Professional AI · Full assessment

Edgar Peña’s Full Professional Assessment

Career direction, work style, skills, industry opportunities, compensation research, AI readiness and a practical development plan.

September 13, 2026 edition · Shared with Edgar’s permission. Personal contact details and private job-search preferences are excluded. Career claims are source-reported; recommendations are assessments, not verified outcomes.

See Edgar’s answers

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Prepared interpretation · AI-estimated · September 14, 2026

Work style: how Edgar approaches the work

Driver-led builder with expressive influence and analytical operating habits

Direct about outcomes, imaginative about opportunities, demanding about evidence and implementation, and relationship-aware without being consensus-first.

Driver is the strongest pattern in this prepared interpretation: establishing a direction, making the objective clear and asking for usable results. Expressive opportunity framing and Analytical evidence-and-structure habits support it equally in this illustration. Amiable contributions appear through reported customer relationships and training, while consensus-first preferences are less clear.

AI-estimated from Edgar’s available career and project history, not a standardized test. Reported prior result: high Driver. The original provider, date and scores were not supplied. These estimates are separate from personally entered ratings.

Prepared work-style affinities

One dot per style. Farther from the center means a stronger match to the prepared reflections. The dashed shape connects independent estimates; its area and horizontal/vertical position are not psychological measurements.

AI estimates · custom 1–5 scale
Style	Two reflections	Mean
Driver	5 and 5	5 of 5
Expressive	4 and 4	4 of 5
Amiable	4 and 2	3 of 5
Analytical	5 and 3	4 of 5

How to read this profile

Each mean uses two newly prepared reflections. Higher is not better. Expressive and Analytical are tied; the chart does not create a statistical distinction. It does not measure assertiveness or responsiveness, predict job success or rank candidates. Exact estimates have low confidence even where the narrative has stronger support.

The eight reflections have different wording and scale anchors from the live questionnaire. No estimate has been copied into Edgar’s account, counted toward completion or relabeled as his saved response.

Driver: Dominant direction-setting and results focus

Edgar’s reported high-Driver outcome is consistent with the available task-focused conversation pattern: set a direction, identify what is missing, ask for a usable deliverable and keep the commercial objective in view.

Why this interpretation: Q-U01 Q-C01 Q-C02 Q-F01

Where it may help

Turning an ambiguous business need into a decision and next action
Holding a product or commercial initiative to an explicit objective
Challenging an output that does not solve the stated problem

Watch-out to test: When a direction changes faster than the brief, collaborators may have to rediscover the target. Treat this as a coordination risk to test, not a proven interpersonal defect.

Another possible explanation: Strong direction in these chats may partly reflect the founder role and repeated correction of incomplete AI outputs.

One practical action: Before a revision, state what changed, what must stay fixed and the one acceptance test. Invite the implementer to name the most important constraint before deciding.

Narrative confidence: high. Not established: Decision quality, team perception, general impatience and an official Driving score.

See the prepared reflections behind this finding ↓

Expressive: Strong opportunity-framing and communication tendency

Edgar repeatedly develops a larger product story and asks how it should attract customers, explain value and lead to action. This supports an expressive, possibility-generating contribution alongside the Driver pattern.

Why this interpretation: Q-C03 Q-C02 Q-F01 Q-H01

Where it may help

Connecting customer problems to a compelling product direction
Exploring new markets and service combinations
Explaining an opportunity across commercial and technical audiences

Watch-out to test: A compelling next idea can enter the scope before the current version has produced usable evidence. New possibilities should not silently become new commitments.

Another possible explanation: The record samples strategy and design work, where possibility generation is expected. It does not establish behavior in routine operations or private social settings.

One practical action: Keep a visible Now/Next/Later board. Give the current hypothesis a review date before promoting the next idea into scope.

Narrative confidence: moderate. Not established: Extraversion, charisma, emotional expressiveness, actual persuasion results or a standardized Expressive subtype.

See the prepared reflections behind this finding ↓

Analytical: Strong evidence-and-structure habit; cautious interpersonal style remains unassessed

Detailed questions, progress logic, source support, salary distinctions and implementation specifications matter to Edgar. The record also shows iterative testing, so analytical habits should not be mistaken for a uniformly reserved or slow decision style.

Why this interpretation: Q-C04 Q-F02 Q-C02

Where it may help

Making requirements and dependencies explicit
Asking how a conclusion connects to evidence
Connecting a recommendation to implementation steps and operating measures

Watch-out to test: Demanding a comprehensive answer while simultaneously pushing for immediate completion can leave the depth-versus-speed tradeoff unstated.

Another possible explanation: Commissioning a rigorous report is evidence of a preference, not proof that Edgar personally conducts every analysis or has a validated Analytical style.

One practical action: Specify the decision, minimum evidence, timebox and acceptable unknowns before beginning. Use stronger checks for irreversible or consequential choices.

Narrative confidence: moderate. Not established: IQ, statistical proficiency, engineering skill, cautious interpersonal behavior or a standardized Analytical score.

See the prepared reflections behind this finding ↓

Amiable: Relationship-aware, with a less consensus-first pattern

Enterprise customers, training and coordination feature in Edgar’s reported work. His direct revisions suggest that preserving agreement is not always the first priority when a result falls short. Both can be true: relationships matter and concerns are stated directly.

Why this interpretation: Q-H01 Q-F01 Q-C02

Where it may help

Understanding the practical needs of customers and colleagues
Helping others through training and cross-functional explanation
Keeping the human usefulness of a product in view

Watch-out to test: Task clarity does not tell us whether quieter stakeholders had space to raise concerns. The report should propose a check, not assume that they did not.

Another possible explanation: Sales and leadership duties can require relationship work without indicating a consensus-oriented preference. AI-task conversations under-sample listening and support.

One practical action: In a difficult decision, summarize the other person’s concern, ask what was missed, then state the decision and rationale. Collect an example of what helped or hindered the handoff.

Narrative confidence: limited to moderate. Not established: Empathy, kindness, honesty, care for people, actual conflict behavior or a standardized Amiable score.

See the prepared reflections behind this finding ↓

How these contributions work together

Driver establishes the outcome; Expressive explores and explains possibilities; Analytical makes requirements and evidence explicit; Amiable keeps the practical needs of customers and colleagues in view. The useful question is which contribution a situation needs, rather than which type is best.

A development experiment: Write one clear project brief in week one. Ask for the main constraint before directing revisions in week two. Keep ideas in a Now/Next/Later list in week three. In week four, compare the briefs and actual feedback. These are proposed actions, not reported outcomes.

See Edgar’s answers

“I answered these questions to the best of my ability.” — Edgar Peña

Eight prepared work-style reflections

Prepared in Edgar’s voice from the supplied history. These are AI-written responses, not quotations he entered in the questionnaire.

Q4D01 · When a project lacks direction, I prefer to set the goal, make a decision, and get it moving.

AI estimate: 5 of 5

Prepared response: I usually want to turn an unclear business problem into a goal, an owner and an executable next step. I would rather establish a direction and improve it than keep discussing possibilities without an outcome.

Basis: Q-U01 Q-C01 Q-F01

Limit: The available record supports a strong direction-setting tendency. The estimate does not mean every decision is fast, correct or made without consultation.

Q4D02 · When work misses the objective, I address the gap directly and ask for a specific correction.

AI estimate: 5 of 5

Prepared response: I tend to say clearly when something misses the point. I want the revision to address the actual problem, preserve what already works and produce something usable.

Basis: Q-C02 Q-C04

Limit: This is visible in project feedback to AI tools. It should not be generalized into how colleagues experience Edgar in disagreement.

Q4E01 · I enjoy explaining a new business idea and getting others interested in the opportunity.

AI estimate: 4 of 5

Prepared response: I like connecting a new idea to a customer problem and a business opportunity. I want people to understand why it matters, not just receive a list of features.

Basis: Q-C03 Q-F01 Q-H01

Limit: Business-development and founder context support this draft. Enjoyment and persuasive effectiveness have not been directly measured.

Q4E02 · Exploring possibilities, shaping the message, and connecting people around an idea energize me.

AI estimate: 4 of 5

Prepared response: I see connections between products, customers, technology and new services. I want the positioning and experience to communicate the larger opportunity, then lead to a concrete action.

Basis: Q-C03 Q-C02

Limit: Idea-generation and positioning requests are visible. This does not establish general extraversion, emotional expression or how much social interaction Edgar prefers.

Q4A01 · I want the numbers, requirements, and implementation steps clear enough to evaluate a proposal.

AI estimate: 5 of 5

Prepared response: I want a recommendation explained in enough detail to judge it. I look for the business logic, question sequence, source support, costs, milestones and the instructions needed to put it into practice.

Basis: Q-C04 Q-F02

Limit: This supports demand for structure and evidence. It does not prove analytical competence or an officially Analytical interpersonal style.

Q4A02 · I prefer to wait for a nearly complete analysis before trying a reversible change.

AI estimate: 3 of 5

Prepared response: It depends on the consequences. I want meaningful detail, but for a reversible prototype I am willing to try a direction, inspect the result and request changes. I would want more checking for a consequential commitment.

Basis: Q-C01 Q-C02 Q-C04

Limit: The midpoint is a proposed context-dependent interpretation, not an observed response. If later evidence is insufficient, replace the estimate with null rather than using 3 as an unknown-value default.

Q4M01 · I invest time in understanding customer or colleague needs and helping them succeed.

AI estimate: 4 of 5

Prepared response: Customer relationships, training and coordination matter in my work. I want the solution to be useful to the people depending on it, and I see understanding their needs as part of doing the job well.

Basis: Q-H01 Q-F01

Limit: Relationship-heavy responsibilities support this possibility. They do not measure compassion, agreeableness, listening quality or behavior in personal relationships.

Q4M02 · When maintaining agreement conflicts with challenging a weak plan, I usually prioritize maintaining agreement.

AI estimate: 2 of 5

Prepared response: I value relationships, but I am generally willing to challenge a weak plan or an output that misses the objective. I would rather make the concern clear and work toward a better result than agree only to keep the discussion comfortable.

Basis: Q-C02 Q-U01

Limit: This estimates a lower consensus-first tendency in observed task discussions. It is not a low-kindness or low-empathy rating and is not a reverse-scored measure of merit.

Edgar’s saved responses

No personally entered responses to these eight supplemental reflections are included in this published edition. The original 86-question assessment and earlier qualitative reflections remain separate.

Sources and interpretation limits

Q-U01 · direct user statement

Edgar reports that a previous assessment ranked him high as a Driver and asks for a history-based four-quadrant assessment for Codex.

No original assessment provider, date, item responses, scale or numerical result was supplied. This is a self-report of a prior outcome, not independent verification.

Q-C01 · observed conversation behavior

Edgar repeatedly specifies a desired business outcome and asks for concrete questions, files, implementation instructions and the next service step.

Communication with an AI assistant is task-specific and may differ from work with colleagues.

Q-C02 · observed conversation behavior

Edgar gives direct revision feedback, rejects designs that do not meet the brief, preserves approved elements and requests narrowly targeted changes.

Correction may reflect an inadequate initial output; it does not establish impatience, hostility or poor collaboration.

Q-C03 · observed conversation behavior

Edgar connects business and professional audiences, free analysis, subscriptions, business-plan adjustment, training and specialist implementation into a broad product vision.

This supports a possibility-generating pattern, not proof of charisma, conversion, implementation or customer traction.

Q-C04 · observed conversation behavior

Edgar requests detailed question sequences, section percentages, report explanations, industry evidence, source-backed salary context, versions and Codex handoff files.

Requesting rigorous work establishes a preference for useful detail, not mastery of statistics, engineering or a cautious interpersonal style.

Q-F01 · author supplied career claim

The working plan describes business ownership, enterprise commercial work, operations and leadership of a 25-person software/UI organization. It separates commercial/product direction from hands-on production engineering.

These are reported professional experiences, not independently audited results. Do not infer a work style from job titles or revenue alone.

Q-F02 · author review document

The companion describes returning to education, completing an AI/work thesis, a 26-interview qualitative study, and the importance of learning, checking and applied evidence.

An author-review document supports the reported learning/research context. It is not an administered personality measure or independent corroboration of repeated career claims.

Q-H01 · user supplied professional history

Edgar describes enterprise account management, sales-team training, customer requirements and cross-functional coordination as parts of his work.

This suggests relationship-oriented responsibilities. It does not directly measure empathy, listening effectiveness, harmony preference or others’ experience of his leadership.

Decision brief

Your strongest professional direction

Section summary

A commercial and product leader who understands physical operations, enterprise customers and practical AI adoption.

Full detail · Your strongest professional direction

A commercial and product leader who understands physical operations, enterprise customers and practical AI adoption.

The clearest near-term opportunity is not to discard your print-on-demand experience and become a generic “AI expert.” It is to use that domain knowledge to lead higher-value products, solutions, accounts and business improvements in print production and commerce technology, then expand into adjacent software and AI transformation work.

This is a recommendation based on the experience in your accessible history and materials. It is not a hiring decision, a verified skills certification or a guarantee of compensation. Your task mix, evidence quality and specific employer requirements still matter.

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Section 01

The decision I would make first

Section summary

I would organize your next move around one primary professional story and two deliberate alternatives.

Full detail · The decision I would make first

I would organize your next move around one primary professional story and two deliberate alternatives.

Priority	Direction	Why it belongs here
Primary	Products, solutions and commercial leadership in print/commerce technology	Uses your industry experience, customer understanding, operating judgment, software-team leadership and sales enablement together.
Adjacent	Strategic partnerships or enterprise account leadership in commerce/operational SaaS	Transfers your ability to connect customer needs, commercial relationships and a working product. Partner economics and attributable results need clearer proof.
Development path	Business-side AI transformation or a focused consulting practice	Fits your research and product interests, but needs more explicit evidence of governed delivery, evaluation, adoption and measurable outcomes.

Your background is unusually broad. That is useful inside a business, but the reader of a résumé still needs to know the problem you solve. The strongest message is not “I have done everything.” It is “I can connect customers, operations and technology to improve a business result.” This interpretation draws on the Wurkflow contribution described in your research companion and the founder experience in your working plan. [F01, F03]

A first-person positioning statement

I lead commercial and operational improvement where customer needs, software and physical delivery have to work together. My background combines enterprise business development, print-on-demand operations, product direction and technology-team leadership. I use AI to improve how the work gets done, while keeping the business case, people and accountability central.

This is draft positioning for your review. It deliberately does not imply that you independently code production systems, have decades of generative-AI experience, or hold an earned MBA.

What I would not lead with

Do not make “Director of AI” your only search phrase. Some such roles may fit your adoption and business leadership; others require hands-on software, data science or model-engineering work that your accessible evidence does not establish. Similarly, do not lead with an entry-level reinvention when an adjacent senior role can use your existing knowledge. A true domain change can still be worthwhile, but its economics and learning requirements deserve separate analysis. [H01, F03]

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Section 02

What the available record actually establishes

Section summary

The evidence is strongest for the shape of your experience, not for every metric or current employment detail. A source reporting a claim is different from an independent reviewer validating it.

Full detail · What the available record actually establishes

The evidence is strongest for the shape of your experience, not for every metric or current employment detail. A source reporting a claim is different from an independent reviewer validating it.

Career element	What this report uses	Evidence status and limitation
Business experience	You describe 25+ years in business; a working plan cites a narrower résumé with 18+ years.	Both are preserved. No synthetic timeline was built to reconcile them. [H01, F01, F04]
Business ownership	A POD business grown beyond $5M annual revenue before acquisition.	Reported in your documents. Financial records, transaction terms and exact attribution were not inspected. [F01, F04]
Software/product leadership	A 25-person software/UI group across three offices and work on a seven-figure SaaS platform.	The companion explains requirements, prioritization and coordination. It explicitly distinguishes this from personally coding the whole platform. [F03]
AI-related commercial results	Your plan reports $1M+ incremental annual AI-related revenue and other revenue accomplishments.	Baseline, costs, period, attribution and overlap need documentation. These are not treated as audited outcomes. [F01]
Education	Latest accessible application draft states a BA in Business Administration/Entrepreneurship at Cal State LA; prior history includes an ELAC Associate’s degree.	The August 2026 degree statement supersedes older “planning to graduate” wording as a source-reported claim. The conferral record was not independently inspected. [H01, F04]
Research	AI/work exploration began in August 2024; thesis completed in May 2026.	The companion distinguishes the wider exploration from the formal interview period. The study does not validate hiring or personality predictions. [F03]
USC	Accepted for Fall 2026; chose not to enroll then and planned to revisit in 2027.	Not an earned MBA, current enrollment or confirmed institutional deferral. [F03]

Personal contact details, compensation preferences and private job-search conditions are excluded from this public edition.

Back to assessment contents ↑

Section 03

Personality and work-style assessment

Section summary

Working description: strategic commercial builder

Full detail · Personality and work-style assessment

Working description: strategic commercial builder

You appear to prefer improving the business system, not merely completing an isolated task inside it.

This is a qualitative interpretation of your projects, stated career history and interactions. It is not a clinical assessment, psychometric score or universal description of your behavior. Chat interactions show how you direct a product task; they do not show every side of your personality.

Pattern	Evidence behind the interpretation	Likely advantage	Counterweight to build	Confidence
Curiosity and openness to new methods	Adult return to education, AI workforce research, experimenting across tools and building a new platform. [H01, F03]	Spotting useful changes and learning across functions.	Choose which new capability matters now; novelty alone is not a business case.	High for observed learning behavior.
Systems-oriented thinking	Connecting customer intake, research, reports, training, business planning and specialist delivery. [H01]	Seeing dependencies that siloed teams may miss.	Ship one complete useful workflow before expanding the system around it.	High in the project context.
Commercial orientation	Business ownership, enterprise accounts, revenue-focused work and repeated attention to monetization. [F01, F03, F04]	Translating technology into a business reason to act.	Test the economics, not just the appeal of the offer.	High as reported experience.
Direct quality standards	You give explicit feedback when design or content misses the intended outcome. [H01]	Protecting the customer experience and challenging weak work.	State acceptance criteria before work begins; distinguish an error from a new preference.	High for visible behavior.
Ownership and autonomy	Founding businesses and taking responsibility across commercial and operating boundaries. [F01, F03]	Leading ambiguous initiatives with multiple stakeholders.	Clarify decision rights so ownership does not become taking on every dependency.	Moderate.
Relationship and teaching orientation	Reported enterprise relationship work and sales-team development. [H01, F03]	Customer discovery, stakeholder alignment and enablement.	Document outcomes and obtain specific feedback; network size is not proof of influence.	Moderate.
Breadth and opportunity generation	Multiple plausible career directions and a broad product vision. [H01, F03]	Connecting ideas and finding adjacent opportunities.	Use a stop-doing list and protect the primary market message.	Moderate.

The likely blind spot

The most actionable concern is scope expansion before proof, not a personality defect. Your ability to see a bigger product or broader career path can also make it harder to choose a small, testable deliverable. In a job search this can become too many positioning narratives. In a product it can become another feature before the first outcome is measured.

A useful operating rule for you would be: one owner, one business decision, one deliverable, one success measure, then a review date. Keep additional ideas in a backlog rather than inside the current commitment. This is a proposed work method, not an observed failure rate.

What this assessment does not claim

It does not assign an IQ, MBTI type, Big Five percentile, emotional-stability rating, diagnosis or probability that you will enjoy a job. It does not infer personality from health history, age, family circumstances, ethnicity or other protected characteristics. No such information was needed for this analysis.

A separate self-report instrument would be necessary for formal personality results. The BFI-2 owner describes it as a self-report inventory and limits its free research use; AI BURRO should not silently copy it into a commercial product. [P02]

Career-interest hypothesis, separate from personality

An Enterprising / Investigative / Social interest pattern is worth exploring: leading commercial initiatives, investigating business problems and developing people. That is an unscored hypothesis, not an O*NET result. O*NET’s Interest Profiler is designed for career-interest exploration and offers 30- and 60-question formats. It measures interests, not proven competence or guaranteed satisfaction. [P01]

The work-style interpretation can be reviewed and challenged. You can disagree with any inference without changing your career achievements or salary evidence.

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Section 04

Skills you are best positioned to use

Section summary

The following ranking is a recommendation about where to focus. “Strong fit” means your reported work supports the capability; it does not mean an independent practical examination has been passed.

Full detail · Skills you are best positioned to use

Skill	Assessment	Best department or work context	Best next evidence
Business-to-technology translation	One of your strongest differentiators. You connect operating needs to requirements and product decisions. [F03]	Product/solutions, transformation, business systems.	A redacted workflow, requirements and decision-tradeoff case.
Enterprise discovery and value framing	Strong reported experience; exact deal attribution needs proof. [F01, F04]	Enterprise accounts, consultative sales, strategic partnerships.	An anonymized account story showing the need, decision and your contribution.
POD/apparel production judgment	Deepest domain advantage in the available record. [H01, F03]	Digital production solutions, fulfillment, commerce operations.	A scenario explaining production constraints, quality and economics.
Cross-functional leadership	Strong reported experience across technical, operating and customer needs. [F03]	Product, operations, customer delivery, business transformation.	Team scope plus a cross-functional delivery example and reference.
Commercial strategy and business building	Credible foundation from ownership and growth work. [F01, F04]	Business development, solutions commercialization, general management.	A business case with baseline, decisions, results and attribution.
Sales enablement and communicating value	Supported by reported sales-training responsibilities. [H01]	Sales enablement, product marketing, partner/customer education.	A short training asset and a measured learning or adoption outcome.
AI use-case framing	Visible in the current product-definition work. [H01, A01]	Business-side AI adoption, consulting, product strategy.	One evaluated workflow with explicit safeguards and outcome measures.
Research synthesis	Supported by thesis/companion work and current research-driven product direction. [F03]	Strategy, market intelligence, workforce/product research.	A source-linked decision brief with limitations and implications.
Partner-program economics	Credible adjacent capability, but program-level proof is incomplete.	Partnerships and ecosystem development.	Partner-sourced versus influenced revenue, incentives and agreement examples.
Hands-on CRM, SQL, analytics and AI engineering	Depth not established by the available record. Do not translate “not assessed” into either expert or incapable.	RevOps or more technical AI positions, conditional on evidence.	A practical exercise and reviewed artifact before claiming proficiency.

Your most valuable combination is commercial judgment + operating domain knowledge + product/technical translation. Any one of those alone is easier to summarize; the combination is where a well-defined role may extract more value from your experience.

What an employer should be able to see

Your portfolio should answer three questions: What did Edgar decide? What did the team or tool do? What changed, and what supports that result? The companion’s Wurkflow example already gives a useful distinction between directing requirements and personally producing code. Build on that distinction rather than hiding it. [F03]

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Section 05

Industries, departments and role priorities

Section summary

1. Digital textile production, POD and apparel-decoration technology

Full detail · Industries, departments and role priorities

1. Digital textile production, POD and apparel-decoration technology

Recommended first market. Consider Director of Products and Solutions, Director of Business Development, Head of Enterprise Accounts, or a commercially accountable operating/product role.

The most relevant departments are Product/Solutions, Commercial Strategy, Sales Enablement, Enterprise Accounts and Operations Transformation. This market uses your knowledge of physical production alongside software and customer experience. The Kornit posting is a concrete example of a role crossing product, marketing, service and sales; it also asks for textile/production knowledge. [M03]

The remaining proof is product-launch responsibility, commercial economics, exact leadership scope and specific results. Do not assume every printer manufacturer or fulfillment company has the same technology or business model.

2. Commerce infrastructure and operational SaaS

Recommended adjacent market. Look at software serving merchants, production businesses, fulfillment networks or enterprise customer workflows. Departments include Strategic Partnerships, Enterprise Customer Success/Accounts, Product Solutions and Business Development.

Your advantage is understanding the operating consequences of a software promise. Your gap is showing platform-specific ecosystem knowledge and partner/customer metrics. The supplied WooCommerce partnerships example is relevant because it connects commerce, integrations and commercial relationships; its scale and work model require a role-specific comparison. [M04]

3. AI consulting and business transformation services

Good direction when the mandate is business adoption, not solo engineering. Departments include Digital Consulting, Transformation, Client Leadership and AI Enablement.

Your transferable story is useful, but a consulting director may be expected to own engagement economics, scope, delivery assurance and executive governance. Trace3’s posting makes those demands explicit and asks for substantial consulting experience. Your general business tenure should not automatically be described as the same number of years of formal consulting. [M05]

Start by assembling a portfolio that demonstrates those responsibilities. A focused domain consulting offer may be a more credible entry than claiming broad expertise in every industry.

4. Workforce learning and enablement technology

A selective secondary path. Your research and training interests give a coherent reason to explore Partnerships, Customer Enablement or Commercial Learning Solutions.

The interest is real, but it does not yet establish leadership of a large learning business. OpenSesame provides a salary and role example centered on partner outcomes; its travel requirement must be weighed against the requirements of a specific role. [M08]

5. Revenue Operations in B2B software or marketplaces

Conditional, not the automatic first choice. You understand the commercial and operational problem. To lead a technically demanding RevOps function, you would need clear evidence of the specific CRM, reporting, forecasting, system-governance and analytics work the job requires.

Roo supplies a Los Angeles-tier compensation example, but experience in printing does not automatically transfer into veterinary labor-market expertise. The right application would explain the transferable operating skill and acknowledge the new domain. [M06]

6. General management in a smaller operating business

Potentially strong when the authority and economics are real. This could combine commercial growth, delivery and technology change in a smaller operating company.

The title alone is not enough. Verify budget responsibility, profit-and-loss accountability, team scope and founder/board expectations. No directly comparable, recent Los Angeles 25–50-person general-manager salary sample was established here, so this report does not invent one.

Directions I would not prioritize immediately

Pure machine-learning engineering leadership, deep cloud architecture, highly specialized regulatory leadership or large-scale operations roles requiring a demonstrably different management scale should remain conditional on new evidence. Medical business brokerage is a previously explored direction, but your application draft shows interest in learning its domain, not completed expertise in valuation, diligence or deal structuring. [F03, F04]

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Section 06

Compensation based on industry data and employer postings

Section summary

First distinguish the pay types

Full detail · Compensation based on industry data and employer postings

First distinguish the pay types

Base salary is fixed pay. Variable pay depends on specified conditions. OTE combines base and target variable, but it is not guaranteed earnings. Equity, benefits and business-owner revenue are separate. A company’s sales revenue is not your personal compensation.

BLS wage medians are valuable context, but the sales-manager figures include commissions and production-target bonuses. They are not a clean base-salary benchmark. These national industry categories also do not isolate director-level POD professionals in Los Angeles. [M01, F06]

National industry benchmarks

The following are May 2025 median annual wages, as recorded in the supplied September 13, 2026 report. They are descriptive market context, not predictions for your offer.

Occupation / industry	Median annual wage	Interpretation
Sales managers, all industries	$148,270	Broad occupation and seniority mix.
Sales managers, professional/scientific/technical services	$173,700	Relevant broad services context, not an AI-director benchmark.
Sales managers, manufacturing	$156,150	Broad manufacturing proxy, not a textile/POD-only sample.
Sales managers, wholesale trade	$149,980	Distribution/commercial context.
Management analysts, all industries	$101,860	Broad analyst/consultant category, not consulting-director pay.
Management analysts, professional/scientific/technical services	$107,330	Broad services median; senior leadership may be mapped differently.

Sources: BLS Sales Managers and Management Analysts. [M01, M02]

Six specific employer examples

This is a purposive sample of six postings, one per example, not a statistically representative survey. The supplied report records these source observations on September 13, 2026; publication here does not refresh their availability. Advertised availability can change; a visible page does not guarantee active interviewing. Unknown publication dates remain unknown.

Employer / role	Advertised USD annual compensation	Geography / work condition	Relevance and limitation
Kornit, Director of Products and Solutions	$150,000–$235,000 salary range	Americas remit; exact worksite and travel need verification.	Strong domain/role combination. Source does not clearly break salary into base and other components. [M03]
WooCommerce, Director of Strategic Partnerships	$105,000–$215,000 salary range	North America; remote.	Commerce/partner path. Other pay components require checking. [M04]
Trace3, Director, Digital Consulting / Transformation	$185,000–$200,000 estimated pay	US remote.	Text distinguishes base from potential variable incentives. Substantial consulting-delivery requirements make fit conditional. [M05]
Roo, Director of Revenue Operations	$160,000–$210,000 Tier 2 pay range	Los Angeles included in Tier 2.	Location-tier example; verify pay components and work arrangement directly. [M06]
Salesforce/Qualified, Commercial Account Executive	$109,500–$208,300 base	California remote listed; posted July 9, 2026.	Base explicitly excludes sales incentive, equity and benefits. OTE not established. [M07]
OpenSesame, Director of Strategic Partnerships	$180,000–$220,000 base	US/Canada remote; about 25% travel.	Eligible for up to 30% bonus and ISOs. Maximum bonus is not a target or a guarantee. [M08]

How to use these salary examples

The advertised ranges help compare role scope and pay components. They are not an estimate of Edgar’s current earnings or a personal acceptance floor. Higher ranges require evidence that matches the employer’s responsibilities. The top of a range is not an expected offer.

What to verify before comparing offers

Ask for the location-specific range, guaranteed base, target variable, quota/ramp, payout rules, historical attainment information the employer is willing to disclose, equity terms and benefit costs. For a consulting or founder route, use a separate business model with expenses and unpaid time; revenue is not salary. No independent-consulting income forecast was generated here.

Confidence: reported as high in the supplied analysis for transcription of the source text; moderate in its relevance across your potential paths; low for predicting your individual offer or a niche small-company Los Angeles median. No representative niche sample size or candidate percentile was established.

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Section 07

How AI changes the value of your work

Section summary

Your own scope correctly separates Role AI Threat Level from Personal AI Resilience. This report retains that separation without calculating unsupported scores. The ILO’s task-based research also distinguishes occupational exposure from individual job loss and emphasizes transformation rather than assuming that every exposed job disappears. [F05, R01]

Full detail · How AI changes the value of your work

The map below is a proposed response to your reported work, not a measured exposure assessment. Frequency and current task allocation still need your confirmation.

Work category	Proposed AI mode	Where your contribution remains important	Evidence to build
Account and market research	Assist and structure.	Decide which evidence matters, verify sources and test assumptions.	Source-linked account brief.
Proposal and enablement drafts	Assist; human approval before use.	Select the value proposition and own factual commitments.	Before/after draft with review notes.
CRM administration and handoff	Consider bounded automation after data/policy review.	Define correct stages, ownership and exceptions.	A permitted or synthetic workflow with an audit trail.
Discovery and negotiation	Human-led with preparation support.	Trust, questioning, commercial judgment and accountability.	Real anonymized case, once permitted.
Pricing and delivery commitments	Human-led; calculations or drafts may assist.	Margin, feasibility, authority and escalation.	A decision rule and case showing when to refuse or clarify.
Product requirements	AI-assisted structuring and review.	Prioritization, scope, acceptance criteria and stakeholder tradeoffs.	Requirements-to-release decision story.
Physical fulfillment and quality exceptions	AI may help triage and analyze.	Production context, inspection and responsibility for delivery.	Operational case with agreed measurements.
Team adoption and training	AI-assisted materials; human-led adoption.	Coaching, behavior change and feedback.	Training asset plus measured adoption feedback.

Your resilience assessment today

Strongest demonstrated starting points: practical business context, experience leading across functions, active AI use for planning, and an explicit interest in learning and applying research. [H01, F03]

Most important missing proof: a measured, safely evaluated workflow; evidence of how you handled failures; source-backed commercial outcomes; and a precise statement of what you personally configured, built, approved or led.

The next step is therefore not simply “use more AI.” It is show one useful AI-assisted business workflow that works under defined conditions, and explain its limits. A tool list and a successful generated sample alone do not establish operational reliability.

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Section 08

Training that would materially improve your position

Section summary

Start with proof, then close the actual gap

Full detail · Training that would materially improve your position

Start with proof, then close the actual gap

You do not need to relearn basic business experience you already have. You do need to make current capability visible and check the technical or domain-specific skills required by your chosen role. This plan prioritizes one primary path; it does not ask you to complete every listed course.

Priority	Capability and resource	Time / cost evidence	Required practical output
Core	AI business-case and adoption design: Microsoft Learn, Transform your business with AI. [T01]	Provider lists 2h34m. Add your own practice time; online learning is separate from live cloud costs.	One-page use case with owner, baseline, costs, value assumptions and adoption plan.
Core	Evaluation and governance: NIST AI RMF Playbook. [T07]	Public reference, not a course or certification. Proposed 3–5 hours focused application, not a provider duration.	Risk log, approval boundary, test cases and stop conditions for your project.
Choose for partnerships/RevOps	HubSpot Revenue Operations Certification. [T03]	Provider lists free. Proposed 6–10 hours including your exercise, not a quoted completion time.	Lifecycle map, metric definitions, handoffs and one revenue-quality dashboard specification.
Choose for analytical systems work	Microsoft Power BI data analyst learning paths and Transact-SQL. [T04, T05]	SQL path lists 5h45m; full analytics practice will take longer. Proposed 15–25 hours for a small portfolio exercise. Software/exam costs separate.	Synthetic pipeline/order data model; queries; dashboard with reconciled totals and documented measures.
Choose for Salesforce/AI sales systems	Design and Implement AI Agents with Agentforce. [T06]	Intermediate administrator trail lists about 8h08m; preparatory CRM study and access may be needed.	Sandbox demonstration, test log and explanation of what the agent is not permitted to do.
Optional signal for business-side AI roles	Google Cloud Generative AI Leader. [T02]	Exam $99 plus applicable tax; 90 minutes; no prerequisites. Preparation time and training access separate.	Business-level explanation of a use case. Pair any earned credential with a project; it does not prove engineering experience.

Role-specific additions

For Products and Solutions, prioritize market segmentation, launch readiness, product adoption metrics, total-cost/value modeling and lifecycle decisions. The best training assignment is a product-commercialization brief using your existing industry knowledge, with assumptions explicitly separated from measured facts.

For Partnerships, practice partner selection, unit economics, commercial agreements and sourced-versus-influenced pipeline measurement. A partner plan and an accurately attributed negotiation example will usually say more about your readiness than an unrelated AI certificate.

For Transformation Consulting, build a statement-of-work sample, dependency/risk log, governance cadence and benefit-realization plan. A formal change-management or project credential should be considered only after target roles repeatedly justify it; none has been assumed necessary or purchased.

For Revenue Operations, test your actual hands-on capability first. A leadership background does not prove Salesforce administration, SQL or analytical-model design. It also does not prove you lack them. Use a practical exercise to decide whether the next step is evidence capture or training.

What I would not buy first

I would not start with another broad degree, multiple introductory AI subscriptions, or an expensive engineering boot camp solely to improve a headline. Your USC decision remains separate from this report. Revisit a major program based on the specific skills, network, time and economics it would add, not because the report claims it is needed to be employable. [F03]

No training provider’s commission or sponsorship influenced this ordering. No course was purchased, enrollment submitted or credential marked earned.

Back to assessment contents ↑

Section 09

The portfolio project I would build around you

Section summary

AI-assisted account-to-commitment decision brief

Full detail · The portfolio project I would build around you

AI-assisted account-to-commitment decision brief

Purpose: demonstrate how you connect commercial opportunity, production constraints, information quality and human approval.

Scope: one synthetic enterprise inquiry workflow. It is not an autonomous sales representative, a production quotation system or a claim about an existing customer deployment.

The input

Use fictional business inquiries, a small synthetic product/policy sheet and example production constraints. Include complete requests as well as missing, conflicting or misleading information. No real customer information, private pricing, live contacts or employer credentials are required.

The output

Produce a short brief containing the customer need, supplied facts, assumptions that must not be treated as facts, missing information, relevant constraints, recommended questions and a proposed next action. Any price or delivery commitment must remain outside the workflow unless explicitly supplied and authorized.

Your contribution

You own the business problem, the workflow, the required information, the acceptance criteria and the interpretation of results. Attribute any AI-generated code and any engineer’s work. A developer or technical reviewer can support implementation without weakening your leadership story; the distinction makes the story more accurate.

An initial evaluation plan

As a proposed practice design, create 20 synthetic cases: 10 normal cases, 5 missing-data cases, 3 contradictory-information cases and 2 attempts to bypass the rules. Reserve some cases for a final check rather than repeatedly optimizing against every example. These counts are a design recommendation, not completed tests.

Track source correctness, gap detection, unsupported claims/commitments, completeness, review effort and total handling time. An unauthorized commitment or disclosure should be treated as a blocking failure. A clean small test set is necessary learning evidence, not proof that a system is safe at production scale.

Measurement

Capacity released per case = comparable manual handling time minus total AI-assisted handling time, including human review and correction.

Multiply by an observed case volume only when that volume is known. Do not translate time released into reduced payroll unless it actually changes cash expense. If the project is entirely synthetic, describe results as simulation findings rather than revenue or customer outcomes.

Finished evidence package

A useful finished package contains the business brief, process map, bounded demo, evaluation results, error analysis, cost/effort assumptions and a one-page explanation of your contribution. That single package can support products/solutions, transformation and commercial conversations in different ways without changing the underlying facts.

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Section 10

A focused 90-day development and pursuit plan

Section summary

The schedule below assumes five hours per week for 12 weeks, solely as a proposed planning scenario. Your availability is unknown. Adjust the pace without pretending the work is complete.

Full detail · A focused 90-day development and pursuit plan

The schedule below assumes five hours per week for 12 weeks, solely as a proposed planning scenario. Your availability is unknown. Adjust the pace without pretending the work is complete.

Period	Proposed effort	Deliverable	Evidence of progress
Weeks 1–2	10 hours	One primary positioning statement; accurate role timeline; first evidence case; a small role-comparison set.	Claims have sources, personal contribution and explicit unknowns.
Weeks 3–4	10 hours	Business-case/governance learning; project scope and acceptance criteria.	Defined inputs, permission boundaries, risks and measurement plan.
Weeks 5–8	20 hours	Build and evaluate the synthetic account-to-commitment workflow.	Working bounded demo, failure log and transparent results.
Weeks 9–10	10 hours	One target-specific learning branch and one role-specific portfolio variation.	A relevant artifact, not merely another badge.
Weeks 11–12	10 hours	Final case study, targeted conversations and review of the primary career direction.	Actual feedback and accurately recorded outcomes.

Days 1–30: make your value legible

Choose Products/Solutions in print/commerce technology as the primary hypothesis to test. Keep partnerships and commercial AI transformation as alternatives. Build a fact sheet so titles, dates and outcomes are consistent across your résumé, LinkedIn and portfolio. Select two reported achievements that can be substantiated without disclosing confidential information.

A useful first milestone is not a target number of applications. It is having someone unfamiliar with your history explain back what business problem you can own and why your evidence supports it.

Days 31–60: make the AI capability observable

Build the bounded project and take the relevant learning path. Ask one appropriate technical reviewer and one commercial/operating reviewer for specific feedback, but do not imply either review has already occurred. Record changes, errors and what you chose not to automate.

Days 61–90: test market response

Use a concise role-specific résumé and the same underlying evidence to pursue a small set of genuinely relevant opportunities. Track role family, source, decision to pursue, whether an introduction was real, conversations, interviews and outcomes. Do not conclude that silence proves a specific screening mechanism.

Days 180 and 365: make a deliberate next investment

At six months, compare actual market feedback and work satisfaction with the initial hypotheses. Expand the capability that created useful results; reconsider a weak target instead of continuously rewriting the same résumé. At 12 months, a deeper credential, degree or consulting expansion should follow an identified need and evidence of value, not a promise that education alone protects a career.

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Section 11

Working conditions and interview questions

Section summary

A good role should use your cross-functional judgment while giving you real authority and competent implementation support. That is a work-fit hypothesis, not a promise about any employer’s culture.

Full detail · Working conditions and interview questions

Question to ask	Why it matters for your decision
What will I personally own after six months, and how will success be measured?	Separates an impressive title from a real mandate.
Which decisions can I make without another approval, and where do dependencies sit?	Tests whether accountability is matched by authority.
Who performs implementation, data/security review and ongoing support?	Clarifies the boundary between leading AI adoption and being the sole engineer.
Is the commercial goal new logos, account expansion, partner revenue or product adoption?	Prevents different sales/partnership roles being treated as interchangeable.
How are priorities changed, and what happens when evidence conflicts with the initial plan?	Tests whether your direct, iterative work style will be useful there.
What does remote or hybrid mean in practice, including client and event travel?	Protects against comparing labels instead of actual working conditions.
What pay is guaranteed, what is contingent, and what information can you share about attainment?	Makes the compensation comparison meaningful.
Why is the role open, and what would make the first year unsuccessful?	Reveals risks that polished employer messaging may not address.

A 25–50-person employer may offer breadth, but that breadth can also mean limited support. A larger organization may offer resources but less authority. Investigate the actual team and role instead of assigning a universal “culture fit” percentage.

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Section 12

What should happen inside AI BURRO next

Section summary

The first report should lead to a Career & AI Readiness Plan that turns analysis into a short set of choices, evidence tasks and evaluated learning projects. Career IQ can then organize the pursuit process, and AI Professional Training can support a demonstrated capability gap. A specialist should be available for interpretation without making every uncertainty a sales opportunity.

Full detail · What should happen inside AI BURRO next

For your own next session, the highest-value agenda is: choose the primary role story, identify the two strongest evidence cases, check technical expectations, and decide which working conditions matter enough to screen early. The specialist should receive your approved summary, only information explicitly approved for that session.

Highest-impact unresolved items

The report can already propose a direction. These gaps would most improve its precision: a final current-role timeline; documentary support for the largest claims; a representative task diary; actual application/outcome records; precise work/travel conditions; and a realistic learning budget and schedule.

These are evidence gaps, not a reason to withhold every useful recommendation. They are also not permission to fill the missing data with flattering numbers.

Confidence summary

Component	Confidence	Reason
Broad commercial/product/operations positioning	High	Repeatedly supported in available career materials and current work.
Qualitative work-style interpretation	Moderate	Behavioral examples exist, but no formal instrument or independent multi-rater review.
Specific employer fit	Moderate	Role descriptions can be compared with experience; critical requirements and conditions still need checking.
Advertised pay transcription	High	Supplied analysis records employer and BLS source observations; components and dates are labeled.
Your individual offer potential	Moderate to low	No representative niche dataset, offer history or verified candidate benchmarking.
Learning priorities	Moderate to high	Closely connected to the recommended roles and identified evidence gaps; available capacity is unknown.
Numerical AI threat, resilience or happiness scores	Not issued	Insufficient confirmed task/evidence inputs and no validated individual predictive assessment.

your most credible advantage is not knowing the most AI tools. It is combining business judgment, customer understanding, operational experience and product leadership, then proving that you can use AI responsibly to improve a specific result.

Back to assessment contents ↑

Sources and evidence

This edition preserves the supplied report’s dated source observations. Publishing it does not independently verify career claims or refresh job openings, salaries, course durations or prices. Check the linked provider before acting. Research and recommendations are distinct from measured results.

H, F and A references identify founder-provided evidence and methodology. The underlying private documents and questionnaire are not published. M, P, T and R references link to market, assessment, training and AI research sources.

H01 · Founder-supplied career history and project observations

User-provided career history and requests available in this conversation. Not a full archive audit. Role dates, financial claims and current conditions may need confirmation.

Report observation: 2026-09-13

F01 · Founder business-plan career evidence

Founder career claims and division of product/commercial leadership versus hands-on engineering. The plan reports résumé claims; it is not independent evidence of business results.

Report observation: 2026-09-13 · Source data/date: 2026-09-05

F03 · Founder research and career reflection

Sections on Wurkflow contribution, evidence, research chronology, learning and USC decision. States 25-person software/UI leadership, not personal authorship of all code; thesis completed May 2026; chose not to enroll at USC in Fall 2026 and planned to revisit in 2027. No formal institutional deferral is established.

Report observation: 2026-09-13 · Source data/date: 2026-09-07

F04 · Founder career and education statement

Source-reported business experience, education and business growth. Degree conferral and financial outcomes were not independently verified.

Report observation: 2026-09-13 · Source data/date: 2026-08-27

F05 · AI BURRO role exposure and resilience method

Keeps role transformation separate from personal resilience, demands source-backed analysis and evidence, and prohibits fabricated replacement probabilities or commission-biased learning recommendations.

Report observation: 2026-09-13 · Source data/date: 2026-09

F06 · AI BURRO compensation evidence method

Compensation requires source, date, geography, role, pay components and confidence; approved evidence is separate from private career data.

Report observation: 2026-09-13 · Source data/date: 2026-09

A01 · Professional questionnaire methodology

Original 60 main questions, two review/consent cards, 24 optional deep dives and one server-only checkpoint. Exact titles and field keys retained.

Report observation: 2026-09-13 · Source data/date: 2026-09-13

M01 · BLS Occupational Outlook Handbook: Sales Managers

May 2025 national and industry median wages. Includes commissions and production-target bonuses; not pure base salary or a POD-specific director distribution. Page updated August 27, 2026.

Report observation: 2026-09-13 · Source data/date: 2025-05

M02 · BLS Occupational Outlook Handbook: Management Analysts

May 2025 national median $101,860; professional/scientific/technical-services median $107,330. Broad occupation, not consulting-director compensation.

Report observation: 2026-09-13 · Source data/date: 2025-05

M03 · Kornit Digital: Director of Products and Solutions

Americas product/solutions and commercial leadership; advertised salary $150,000–$235,000. Exact worksite, travel and base-versus-total classification are not established by the retrieved description.

Report observation: 2026-09-13

M04 · Automattic/WooCommerce: Director of Strategic Partnerships

North America, remote. Advertised salary $105,000–$215,000 USD; eight-plus years in relevant partnerships/business development; compensation components beyond salary need checking.

Report observation: 2026-09-13

M05 · Trace3: Director, Digital Consulting | Transformation

United States remote. Estimated pay $185,000–$200,000; accompanying text distinguishes base from possible variable incentives. Requires 12+ years in management or technology consulting and demonstrated account/program ownership.

Report observation: 2026-09-13

M06 · Roo: Director of Revenue Operations

Los Angeles is in Tier 2: advertised pay $160,000–$210,000 USD. Use as a location-tier example, not a base/OTE assumption or proof of sector expertise.

Report observation: 2026-09-13

M07 · Salesforce/Qualified: Commercial Account Executive, JR343654

Posted July 9, 2026. California remote base $109,500–$208,300; sales incentives, equity and benefits excluded. OTE and quota attainment are not disclosed in reviewed salary text.

Report observation: 2026-09-13 · Source data/date: 2026-07-09

M08 · OpenSesame: Director of Strategic Partnerships

US/Canada remote, approximately 25% travel, base $180,000–$220,000; eligible for up to 30% bonus and ISOs. Maximum bonus is not target or guaranteed pay.

Report observation: 2026-09-13

P01 · O*NET Interest Profiler

RIASEC interest exploration; 30-item Mini-IP and 60-item short form. No instrument was administered to Edgar and no scores were inferred.

Report observation: 2026-09-13

T01 · Microsoft Learn: Transform your business with AI

Business-leader learning path covering planning, value, responsible AI and scaling. Provider duration 2h34m; online content, not a professional license.

Report observation: 2026-09-13

T02 · Google Cloud: Generative AI Leader

No prerequisites; business-level generative-AI knowledge. Exam $99 plus applicable tax, 90 minutes; preparation and project time are separate. Not proof of production engineering capability.

Report observation: 2026-09-13

T03 · HubSpot Academy: Revenue Operations Certification

Provider lists free certification course on business/sales/marketing operations. Preparation time in the plan is our estimate, not a quoted provider duration.

Report observation: 2026-09-13

T04 · Microsoft Learn: Data Analyst training paths

Paths cover Power BI preparation, data modeling and analytical reporting. Certification and software/licensing costs are separate from online learning content.

Report observation: 2026-09-13

T05 · Microsoft Learn: Query and modify data with Transact-SQL

Beginner path; 5h45m listed, spreadsheet-table familiarity prerequisite. Practice time additional; use synthetic data, not a production database.

Report observation: 2026-09-13

T06 · Salesforce Trailhead: Design and Implement AI Agents with Agentforce

Intermediate administrator trail; about 8h08m listed, planning through monitoring. Introductory CRM learning may be needed; org access and production licenses separate.

Report observation: 2026-09-13

T07 · NIST AI RMF Playbook

Voluntary framework guidance on Govern, Map, Measure and Manage. Reference material, not a certification or assurance of legal compliance.

Report observation: 2026-09-13

R01 · ILO: Generative AI and jobs, 2025 update

Task-based occupational exposure is not an individual job-loss forecast; transformation rather than whole-job redundancy is the central distinction.

Report observation: 2026-09-13 · Source data/date: 2025-05-20

P02 · Berkeley Personality Lab: Big Five Inventory 2

BFI-2 is a self-report inventory. It is copyrighted and free for non-commercial research, not automatically licensed for a commercial product. No BFI-2 questions or scores are reproduced here.

Report observation: 2026-09-13

Turn your findings into next steps.

Review your career direction, evidence and AI learning priorities with an AI specialist in a free consultation.

Earlier assessment · September 10, 2026

Preserved earlier edition and interactive explorations. The full September 13 assessment above contains the latest supplied analysis.

Career IQ / Edgar’s public example

Experience meets
the next chapter.

Edgar Peña’s AI career analysis for business development in print on demand and apparel printing.

Explore how his experience connects to changing work, what still needs evidence, and which next steps are worth testing.

Explore report Meet Edgar

Prepared September 10, 2026 · Sources and assumptions included

Turn experience into proof.

The strongest direction is commercial leadership that connects customer needs, production and practical AI adoption. Start with a defensible account-growth case and a quote-to-fulfillment demonstration.

Resume claims are self-reported.
Missing evidence does not mean missing ability.

Look inside the analysis

12 topics · 16 sources · 10 resume excerpts

Edgar’s experience, in context

Your submitted evidence supports commercial leadership and cross-functional delivery as a positioning hypothesis. AI evaluation, unit economics and customer-data controls require additional artifacts before they can be assessed. Use existing work first and build only the missing proof. Compare duties and evidence with public occupational frameworks; no matched-peer dataset supports a personal ranking or readiness percentage.

Counts describe the requirements in this report and the submitted experience. They are not a readiness score or a prediction of job security. Select a count to filter.

Commercial attributionPartly supported

Specific results claimed; supporting artifacts missing. A target employer needs a defensible path to profitable growth. Missing artifacts do not establish inability.

Standard: occupational framework

Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.E2 · Public resume excerpt; self-reported, not independently verified

Architected and commercialized AI-enabled customer experience solutions, including a proprietary AI CRM platform and intelligent voice-agent capabilities, contributing more than $1M in incremental annual revenue and supporting 25% year-over-year growth.E5 · Public resume excerpt; self-reported, not independently verified

Established operating procedures, KPIs, forecasting practices, and performance reporting used to identify risks, prioritize resources, and guide strategic decisions.E8 · Public resume excerpt; self-reported, not independently verified

Next step: Create the two-page P3 case first. Use one initiative and a defined period, distinguish cumulative from annual revenue, identify your contribution and other causes, and show reproducible arithmetic. An explicitly hypothetical case proves reasoning, not historical achievement.

www.onetonline.org · View evidence

AI reliability and evaluationNot yet evidenced

The submitted evidence describes AI leadership, but does not contain a test set, error analysis or evaluation results. This competency cannot be assessed from the supplied artifacts. A fast quote assistant can still create wrong prices or impossible promises; missing evidence does not establish inability.

Standard: inference

No supporting excerpt was identified in the submitted evidence.

Next step: Use the same twenty synthetic cases from P1/P2. Preserve expected fields, errors, correction time and the revision history. Missing required facts must remain unknown; unauthorized promises fail the case. Publish observed results only after running the test.

www.nist.gov · View evidence

AI unit economicsNot yet evidenced

The supplied evidence describes revenue and KPI work but does not show AI-specific review, correction, usage or subscription costs. Those costs are needed to assess net value. The current evidence cannot establish this competency; it does not establish inability.

Standard: inference

No supporting excerpt was identified in the submitted evidence.

Next step: Add a per-case worksheet to P3: handling + review + correction time, attributable tool costs and contribution assumptions. State the comparator. Capacity released is not automatically cash savings; use a separate incremental-revenue scenario if demand is unproved.

www.nist.gov · View evidence

Method-specific production constraintsPartly supported

General domain experience present; detailed proof absent. Customers buy suitability and delivery reliability, not printer labels. Missing artifacts do not establish inability.

Standard: inference

Built and scaled a technology-enabled B2B and e-commerce business to more than $5M in annual revenue before acquisition by an international corporation.E1 · Public resume excerpt; self-reported, not independently verified

Developed integrated e-commerce and digital workflows connecting CRM, order management, production, fulfillment, customer service, and automation systems to improve end-to-end execution.E6 · Public resume excerpt; self-reported, not independently verified

Next step: One actual or clearly hypothetical job comparing material, art, method, setup, finishing, quality, service, and lead-time constraints.

Integration resiliencePartly supported

Workflow experience described; failure-recovery evidence absent. Duplicate events or stale statuses can trigger duplicate fulfillment or mislead customers. Missing artifacts do not establish inability.

Standard: inference

Developed integrated e-commerce and digital workflows connecting CRM, order management, production, fulfillment, customer service, and automation systems to improve end-to-end execution.E6 · Public resume excerpt; self-reported, not independently verified

Technology leadership: product roadmap prioritization, business and workflow architecture, requirements definition, UI/UX direction, implementation planning, release coordination, and executive reporting.E7 · Public resume excerpt; self-reported, not independently verified

Next step: Use P2: state diagram plus ten failure/recovery cases, reviewed with a technical partner if available. Distinguish a designed control from an implemented and tested control.

developers.printful.com · View evidence

Customer data controlsNot yet evidenced

No operating rules, access-control record or retention procedure is included in the supplied evidence. General AI leadership or degree statements do not demonstrate these controls. This is an evidence gap, not a conclusion that the customer lacks the skill.

Standard: inference

No supporting excerpt was identified in the submitted evidence.

Next step: Add a one-page data-use and approval policy to P1/P2: permitted fields, redaction, access, retention, owner and stop/escalation behavior. Demonstrate with synthetic records; list missing implementation evidence separately.

www.nist.gov · View evidence

Sales leadership proofPartly supported

KPI/forecasting work described; quota/coaching artifacts missing. A small-employer player-coach role may require personal closing and team development. Missing artifacts do not establish inability.

Standard: occupational framework

Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.E2 · Public resume excerpt; self-reported, not independently verified

Established operating procedures, KPIs, forecasting practices, and performance reporting used to identify risks, prioritize resources, and guide strategic decisions.E8 · Public resume excerpt; self-reported, not independently verified

Next step: For the player-coach path, add a forecast with stage definitions, personal/team quota split and assumptions plus a coaching example. For a strategic-account individual-contributor path, prioritize account growth and negotiation proof; do not invent team-management requirements.

www.onetonline.org · View evidence

Technical ownership boundariesPartly supported

The submitted resume describes business/workflow architecture, product priorities, requirements, UI/UX direction and coordination with technical teams. These support a claimed leadership scope but do not establish personal coding, model engineering or production infrastructure ownership. Evaluate actual role requirements; missing implementation proof does not establish inability.

Standard: inference

Technology leadership: product roadmap prioritization, business and workflow architecture, requirements definition, UI/UX direction, implementation planning, release coordination, and executive reporting.E7 · Public resume excerpt; self-reported, not independently verified

Next step: For one project, identify Edgar's own design, implementation, review and operating responsibilities; distinguish them from team contributions. Match that demonstrated scope to each actual role.

www.nist.gov · View evidence

Where AI changes the work

Prospect research and first-draft outreachassisted · emerging

The printing study reports AI use in customer communications, while the training material covers sales assistance. Extending this to account research and first drafts is an analyst use-case assessment, not measured adoption of this exact task in apparel employers. Validate account facts and approve each communication. For Edgar: E2/E9 suggest account-development context, not verified AI research performance. Proof: two prospect briefs with source links, one rejected unsupported claim and a human-approved draft.

Human responsibility: Define the customer profile, validate account facts, understand the buying group, and approve outreach.

RFQ extraction and CRM entryassisted · emerging

Field extraction is an analyst-proposed use case supported by broader reports of estimating and workflow automation. The cited Printful API is ordinary integration infrastructure; it does not prove AI extraction accuracy or apparel-wide deployment. Missing variants, dates or quantities must remain visible for review. For Edgar: E6 is relevant workflow testimony. Proof: ten synthetic RFQs including contradictory quantities and dates; compare exact fields with an answer key and surface every required unknown.

Human responsibility: Define required inputs, never infer missing specifications silently, own exception handling.

Estimating and quote preparationtransformed · emerging

Tools can draft explanations and comparisons; validated arithmetic, costs, margins, and capacity still govern the offer. Adoption refers to capabilities described by public sources, not proven use by Edgar or a target employer. No job-loss probability is implied. For Edgar: E1/E8 suggest commercial and measurement context. Proof: one quote with reproducible contribution arithmetic and documented margin/capacity approval; zero invented prices or dates in the test set.

Human responsibility: Own pricing/margin rules and approve deviations; distinguish numerical calculation from generated prose.

Artwork and prepress handofftransformed · emerging

The printing announcement describes AI-related prepress use; it does not establish prevalence at apparel employers or adoption by Edgar. Concepts and triage may be assisted, while rights, production quality and customer acceptance require separate review. For Edgar: E6 suggests production-handoff context. Proof: one artwork/version checklist with separate rights, file-quality and customer approvals; a generated concept is not a production release.

Human responsibility: Coordinate customer, designer, and production specialist; retain approved artwork version.

Order status summariesassisted · unknown

Printful documents shipment and order events with retries. That establishes integration capabilities, not deployment of AI-generated summaries. The proposed AI layer is unverified here; an event-based customer view can also work without an LLM. Reconcile duplicates, stale state and partial shipments before communicating status. For Edgar: E6/E7 suggest integration coordination. Proof: ten incident cards covering duplicate, stale and partial events; explain the source of truth, owner and safe next communication.

Human responsibility: Provide a trustworthy customer view and accountable recovery when system states conflict.

developers.printful.com · View evidence

Production method and delivery promisetransformed · unknown

The equipment sources describe physical production and automation capabilities. They do not establish deployed AI that selects the right print method or predicts a specific shop delivery date. Such assistance is a proposed use case; production staff must verify substrate, quality, capacity and maintenance constraints. For Edgar: E1/E6 support domain-experience claims. Proof: a garment/method decision sheet reviewed for actual material and process constraints; mark any untested assumption explicitly.

Human responsibility: Turn customer requirements into commitments explicitly accepted by production.

Negotiation and account recoveryhuman led · unknown

AI can prepare alternatives, while trust, commercial authority, and consequence ownership remain central. Adoption refers to capabilities described by public sources, not proven use by Edgar or a target employer. No job-loss probability is implied. For Edgar: E2/E9 support enterprise-relationship claims. Proof: a permitted or hypothetical recovery brief with alternatives, margin/capacity tradeoffs, authority and an accepted next step.

Human responsibility: Own discovery, negotiation, escalation, renewal, and recovery promises.

www.onetonline.org · View evidence

AI rollout and team adoptiontransformed · emerging

Templates accelerate planning; use-case decisions, adoption, testing, and governance require accountable leadership. Adoption refers to capabilities described by public sources, not proven use by Edgar or a target employer. No job-loss probability is implied. For Edgar: E4/E7 support claimed AI initiative leadership. Proof: one pilot charter, responsibility map, test log and adoption review; state which work you performed and which the team performed.

Human responsibility: Lead a small pilot with engineers and operators, train users, and log defects before expansion.

Edgar’s salary preference

Customer-stated target: minimum USD $100,000 annual base salary plus commission, with no upper base limit supplied. The desired commission amount and total on-target earnings remain unspecified. This is desired pay, not current compensation or an employer offer. The BLS national wage measure includes variable pay, so it cannot establish attainable base pay at a 25-50-person Los Angeles apparel/POD employer. No local vacancy/pay sample was collected.

Edgar’s work arrangement

Desired work arrangement: hybrid in Los Angeles, California, United States, for an employer with 25-50 employees. Whittier is the separately supplied residence city, not the job-search target. Onsite days, commute/travel, and whether headcount means the whole company, site, or team remain unknown. No actual employer fit was established.

Edgar’s next 90 days

LEARN
Choose the first role path and inspect existing proof before selecting any refresher.
Deliverable: One commercial-case outline with provenance, responsibility boundaries and a short list of actual evidence gaps.
BUILD
Build the linked RFQ and exception portfolio with twenty synthetic cases and explicit approval rules.
Deliverable: RFQ checklist, contribution calculation, order-state map, expected-result table and observed test log when executed.
PROVE
Obtain critical review, package permitted evidence and assess actual employer compensation/work terms.
Deliverable: Redacted case and demonstration, dated employer-source shortlist, and base/variable offer comparison with unknowns visible.

What this comparison cannot establish

Career achievements are self-reported. No employer reference checks, financial schedules, degree verification or project evaluation logs were reviewed.
No representative Los Angeles vacancy/base-pay sample or POD/apparel market-growth forecast was established. National occupational data span industries and are not employer offers.
Task and evidence labels are qualitative assessments, not validated job-loss probabilities, career scores or peer percentiles. Missing proof does not establish missing ability.
Role paths and adoption scenarios are planning hypotheses. No representative matched-professional sample or causal training-to-salary evidence was reviewed.
Training is optional and should follow a demonstrated evidence gap. Suggested effort is adjustable planning time; course completion, a portfolio or a 90-day plan does not guarantee competence, employment or pay.

Sources & limitations · 16 references

Sources were reviewed while preparing this report in Codex. This does not independently verify the claims, chart figures, publisher names or quoted excerpts. Open the original source for consequential decisions.

PRINTING United AI adoption research announcement
printing.org · 2025-09-23
Epson G6070 DTFilm product specification
epson.com · Publication date not established
Epson G6070 maintenance guidance
epson.com · 2025-04-22
Kornit Apollo production workflow
kornit.com · 2025-05-27
Printful API documentation
developers.printful.com · Publication date not established
Shopify Sidekick setup and limitations
help.shopify.com · Publication date not established
O*NET Sales Managers
onetonline.org · 2026
BLS Sales Managers Occupational Outlook Handbook
bls.gov · 2026-08-27
NIST AI Risk Management Framework
nist.gov · Publication date not established
HubSpot Academy AI for Sales
academy.hubspot.com · Publication date not established
Microsoft Learn Transform your business with AI
learn.microsoft.com · Publication date not established
Microsoft Learn cost and sandbox FAQ
learn.microsoft.com · Publication date not established
Google Analytics Academy
support.google.com · Publication date not established
US Copyright Office AI report announcement
copyright.gov · 2025-01-29
BLS Wholesale and Manufacturing Sales Representatives
bls.gov · 2026-08-27
BLS Sales Engineers
bls.gov · 2026-08-27

Career achievements are self-reported. No employer reference checks, financial schedules, degree verification or project evaluation logs were reviewed.
No representative Los Angeles vacancy/base-pay sample or POD/apparel market-growth forecast was established. National occupational data span industries and are not employer offers.
Task and evidence labels are qualitative assessments, not validated job-loss probabilities, career scores or peer percentiles. Missing proof does not establish missing ability.
Role paths and adoption scenarios are planning hypotheses. No representative matched-professional sample or causal training-to-salary evidence was reviewed.
Training is optional and should follow a demonstrated evidence gap. Suggested effort is adjustable planning time; course completion, a portfolio or a 90-day plan does not guarantee competence, employment or pay.
Sources were reviewed September 9-10, 2026. Product capabilities, course details and labor projections can change. The employment chart has two endpoints and is not an observed annual series.

Career IQ · Explore your options

Skills, salary and industry scenarios

Choose a role, build a learning plan and test your pay assumptions. Keep the evidence and the decisions in view.

These are planning tools. Changing a selection does not change the report’s evidence, predict a raise or update your saved career information.

01 / Role and market

Start with the work you want to do

Explore a role path

E1, E2, E6 and E9 describe customer growth and delivery complexity. Position the account-growth story around profitable programs and dependable commitments.

Proof to develop for this path

One defensible account-growth case
Personal quota, territory and negotiation scope
Current local employer base and commission evidence

Published wage distribution

Wholesale and manufacturing sales representatives, except technical and scientific products

United States; all industries · May 2025 · SOC 41-4012

10th percentile: $39,090
Median: $72,080
90th percentile: $137,550

$0$160,000

Pay definition: Annual occupational wages including commissions and production bonuses; not base salary or OTE

Broad goods-sales context that includes clothing, not a study of senior apparel/POD account leads. Do not compare its total-wage median directly with a desired fixed base or infer Edgar's attainable pay.

These published percentiles describe an occupation’s wages. They do not place you at a percentile or estimate a skill’s pay premium.

Read the wage source · www.bls.gov

02 / Skills and proof

Build a plan you can demonstrate

Select what you want to practice. The plan adds the report’s estimated time and resource costs; it does not add a salary premium or mark a skill as mastered.

Defensible strategic-account growthRelevant to this role path

Two-page account case: problem, period, baseline, buying group, own/team role, outcome and supporting record.

4–6 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: A reviewer can distinguish annual/cumulative results and personal/team contribution; hypothetical numbers are labeled.

You may already have this proof: You already have a permitted, well-supported account-growth case.

Time basis: editable planning assumption

Resource cost: $0. Local case preparation using existing tools; excludes time and optional coaching.

Report evidence references: E1, E2, E9

Optional HubSpot AI for Sales refresher (36-minute listed course) · academy.hubspot.com

RFQ, margin and production decisionsRelevant to this role path

Required-field checklist, ten RFQs, expected fields, quote arithmetic and approval map.

6–10 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: Unknown quantities, artwork status and delivery facts stay visible; arithmetic is reproducible and consequential promises require approval.

You may already have this proof: You can already show a relevant permitted quoting workflow and explain its failure cases.

Time basis: editable planning assumption

Resource cost: $0. Synthetic local RFQs and spreadsheet; no paid model, real order or equipment purchase.

Report evidence references: E1, E6, E8

Sales Managers task framework · www.onetonline.org

Order integration and exception recoveryRelevant to this role path

State diagram and ten duplicate, stale, missing and partial-shipment incident cards.

6–10 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: Each case has a trusted state, responsible owner and recovery action; designed versus executed controls are distinguished.

You may already have this proof: Equivalent safe failure-recovery evidence already exists and you can explain your role.

Time basis: editable planning assumption

Resource cost: $0. Synthetic event/tabletop exercise with existing tools; no live API calls or orders.

Report evidence references: E6, E7

Printful order/event documentation · developers.printful.com

AI workflow evaluation

Expected-result rubric and review log for the shared twenty RFQ/exception cases.

5–8 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: Required unknowns remain unknown; unsupported price/delivery promises fail; actual results require execution and a recorded revision.

You may already have this proof: You already have a relevant test set, error analysis and documented evaluation results.

Time basis: editable planning assumption

Resource cost: $0. Local evaluation design/tabletop using existing tools; does not purchase or call a model.

Report evidence references: E4, E7

NIST AI Risk Management Framework · www.nist.gov

AI value and operating costs

Per-case baseline, handling/review/rework time, attributable cost and a labeled contribution scenario.

4–6 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: Arithmetic is reproducible; capacity, cash savings and incremental revenue remain separate; unobserved assumptions are labeled.

You may already have this proof: A comparable measurement/cost case already demonstrates the intended skill.

Time basis: editable planning assumption

Resource cost: $0. Local measurement worksheet using existing tools; no paid advertising or cloud resources.

Report evidence references: E5, E8

Sales forecast and coaching

Stage definitions, forecast example, quota split and one coaching conversation outline.

3–5 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: Observed history is separated from assumptions; product-team leadership is not silently relabeled sales-team leadership.

You may already have this proof: Existing permitted sales-management artifacts already demonstrate these responsibilities.

Time basis: editable planning assumption

Resource cost: $0. Synthetic forecast and coaching outline in existing document tools.

Report evidence references: E2, E3, E8

O*NET Sales Managers · www.onetonline.org

Customer-data and action controls

Allowed fields, redaction, access, retention, action approvals and escalation rules.

2–3 hours · Planning estimate

Evidence, completion and learning resources

Evidence to produce: A reviewer can identify who may do what and what must stop; a policy is not proof that controls are implemented.

You may already have this proof: Existing permitted controls and demonstrations cover the relevant workflow.

Time basis: editable planning assumption

Resource cost: $0. One-page policy and synthetic examples; excludes production security implementation.

NIST AI Risk Management Framework · www.nist.gov

Time available each week (hours)Editable planning assumption. Zero leaves the plan unscheduled.

Additional training budget ($)Your optional budget for coaching, certificates or tools. This is not an AI BURRO price quote.

Selected skills0

Estimated effort0–0 hours

At your weekly paceChoose skills

Known costs + your budget$0

Time estimates assume the work is additive and do not account for overlap, prior learning or completion delays. Check each learning resource for certificate, software and optional paid-service charges.

Choose training only where proof is missing

Try the artifact with existing knowledge first.
Use free selected reading if it resolves a specific gap.
If you need structured practice or feedback, request a sample task, review rubric, prerequisites, total time and total price before comparing paid options.
Evaluate the resulting work with the same rubric used before training; do not equate a certificate with demonstrated ability.

Use the report to discuss a tailored learning plan or request information. No course availability, enrollment, purchase, certification or salary outcome is implied.

03 / Pay planning

Explore a possible offer

The starting $100,000 is the requested annual base target. Current earnings are not supplied. Commission is modeled separately.

Desired annual base target ($)A goal, not current income.

Hypothetical offer base ($/year)Enter a possible offer or your own scenario.

Target annual variable pay ($)Commission or bonus at 100% target attainment. The starting amount is illustrative, not a supplied pay preference.

Assumed variable payout (%)0–200% assumption. Actual plans may use different thresholds, caps and rules.

Current annual base ($, optional)Only enter this if you want a comparison to current base. It stays in this view.

Hypothetical annual cash pay$130,000Before tax · Benefits and equity excluded

Base: $100,000
Assumed variable pay: $30,000

Base + target variable pay × assumed payout

Offer base vs desired base$0 / year

This hypothetical base meets the entered base target. Variable pay does not close a guaranteed-base gap.

Offer base vs current baseCurrent base not entered

No raise or current-pay percentage can be calculated from a desired salary alone.

Pay assumptions and limits

The $30,000 target variable is an invented example for arithmetic, not Edgar's stated goal or an observed employer range.
Payout fraction means the share of target variable actually paid. It is not quota attainment or the probability of success; actual plans may have nonlinear thresholds or accelerators.
The arithmetic assumes a full employment year, no recoverable draw, no clawback, and no other bonus, equity or benefits. It excludes taxes.
Compare the hypothetical base only with the desired base floor. Do not describe total illustrative compensation as guaranteed or compare it with current income, which is unknown.
Selecting skills never alters these inputs automatically. A user can change them explicitly after reviewing actual employer evidence.

Published occupational wages are shown separately because their pay definition can include commission and production bonuses. They are not a matched base-pay, offer or personal-earnings estimate.

04 / Industry futures

What changes if adoption moves faster?

Explore conditional paths over next 12–24 months. These scenarios carry no assigned probability or personal job-loss estimate.

Choose an adoption assumptionSlow adoptionSelective adoptionAccelerated adoption

Assumption: Planning case: employers adopt narrow assistance for high-volume tasks while retaining human approval for commercial decisions. This is the default exploration case, not a forecast.

Work that changes

RFQ extraction, draft quotes and status summaries become supervised workflow steps.
More time moves toward exceptions, quality review and account development.
Adoption decisions depend on measured correction time and costs.

Where people add value

Own evaluation, margin rules and exception handling.
Translate customer requirements into an operationally accepted promise.

Signals to watch

Named pilot owner and a before/after measurement plan.
Human approvals logged for price, artwork and delivery.
Regular review of errors and staff adoption.

Skills to consider: RFQ, margin and production decisions; AI workflow evaluation; AI value and operating costs; Customer-data and action controls

The scenario does not select training for you or alter the wage benchmark.

05 / Evidence against expectations

Compare evidence with role standards

Public occupational frameworks and analyst-designed proof criteria; these are not measurements from a matched sample of professionals.

No representative peer cohort is supplied, so this view cannot rank you against other professionals. Missing proof means evidence still needs to be supplied or tested; it does not establish that a skill is absent.

Strategic account growth

Role expectation: Goods-sales work includes customer needs, negotiation and follow-through.
Supplied evidence: E1/E2/E9 describe growth and enterprise relationships; self-reported.
Proof still needed: A dated case with attributable results and permitted supporting evidence.

Read role evidence · www.bls.gov

Sales leadership

Role expectation: Sales managers direct goals, forecasts and staff development.
Supplied evidence: E3/E8 describe team and measurement work; sales-coaching scope is not established.
Proof still needed: Forecast, personal/team quota split and actual sales coaching example.

Read role evidence · www.onetonline.org

Technical solutions work

Role expectation: Sales engineering involves technical product expertise and customer-facing solution explanation.
Supplied evidence: E7 describes product/requirements coordination; hands-on technical demonstration is unassessed.
Proof still needed: Role-matched discovery and demonstration with personal responsibility boundaries.

Read role evidence · www.bls.gov

Responsible AI workflow

Role expectation: The NIST framework provides risk-management structure; it is not a personal certification or career scoring system.
Supplied evidence: E4/E5 describe AI initiatives; evaluation and controls were not supplied.
Proof still needed: Evaluation cases, review/rework cost and an inspectable data/action policy.

Read role evidence · www.nist.gov

No defensible claim can be made that Edgar ranks above or below other professionals on AI readiness. Wage percentiles are occupational pay statistics, not skill or employability percentiles.

Career IQ · Employer comparison

Company fit for Edgar Pena

10 employer groups · Sources reviewed 2026-09-10 UTC · Unranked selection

Your target: Business development; Los Angeles, California, United States; hybrid; 25–50 employees; at least $100,000 annual base plus commission.

Actual hiring decision: UNKNOWN for every company. A simulated resume screen is an analyst assessment of the supplied evidence. It is not employer feedback or an offer.

Selection method, decisions and limits

Ten prominent relevant employer groups; illustrative shortlist, not an objective top-ten ranking.

Purposive coverage of six POD/custom-apparel commerce or fulfillment groups and four apparel/printing supply-chain technology groups, using official company and careers sources. Prominence is a qualitative selection judgment based on the documented business scope; no revenue, market-share or hiring rank is asserted.

Printful, Printify and Snow Commerce count as FYUL once; Gooten/Taylor count once; Redbubble/TeePublic count as Articore once; other affiliated brands are not duplicated. Known relationships are recorded; ultimate beneficial ownership was not audited.

Prominent industry groups are often outside a 25–50-person employer target. This shortlist exposes mismatches and unknowns instead of silently changing that preference. Equipment examples include adjacent roles and are labeled accordingly.

Simulated resume screen

YES: The self-reported evidence plausibly aligns with the reviewed role's central scope. It is a preliminary analyst screen, subject to verification and explicitly listed gaps.
NO: The supplied evidence does not yet support prioritizing the specific reviewed role; missing evidence does not prove inability.
UNKNOWN: No sufficiently current/specific requirements were available, or the review stopped at a clear target mismatch. No inferred hiring rejection.

Application recommendation

YES — worth applying now: An identified current role is sufficiently supported and practical constraints are sufficiently resolved to recommend an application. No company reaches that threshold in this bounded review.
NO — prepare or resolve the mismatch first: Do not prioritize the reviewed role under the current evidence/targets; prepare specified proof or resolve the documented mismatch first.
UNKNOWN — need job details: Industry/profile affinity may be promising, but vacancy status or required practical terms must be confirmed.

The only candidate evidence used is the ten supplied/public-approved resume excerpts. Achievements, employment, customer relationships, revenue, role dates and education have not been independently verified.
A career page or open application link is not proof the role is still accepting candidates. Current indexes, ambiguous pay wording and retrieval failures are noted.
Official corporate descriptions are first-party claims; historical and internally inconsistent staff figures are labeled. Unknowns remain null or explicit rather than estimated.
The public report should not convert simulated YES/NO screens into 'this company would/would not hire Edgar.' No actual employer decision or probability is available.
Base pay, commission, bonus and total earnings are distinct. Partial overlap with a range does not establish an offer; up-to variable pay is not guaranteed.
Published remote or generic hybrid policies do not establish LA-based eligibility. Corporate HQ is not necessarily the worksite or territory.
The absence of a qualification artifact is an evidence gap, not a conclusion of inability. No protected traits are inferred or used.
Role scope and company fit are evaluated as of the stated UTC review date; recheck before relying on a vacancy or submitting an application.

FYUL strategic accounts and Kornit's workflow-sales archetype provide the clearest commercial matches to the submitted evidence. Kornit's vacancy is unconfirmed. None of the ten has a verified current opening that satisfies all four requirements together: business development, LA hybrid, 25–50 employees, and at least $100,000 base plus commission. This is a bounded evidence result, not proof that such roles do not exist.

Choose a company (10)

Company

Company and role assessment

FYUL — Printful, Printify and Snow Commerce

On-demand merchandise fulfillment, e-commerce technology and enterprise merchandise services. Sources: S1: FYUL group and careers (opens in a new tab)

One consolidated group; Printful, Printify and Snow Commerce are not counted as three independent employers.

Company website Official careers

Simulated resume screen

YES

Plausible initial resume screen for this role, assuming the supplied claims can be substantiated; not a hiring prediction.

E2 and E9 connect enterprise relationship development with implementation and account expansion; E3 supports claimed staff leadership and E8 forecasting. These are relevant to portfolio growth and retention. Confirm degree status (E10), chronology and direct quota contribution before representing all requirements as satisfied.

Actual employer hiring decision: UNKNOWN

Application recommendation

UNKNOWN — need job details

The resume offers a credible commercial fit. The job's geography, pay wording and variable compensation do not yet establish the requested package. A positive simulated screen therefore does not equal an instruction to apply under the original constraints.

Strategic Account Manager

Listed on the official board at review

Strategic accounts / business development

At least five years of relevant account/BD/strategy work, staff management, e-commerce or related experience, CRM capability and a business-related degree.

Read the official role page (opens in a new tab)

Sources: S2: FYUL Strategic Account Manager, S3: FYUL current openings

Location: Cincinnati, Ohio; remote work may be approved by the manager. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)
Work arrangement: Tagged hybrid; manager-approved remote alternative. No Los Angeles hybrid office established. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)
Employer size: Not verified
Pay: $80,000–$115,000 per year (salary range; base wording unconfirmed). Additional 10% based on company and individual performance; commission not established. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)

Fit with your preferences

Business development: Account growth aligns with business development. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)
Los Angeles + hybrid: Cincinnati hybrid is not the requested LA hybrid; remote exception needs confirmation. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)
25–50 employees: No verified whole-employer headcount; multiple brands do not establish a 25–50-person company.
$100,000+ base and commission: Upper salary range reaches the target; exact base and commission structure remain unconfirmed. Sources: S2: FYUL Strategic Account Manager (opens in a new tab)

Matched resume evidence

Submitted claims; not independently verified. Missing proof does not mean missing ability.

E2 · Enterprise lifecycle and account growth

Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.

Submitted resume; self-reported, not independently verified.

E9 · Executive relationships and tailored fulfillment solutions

Developed and maintained executive relationships with strategic enterprise customers including Uber, Google, Meta, and Coca-Cola, translating business needs into customized fulfillment, e-commerce, software integration, and operational solutions.

Submitted resume; self-reported, not independently verified.

E3 · Team leadership

Helped build and scale Wurkflow.net into a seven-figure SaaS platform; led a 25-person team of software developers and UI/UX designers across three corporate offices, setting product priorities, business requirements, workflow logic, customer experience standards, and release objectives.

Submitted resume; self-reported, not independently verified.

E8 · Forecasting and KPIs

Established operating procedures, KPIs, forecasting practices, and performance reporting used to identify risks, prioritize resources, and guide strategic decisions.

Submitted resume; self-reported, not independently verified.

E10 · Degree status requires confirmation

Bachelor of Business Administration, Entrepreneurship | California State University, Los Angeles | 2026

Submitted resume; self-reported, not independently verified.

Gaps and unknowns

Account retention, portfolio margin and quota-attainment evidence are not supplied.
Degree awarded/expected status and exact relevant-role dates are unresolved.

Next actions

Confirm LA-based arrangement and whether remote work would satisfy the preference.
Confirm at least $100,000 base, commission versus performance bonus, and legal-employer headcount.
Prepare one evidence-backed account growth/retention case with Edgar's personal contribution.

Company sources reviewed 2026-09-10. Recheck role availability and terms before applying.

Career IQ · Public profile comparison

LinkedIn and resume, compared.

Edgar O. Peña · Prepared September 10, 2026

View public LinkedIn (opens in a new tab)

The accessible LinkedIn material is broadly consistent with a business-development profile interested in practical AI, strategy and team adoption. It adds self-reported research and publishing activity, but it does not independently verify the resume's commercial achievements or resolve education and employment chronology. For the apparel/POD target, the strongest improvement is to connect that broad AI story to a specific customer problem and defensible commercial proof. No contradiction is established from the limited public view.

What was visible, and what was outside the review

Reviewed public material

Visible profile top card and one truncated About sentence
Visible professional service labels
Two truncated project entries
Eight visible professional activity entries, including one article reshare
Three article titles, dates and short previews

Not reviewed

Expanded About: sign-in required
Full Experience and Education: masked/sign-in required
Expanded project descriptions
Full linked article bodies
Complete activity history, comments or reactions
Employment records, academic records, references or customer records

Relative labels such as 3w and 4mo are preserved as displayed; no exact post date was inferred from them or from LinkedIn activity identifiers.

How the two accounts connect

7 topics · Open a comparison to inspect its evidence

AI-enabled business development positioningConsistent self-reported accounts

The public material emphasizes AI strategy, business use cases and human-led adoption, consistent with the resume's stated customer lifecycle and AI/product leadership scope.

Evidence limits

Two accounts authored by the same person are not independent corroboration. Neither source alone proves operating reliability, hands-on engineering ownership or the quality of the work.

Next step

Connect one public AI/business post to a concrete apparel customer problem, Edgar's contribution and a clearly supported result.

Compare the supporting excerpts 4 public references · 3 resume references

Public profile and activity

P2 Services
Visible service labels include business/management consulting, strategic planning, project management, coaching and marketing services.
These are self-selected service categories, not independent certifications or proof of completed consulting engagements.
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P5 Agentic AI post
Where are people stuck, frustrated, or underutilized?
The post frames agent work around people, workflow problems, organizational change and support for teams.
This aligns with a human-led implementation/adoption narrative but does not demonstrate a running system or measured outcome.
7mo displayed; exact date unverified
- L5 · Agentic AI and supporting people at work (opens in a new tab)
P6 Mindset post
Creativity to see what others miss.
The post presents creativity, curiosity and a creator mindset as lessons from thesis research interviews.
This is the author's research interpretation and professional viewpoint, not a representative labor-market finding or hiring standard.
4mo displayed; exact date unverified
- L4 · Creativity, curiosity and creator mindset (opens in a new tab)
P9 Articles and newsletter activity
The profile lists three dated AI/strategy newsletter or article entries, and a visible post describes a strategy article informed by 25+ interviews.
This establishes visible publishing activity and topics. Article bodies were not assessed, and 25+ interviews is compatible with the later stated 26 rather than evidence of inconsistent counts.
Article dates visible: 2026-03-04, 2025-11-25, 2025-11-09

Submitted resume

E2 · Resume excerpt
Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.
Submitted resume excerpt; self-reported and not independently verified.
E4 · Resume excerpt
Currently lead AI initiatives involving generative AI, large language models, conversational AI, intelligent voice agents, CRM automation, and analytics, identifying practical use cases and partnering with technical teams on implementation and rollout.
Submitted resume excerpt; self-reported and not independently verified.
E7 · Resume excerpt
Technology leadership: product roadmap prioritization, business and workflow architecture, requirements definition, UI/UX direction, implementation planning, release coordination, and executive reporting.
Submitted resume excerpt; self-reported and not independently verified.

Apparel/POD relevance for the target searchClarify the target story

The accessible profile foregrounds broad AI/strategy interests. The supplied resume gives a more specific commercial story around e-commerce, fulfillment, enterprise relationships and growth.

Evidence limits

The full About and Experience are hidden; their absence from the public capture cannot establish that the complete profile omits apparel/POD experience.

Next step

When revising the profile, make the bridge to the chosen business-development target easy to find: apparel/POD buyer problems, enterprise account work and quote-to-fulfillment execution.

Compare the supporting excerpts 5 public references · 4 resume references

Public profile and activity

P1 Top card and About preview
Most executives build their career in one industry. I built mine across several, on…
The top card displays clickpeel.com and Los Angeles Metropolitan Area. The visible About introduces cross-industry executive experience and stops mid-sentence.
This establishes visible positioning only; it does not reveal the full headline, About text, job title, current employment status or tenure.
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P2 Services
Visible service labels include business/management consulting, strategic planning, project management, coaching and marketing services.
These are self-selected service categories, not independent certifications or proof of completed consulting engagements.
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P3 Projects
Two entries carry the same AI-agents/economic-impact thesis project title. One shows Aug 2024; another says work began in 2024. Both visible descriptions are truncated.
The project describes research intent and practical-business interests. A listed project start and overlapping descriptions do not establish two completed studies, thesis acceptance or evaluated deployment.
Aug 2024 displayed on one entry; second entry has no calendar date
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P4 Thesis defense post
I had the opportunity to interview 26 industry professionals
The post announces a forthcoming Cal State LA Honors College thesis defense, names a thesis about mindset and AI, and describes interviews across multiple industries including printing.
Self-reported research activity can support a thought-leadership narrative; the post does not itself verify thesis completion, methods quality, representativeness, peer review or a degree award.
4mo displayed; exact date unverified
- L3 · AI thesis defense announcement (opens in a new tab)
P9 Articles and newsletter activity
The profile lists three dated AI/strategy newsletter or article entries, and a visible post describes a strategy article informed by 25+ interviews.
This establishes visible publishing activity and topics. Article bodies were not assessed, and 25+ interviews is compatible with the later stated 26 rather than evidence of inconsistent counts.
Article dates visible: 2026-03-04, 2025-11-25, 2025-11-09

Submitted resume

E1 · Resume excerpt
Built and scaled a technology-enabled B2B and e-commerce business to more than $5M in annual revenue before acquisition by an international corporation.
Submitted resume excerpt; self-reported and not independently verified.
E2 · Resume excerpt
Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.
Submitted resume excerpt; self-reported and not independently verified.
E6 · Resume excerpt
Developed integrated e-commerce and digital workflows connecting CRM, order management, production, fulfillment, customer service, and automation systems to improve end-to-end execution.
Submitted resume excerpt; self-reported and not independently verified.
E9 · Resume excerpt
Developed and maintained executive relationships with strategic enterprise customers including Uber, Google, Meta, and Coca-Cola, translating business needs into customized fulfillment, e-commerce, software integration, and operational solutions.
Submitted resume excerpt; self-reported and not independently verified.

Commercial achievements and implementation proofClaims remain self-reported

Public AI discussion adds context but does not independently substantiate the resume's $5M business scale, $10M revenue, $1M incremental annual contribution, 25% growth, team size or named customer relationships.

Evidence limits

These claims were not disproved. The capture is incomplete, and follower counts, article activity and post engagement are not substitutes for revenue or delivery evidence.

Next step

Prepare one approved/redacted commercial case identifying time period, baseline, revenue type, personal/team attribution, margin or retention context and a defensible evidence source.

Compare the supporting excerpts 3 public references · 7 resume references

Public profile and activity

P3 Projects
Two entries carry the same AI-agents/economic-impact thesis project title. One shows Aug 2024; another says work began in 2024. Both visible descriptions are truncated.
The project describes research intent and practical-business interests. A listed project start and overlapping descriptions do not establish two completed studies, thesis acceptance or evaluated deployment.
Aug 2024 displayed on one entry; second entry has no calendar date
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P5 Agentic AI post
Where are people stuck, frustrated, or underutilized?
The post frames agent work around people, workflow problems, organizational change and support for teams.
This aligns with a human-led implementation/adoption narrative but does not demonstrate a running system or measured outcome.
7mo displayed; exact date unverified
- L5 · Agentic AI and supporting people at work (opens in a new tab)
P9 Articles and newsletter activity
The profile lists three dated AI/strategy newsletter or article entries, and a visible post describes a strategy article informed by 25+ interviews.
This establishes visible publishing activity and topics. Article bodies were not assessed, and 25+ interviews is compatible with the later stated 26 rather than evidence of inconsistent counts.
Article dates visible: 2026-03-04, 2025-11-25, 2025-11-09

Submitted resume

E1 · Resume excerpt
Built and scaled a technology-enabled B2B and e-commerce business to more than $5M in annual revenue before acquisition by an international corporation.
Submitted resume excerpt; self-reported and not independently verified.
E2 · Resume excerpt
Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.
Submitted resume excerpt; self-reported and not independently verified.
E3 · Resume excerpt
Helped build and scale Wurkflow.net into a seven-figure SaaS platform; led a 25-person team of software developers and UI/UX designers across three corporate offices, setting product priorities, business requirements, workflow logic, customer experience standards, and release objectives.
Submitted resume excerpt; self-reported and not independently verified.
E5 · Resume excerpt
Architected and commercialized AI-enabled customer experience solutions, including a proprietary AI CRM platform and intelligent voice-agent capabilities, contributing more than $1M in incremental annual revenue and supporting 25% year-over-year growth.
Submitted resume excerpt; self-reported and not independently verified.
E6 · Resume excerpt
Developed integrated e-commerce and digital workflows connecting CRM, order management, production, fulfillment, customer service, and automation systems to improve end-to-end execution.
Submitted resume excerpt; self-reported and not independently verified.
E8 · Resume excerpt
Established operating procedures, KPIs, forecasting practices, and performance reporting used to identify risks, prioritize resources, and guide strategic decisions.
Submitted resume excerpt; self-reported and not independently verified.
E9 · Resume excerpt
Developed and maintained executive relationships with strategic enterprise customers including Uber, Google, Meta, and Coca-Cola, translating business needs into customized fulfillment, e-commerce, software integration, and operational solutions.
Submitted resume excerpt; self-reported and not independently verified.

Employment and business-identity continuityTimeline needs clarification

The public top card shows clickpeel.com, while the supplied resume/report discusses OmniPrint and concurrent Wurkflow responsibilities. The visible capture does not show the titles and dates needed to relate these organizations.

Evidence limits

Do not infer a job change, inconsistency, omission or simultaneous full-time jobs from a top-card association. Concurrent business interests, branding and employment are different possibilities that remain unresolved.

Next step

Confirm what clickpeel represents, its dates/current status, and how it relates to OmniPrint and Wurkflow; keep distinct organization/role timelines in any future update.

Compare the supporting excerpts 1 public references · 1 resume references

Public profile and activity

P1 Top card and About preview
Most executives build their career in one industry. I built mine across several, on…
The top card displays clickpeel.com and Los Angeles Metropolitan Area. The visible About introduces cross-industry executive experience and stops mid-sentence.
This establishes visible positioning only; it does not reveal the full headline, About text, job title, current employment status or tenure.
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)

Submitted resume

E3 · Resume excerpt
Helped build and scale Wurkflow.net into a seven-figure SaaS platform; led a 25-person team of software developers and UI/UX designers across three corporate offices, setting product priorities, business requirements, workflow logic, customer experience standards, and release objectives.
Submitted resume excerpt; self-reported and not independently verified.

Research and practical AI thought leadershipAdds professional context

Visible projects, thesis-defense discussion and dated articles add a research-and-communication dimension to the resume. The earlier 25+ interview statement and later 26 interview statement are numerically compatible.

Evidence limits

The public snippets do not establish a completed/accepted thesis, research quality or representative findings. Broad AI interviews cannot be converted into apparel-industry adoption percentages or individual hiring probabilities.

Next step

Provide the approved thesis title/version, completion status and a short methods/limitations note. If the two project entries represent the same project, consolidate them after confirming that their purposes do not differ.

Compare the supporting excerpts 5 public references · 3 resume references

Public profile and activity

P3 Projects
Two entries carry the same AI-agents/economic-impact thesis project title. One shows Aug 2024; another says work began in 2024. Both visible descriptions are truncated.
The project describes research intent and practical-business interests. A listed project start and overlapping descriptions do not establish two completed studies, thesis acceptance or evaluated deployment.
Aug 2024 displayed on one entry; second entry has no calendar date
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
P4 Thesis defense post
I had the opportunity to interview 26 industry professionals
The post announces a forthcoming Cal State LA Honors College thesis defense, names a thesis about mindset and AI, and describes interviews across multiple industries including printing.
Self-reported research activity can support a thought-leadership narrative; the post does not itself verify thesis completion, methods quality, representativeness, peer review or a degree award.
4mo displayed; exact date unverified
- L3 · AI thesis defense announcement (opens in a new tab)
P5 Agentic AI post
Where are people stuck, frustrated, or underutilized?
The post frames agent work around people, workflow problems, organizational change and support for teams.
This aligns with a human-led implementation/adoption narrative but does not demonstrate a running system or measured outcome.
7mo displayed; exact date unverified
- L5 · Agentic AI and supporting people at work (opens in a new tab)
P6 Mindset post
Creativity to see what others miss.
The post presents creativity, curiosity and a creator mindset as lessons from thesis research interviews.
This is the author's research interpretation and professional viewpoint, not a representative labor-market finding or hiring standard.
4mo displayed; exact date unverified
- L4 · Creativity, curiosity and creator mindset (opens in a new tab)
P9 Articles and newsletter activity
The profile lists three dated AI/strategy newsletter or article entries, and a visible post describes a strategy article informed by 25+ interviews.
This establishes visible publishing activity and topics. Article bodies were not assessed, and 25+ interviews is compatible with the later stated 26 rather than evidence of inconsistent counts.
Article dates visible: 2026-03-04, 2025-11-25, 2025-11-09

Submitted resume

E4 · Resume excerpt
Currently lead AI initiatives involving generative AI, large language models, conversational AI, intelligent voice agents, CRM automation, and analytics, identifying practical use cases and partnering with technical teams on implementation and rollout.
Submitted resume excerpt; self-reported and not independently verified.
E7 · Resume excerpt
Technology leadership: product roadmap prioritization, business and workflow architecture, requirements definition, UI/UX direction, implementation planning, release coordination, and executive reporting.
Submitted resume excerpt; self-reported and not independently verified.
E10 · Resume excerpt
Bachelor of Business Administration, Entrepreneurship | California State University, Los Angeles | 2026
Submitted resume excerpt; self-reported and not independently verified.

Education chronologyConfirm education status

The resume's Cal State LA 2026 degree entry still needs an awarded/expected status. The newer visible USC post appears to describe choosing a year away after acceptance announced earlier.

Evidence limits

A defense announcement is not degree verification. Acceptance is not enrollment or an earned MBA; a personal plan to return in 2027 is not proof of an institutional deferral. USC posts do not by themselves conflict with the separate Cal State LA degree entry.

Next step

Confirm Cal State LA award status/month. If USC is mentioned in future career materials, use only the exact current, supportable status and avoid describing an MBA as completed or underway without evidence.

Compare the supporting excerpts 3 public references · 1 resume references

Public profile and activity

P4 Thesis defense post
I had the opportunity to interview 26 industry professionals
The post announces a forthcoming Cal State LA Honors College thesis defense, names a thesis about mindset and AI, and describes interviews across multiple industries including printing.
Self-reported research activity can support a thought-leadership narrative; the post does not itself verify thesis completion, methods quality, representativeness, peer review or a degree award.
4mo displayed; exact date unverified
- L3 · AI thesis defense announcement (opens in a new tab)
P7 Earlier education post
I have officially been accepted into the USC Marshall Executive MBA program!
The earlier post announces acceptance and asks whether pursuing the program would be worthwhile.
This is self-reported acceptance, not enrollment or an earned MBA.
4mo displayed; exact date unverified
- L2 · USC Marshall Executive MBA acceptance announcement (opens in a new tab)
P8 Newer education post
I decided to take a year off, invest that time into myself and AI, and circle back in 2027.
The newer post describes acceptance for Fall 2026 followed by choosing to postpone further study while focusing on AI and building.
This appears to update the earlier acceptance announcement. It does not prove a university-approved deferral, enrollment, re-admission or a degree.
3w displayed; exact date unverified
- L1 · AI, MBA and the decision to defer further study (opens in a new tab)

Submitted resume

E10 · Resume excerpt
Bachelor of Business Administration, Entrepreneurship | California State University, Los Angeles | 2026
Submitted resume excerpt; self-reported and not independently verified.

Location, employer size and pay preferencesPublic view cannot assess

The visible Los Angeles Metropolitan Area label is broadly compatible with the requested search region. The profile capture does not establish willingness to work hybrid, a 25–50-person employer preference or at least $100,000 base plus commission.

Evidence limits

Those are supplied job-search preferences. They must not become claims about current earnings, a current employer's size, commute feasibility or a prospective offer.

Next step

Retain the submitted targets in job matching and verify each actual role's location, onsite days, legal-employer headcount, base pay and variable compensation separately.

Compare the supporting excerpts 1 public references · 0 resume references

Public profile and activity

P1 Top card and About preview
Most executives build their career in one industry. I built mine across several, on…
The top card displays clickpeel.com and Los Angeles Metropolitan Area. The visible About introduces cross-industry executive experience and stops mid-sentence.
This establishes visible positioning only; it does not reveal the full headline, About text, job title, current employment status or tenure.
- L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)

Submitted resume

This topic uses the supplied search preferences; no resume excerpt is cited.

Practical next steps

Confirm the employment and education timeline.
Prepare: Exact titles/dates for clickpeel, OmniPrint and concurrent Wurkflow scope; Cal State LA awarded/expected status and month; accurate current USC status if included.
A clear timeline prevents readers from making unsupported assumptions from separate profile fragments.
Lead the public career story with the chosen buyer and commercial contribution.
Prepare: A concise headline/About opening connecting business development, apparel/POD, enterprise accounts and practical workflow improvement, using only supportable experience.
The visible public AI discussion is broad; a targeted opening makes relevance to the chosen role easier to assess.
Publish or privately present one evidence-backed case when approved.
Prepare: A redacted or synthetic quote/order workflow plus a separately supported commercial case, clearly distinguishing measured outcomes from demonstration assumptions.
This provides role-relevant proof beyond social activity and broad AI tool familiarity.
Make the research work easy to inspect.
Prepare: One clear project entry if the duplicates are the same work, with the approved thesis title, status, methods, limitations and permitted artifact link.
Research activity can strengthen the narrative without being overstated as an awarded credential or validated market forecast.

Public sources and review limits 10 references

These links identify the public material used in this companion. Source coverage below distinguishes visible snippets from full pages. The profile and posts remain self-reported.

L0 · Edgar O. Peña — public LinkedIn profile (opens in a new tab)
Public top card, truncated About, service labels, project snippets and visible article metadata. Full Experience/Education and expanded About inaccessible.
Observed 2026-09-10
L1 · AI, MBA and the decision to defer further study (opens in a new tab)
Professional post text visible in profile capture; standalone post was not separately opened.
Observed 2026-09-10 · Date shown on LinkedIn: 3w
L2 · USC Marshall Executive MBA acceptance announcement (opens in a new tab)
Professional post text visible in profile capture; standalone post was not separately opened.
Observed 2026-09-10 · Date shown on LinkedIn: 4mo
L3 · AI thesis defense announcement (opens in a new tab)
Professional post text visible in profile capture; standalone post was not separately opened.
Observed 2026-09-10 · Date shown on LinkedIn: 4mo
L4 · Creativity, curiosity and creator mindset (opens in a new tab)
Professional post text visible in profile capture; standalone post was not separately opened.
Observed 2026-09-10 · Date shown on LinkedIn: 4mo
L5 · Agentic AI and supporting people at work (opens in a new tab)
Professional post text visible in profile capture; standalone post was not separately opened.
Observed 2026-09-10 · Date shown on LinkedIn: 7mo
L6 · Corporate AI strategy newsletter announcement (opens in a new tab)
Professional post text visible in profile capture; linked newsletter body was not reviewed.
Observed 2026-09-10 · Date shown on LinkedIn: 6mo
L7 · Corporate Strategy with AI: What It Actually Means (and How I’m Using It) (opens in a new tab)
Title, date and short preview only; full article not reviewed.
Observed 2026-09-10 · Date shown on LinkedIn: Mar 4, 2026
L8 · Issue #1: Don't Get Replaced. Get Leverage. (opens in a new tab)
Title, date and short preview only; full article not reviewed.
Observed 2026-09-10 · Date shown on LinkedIn: Nov 25, 2025
L9 · Welcome to EdgarIQ: The Agent Memo (opens in a new tab)
Title, date and short preview only; full article not reviewed.
Observed 2026-09-10 · Date shown on LinkedIn: Nov 9, 2025

Limits of this comparison

All professional profile/post statements are attributable to the profile holder and remain self-reported; repeated statements do not become independent corroboration.
The public view is incomplete and dynamic. Missing visible content does not establish missing ability, experience or credentials.
No peer ranking, follower-based credibility score, recruiter feedback, employer decision or hiring probability was inferred.
Article publication metadata and previews were visible; article bodies were not reviewed.
No protected traits were inferred or used. Personal/holiday content and third-party comments were excluded.
Recommendations are draft improvements for review; no LinkedIn profile edits or public publication were performed.

The experience behind the analysis

These selected excerpts are shared with Edgar’s permission. They are his submitted claims, not independently verified achievements. E references let you trace the excerpts cited in the report.

E1
Built and scaled a technology-enabled B2B and e-commerce business to more than $5M in annual revenue before acquisition by an international corporation.
Submitted resume excerpt; self-reported and not independently verified.
E2
Generated more than $10M in revenue across services, consulting, and SaaS by owning the enterprise customer lifecycle from opportunity development and solution strategy through implementation, executive escalation, adoption, and long-term account growth.
Submitted resume excerpt; self-reported and not independently verified.
E3
Helped build and scale Wurkflow.net into a seven-figure SaaS platform; led a 25-person team of software developers and UI/UX designers across three corporate offices, setting product priorities, business requirements, workflow logic, customer experience standards, and release objectives.
Submitted resume excerpt; self-reported and not independently verified.
E4
Currently lead AI initiatives involving generative AI, large language models, conversational AI, intelligent voice agents, CRM automation, and analytics, identifying practical use cases and partnering with technical teams on implementation and rollout.
Submitted resume excerpt; self-reported and not independently verified.
E5
Architected and commercialized AI-enabled customer experience solutions, including a proprietary AI CRM platform and intelligent voice-agent capabilities, contributing more than $1M in incremental annual revenue and supporting 25% year-over-year growth.
Submitted resume excerpt; self-reported and not independently verified.
E6
Developed integrated e-commerce and digital workflows connecting CRM, order management, production, fulfillment, customer service, and automation systems to improve end-to-end execution.
Submitted resume excerpt; self-reported and not independently verified.
E7
Technology leadership: product roadmap prioritization, business and workflow architecture, requirements definition, UI/UX direction, implementation planning, release coordination, and executive reporting.
Submitted resume excerpt; self-reported and not independently verified.
E8
Established operating procedures, KPIs, forecasting practices, and performance reporting used to identify risks, prioritize resources, and guide strategic decisions.
Submitted resume excerpt; self-reported and not independently verified.
E9
Developed and maintained executive relationships with strategic enterprise customers including Uber, Google, Meta, and Coca-Cola, translating business needs into customized fulfillment, e-commerce, software integration, and operational solutions.
Submitted resume excerpt; self-reported and not independently verified.
E10
Bachelor of Business Administration, Entrepreneurship | California State University, Los Angeles | 2026
Submitted resume excerpt; self-reported and not independently verified.

Read every topic in one place

Edgar's current profession and desired role are business development in print on demand/apparel. This analysis uses his submitted career information and published sources. The target is Los Angeles, California, United States, with hybrid work at a 25-50 employee company and at least USD $100,000 annual base salary plus commission. Whittier is his residence city. Goals are preferences, not current pay or verified employer opportunities. Resume achievements are self-reported. Recommendations are reasoned career guidance, not a guarantee of outcomes.

Your strongest positioning is business-development leadership that turns complex apparel and print-on-demand customer needs into profitable, deliverable programs. Your submitted resume connects enterprise accounts, print operations, e-commerce workflows and AI initiative leadership; these remain self-reported claims. The next step is to make that combination inspectable through one defensible commercial case and a quote-to-fulfillment demonstration. AI assistance can reduce the value of routine research, first drafts, CRM entry and status summarization. Your opportunity is to own the buying decision, margin, production commitment, recovery and adoption of a reliable process. This is a task-level assessment, not a prediction that your job will disappear. Test three paths first: strategic apparel/POD accounts, a sales player-coach role, and commercial leadership for print-workflow or fulfillment software. Technical solutions sales is conditional on a credible discovery and demonstration artifact; the resume alone does not establish hands-on engineering scope. The evidence supports commercial and coordination experience, while AI evaluation, operating costs and customer-data controls still need proof. That is an evidence gap, not a diagnosis of missing ability. Your target remains at least USD $100,000 annual base plus commission, hybrid in Los Angeles at a 25-50-person employer. Current income is unknown. National occupational wages are context; local base pay, commission terms and actual openings remain unverified. Start with proof you already have, then use focused learning only where a specific deliverable exposes a gap.

Industry landscape & business model

inference · medium evidence confidence

Use four business models to assess each employer: a brand or merchant creates demand and controls its customer offer; a decorator sells production capacity and quality; an integrated POD/fulfillment provider connects orders to production and delivery; a workflow-software provider sells process capability and adoption. One business can combine these models. Their economic questions differ: acquisition and repeat orders; contribution per job and usable capacity; exception/reprint cost and service reliability; or implementation effort and retention. This is an analyst lens, not a measured market segmentation. Edgar's E1, E2 and E6 claims suggest a useful cross-model commercial story, subject to supporting records.

Printful API documentation O*NET Sales Managers

fact · high evidence confidence

Printful documents order APIs, shipment/product events, and webhook retries.

Printful API documentation

inference · medium evidence confidence

Business value includes handling duplicates, partial shipments, stale status, and exception ownership rather than merely connecting two systems. No claim Edgar implemented Printful or that an API guarantees fulfillment quality.

Printful API documentation

recommendation · medium evidence confidence

Show a synthetic order-state diagram and failure/recovery table.

Printful API documentation

recommendation · medium evidence confidence

P2 An account view that survives fulfillment exceptions. Priority: Second portfolio project. Problem: Sales, e-commerce, and production show conflicting order states, creating misleading updates and account risk. Inputs: Synthetic external/internal order IDs; Item and shipment state; Approved delivery commitment; Last confirmed event; Account/support owner. Process: 1) Map receipt, validation, production acceptance, shipment, and closure separately. 2) Handle repeated events without duplicate orders. 3) Represent partial shipments at item/package level. 4) Distinguish confirmed facts from stale or missing state. 5) Create an exception queue with owner and next action; obtain approval before customer communication. Human approvals: Cancellation or change after production starts; Refund/reprint commitment; Customer-facing recovery promise.

Printful API documentation

recommendation · medium evidence confidence

P2 proof and testing. Cases: Duplicate order-created event; Out-of-order status; Partial shipment; Missing shipment event; Cancellation after production acceptance; Supplier outage. Measures: Duplicate actions prevented; Status accuracy on audited sample; Age of unresolved exceptions; Time to verified customer update. First deliverable: State diagram and ten synthetic incident cards with expected recovery. No-cost execution: Tabletop review using synthetic events. No Printful production API or actual order. Completion rule: Technical partner can explain how each failure is detected and recovered; do not claim implemented reliability from a diagram. Resume evidence connections: E6, E7.

Printful API documentation

What remains uncertain

No employer financial or operating records were reviewed.

Market forces & recent developments

fact · high evidence confidence · 2025-09-23

PRINTING United describes AI use in customer communications, estimating, prepress, and workflow automation, with organizational skills and use-case clarity important barriers.

PRINTING United AI adoption research announcement

inference · medium evidence confidence

Three technology layers affect the offer differently: equipment automation changes physical production tasks; APIs connect order and shipment records; generative AI assists language, content and interpretation. They do not establish the same capability or adoption rate. A commercial leader should ask which layer changes customer value, cost or the failure path. The printing announcement supports use-case activity, not apparel-wide adoption, job-loss odds or a POD revenue forecast.

PRINTING United AI adoption research announcement

recommendation · medium evidence confidence

Watch observable buying signals before changing strategy: customers requesting smaller repeated programs, documented RFQ delays, reprints or missed commitments, system fragmentation, and willingness to pay for a managed workflow. These are investigation prompts, not observed market trends. Compare slow, selective and accelerated AI-adoption scenarios over the next 12-24 months; each is a planning assumption. The same useful preparation is a documented commercial case and a reliable quoting/exception process.

PRINTING United AI adoption research announcement

fact · high evidence confidence · 2025 baseline; 2035 forecast; source updated 2026-08-27

BLS projects national Sales Managers employment to increase from 650,100 in 2025 to 679,100 in 2035, a rounded 4% increase. This broad occupation includes industries beyond apparel and print on demand.

BLS Sales Managers Occupational Outlook Handbook

inference · medium evidence confidence

The national occupational outlook is compatible with continuing demand for commercial leadership, but it does not show which apparel employers will hire or how AI will affect a specific role. Use the workflow and commercial-case projects to test a concrete positioning hypothesis, then validate actual openings and requirements.

BLS Sales Managers Occupational Outlook Handbook

What remains uncertain

No representative apparel-only adoption measure was established.
No representative print-on-demand or apparel-decoration market growth forecast was verified. National occupational projections do not fill that gap.

Regional demand & employer landscape

recommendation · medium evidence confidence

Build a deliberately small, nonrepresentative shortlist of 10 Los Angeles-area opportunities from employer career pages. Record capture date, work site, hybrid days, whole-company headcount when known, role duties, fixed base range, variable-pay terms and source URL. Mark unknowns; do not infer company size from a team count or treat an expired listing as open. Reject roles that explicitly miss the desired base or work arrangement, and flag undisclosed fields for clarification. Whittier is the residence/commute origin, not the analysis market. No such vacancy sample has been collected for this report.

O*NET Sales Managers BLS Sales Managers Occupational Outlook Handbook

inference · medium evidence confidence

For a 25-50-person employer, a player-coach may sell, scope customer requirements, coordinate production, and improve workflow with limited specialist support. Confirm this rather than assuming it from headcount.

O*NET Sales Managers

What remains uncertain

No current employer-level vacancy, base-pay or hybrid sample was established.
Desired commission/OTE, acceptable ramp and downside terms remain unspecified.
Onsite days, travel and commute limit from Whittier remain unspecified.
Whether 25-50 employees refers to the whole employer, a site or a team remains unspecified.

Responsibilities, titles & business outcomes

fact · high evidence confidence · 2026

The Sales Managers occupational framework covers customer needs, profitability, pricing, forecasting, commercial planning, and sales-team development.

O*NET Sales Managers

recommendation · medium evidence confidence

Compare duties before titles. Strategic-account business development should demonstrate discovery, profitable proposals, negotiation and account growth. A player-coach also needs forecast discipline, quota ownership and coaching proof. An adjacent workflow-software commercial role needs a clear implementation/adoption story. Technical solutions sales needs product-level discovery and demonstration competence. These are role hypotheses derived from occupational frameworks, not verified vacancies or a claim Edgar already meets every requirement.

O*NET Sales Managers BLS Wholesale and Manufacturing Sales Representatives BLS Sales Engineers

What remains uncertain

Individual-contributor versus team-lead preference remains unknown.
The uploaded resume targets Consulting Account Lead, while the chosen search targets business development. Lead with relevant commercial/domain experience. Clarify strategic-account individual contribution versus player-coach leadership before comparing duties, quota, and compensation; assess engineering-heavy roles separately.

Qualifications, education & experience

fact · high evidence confidence · 2026-08-27

BLS describes a bachelor's degree and related experience as typical for sales managers, while noting some roles accept a high-school diploma.

BLS Sales Managers Occupational Outlook Handbook

recommendation · medium evidence confidence

Confirm the degree's awarded/expected status and dates for each responsibility. The uploaded resume describes business/product priorities and technical-team coordination; it does not establish how much software or model engineering Edgar personally performed.

O*NET Sales Managers

What remains uncertain

No independent degree, employment, or achievement verification.
Separate the dates and responsibilities of the concurrent Wurkflow leadership scope from the broader OmniPrint tenure. Do not assign every responsibility to the full employer period.
A Senior Software Architect title and cloud familiarity are stated. Personal implementation, model engineering and production operating responsibilities need a project-level explanation; a title alone proves neither presence nor absence of those skills.
The education entry gives 2026 without an awarded or expected month. Confirm degree status before describing it as awarded.

Industry knowledge & competencies

fact · high evidence confidence

Epson markets G6070 automated maintenance but also documents continuing cleaning and consumable-related maintenance.

Epson G6070 DTFilm product specification Epson G6070 maintenance guidance

inference · medium evidence confidence

Sell the whole production outcome: artwork, substrate, transfer process, operator tasks, service contingency, quality, and delivery. No equipment purchase or saving is justified without the employer's process data.

Epson G6070 DTFilm product specification Epson G6070 maintenance guidance

recommendation · medium evidence confidence

Build a job decision sheet for method, touches, consumables, waste, maintenance, turnaround, and service needs.

Epson G6070 DTFilm product specification Epson G6070 maintenance guidance

recommendation · medium evidence confidence

P1 From apparel RFQ to an approved profitable quote. Priority: First portfolio project. Problem: Customer specifications arrive incomplete or inconsistent, slowing quotes and increasing rework. Inputs: Buyer use case; Garment variants and quantities; Decoration placements and method constraints; Artwork version and approval status; Delivery date and destination region; Approved price/cost rules; Account owner. Process: 1) Record source information with unknown fields left blank. 2) Flag missing or contradictory variants, quantities, dates, and artwork. 3) Have production accept method, quality, capacity, and timing assumptions. 4) Use approved deterministic cost/margin calculations; AI may explain, not invent the numbers. 5) Have an authorized person approve and send the quote; record the final accepted specification. Human approvals: Artwork/rights; Pricing or margin deviation; Delivery commitment; Sending quote.

PRINTING United AI adoption research announcement O*NET Sales Managers

recommendation · medium evidence confidence

P1 proof and testing. Cases: Missing size split; Conflicting quantity totals; Rush date with no capacity confirmation; Unsupported guarantee; Unapproved artwork; Unrecognized garment variant. Measures: Median comparable-case quote turnaround; Exact-field accuracy; Required-field omission rate; Quote correction rate; Realized contribution after rework/freight. First deliverable: One workflow diagram, one required-field checklist, ten synthetic RFQs, and an expected-result table. No-cost execution: Build the documents and spreadsheet locally. No API, paid model, real quote, or outbound message is required. Completion rule: The process exposes missing facts and approval points. Savings are not claimed until comparable cases are actually measured. Resume evidence connections: E1, E2, E6, E8.

PRINTING United AI adoption research announcement O*NET Sales Managers

What remains uncertain

No machine-specific operating demonstration or order economics supplied.
Method-specific work personally operated, supervised or reviewed remains to be distinguished.

Tools, systems & emerging skills

fact · high evidence confidence

Shopify documents contextual assistance and review/approval of changes; the cited setup page distinguishes it from handling customer-support conversations.

Shopify Sidekick setup and limitations

inference · medium evidence confidence

Routine drafting and summaries face automation; decision quality and accountable execution become stronger differentiators. These are vendor-specific capabilities, not universal AI behavior.

Shopify Sidekick setup and limitations

recommendation · medium evidence confidence

Separate information retrieval, suggested action, approval, and execution in a task policy.

Shopify Sidekick setup and limitations

recommendation · medium evidence confidence

Write a one-page risk-aware pilot charter. A worksheet is not certification, compliance, or proof of system reliability.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

L4 NIST AI RMF and GenAI Profile guided reading. Timing: During pilot design. Duration: Self-directed public reading. Cost: Free public material; no paid assessment/certification. Practice: Risk register for wrong quotes, unsupported delivery promises, private data exposure, and unauthorized actions. Evidence to keep: Owner, impact, test, mitigation, and review date for each risk. Learning question: Which action needs human approval because an error creates a commercial obligation?

NIST AI Risk Management Framework

What remains uncertain

No actual deployed-system evaluation or access-control artifacts supplied.
A safe demonstration of a deployed system, actual users and permitted evaluation results was not supplied.

Compensation & evidence limits

fact · high evidence confidence · May 2025 wages; pages updated 2026-08-27

BLS May 2025 national annual occupational wages: Sales Managers median $148,270, with 10th/90th-percentile thresholds $73,170/$290,540; nontechnical wholesale/manufacturing sales representatives $72,080 ($39,090/$137,550); Sales Engineers $124,900 ($73,870/$195,270). These are broad occupation distributions, not personal salary estimates, entry-to-senior progression bands or local employer offers.

BLS Sales Managers Occupational Outlook Handbook BLS Wholesale and Manufacturing Sales Representatives BLS Sales Engineers

recommendation · medium evidence confidence

Keep three quantities separate: desired base, an employer's stated base/variable plan, and occupational wages. Wage measures include commissions and production bonuses; they cannot validate a $100,000 base at a 25-50-person Los Angeles employer. Use the matching occupation only after checking actual duties. A software account role is not automatically a Sales Engineer. No observed skill premium or salary increase from training is established.

BLS Sales Managers Occupational Outlook Handbook BLS Sales Engineers

recommendation · medium evidence confidence

For a potential offer, record fixed base and target variable separately. Then test zero, partial and full variable payout as hypothetical cash scenarios; payout is not the probability of success and need not equal quota attainment. Confirm territory, lead supply, ramp, crediting, margin/bookings/collections basis, threshold, accelerators, caps, cancellations, clawbacks and payment timing. Ask for actual attainment distributions for comparable incumbents. Do not treat OTE as guaranteed pay.

BLS Sales Managers Occupational Outlook Handbook

recommendation · medium evidence confidence

Select skills to identify relevant role paths and portfolio work, not to add an automatic wage premium. Training can help produce better evidence; the employer still prices role scope, territory, experience and the compensation plan. Leave current earnings blank unless supplied. Compare the hypothetical offer's base with the desired minimum; compare wages only with a compatible pay definition.

O*NET Sales Managers BLS Sales Managers Occupational Outlook Handbook

What remains uncertain

No representative local comparable base-pay range or verified offer was collected.
Desired variable pay and acceptable ramp/downside terms are unknown.
The resume's revenue claims need separate periods, definitions, personal/team contribution and supporting records; they do not prove a compensation entitlement.

How AI changes the work

fact · high evidence confidence · 2025-01-29

The US Copyright Office emphasizes human expressive contribution for copyrightability; prompts alone are insufficient.

US Copyright Office AI report announcement

inference · medium evidence confidence

Request artwork provenance and approvals. Keep rights review separate from file-quality review and do not promise automatic exclusivity. No legal clearance of individual artwork or permission to use a client logo.

US Copyright Office AI report announcement

recommendation · medium evidence confidence

Include approval status and unclear-rights escalation in artwork intake.

US Copyright Office AI report announcement

inference · medium evidence confidence

Task exposure is uneven. Standardized digital inputs make drafting, extraction and summarization candidates for assistance, but errors can propagate into quotes and customer promises. Relationship work is also assisted; human-led does not mean untouched by AI. Labels summarize analyst judgment about work design and accountability, not a measured personal threat, observed time saving or likelihood of redundancy. Review the eight task rows and their concrete proof tests; do not average the labels into a readiness score.

PRINTING United AI adoption research announcement Printful API documentation O*NET Sales Managers

recommendation · medium evidence confidence

Keep human approval for artwork rights, margin exceptions, delivery commitments, customer communication, and order release. Use deterministic calculations for approved price/cost rules.

NIST AI Risk Management Framework

What remains uncertain

No job-loss or personal readiness percentage is justified.
AI-project start dates and personal responsibilities remain unconfirmed; employer tenure is not generative-AI tenure.

Human contribution & readiness

inference · medium evidence confidence

Your evidence supports a credible commercial positioning hypothesis: E1/E2 describe growth, E6 operating integration, E7 coordination and E8 business measurement. The report has not evaluated real systems or verified results. The highest-return evidence work is to make one contribution, one failure-recovery process and one outcome reproducible. This is preferable to repeating basic training already covered by your experience; it does not establish superiority over other professionals.

O*NET Sales Managers

recommendation · medium evidence confidence

Prepare a redacted commercial case, RFQ workflow, and order-exception artifact. Separate customer claims, personal leadership, team implementation, and measured results.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

P3 A defensible AI commercial case. Priority: Highest evidence priority. Problem: Large achievement figures do not reveal causality, contribution, cost, or repeatability. Inputs: One permitted initiative; Dates/baseline; Edgar's responsibility; Team role; Definition of revenue or time outcome; Cost categories; Supporting record. Process: 1) Separate the business problem from the AI feature. 2) Define the metric and comparison before calculating improvement. 3) Identify other changes that affected results. 4) Include review, rework, subscription, and usage costs. 5) State what the evidence proves and what remains uncertain; redact customers and confidential values when needed. Human approvals: Use of employer/customer data; Publication of financial or client details.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

P3 proof and testing. Cases: Missing baseline; Annual and cumulative revenue mixed; Time released called cash savings; Team outcome attributed to one person; Projected ROI presented as actual. Measures: Baseline and outcome definitions complete; Reproducible arithmetic; Supported attribution; Cost and caveat completeness. First deliverable: A redacted two-page case or explicitly labeled hypothetical model. No-cost execution: Use permitted existing evidence and local calculations only. Completion rule: A skeptical reviewer can reproduce the arithmetic and see the limits without needing trust in an AI-generated narrative. Resume evidence connections: E2, E5, E8.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

Commercial measurement: Order contribution equals revenue minus agreed attributable garment, decoration, labor, spoilage/reprint, fulfillment, shipping, commission, and tool costs. Avoid double-counting costs included in another category. Compare similar cases. Subtract review/rework and tool expense. Time released is capacity, not automatically cash savings or revenue.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

Compare yourself with role standards through artifacts, not a fabricated peer percentile: account leaders need a defensible account-growth case; sales managers need a forecast/coaching example; technical solutions sellers need credible discovery and a product demonstration. National wage distributions describe occupations, not peer AI competence. No representative matched-professional dataset was reviewed, so no claim that Edgar is ahead of or behind other professionals is justified.

O*NET Sales Managers BLS Wholesale and Manufacturing Sales Representatives BLS Sales Engineers

What remains uncertain

Resume achievements remain customer testimony.
Publication of these self-reported career claims does not establish employer/customer endorsement or independent verification.
Before adding future project screenshots, unpublished customer records or financial schedules to a portfolio, confirm permission and redact confidential details. Public career claims do not grant access to underlying employer data.

Progression & adjacent opportunities

recommendation · medium evidence confidence

Focus on apparel/POD business-development and adjacent print-workflow/fulfillment software. Coding-heavy AI or ML roles need evidence beyond this resume.

O*NET Sales Managers

inference · medium evidence confidence

Recurring B2B merchandise programs with ordering and fulfillment complexity; Strategic accounts with multiple garments, locations, approval steps, or deadlines; Print workflow or fulfillment software selling operational outcomes; Established apparel decorators seeking better quote conversion and account retention. These are opportunity hypotheses, not open vacancies.

PRINTING United AI adoption research announcement Printful API documentation O*NET Sales Managers

recommendation · medium evidence confidence

Use the smallest learning intervention that closes a demonstrated gap. If you can already produce and explain the artifact, skip the introductory module. If a project stalls, consider targeted instruction or coached feedback with a sample assignment, a clear review rubric, stated prerequisites, total time/cost and an inspectable output. A paid program should earn its place through useful feedback and practice; it cannot establish a salary premium or substitute a certificate for results. AI BURRO can use this report to scope a training inquiry without implying a course purchase or enrollment.

O*NET Sales Managers NIST AI Risk Management Framework

What remains uncertain

Every employer's vacancy, size, work arrangement, and compensation need verification.

A practical 30 / 60 / 90-day plan

recommendation · medium evidence confidence

Days 1-30: choose strategic accounts or player-coach as the first positioning hypothesis; reuse one permitted commercial case and record missing evidence. Draft the RFQ checklist, ten synthetic RFQs and ten order-exception cases. Start with a 5-hour weekly planning budget, adjustable to your availability. Suggested document-building effort is 21-34 hours across the first commercial case, RFQ workflow, evaluation and exception work; it is an analyst estimate, not a course duration or time to competence. At 5 hours/week that is roughly 5-7 weeks, so extend this stage or reuse existing proof. Do not add these hours again if selected skills packages already include the same work.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

Days 31-60: review the synthetic cases with a production or technical partner when available. Record expected vs actual output, missing-field handling, forbidden claims, correction time and unresolved defects. Any unsupported price, delivery promise or unauthorized action fails that case and must be fixed before reuse. A tabletop can prove the plan is inspectable; only an executed test can establish behavior. Compare observed cases with the same baseline and include human review/rework time. No paid lab or customer transaction is needed for the proposed portfolio.

NIST AI Risk Management Framework

recommendation · medium evidence confidence

Days 61-90: publish only permitted, redacted proof: a two-page commercial case, RFQ example, exception map and a short explanation of your own responsibilities. Build the employer-sourced shortlist and use the salary scenarios to inspect base/variable tradeoffs. Track applications and conversations separately from offers. Completion means a reviewer can inspect the work and limitations; it does not mean a verified skill certification or a guaranteed job in 90 days.

O*NET Sales Managers

recommendation · medium evidence confidence

Optional refresher: HubSpot Academy AI for Sales. Provider listing: 36 minutes, 2 lessons, 7 videos, 2 quizzes; free course. Practice: create one ideal-customer profile and two source-checked account hypotheses. Allow 1-2 additional hours for that local exercise as an adjustable planning assumption. Skip the course if you can already demonstrate the skill. Keep the brief and stage checklist; a completion badge does not verify commercial results.

HubSpot Academy AI for Sales

recommendation · medium evidence confidence

Optional planning support: Microsoft Learn Transform your business with AI, a four-module self-paced path. Reading is free; provider duration is not fixed here. Budget 2-4 hours for selected reading and a local pilot charter as an adjustable assumption, not a promise to complete all modules. Define objective, owner, baseline, responsibilities, review and stop conditions. Skip subscription exercises; the provider FAQ says former free sandboxes are no longer available. No paid resources are required for this plan.

Microsoft Learn Transform your business with AI Microsoft Learn cost and sandbox FAQ

recommendation · medium evidence confidence

Optional measurement support: Google Analytics Academy offers free e-learning; no paid advertising is needed. Choose only content relevant to your source-to-inquiry-to-quote-to-order measurement plan. Provider duration is unspecified here; allow 2-4 hours for selected reading and the local worksheet as an adjustable planning assumption. Separate web events from accepted orders and contribution. Course completion is not proof of measurement implementation.

Google Analytics Academy

What remains uncertain

Weekly availability and reusable artifacts remain unconfirmed; the default time budget is editable.
Zero incremental cash assumes existing equipment, internet and local document tools. It excludes your time, paid coaching, subscriptions and any production implementation.
Course times and planning estimates describe different quantities. A 90-day sequence is a flexible plan, not an employability deadline.

Sources and limits

PRINTING United AI adoption research announcementprinting.org · 2025-09-23
Epson G6070 DTFilm product specificationepson.com · Publication date not established
Epson G6070 maintenance guidanceepson.com · 2025-04-22
Kornit Apollo production workflowkornit.com · 2025-05-27
Printful API documentationdevelopers.printful.com · Publication date not established
Shopify Sidekick setup and limitationshelp.shopify.com · Publication date not established
O*NET Sales Managersonetonline.org · 2026
BLS Sales Managers Occupational Outlook Handbookbls.gov · 2026-08-27
NIST AI Risk Management Frameworknist.gov · Publication date not established
HubSpot Academy AI for Salesacademy.hubspot.com · Publication date not established
Microsoft Learn Transform your business with AIlearn.microsoft.com · Publication date not established
Microsoft Learn cost and sandbox FAQlearn.microsoft.com · Publication date not established
Google Analytics Academysupport.google.com · Publication date not established
US Copyright Office AI report announcementcopyright.gov · 2025-01-29
BLS Wholesale and Manufacturing Sales Representativesbls.gov · 2026-08-27
BLS Sales Engineersbls.gov · 2026-08-27

Career achievements are self-reported. No employer reference checks, financial schedules, degree verification or project evaluation logs were reviewed.
No representative Los Angeles vacancy/base-pay sample or POD/apparel market-growth forecast was established. National occupational data span industries and are not employer offers.
Task and evidence labels are qualitative assessments, not validated job-loss probabilities, career scores or peer percentiles. Missing proof does not establish missing ability.
Role paths and adoption scenarios are planning hypotheses. No representative matched-professional sample or causal training-to-salary evidence was reviewed.
Training is optional and should follow a demonstrated evidence gap. Suggested effort is adjustable planning time; course completion, a portfolio or a 90-day plan does not guarantee competence, employment or pay.
Sources were reviewed September 9-10, 2026. Product capabilities, course details and labor projections can change. The employment chart has two endpoints and is not an observed annual series.

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