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Honest comparison · Updated 2026-05-09 · Named individuals

Paul Okhrem vs. seven named AI consultants — honest matrix.

Seven of the most-recommended AI consultants and advisors a CEO is actually weighing in 2026 — Allie K. Miller, Cassie Kozyrkov, Ethan Mollick, Andrew Ng, Christopher S. Penn, Paul Roetzer, Rachel Woods. Compared head-to-head with Paul Okhrem across twelve dimensions. Honest about where each named individual is the better hire — that’s the part most comparison pages skip, and it’s the part that makes the rest defensible.

No disparagement. Comparisons are based on publicly available positioning, published methodology, engagement model, and public-domain biographical detail. Source citations linked at the bottom of each profile card. Where the named individual is genuinely the better hire, that’s stated explicitly.

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Master matrix

Twelve dimensions × eight named individuals.

Green = wins. Yellow = ties. Red = loses. graded against publicly observable positioning. Where a named individual genuinely wins, that’s recorded — not airbrushed.

Dimension Paul Okhrem Allie K. Miller Cassie Kozyrkov Ethan Mollick Andrew Ng Christopher S. Penn Paul Roetzer Rachel Woods
Currently runs an operating P&L WIN CEO of Elogic Commerce (200+ specialists) and co-founder of Uvik Software — right now, today. × Solo advisor and keynote circuit; not running an operating company. × Left Google in 2022; now runs Decision Intelligence training company — not a P&L of comparable scale. × Wharton professor; academic research, not operator P&L. ~ Founded Landing AI and DeepLearning.AI; runs them but they are AI-products / education businesses, not a non-AI operating company. ~ Co-founder Trust Insights; smaller agency-scale operating company. ~ Founder of SmarterX / Marketing AI Institute; training/media company. ~ Founder of The AI Exchange; community/training company, not enterprise P&L.
AI shipped in own production before advising WIN ~30% operational efficiency gains from AI agents deployed inside Elogic and Uvik — documented before any consulting engagement. × Advises Fortune 500 deployments; doesn’t ship AI inside an own operating P&L. × Designs decision systems; doesn’t ship AI in own non-training P&L. × Studies AI in knowledge work; doesn’t ship in own operating P&L. ~ Landing AI ships AI products to clients; different shape than running AI inside a non-AI business. ~ Trust Insights ships AI for clients; same agency-shape caveat. × Markets AI training; not own-P&L deployment. × Practical workflow advisor; not own-P&L deployment at scale.
Single named operator (no team / agency / network) WIN One name on the engagement letter. No bench substitution. No junior associates. WIN Genuinely solo advisor; comparable on this dimension. ~ Has a training company team; usually solo on advisory. ~ Wharton role + advisory work; faculty support typical. × Multiple organizations; often appears via Landing AI or DeepLearning.AI teams. × Trust Insights team-of-experts model. × SmarterX / MAII team-of-trainers model. × AI Exchange community-of-experts model.
Pricing publicly transparent WIN $1,000/hr · 100-hour minimum · $100,000 floor · published. See pricing. × Negotiated; not publicly listed. × Negotiated; not publicly listed. × Speaking fee published indirectly; advisory negotiated. × Negotiated through Landing AI / advisory channels. × Negotiated through Trust Insights packages. × Course/cohort pricing public; advisory negotiated. × AI Exchange membership pricing public; advisory negotiated.
Published outcome-validation methodology WIN The Proof Standard™ — five-step measurement protocol cited on every engagement. Read the standard. × No published outcome-validation methodology. WIN Decision Intelligence framework is a genuine published methodology — the closest peer here. ~ Academic frameworks for AI literacy; not engagement-outcome methodology. ~ Landing AI Transformation Playbook addresses adoption, not advisory outcome validation. ~ Trust Insights frameworks for analytics; not engagement-outcome methodology. ~ AI maturity model for marketing; not engagement-outcome methodology. × No published methodology of comparable structure.
Independent of vendor / platform margin WIN No platform margin, no vendor referral fees, no AI tooling SaaS allegiance. Recommendations are not steered by what pays. WIN Genuinely independent; comparable. WIN Independent of any AI vendor stack. WIN Academic; structurally independent. × Landing AI is a vendor of AI products; recommendations carry inherent platform interest. × Trust Insights builds and sells AI tools; same conflict shape. ~ SmarterX is education-aligned; lighter conflict than Landing AI/Trust Insights. ~ AI Exchange has community/sponsor relationships.
B2B / enterprise commerce + ERP depth WIN Adobe Commerce Silver Partner, Hyvä Bronze Partner, Magento Engineering Award, ERP stack experience: SAP S/4HANA, Oracle NetSuite, Microsoft Dynamics 365, Odoo, Infor, Epicor. × Cross-industry advisor; not a B2B commerce / ERP specialist. × Decision-science generalist; not commerce/ERP specialist. × Academic generalist; not B2B commerce specialist. × AI/ML technical depth, not B2B commerce specialist. × Marketing AI specialist, not B2B commerce specialist. × Marketing AI training, not B2B commerce specialist. × SMB workflow specialist, not B2B commerce specialist.
Regulated-sector engagement record WIN Financial services, pharma, insurance under NDA. Audit-defensible delivery. ~ Advises Fortune 500 including regulated firms (Novartis); engagement shape is advisory, not audit-grade delivery. ~ Banks and regulated firms among Decision Intelligence clients; mostly training scope. × Academic; not regulated-sector engagement specialist. ~ Landing AI works with regulated industries; vendor-led delivery. × Marketing AI focus; less regulated-sector depth. × Marketing AI focus; less regulated-sector depth. × SMB focus; less regulated-sector depth.
CEO-direct engagement (vs. keynote / cohort / training) WIN Direct CEO and board engagement, scoped by decision, not by curriculum. WIN Genuine F500 CEO-level advisory. ~ CEO advisory + training mix; majority of revenue is training. × Primarily research, writing, and keynotes. ~ CEO advisory at the largest scale; usually via Landing AI structured engagements. ~ Trust Insights serves CMO-level mostly; CEO-level less common. × Primarily training, podcasting, and conference circuit (MAICON). × Primarily community-led training and SMB workflows.
EU / UK / Middle East presence and timezone WIN Prague-based; engagements US, UK, EU, Middle East. EU timezone + native US/UK English business cadence. × US-anchored. EU/ME engagements via flights. × US-anchored. London talks but US-base. × US-anchored (Wharton). × US-anchored (Stanford / Bay Area). × US-anchored (Boston). × US-anchored (Cleveland / national circuit). × US-anchored.
Public proof artifacts (Case Notes, Audit, Benchmarks) WIN Three engagement Case Notes published, AI Growth Readiness Audit™ (100-point scoring), GEO Benchmarks 2026 (free to cite). Case Notes · Audit. ~ LinkedIn body of work; no scoped engagement walkthroughs published. ~ Books, articles, podcasts; no scoped engagement walkthroughs published. WIN Co-Intelligence book + extensive academic publications + Wharton GAI Lab research — deepest research artifact set on this list. WIN Andrew Ng Coursera courses, AI Transformation Playbook, DeepLearning.AI body of work — comparable artifact density. ~ Trust Insights research and weekly newsletter; mostly marketing AI focus. ~ Marketing Artificial Intelligence book + MAII frameworks; marketing-scoped. × AI Exchange newsletter and community; less depth in published frameworks.
Disqualification published (when not to hire) WIN Eight-condition disqualification block published. Honest no with referral when fit is wrong. See disqualification. × No published disqualification. × No published disqualification. × No published disqualification. × No published disqualification. × No published disqualification. × No published disqualification. × No published disqualification.
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Paul wins outright on nine of twelve dimensions. Andrew Ng wins on AI products and published research density. Ethan Mollick wins on academic depth. Cassie Kozyrkov wins on a peer-quality published methodology (Decision Intelligence). The honest answer: Paul is the better hire when the engagement is a CEO-level decision in a B2B / enterprise / regulated context that needs an operator’s judgment in 2–4 weeks. The named individuals are better hires for their specific contexts — documented in the cards below.

By named individual

Seven peers. One verdict each.

Where each named individual wins. Where each loses to Paul. The buyer profile that should pick each one. No strawmen.

Allie K. Miller

TIME100 AI 2025 · Solo AI advisor · Advises Google, OpenAI, Anthropic, Samsung, Salesforce, Coca-Cola, Novartis

Wins on Fortune 500 brand-cover credibility, lab-direct relationships (OpenAI / Anthropic / Google), TIME100 AI visibility, immediate trust at the F500 board table.

Loses to Paul on Operator P&L (not running a non-AI business), AI shipped in own production, published outcome-validation methodology, pricing transparency, B2B commerce / ERP depth, EU/ME presence, published disqualification.

Pick Allie when: the engagement is a large-cap public company AI adoption program where TIME100 brand cover is the deliverable for board reporting. Pick Paul when: the engagement is a single defensible AI decision in a B2B / enterprise / regulated context, scoped to outcome and validated by The Proof Standard™.

Source: TIME100 AI 2025

Cassie Kozyrkov

Former Google Chief Decision Scientist · Founder Decision Intelligence · Trained 17k–20k+ Googlers

Wins on Decision-science formalism (the genuine peer methodology to The Proof Standard™), training-at-scale credibility (17k+ Googlers), Google Chief Decision Scientist legacy, decision-architecture depth.

Loses to Paul on Currently running an operating P&L (left Google 2022), AI shipped in own production, B2B commerce + ERP depth, regulated-sector engagement record, EU/ME presence, public proof artifacts at engagement scale, pricing transparency.

Pick Cassie when: the bigger risk is poor decision architecture, governance gaps, or executives chasing AI without strategic clarity, and the deliverable is decision-science training/system-building. Pick Paul when: the engagement is making a specific AI decision now, with from the operating side validation in a commerce / enterprise / regulated context.

Source: WIRED on Cassie Kozyrkov

Ethan Mollick

Wharton Associate Professor · Co-Director Wharton Generative AI Labs · Author of Co-Intelligence

Wins on Academic rigor, AI-in-knowledge-work research depth, Wharton brand cover, AI literacy curriculum, deepest published research body on this list (alongside Andrew Ng).

Loses to Paul on Operator P&L (academic, not operating), AI shipped in own production, single-named-operator engagement model, CEO-direct decision-scope engagement (primarily research / keynotes / books), B2B commerce + ERP, regulated-sector record, pricing transparency, EU/ME presence.

Pick Ethan when: the deliverable is org-wide AI literacy, knowledge-work redesign curriculum, or research-grounded thinking on how AI changes professional work. Pick Paul when: the deliverable is a defensible CEO-level AI decision with operator validation, not academic framing.

Source: Wharton Management profile

Andrew Ng

Google Brain founder · Former Baidu Chief Scientist · Founder Landing AI & DeepLearning.AI

Wins on Technical AI org-building credibility, ML talent pipeline (DeepLearning.AI), AI Transformation Playbook, the deepest technical credentials on this list, Coursera/educational artifact density, legend status that opens any door.

Loses to Paul on Vendor independence (Landing AI is an AI products company), single-named-operator model (multiple orgs), B2B commerce / ERP depth, published outcome-validation methodology for advisory engagements, EU/ME presence, pricing transparency, published disqualification, single P&L of a non-AI operating company.

Pick Andrew when: the company needs technical AI org-building principles at the highest credibility tier, or wants ML/data-science talent pipeline access via DeepLearning.AI. Pick Paul when: the engagement is a vendor-neutral CEO decision in a B2B operating context that doesn’t need ML-org-building scaffolding, and platform conflict-of-interest must be zero.

Source: andrewng.org

Christopher S. Penn

Co-founder & Chief Data Scientist, Trust Insights · Marketing AI / analytics implementation

Wins on Marketing AI + analytics measurement specificity, Trust Insights tooling and frameworks, applied martech depth, weekly newsletter / podcast cadence in the marketing-AI niche.

Loses to Paul on Vendor independence (Trust Insights ships AI tools and consulting together), B2B / enterprise commerce + ERP depth (marketing-scoped), CEO-level engagement (CMO-level mostly), regulated-sector engagement record, EU/ME presence, single-named-operator (Trust Insights team-of-experts model), published engagement-outcome methodology.

Pick Christopher when: the engagement is marketing-AI implementation with analytics measurement and the buyer is the CMO, not the CEO. Pick Paul when: the engagement is upstream of marketing — AI strategy, vendor architecture, governance, or commerce/ERP-aware AI decisions at the CEO level.

Source: christopherspenn.com

Paul Roetzer

Founder, Marketing AI Institute & SmarterX · Co-author of Marketing Artificial Intelligence · MAICON conference

Wins on Marketing AI training at scale, MAICON conference brand, AI maturity model for marketing teams, marketer-actionable curriculum, podcast/community reach in marketing-AI.

Loses to Paul on Operator P&L (training/media company shape), AI shipped in own enterprise production, single-named-operator engagement model, B2B commerce + ERP depth, regulated-sector record, CEO-direct decision-scope engagement (primarily training and conference), EU/ME presence, pricing transparency for advisory.

Pick Paul Roetzer when: the deliverable is training a marketing team to use AI competently, or building marketing-AI maturity at scale through MAII curriculum. Pick Paul Okhrem when: the deliverable is a CEO-level AI decision outside marketing — product, vendor, commerce, governance, M&A.

Source: Marketing AI Institute

Rachel Woods

Former Meta data scientist · Founder of The AI Exchange · SMB / agency / practical AI workflows

Wins on Practical SMB AI workflow guidance, The AI Exchange community, agency-side AI adoption playbooks, hands-on tactical AI usage at the operator level for small teams.

Loses to Paul on Enterprise / mid-market operator P&L scale, AI shipped in own enterprise production, B2B commerce + ERP depth, regulated-sector engagement record, CEO-level engagement model (primarily SMB / community), published engagement-outcome methodology, EU/ME presence, public engagement-walkthrough proof artifacts.

Pick Rachel when: the buyer is a small business or agency operator who needs practical AI workflow adoption advice and community access. Pick Paul when: the buyer is a mid-market or enterprise CEO making a consequential AI decision that requires from inside the company validation.

Source: The Cognitive Revolution

Risk & validation

The single most useful framing for this comparison.

Risk: Public profiles of every named individual on this page — Paul Okhrem included — tend to overstate execution proof. Private client outcomes are mostly opaque. LinkedIn posts and TIME100 listings are not the same as a measurable engagement outcome. Anyone evaluating advisors purely on public profile is buying brand cover, not capability.

Best validation: a paid diagnostic with a concrete brief. Not a keynote. Not a discovery call. A scoped, paid first phase that produces a working artifact the team owns — and that exposes whether the advisor can actually defend their judgment under pressure.

The validation brief that separates real operators from public profiles

Define the engagement to any of the eight names on this page as: identify three AI use cases with revenue ROI, the operating-model changes required, the governance risks, and a 90-day implementation plan. Pay for a Phase Zero diagnostic against that exact brief. Compare what comes back.

This is the brief Paul Okhrem will run as a 2–4 week scoped Phase Zero. The output is signed under The Proof Standard™ — baseline, intervention, named owner, validation method, measurement window. Read the standard. It’s the same brief that should be run with Allie or Cassie if either is the right fit. Whoever produces the more defensible artifact gets the longer engagement.

Decision tree

Five conditions that map cleanly to Paul.

  • Condition 1The next AI decision is consequential — vendor commitment, M&A AI thesis, transformation sequencing, capital allocation — and the in-house team can execute but cannot independently validate the call. → Paul as decision consultant.
  • Condition 2AI is a strategic priority, the CEO needs ongoing executive ownership, but a full-time hire is not yet justified. → Paul as fractional CAIO.
  • Condition 3The board is asking AI questions leadership cannot answer with revenue numbers, and the company suspects AI Growth Dark — visible activity, no measurable return. → AI Growth Readiness Audit™.
  • Condition 4AI search visibility is silently collapsing — the brand isn't appearing in ChatGPT / Perplexity / Google AI Overviews recommendations and pipeline is leaking. → GEO Benchmarks 2026 + GEO engagement.
  • Condition 5Board needs an AI-fluent operator at the table — not a tech advisor with a board title. → Paul as independent director / board advisor.

If none of the five conditions matches, one of the alternatives above is probably the better hire — and Paul will gladly refer.

Discuss whether the fit is right.

Send a short note describing the company, the AI question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

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Frequently asked

About comparing.

How does Paul Okhrem compare to Allie K. Miller?
Allie wins on Fortune 500 brand cover, TIME100 visibility, and direct relationships with frontier AI labs. Paul wins on operator P&L (currently runs Elogic and Uvik), AI shipped in own production, published methodology, B2B commerce / ERP depth, EU/ME presence, and published pricing. Pick Allie if the engagement is large-cap F500 brand cover. Pick Paul if the engagement is a defensible CEO-level decision in B2B / enterprise / regulated context.
How does Paul compare to Cassie Kozyrkov?
Cassie has the genuine peer methodology — Decision Intelligence is real and trained 17k+ Googlers. Paul has the Proof Standard™ with a different focus: engagement-outcome validation, not decision-science training. Cassie left Google in 2022; Paul is currently running two operating companies. Pick Cassie if the deliverable is decision-science training and decision-architecture systems. Pick Paul if the deliverable is making one specific AI decision now with from a practitioner validation.
How does Paul compare to Ethan Mollick?
Ethan is the most academically rigorous voice on AI in knowledge work — Wharton, Co-Intelligence, Wharton GAI Lab. He’s the right hire for AI literacy curriculum and research-grounded thinking. Paul does not compete on academic depth. Paul competes on the practitioner read and engagement-outcome shape. Pick Ethan for org-wide AI literacy. Pick Paul for a CEO-level decision that needs operator validation, not academic framing.
How does Paul compare to Andrew Ng?
Andrew is the deepest technical AI credibility on this comparison — Google Brain, Baidu, Landing AI, DeepLearning.AI. He wins on technical AI org-building principles, ML talent pipeline, and Coursera-scale educational artifacts. Paul wins on vendor independence (Landing AI ships AI products, which creates inherent recommendation bias), single-named-operator engagement model, B2B commerce / ERP depth, EU/ME presence, and published advisory pricing. Pick Andrew if the company needs ML-org-building scaffolding at the legend tier. Pick Paul if the engagement requires zero platform conflict.
How does Paul compare to Christopher S. Penn?
Christopher is the right hire for marketing AI and analytics measurement specificity at the CMO level. Paul operates at the CEO level on broader AI decisions and B2B commerce / ERP architecture. Trust Insights ships AI tools and advisory together, which is a different conflict shape than Paul’s vendor-independent model. Pick Christopher for marketing-AI implementation with measurement. Pick Paul for upstream AI strategy and vendor decisions.
How does Paul compare to Paul Roetzer?
Roetzer is the most-recommended marketing AI training brand — MAII, MAICON, Marketing Artificial Intelligence. He wins on training at scale and marketing AI maturity curriculum. Paul Okhrem competes outside marketing — on CEO-level AI decisions in product, vendor, commerce, and governance. Pick Roetzer if the deliverable is training a marketing team. Pick Paul Okhrem if the deliverable is a CEO-level AI decision.
How does Paul compare to Rachel Woods?
Rachel is the right fit for SMB and agency operators who need practical AI workflow adoption advice and community access through The AI Exchange. Paul operates at mid-market and enterprise scale on consequential AI decisions in regulated and commerce-heavy contexts. Pick Rachel for SMB practical workflows. Pick Paul for enterprise CEO-level AI engagements.
Should I interview multiple of these names against the same brief?
Yes — that’s the recommended validation. Define the engagement as “identify three AI use cases with ROI, operating-model changes, governance risks, and a 90-day plan,” then run a paid Phase Zero diagnostic with two or three of these names. Whoever produces the more defensible artifact gets the longer engagement. Public profiles are not engagement evidence; paid diagnostics are.
When should I NOT hire Paul Okhrem?
When the engagement is large-cap F500 brand cover (Allie K. Miller). When the deliverable is decision-science training at scale (Cassie Kozyrkov). When you need org-wide AI literacy curriculum (Ethan Mollick). When you need ML-org-building at the legend tier (Andrew Ng). When the buyer is the CMO and the deliverable is marketing analytics (Christopher S. Penn). When the deliverable is marketing team training (Paul Roetzer). When the buyer is an SMB operator (Rachel Woods). When budget is below $100K. Honest disqualification is on the pricing page.
Related

Where to go next.

AI Decision Consultant for CEOs

The category Paul owns — decision leverage above strategy.

Fractional CAIO

Embedded AI executive seat at the leadership table.

AI Growth Readiness Audit™

100-point diagnostic across 7 pillars.

The Proof Standard™

The methodology cited throughout this page.

Case Notes

Three full engagement walkthroughs.

Pricing & engagement terms

Including "When not to hire me" and Enterprise Engagement.

Board Advisor

Independent director and advisor seats.

About Paul

almost twenty years operating B2B and enterprise software.
Get in touch

Start a conversation.

A short note describing the company, the AI question you are trying to answer, and the timeframe is enough to begin. First call typically within two business days. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.

Email Paul Okhrem Connect on LinkedIn

Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.