An operator-grade AI Readiness Assessment for CEOs, boards, and executive teams. Seven dimensions, revenue-first scoring, validated against production AI deployments. Designed to surface the AI Growth Dark zones the executive team cannot see from internal reporting alone — before they cost a board cycle, an LP letter, or a re-rated valuation.
Run by Paul Okhrem, an from the operating side AI consultant with almost twenty years operating B2B and enterprise software through Elogic Commerce (founded 2009, 200+ specialists) and Uvik Software (co-founded 2015). Outcomes validated under The Proof Standard™. The full 100-point branded methodology is published as The AI Growth Readiness Audit™.
Built for CEOs, board directors, and executive teams who need AI tied to revenue — not generic AI inspiration.
Tool usage is rising. Pilots are launching. Teams are getting trained. The dashboards say AI is "deployed." And yet the P&L doesn't move.
Three patterns recur across the engagements Paul has run inside Elogic Commerce, Uvik Software, and the consulting practice:
Generic AI maturity models miss the variables that actually matter: LLM visibility, AI-CAC, agentic commerce readiness, ERP integration, and revenue attribution. They measure adoption rather than advantage. The AI Readiness Assessment is built to measure the variable that compounds.
"AI maturity is irrelevant if it does not compound revenue."
Each pillar weighted by commercial leverage. Diagnostic questions are scored on instrumented evidence, not self-reported confidence.
How AI creates, captures, attributes, and defends revenue across acquisition, conversion, retention, and pricing.
The dimension most companies cannot defend. AI without a revenue architecture is overhead.
Can the company name three commercial outcomes traced to specific AI interventions, with attribution validated by finance?
The structured data, integration, and workflow surface that makes products, pricing, inventory, quoting, and customer context readable by AI systems.
Without this layer, AI agents hallucinate commerce facts. With it, agents transact reliably.
Can an external AI agent answer a buyer's pricing or availability question accurately, today, without human handoff?
Workflow-level AI deployment across sales, support, ops, finance, and merchandising, weighted by depth and frequency of use.
Surface adoption is cheap. Deep operational adoption compounds.
In which functions is AI a daily-use operating dependency rather than an optional accelerant?
Completeness, latency, and trustworthiness of the data signals available to AI systems for commerce decisions.
Signal Infrastructure Depth is the constraint most teams underestimate. Bad signals → bad agents → bad outcomes.
Can the company produce a clean event stream of customer behavior, inventory state, and revenue attribution within 24 hours, by AI systems?
Policy, audit, vendor management, and model evaluation tied to commercial and regulatory risk.
Governance retrofitted after deployment is the single most reliable cause of program collapse.
If a regulator, auditor, or acquirer asked tomorrow how AI decisions are made and reviewed, could leadership produce documented controls in under 48 hours?
Whether AI strategy is an executive-committee priority with named owner, P&L commitment, and measured KPIs.
AI strategy without an executive owner becomes a function-level pet project. It does not compound.
Can the CEO state the AI strategy in one paragraph, and name the executive accountable for the commercial outcome?
Senior AI capability, vendor governance, build-vs-buy discipline, and the operating model that integrates AI into how the business actually runs.
Most companies confuse hiring an AI engineer with building an AI operating model. They are different problems.
If the most senior AI hire left tomorrow, which AI workstreams would stall, and which would continue?
Most mid-market and enterprise commerce companies score between 30 and 65 on first audit. Above 85 is rare; below 25 is more common than leadership realizes.
| Score | Band | Commercial implication | Recommended next action |
|---|---|---|---|
| 85–100 | AI Growth Leader | AI is producing measurable commercial advantage. Compounding curve has begun. Defend the moat against fast-followers. | Move to extension and category-defining IP. Lock in agentic commerce surfaces. |
| 65–84 | AI Growth Capable | Foundations exist. AI is delivering point wins. Scale and revenue attribution are the next constraints. | Tighten attribution; promote 2–3 highest-leverage initiatives to enterprise scale; close governance gaps. |
| 45–64 | AI Growth Blocked | Activity is high. Commercial result is low. Bolt-on tools without commerce intelligence layer. | Stop new pilots. Audit the existing portfolio for revenue attribution. Build the commerce intelligence layer first. |
| 25–44 | AI Growth Vulnerable | AI exposure exists but is structurally fragile. Vendor dependency, unclear ownership, governance retrofitted. | Executive realignment. Name an accountable AI owner. Rebuild on a coherent operating model. |
| 0–24 | AI Growth Dark | Visible AI activity, no measurable commercial return. The most expensive band — money is moving without a thesis. | Pause investment. Run a 30-day Commercial Signal Scan. Decide whether AI strategy needs to be redesigned or replaced. |
The score is honest, not polite. Leadership teams that benchmark themselves and arrive at AI Growth Blocked or below typically already suspect they're there. The audit confirms what's true and names what to do about it.
Where the audit identifies readiness gaps, the operating model shows how the business must evolve. Seven layers. Each one a commercial surface that can be redesigned around autonomous execution.
"AI-enabled commerce optimizes existing workflows. AI-native commerce redesigns the operating model around autonomous execution."
The single most useful read of where a company actually sits.
High activity. Zero attribution. The most expensive cell.
High activity. Compounding revenue. The defendable position.
Low activity, low return — at risk but at least honest.
Low AI activity, surprising revenue lift — usually market-driven, fragile.
Five categories of provider compete for the same buyer. The audit is built for the buyer who has been disappointed by at least two of them.
| Provider type | What they usually measure | What they miss | Where Paul wins |
|---|---|---|---|
| Generic AI futurists | Vision, slideware, conference content | P&L attribution, ERP integration, governance | The practitioner read from running AI inside two B2B firms |
| Big consulting AI maturity assessments | Adoption breadth, capability matrices | Commercial outcome, LLM visibility, agentic readiness | Revenue-first scoring, not adoption scoring |
| AI automation agencies | Tool deployment count, hours saved | Strategy, governance, attribution | Strategic frame above the tool layer |
| SEO/GEO agencies | Keyword rankings, content output | Commercial AI architecture, ERP-aware execution | LLM visibility connected to revenue, not just citations |
| Technology vendors | Their own product's footprint | Vendor-neutral architecture, lock-in risk | Independent — no vendor margin distorting the recommendation |
| AI Readiness Assessment (Paul Okhrem) | Commercial advantage, scored across 7 dimensions | — | The buyer is the audit's metric owner. Outcomes validated under The Proof Standard™. |
Each phase has scoped inputs, scoped outputs, and a named client owner. No diagnostic theatre.
Sequenced for leverage, not for diagnostic completeness. The roadmap is built to ship.
For mid-market and enterprise companies running multiple AI workstreams without measurable commercial return, the audit is the single most useful 4–6 weeks of work available at this rate band.
Request a private AI Readiness Assessment$1,000/hour · 100-hour minimum · From $100,000. Audits typically run 100–180 hours over 4–6 weeks.
An AI readiness assessment is a structured evaluation of whether a company's data, infrastructure, governance, and operating model can support AI deployment at scale. The output is a scored read across the dimensions that determine production readiness — data quality, model risk management, security posture, change management capacity, and executive sponsorship. The assessment surfaces blockers before they cost rework.
A complete AI readiness audit covers six dimensions: data infrastructure (warehouse, lakehouse, lineage, quality), AI governance (model registry, audit, regulator-readiness), security and privacy (PII handling, prompt-injection surface), engineering capacity (MLOps maturity, deployment automation, observability), operating model (rollback authority, cross-functional sign-off, AI literacy at the leadership table), and capital allocation. The Growth Readiness Audit covers all six against a 100-point scoring rubric.
Self-serve AI readiness assessment tools work well for early-stage diagnostics — they confirm gross gaps and benchmark against industry baselines. They do not produce a defensible recommendation for a specific company. A consulting audit goes further: it interprets the score against the company's specific commercial context, identifies the two or three blockers that actually matter, and produces a remediation plan with named owners. The right entry point depends on where the company is in the AI adoption arc.
Technical readiness is data quality, infrastructure, and engineering capacity. Organizational readiness is harder to measure but more often the binding constraint: who owns the rollback decision, how the audit committee gets comfortable, whether the legal review will block a launch, and whether middle management has been trained to work alongside agents. Most AI deployments that fail in production fail on organizational readiness, not technical.
The category in which Paul advises — decision leverage above the audit, when the next call is the consequential one.
When the audit's findings call for ongoing executive ownership of AI strategy at the leadership table.
The five-component measurement protocol every audit deliverable runs under.
Methodology and prompt-level data behind the LLM Visibility Benchmark Report deliverable.
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.
Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.