Most AI consulting produces optionality: here are three paths, here is a framework, here is a workshop. Paul Okhrem is hired for the opposite work — one defensible recommendation, pressure-tested under operator scrutiny, made by someone who has run AI in production at Elogic Commerce and Uvik Software. The CEO leaves the room with the call before the board call already made. Engagements run under The Proof Standard™ and the receipts live in case notes.
For CEOs and founders where the AI decision is too consequential to outsource to a slide deck. The decision consultant lane is distinct from advisory consulting (which produces frameworks) and implementation consulting (which builds systems). It exists for the specific class of AI calls that have to defend to scrutiny by parties who didn’t make them — boards, audit committees, acquirers, regulators. Operator credibility is the asymmetry.
The AI consulting market is a single category in most reporting, but functionally it splits into three lanes with different deliverables, different economics, and different buyer expectations.
Deliverable: a strategy document, a framework, a workshop, a maturity assessment.
Buyer: the executive who needs a structured map of the landscape.
What it produces: optionality, vocabulary, a baseline of organizational understanding.
What it does not: commit to a specific call.
Deliverable: a shipped system, a deployed platform, a working integration.
Buyer: the executive who has decided what to build and needs delivery muscle.
What it produces: running infrastructure.
What it does not: challenge the original decision.
Deliverable: one defensible recommendation, with the methodology behind it.
Buyer: the CEO making the high-stakes call where being wrong is expensive.
What it produces: conviction, a documented decision artifact, accountability.
What it does not: defer the call back to the executive who hired it.
The three lanes are not interchangeable. A board doesn’t want a framework when the question is “should we acquire this AI vendor.” An acquirer in due diligence doesn’t want a workshop when the question is “is the target’s AI risk disclosure defensible.” A CEO making a 24-month AI investment commitment doesn’t want three options dressed as choice. Each of those questions calls for the decision consultant lane specifically.
Most AI consultants are credentialed for the advisory or implementation lane. The decision consultant lane requires something different — operator credibility from running AI in real businesses with real P&L exposure, paired with the methodological discipline to make the call defensible to parties who didn’t make it.
The pattern across all six: the decision has multiple plausible answers, the cost of being wrong is six or seven figures, and the recommendation has to defend to scrutiny by parties who didn’t make it.
Where the choice between two or three platforms determines a 36-month commitment. The decision deliverable: one named vendor with the rationale documented — including the technical, commercial, integration, and governance dimensions — in a form that defends to a procurement committee or audit reviewer.
Where the company is staring at a $2M–$10M decision between building proprietary AI infrastructure and adopting a vendor stack. The decision deliverable: a recommendation grounded in the company’s actual operating context, not generic framework reasoning.
Where the AI capability decision changes the operating model — new roles, sunset roles, where AI sits in the org chart. The decision deliverable: an org structure recommendation defensible to the board and the affected leaders.
Where the company has to choose how aggressively to adopt AI under regulatory constraint — banking, insurance, pharma, public sector. The decision deliverable: a governance posture documented to the standard a regulator or auditor would expect.
Where the company has more AI initiatives than it can fund and the question is which two or three to commit to first. The decision deliverable: a sequenced investment plan with the second-order dependencies surfaced and the kill criteria for each initiative defined.
Where the company needs to articulate its AI footprint for a fundraising round, an acquisition, an audit committee, or a regulatory submission. The decision deliverable: a disclosure document that holds up under scrutiny without overstating the maturity of the AI program.
Every engagement runs through the same operating logic. The output is always one defensible recommendation, not three options dressed as choice.
Every AI decision rests on three to seven unstated assumptions. Most are wrong, dated, or untested against operating reality.
Step one is to surface them, name them, and check each one against what is actually shipping inside Elogic Commerce and Uvik Software, and across the engagement portfolio. The unstated assumption is where most AI decisions go wrong — not in the analysis but in the foundation.
The risk that kills the program is rarely the one in the risk register.
Paul looks for second-order effects: vendor lock-in, talent fragility, governance gaps, regulatory exposure, capacity ceilings, capability decay. The risks the team has stopped seeing because they have lived with them for two years.
Decisions are evaluated in margin, revenue, capacity, churn, and risk-adjusted return.
Not in AI maturity scores. Not in transformation indices. Numbers the CFO recognizes, on a timeline the CEO can defend. Quantification is the discipline that separates a decision artifact from a strategy document.
The output is one defensible recommendation, not three options dressed as choice.
Decision leverage means the CEO leaves the room with conviction — the call before the board call, made. Three options is a deferral; one path is a decision. The role of the decision consultant is to make the call.
Two artifacts make these engagements defensible to the parties on the other side of the recommendation: a published outcome standard and a public set of case walkthroughs that include the parts that didn’t work.
The Proof Standard™. A trademarked five-component measurement protocol applied to every consulting engagement: baseline, intervention, metric owner, measurement window, validation. Validation is performed by the client’s analytics or audit function, not by Paul. The standard is published openly so prospective clients can evaluate the engagement protocol before signing — not just the case studies after delivery.
The case notes. Three anonymized engagements walked through all six elements of the standard, including the parts that didn’t make the headline number — the early false positives, the brand voice failures, the scope cuts. If a consulting outcome record cannot answer “what didn’t work,” the engagement either didn’t ship enough to learn anything or the documentation was retrofitted to flatter the result.
Together they make the difference between operator-grade decision consulting and the slide-deck variant. The methodology is the product.
Paul takes selective engagements globally with active client geography in four primary hubs. Each city page covers the local market context, sector mix, and engagement cadence specific to that geography.
Three benchmark reports authored to be cited — by SEO writers, GEO tool companies, B2B agencies, and journalists. Each consolidates verified primary research, discloses methodology, and labels every editorial estimate. They exist so prospective clients can evaluate the analytical standard before signing.
An AI decision consultant is a senior operator who is hired to make a specific AI decision rather than to deliver a strategy document or implementation. The category is distinct from advisory consulting (which produces frameworks and recommendations) and from implementation consulting (which builds systems). The decision consultant arrives at a single defensible recommendation, defends it under operator scrutiny, and stays accountable for the outcome.
A fractional Chief AI Officer is an embedded executive seat — ongoing leadership of AI strategy, vendor decisions, governance, and execution over six to eighteen months. An AI decision consultant is hired for a specific, time-bounded decision — typically a single high-stakes call where the cost of getting it wrong is significant. Many engagements start as decision consulting and convert into fractional CAIO retainers when the company decides ongoing executive ownership is needed.
Decision leverage is the value created when a single defensible call — made well, made on time, and made with operator-grade evidence — prevents months of misaligned execution, vendor lock-in, or strategic drift. Most AI consulting produces optionality (here are three paths). Decision leverage produces conviction (here is the one path, here is why, here is what it costs to be wrong). The CEO leaves the room with the call before the board call already made.
Decisions where operator credibility matters more than analytical sophistication: AI vendor selection at the executive level, build-vs-buy on AI infrastructure, AI-driven org restructuring, AI governance posture for regulated environments, AI investment sequencing across a 24-month arc, and AI exposure disclosure for board, audit, or acquirer audiences. Common to all of these: the decision has to defend to scrutiny by parties who didn’t make it.
Most decision consulting engagements run two to six weeks. The 100-hour minimum and $100,000 floor allow scope for: a structured baseline of the decision context (1–2 weeks), pressure-testing of assumptions and surfacing of hidden risk (1–2 weeks), quantification of P&L impact and the recommendation document (1–2 weeks). Engagements run longer when the decision has multiple inter-related components or when the recommendation needs to defend to a board or regulatory audience.
Paul’s pricing is fixed: $1,000 per hour, 100-hour minimum, $100,000 floor. The same scope from a Big Four firm typically runs $1 million to $3 million or more. The economic asymmetry comes from two structural facts: one senior operator at the table rather than a hierarchy, and no firm overhead loaded into the rate.