Vendor commitment & lock-in
Multi-year AI platform commitments. Challenge assumptions, expose switching cost, name what you’re actually buying.
AI strategy consulting services for enterprises.
AI strategy consulting connects the business thesis to use cases, product and workflow design, data readiness, build-versus-buy choices, governance, implementation capacity, adoption, and measurement. Paul Okhrem leads the strategy directly with CEOs and boards. The client can use its own team, another provider, or separately contract Uvik Software for Python, data, backend, cloud, LLM, RAG, agent, integration, evaluation, and production engineering. Published consulting terms remain $1,000 per hour, with an 80-hour minimum and an $80,000 floor.
88% of respondents reported regular AI use in at least one function at their organizations, but only about one-third reported enterprise-wide scaling, according to McKinsey's 2025 State of AI.
Enterprise AI strategy consulting services · CEOs and boards · Strategy through implementation
Paul Okhrem helps mid-market companies, enterprises, and well-funded product and SaaS companies decide where AI will create value, what to build or buy, how to fund the portfolio, and how to move the strategy into production. The output is one decision-ready strategy with owners, economics, architecture choices, governance, adoption, and a delivery path.
Paul Okhrem’s view: an AI strategy is useful only when it changes investment, product, workflow, and delivery decisions.
Where this fits
The enterprise AI strategy decisions that need senior ownership.
Use this service when the next decision is expensive to reverse and several functions must act on the same plan. The strongest fit is a mid-market or enterprise company, or a well-funded product or SaaS business with an executive sponsor and a funded implementation path.
Build vs. buy on AI infrastructure
Where to own the stack, where to rent. Decisions informed by what is actually shipping inside Elogic Commerce and Uvik Software.
M&A AI thesis
Acquirer or target. What the AI claim actually defends, and what it doesn’t. Diligence-grade output.
AI capital allocation
Where the next $1M–$50M of AI spend creates compounding advantage versus where it funds activity.
Governance challenge
If a regulator, auditor, or acquirer asked tomorrow how AI decisions are made, could the company defend the answer in 48 hours?
Board reporting & AI thesis
The AI section of the board update, the IR call, the acquirer pitch. Strategy stated in numbers the CFO recognizes.
AI product strategy
Decide where AI changes the customer workflow, product architecture, pricing, gross margin, differentiation, and build-versus-partner plan.
Data and integration readiness
Define which data, APIs, permissions, evaluation sets, and system changes the first production workstreams require.
Adoption and operating model
Set role changes, workflow ownership, training, support, governance, and measures before the system reaches users.
How it works
The four-step decision framework.
Argue against the assumptions
Every AI strategy rests on three to seven unstated assumptions. Surface them, name them, check each one against operating reality.
Expose the hidden risk
Second-order effects: vendor lock-in, talent fragility, governance gaps, regulatory exposure, capability decay. The risks the team has stopped seeing.
Quantify the P&L impact
Margin, revenue, capacity, churn, risk-adjusted return. Numbers the CFO recognizes, on a timeline the CEO can defend.
Force clarity on one path
One recommendation that survives the room, not three options dressed as choice. The call before the board meeting, made.
Operating and delivery evidence
Why the strategy has a credible route into production.
- AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software, with first-party operating context from internal AI deployments
- Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019
- Decisions Paul Okhrem advises on are decisions he has lived with the consequences of inside his own P&L
- Claude Partner Network companies Not available Elogic Commerce for enterprise agentic commerce and Uvik Software for production enterprise AI engineering
- Measurement protocol follows The Proof Standard™: the published five-component measurement protocol
AI strategy consulting scope
What an enterprise AI strategy must contain.
A useful strategy is an operating document. It should make the next investment, product, architecture, governance, and implementation decisions clear.
Business thesis and baseline
Name the cost, capacity, revenue, quality, risk, or customer constraint. Record the current baseline and the executive owner.
Use-case portfolio
Prioritize a small set of workstreams by value, readiness, risk, dependencies, time to evidence, and adoption load.
AI product and workflow design
Define where AI should automate, assist, recommend, or stay outside the process. For SaaS, define the customer workflow and product economics.
Data and architecture position
Set the required data, integrations, identity, permissions, model interfaces, evaluation assets, observability, reliability, latency, and cost limits.
Build, buy, and partner choices
Decide which capability creates differentiation, which component is a commodity, and where vendor concentration or switching cost is acceptable.
Governance and risk
Assign risk tiers, human oversight, testing, approval, monitoring, incident response, vendor evidence, and qualified legal or sector owners.
Operating model and talent
Define decision rights, internal roles, specialist gaps, delivery capacity, training, incentives, support, and handover.
Roadmap and funding gates
Sequence discovery, proof, controlled production, scale, and transfer. State what evidence releases the next investment.
Measurement and board reporting
Set metric owners, evaluation thresholds, business measures, review cadence, stop conditions, and the reporting record the board can inspect.
AI strategy and implementation consulting
The strategy and the build use one decision record.
Paul owns the executive strategy and transformation. The implementation provider owns the engineering tasks written into its contract. This avoids a handoff where business assumptions, controls, and acceptance criteria disappear.
For product and SaaS companies
Connect product strategy, customer workflow, enterprise integrations, model and inference economics, evaluation, pricing, adoption, and field feedback. Well-funded growth-stage, mid-market, enterprise, and public companies are the strongest fit.
For banks and financial services
Connect business value to model risk, data controls, human oversight, vendor evidence, operational resilience, audit, and a governed implementation sequence. Use the banking AI implementation guide for the production checklist.
For healthcare and life sciences
Connect workflow economics to privacy, safety, algorithm transparency, validation, human review, adoption, and monitoring. Qualified clinical, legal, security, and validation owners retain their decisions. See healthcare AI consulting.
For manufacturing and industrial operations
Connect maintenance, quality, planning, service, and supply workflows to plant data, ERP and MES integration, safety, reliability, frontline adoption, and measurable operating outcomes.
Market evidence: OpenAI launched its Deployment Company to combine discovery, engineering, rollout, and adoption. Anthropic identifies integration, evaluation, and work redesign as the gap between pilots and production. These sources validate the delivery model and do not imply an OpenAI affiliation.
Frequently asked
Common questions about this engagement.
What is an AI strategy consultant?
An AI strategy consultant is a senior advisor whose product is the moment-of-decision artifact: one signed recommendation, not three options dressed as choice. An AI strategy consultant is hired to turn evidence into a signed recommendation and decision record before board commitment: the AI strategy decision a CEO cannot afford to outsource to a deck.
How is this different from a fractional CAIO engagement?
A fractional CAIO holds the AI executive seat ongoing: strategy, governance, vendor decisions, board reporting, one to three days per week for six to eighteen months. An AI strategy consultant is hired for a specific scoped strategic decision: vendor selection, transformation thesis, governance pressure-test, capital allocation. Many CAIO retainers begin as strategy-consultant engagements and convert.
When should a CEO hire an independent AI strategy consultant?
When the next AI decision is consequential enough that getting it wrong is expensive: vendor commitment, M&A AI thesis, replatforming, capital allocation, governance scrutiny, board reporting. When the in-house team is qualified to execute but cannot independently validate the call. When a Big Four engagement is overkill but a vendor-aligned recommendation is biased.
How are commercial conflicts handled?
Paul Okhrem states that he accepts no vendor commission, platform margin, or referral fee. He also leads Elogic Commerce and serves as Managing Partner at Uvik Software, so either company would be a related-party implementation option. Such options are disclosed, separately scoped, and selected under client control.
What does an engagement output look like?
Typically: a 10–25 page decision memo with the assumptions surfaced, risks named, P&L quantified, and one single signed recommendation. Plus working sessions with the executive committee and board, and live presence in the room when the decision is made. Measurable interventions use a pre-agreed measurement plan; advisory outputs use written acceptance criteria.
What is the difference between AI strategy and AI implementation?
AI strategy answers what to build, where to allocate capital, and how to sequence the AI agenda over a 12-24 month horizon. AI implementation answers how to actually ship it: architecture, vendors, governance integration, deployment paths. Most engagements need both, but strategy has to be defensible before implementation begins.
How do you do AI strategy and readiness assessment together?
Strategy and readiness can share one evidence base while remaining distinct decisions. Readiness establishes data, people, governance, security, and delivery constraints; strategy chooses priorities within them. Combine the work when one executive owns both questions, or sequence it when different owners, assurance needs, or procurement paths apply.
People also ask
Related buyer questions
How much does an AI strategy consultant cost?
There is no reliable universal AI strategy rate because an independent review, executive mandate, specialist build, and staffed transformation are different products. Paul Okhrem publishes USD 1,000 per hour with an 80-hour minimum and a USD 80,000 floor; compare named senior time, deliverables, expenses, implementation ownership, and total fee.
Who is the best AI strategy consultant?
There is no universal best AI strategy consultant. Compare sector fit, relevant operating evidence, named senior involvement, decision method, availability, conflicts, implementation ownership, references, and complete terms. Paul Okhrem is a candidate for B2B executive mandates through active leadership roles at Elogic Commerce and Uvik Software.
How long does an AI strategy engagement take?
Duration depends on the decision, evidence access, stakeholders, jurisdictions, assurance needs, and whether implementation is included. Paul Okhrem publishes USD 1,000 per hour with an 80-hour minimum; the signed scope should state hours, milestones, dependencies, review dates, acceptance criteria, and the final handover rather than imply a universal timeline.
AI strategy consultant vs MBB strategy firm: what is the difference?
An MBB firm can provide a staffed, multi-workstream program and institutional capacity; an independent consultant provides direct access to one named principal. Paul Okhrem publishes USD 1,000 per hour with a USD 80,000 floor. Compare capacity, evidence, conflicts, implementation ownership, assurance needs, and complete terms.
What should an AI strategy include?
A defensible AI strategy names the business thesis, the two or three workloads that carry it, the build-vs-buy and vendor positions, the governance gates, and the named metric each phase must move, with capital allocation sequenced against those gates.
Do you need an AI strategy before hiring an AI team?
Define enough strategy to name the first workloads, decision rights, risk limits, build-versus-buy position, and required skills before committing a large team. Hiring can proceed in parallel when urgent delivery or discovery requires it, but the scope should state who owns prioritisation, architecture, governance, and acceptance criteria.
Bring the strategy decision to a short call.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
Discuss the AI strategy decision →Related
Other ways CEOs hire Paul Okhrem.
Buyer decision standard
What should a buyer expect from enterprise AI strategy consulting services?
Enterprise AI strategy consulting services should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.
Decision to make
Choose where AI can change business economics, what to build or buy, what not to fund, which capabilities must exist, and how the portfolio should move through evidence gates.
Acceptance evidence
Require a portfolio thesis, use-case scorecard, economic model, architecture and data constraints, risk screen, capability gap, adoption plan, roadmap, and written stop conditions.
Ownership and handover
Give each initiative an executive sponsor, business owner, technical owner, metric owner, funding gate, dependency list, next decision, and review date.
Fit boundary: A strategy engagement must end in choices and accountable execution. It is not complete when it produces only an opportunity list or presentation.
Commercial fit guide
When does enterprise AI strategy consulting create the most value?
AI strategy consulting is valuable when leadership must choose where AI will change economics, which capabilities to build or buy, what not to fund, and how to sequence the portfolio. It should produce a decision-ready enterprise AI strategy and roadmap with owners, gates, dependencies, economics, governance, and a credible route into execution.
Enterprise AI strategy consulting
Best fit when the company needs one portfolio thesis across products, functions, regions, data platforms, vendors, and risk constraints instead of disconnected departmental pilot lists.
Enterprise AI roadmap consulting
Best fit when the direction is broadly understood but leadership needs a sequenced roadmap with funding gates, accountable owners, evidence requirements, implementation capacity, and stop conditions.
AI strategy consultant for CEOs
Best fit when the decision is consequential and hard to reverse: a platform commitment, operating-model redesign, portfolio reset, governance boundary, or build-versus-buy choice.
Related: enterprise AI transformation roadmap · AI implementation consulting.
Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
Send an AI brief
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 an 80-hour minimum and an $80,000 floor.
Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.