AI strategy for regulated teams
AI strategy consulting: decisions your team can act on, not a deck
What an AI strategy engagement should leave behind, the questions a sound AI business strategy answers in order, how to judge an AI strategy consultant, and what Kriv AI does today.
AI strategy consulting helps leadership decide where AI is worth applying, which use cases come first, which are ruled out, and who owns AI decisions. A useful engagement ends in written decisions rather than a slide deck: a ranked use-case portfolio, value and risk criteria, an operating model, governance rules and a sequence your team can act on.
what ai strategy consulting delivers
What AI strategy consulting delivers: decisions, not decks
An AI strategy is a short set of choices about where AI earns its place in your business and where it does not. AI strategy consulting services are worth paying for only if those choices are written down, owned by a named person and specific enough to act on.
Who this is for: leadership teams asked for an AI plan, organisations with scattered pilots and no shared rules, and regulated firms whose auditor or board has asked what AI is in use. Who it is not for: teams that already know their use case and need it built, which is implementation work, or anyone wanting AI research.
| Decision | What it should say in writing |
|---|---|
| Use-case portfolio | A ranked list of use cases worth doing, each with a suggested owner, plus a list of the ones ruled out and why. |
| Value and risk criteria | The tests every idea is scored against: what it would change, what data it touches, what happens if it is wrong, and whether a person can check its output. |
| Operating model | Who proposes AI use, who approves it, who owns each system and who decides when one stops. For the deeper version, see our guide to AI center of excellence governance. |
| Governance | Rules for which data may go into which tool, which actions need a person's approval, what is logged and how changes are reviewed. |
| Sequencing | What happens first, what waits, and what each step depends on. Order and sign-off points, not a calendar promised before anyone has seen your systems. |
ai business strategy framework
An AI business strategy framework, as a sequence of questions
This is the framework Kriv AI works through with clients: our own practice, not an external or published method. It is a sequence because each answer constrains the next; there is no point ranking use cases before you know what the business is trying to change.
Readiness feeds questions two, three and seven. If you have no clear picture of your data, tools and gaps today, start with our AI readiness and governance assessment; the strategy builds on its findings.
AI implications for business strategy
AI in business strategy changes questions you already answer: which work you keep in-house, what staff must be trained to do, how vendors use your data, and what you must be able to explain to an auditor. A strategy should state its position on each, even where the answer is to wait.
An AI strategy plan versus an implementation plan
An AI strategy plan covers the organisation: portfolio, criteria, owners, rules and order. An implementation plan covers one use case: data and access, pilot, evaluation, rollout and handover. Strategy says what to do and why; implementation says how to deliver it. A strategy that cannot hand its first use case to an implementation plan, with an owner and measures, is not finished.
Enterprise AI strategy and generative AI strategy
An enterprise AI strategy covers every kind of AI the organisation uses or buys, including features inside existing software. A generative AI strategy is narrower: assistants such as Claude, ChatGPT or Copilot that draft, summarise and answer, where the strategy turns on data rules, access, review of output and training. Systems that act on their own need stricter design rules.
1. What is the business trying to change?
Start from the goals leadership already has, such as cost, capacity, quality or risk, not from what AI can do. An AI business strategy that cannot name the problem is a technology wish list.
2. Where is AI already in use?
Inventory the tools staff already use, approved or not, and the licences already bought. Ignoring existing use starts the strategy from a false baseline.
3. Which work is a candidate?
Processes where people read, sort, draft or summarise, and where the steps are written down well enough to check what an AI produces.
4. What is each candidate worth, and what could go wrong?
Score each against the value and risk criteria: the change it makes, the data it touches, the cost of a wrong output, and whether a person stays in the decision.
5. What do we rule out?
Name what you will not pursue now and why: data you cannot use, a risk you will not carry, or a process not yet stable. Saying no is the part of a strategy that saves money.
6. Who owns AI decisions?
Who approves new AI use, who owns each system in production, and who reviews changes. Without a named owner, governance becomes nobody's job.
7. What rules apply before anything is built?
Write the data, access, approval and logging rules once, so each new use case does not reopen the same debate with security and compliance.
8. What comes first, and how will we know it worked?
Pick the first use case, its owner and the measures it will be judged on, set before it starts. A no-go is a valid result; the strategy should say what follows one.
choose an ai strategy consultant
How to choose an AI strategy consultant
Ask these questions of any AI strategy consultant, or firm of AI strategy consultants, before you sign.
Red flags in an AI strategy proposal
Results or savings figures promised before anyone has seen your data. A deliverable described only as a report or a deck. No ruled-out list. No named owner on your side. A claim that the strategy will make you compliant. Every recommended next step is more work for the same firm.
A consultant or an AI leader?
If your gap is a person in the AI leadership seat rather than a scoped engagement, read about our fractional Chief AI Officer service, which covers strategy as part of a leadership role.
| Question to ask | What a good answer looks like |
|---|---|
| What will we have in writing at the end? | Named decisions: portfolio, ruled-out list, criteria, owners, rules and sequence. Not only findings and recommendations. |
| Who does the work after the sale? | A named person, ideally the one on the first call, not a team you meet after signature. |
| How does governance enter the strategy? | From the first conversation, written to the policies and frameworks you already answer to, not as a final chapter. |
| What if the answer is to do less? | A consultant who will say a use case should wait, or that you need no outside help yet, and put it in writing. |
regulated industries
AI strategy in regulated industries
Kriv AI's niche is healthcare, life sciences, financial services and insurance, where an AI decision can touch patient, client or claims data and someone outside the project will ask how it was made.
Here governance is a strategy decision, not a later compliance step. We write the rules to the policy and frameworks you already answer to, such as HIPAA, 21 CFR Part 11, SR 26-2, the NAIC Model Bulletin or the NIST AI RMF. A strategy document does not make an organisation compliant, and we do not certify compliance.
For banking teams: SR 26-2, the model risk guidance, states that generative AI and agentic AI models are not within its scope. Your own risk management should still govern them, and your AI strategy should say how.
Before signing off a strategy, read why enterprise AI pilots stall before production.
where kriv fits
Where Kriv AI fits as an AI strategy consulting partner
Kriv AI sends a senior practitioner who both scopes and delivers: no handoffs, no bench, no discovery theater.
Our AI strategy consulting services sit within what we call AI readiness and strategy: an inventory of where AI is already used, a ranked list of use cases worth doing, and the governance gaps that would block them. It is one of six services on our AI consulting services page, and it leaves written artifacts such as a prioritised use-case portfolio with suggested owners, a governance and risk register, and an operating model setting out roles and decision rights.
Our method runs in five chapters, Assess, Govern, Build, Validate and Operate, each leaving an artifact your team keeps. Because we can also build what a strategy recommends, we say plainly when the right next step is to build nothing yet.
Partner credentials, in the exact wording each program allows: Claude Partner Network, Select partner · Services Track (program details at claude.com/partners; written confirmation from the Claude Partner Network team available on request; more on our Anthropic partner credentials). OpenAI Partner Network · Select tier. Solutions Partner for Cloud & AI Platforms · Partner ID 7036589. Databricks partner. AWS Marketplace seller, 59 live listings. We are also a Zapier Silver Solution Partner.
Our Claude Code training delivery record: for a multi-billion-dollar distribution enterprise with 2,000+ associates across 122 locations, we delivered 12 live training sessions, 26 training documents and 5 role-specific starter kits.
The honest gap: that record is training work. We do not yet have a named case study for an AI strategy or governance engagement, so this page quotes no strategy results.
how an engagement runs
How an AI strategy engagement with Kriv AI runs
Every engagement is scoped and priced after a short discovery call, and the length of each step is agreed in the written scope.
1. Discovery call
The business problem, the AI already in use, the regulator or auditor you answer to and who will own the result. If we are not the right fit, you hear it here.
2. Readiness review
AI tools in use, policies, data access and logging, summarised in a short written assessment the strategy builds on.
3. Scoped proposal
Named deliverables and sign-off points, scoped and priced after the discovery call and readiness review.
4. Strategy work
Working sessions with the people who own the business problems, the data and the risk, until each framework question has a written answer.
5. Governance documentation
The owners, rules and change-control approach your compliance or risk team will want. A formal sign-off pack can be prepared on request.
6. Handover
Your named owner takes the portfolio, criteria, rules and sequence, with the first use case ready for an implementation plan.
get started
Get started
Bring the business problem, the AI tools already in use and the name of whoever will own AI decisions. A short call shows whether you need a readiness review, a strategy engagement, or nothing from us yet. Book an AI strategy discovery call, or see how engagements are scoped and priced.
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Straight answers
Frequently asked questions about AI strategy consulting: decisions your team can act on, not a deck
What is AI strategy consulting?
AI strategy consulting helps leadership decide where AI is worth applying, which use cases come first, which are ruled out, and who owns AI decisions. A useful engagement leaves written decisions: a ranked use-case portfolio, value and risk criteria, an operating model, governance rules and a sequence. It stops where implementation begins.
What does an AI strategy consultant do?
An AI strategy consultant works with the people who own your business problems, data and risk to answer a fixed set of questions: what the business wants to change, where AI is already used, what to rule out, who owns AI decisions and what comes first. At Kriv AI, a senior practitioner who both scopes and delivers does this work.
What is an AI strategy framework?
An AI strategy framework is a structured way to reach AI decisions in a sensible order. Kriv AI's own framework is a sequence of eight questions, from what the business is trying to change, through value and risk scoring, ruled-out use cases and ownership, to which use case comes first and how it will be judged.
What should an AI strategy plan include?
A ranked use-case portfolio with owners, a list of what is ruled out and why, the value and risk criteria used to score ideas, an operating model with decision rights, governance rules for data, access, approval and logging, and the order of work. It should end by naming the first use case, its owner and its measures.
How is AI strategy different from AI implementation?
Strategy decides what to do and why across the organisation: which use cases, in which order, under which rules and owners. Implementation takes one chosen use case and delivers it, from data and access through pilot, evaluation and rollout to a working system your team owns. Without implementation, a strategy stays on paper.
How much does AI strategy consulting cost?
Cost depends on the number of business areas the strategy covers, how much AI is already in use, and the review your organisation requires, so there is no honest list price. At Kriv AI every engagement is scoped and priced after a short discovery call, and the written scope names deliverables and sign-off points before work begins.
What is the best AI for business strategy?
There is no single best AI for business strategy. Assistants such as Claude, ChatGPT or Copilot can help draft analysis and summarise research, but the strategy itself is a set of decisions your leadership owns. Choose a tool your organisation can govern with data rules, access controls and logging. Kriv AI deploys and governs Claude, OpenAI models and Microsoft Copilot.
What should an AI strategy for business leaders cover?
Start from the business problem, not the technology. Leaders do not need to understand model internals, but those using AI for business strategy should personally own three things: the problems AI is pointed at, the risks the organisation will and will not carry, and the name of the person accountable for AI decisions. Then insist the strategy names what is ruled out.
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