AI Strategy
AI Strategy Consulting - Roadmaps That Get Built, Not Filed
AI strategy consulting identifies where artificial intelligence creates measurable value in your specific business, scores each opportunity on value, feasibility, and risk, and sequences the winners into a costed roadmap with owners and success metrics. The deliverable is a build plan your teams can start on Monday - not a vision deck.
What We Produce
Ask ten leaders in the same company where AI should go first and you’ll get twelve answers. That isn’t a failure of imagination; it’s the absence of a shared scoring system. Everyone is arguing from a different definition of “value.” An AI strategy is that scoring system. It replaces the loudest opinion in the room with a defensible portfolio - and it tells you just as clearly what not to build this year.
Use-Case Inventory
A use-case inventory built from the floor up. We interview the people doing the work, not only the people describing it. Executives know where the strategy hurts; operators know where the hours go. Both lists matter, and they rarely match.
Value, Feasibility & Risk Scoring
Value, feasibility, and risk scoring. Every candidate gets scored on the same axes: financial impact, effort, data readiness, regulatory exposure, and change difficulty. When your CFO asks why project A precedes project B, the answer is a scorecard, not a preference.
Phased Roadmap
A phased roadmap. Quick wins in weeks to build credibility and cashflow, structural plays in quarters to build advantage. Most AI programs stall because everything in them was ambitious; sequencing is what keeps sponsors engaged.
Build, Buy, or Partner Calls
Build, buy, or partner calls. For each initiative, an honest recommendation - including where a subscription tool beats a custom build. We build custom AI for a living, which is exactly why we’ll tell you when not to.
Business Cases & Metrics
Business cases and metrics. Cost estimates, expected benefit, and the specific metric each initiative moves. If nobody can name the number, it isn’t an initiative - it’s an interest.
How the Engagement Runs
| Phase | Duration | Output |
|---|---|---|
Discovery | Weeks 1–2 | Stakeholder interviews, systems and data review, use-case harvest |
Analysis | Weeks 2–4 | Scored opportunity portfolio, feasibility flags |
Decision | Weeks 4–6 | Prioritized roadmap, business cases, build/buy calls |
Mobilization | Weeks 6–8 | Delivery plan, governance, first initiative kicked off |
Four to eight weeks, depending on organizational complexity. We’d rather compress the analysis than dilute the decisions.
Ready to Stop Debating and Start Deciding?
Bring your three most contested AI ideas. We’ll score them live and you’ll leave knowing which one to fund.
What Makes Our Roadmaps Different
We have to deliver ours. Claravance designs, builds, secures, and runs AI systems - so every recommendation is written by people who will be held accountable for it. Strategy without delivery exposure produces optimistic timelines and convenient omissions. Strategy with it produces plans that survive contact with engineering.
The Scoring Model We Use
Opinions become decisions when everyone scores against the same axes. Every candidate use case gets rated on five:
| Axis | The question it answers |
|---|---|
Value | What does success move - revenue, cost, cycle time, risk - and by roughly how much? |
Feasibility | Can current technology do this reliably, at acceptable cost, at your volume? |
Data readiness | Does the data exist, is it good enough, and can you legally use it this way? |
Adoption difficulty | How much does someone’s daily work change, and who owns making that happen? |
Risk exposure | Regulatory, reputational, and operational consequences if it behaves badly |
Weights shift by organization - a regulated financial services firm weights risk far more heavily than a consumer marketplace does. What matters is that the weights are agreed before anyone scores their favorite project, which is why we set them in the first workshop rather than the last. The most common surprise: the use case with the highest theoretical value frequently scores worst on adoption. Value that nobody adopts is not value.
Five Patterns That Kill AI Strategies
We see the same failures repeatedly, across industries and company sizes:
The technology-led roadmap. Built around what a vendor demoed rather than what the business needs. Recognizable because the use cases match the vendor’s feature list suspiciously well.
The everything-at-once portfolio. Fourteen initiatives, no sequence, no owner, and no capacity. Nothing finishes and the program loses sponsorship by month five.
The unowned initiative. A project with a budget but no named business owner accountable for the outcome. These reliably deliver a working system that nobody uses.
The undated business case. Benefits stated with no baseline and no measurement plan, so success can never be proven or disproven - which means the next round of funding is a matter of faith.
The data assumption. A roadmap that assumes data quality nobody verified. This is the single most common reason a well-received strategy stalls at implementation, and the reason we test data readiness during the strategy, not after it.
A strategy that survives its first year usually does so because it named fewer things and owned them properly.
What Happens After the Roadmap
A roadmap that nobody governs decays within two quarters. We set up the operating rhythm alongside it: a quarterly portfolio review that re-scores initiatives against what’s been learned, a stage-gate for moving from idea to prototype to build, and a standing decision forum with the authority to stop things. The ability to kill an initiative is the feature that makes a roadmap credible - without it, everything stays perpetually “in progress” and the portfolio only grows.
Frequently Asked Questions
Score it on value, feasibility, data readiness, adoption difficulty, and risk - using the same scale as every other candidate. A use case worth pursuing has a named business owner, a measurable baseline, data that already exists in usable form, and a change footprint someone is accountable for managing.
A digital transformation strategy covers the whole technology and operating-model agenda. An AI strategy is narrower and more concrete: which specific AI capabilities to build or buy, in what order, with what data prerequisites. AI strategy should sit inside the broader plan rather than duplicating it - if the two documents contradict each other, the transformation plan wins.
A scored use-case portfolio, a readiness view of data and systems, a phased roadmap with owners and budgets, build/buy recommendations, success metrics per initiative, and a governance model. Anything without owners and metrics is a manifesto, not a strategy.
Typically four to eight weeks for a mid-market or enterprise organization. Faster is possible for a single business unit; much slower usually signals scope confusion rather than diligence.
Executive sponsorship, plus access to operational leads, data owners, IT/security, and finance. Roughly six to twelve hours of stakeholder time in total - the interviews are where the real use cases surface.
Yes, and it’s a common starting point. We validate the list against real feasibility and data readiness, add what’s missing, score everything on one scale, and hand back a sequenced roadmap.
The roadmap accounts for that - remediation work is sequenced ahead of the initiatives that depend on it. See Readiness Assessment and Data Strategy.
Get a Roadmap You Can Hand to Engineering.
Or request the engagement outline (PDF).