Find Where AI Actually Pays Off, Before You Build Anything
AI strategy that pinpoints the highest-value use cases for your business before recommending a single tool.
AI strategy is the process of identifying which specific business problems AI can realistically solve for you, in what order, and whether to build, buy, or leave a given use case alone for now.
What is AI Strategy?
AI strategy is the process of identifying which specific business problems AI can realistically solve for you, in what order, and whether to build, buy, or leave a given use case alone for now.
Skip the strategy step and most AI initiatives stall on a mandate instead of a use case - "figure out how we use this," with no specific problem attached. Teams respond by piloting a tool or hiring for an AI role, and months later there's activity but no clear answer to what actually got better.
It's different from AI consulting, which covers broader organisational and technology guidance, and from an AI-powered application build, which is the implementation that happens once a use case has already been prioritised here.
Common problems we solve
Open-ended AI mandate
A directive to "use AI more" with no specific problem attached to it.
Tool bought before use case
A tool gets purchased first, and a use case is found for it afterward.
Unready data and systems
A promising use case depends on data that is not clean or connected yet.
No prioritisation
Multiple candidate use cases with no ranking on feasibility or value.
Compliance risk surfacing late
Data privacy and vendor risk discovered mid-build, when they are expensive to fix.
Everything covered in this service
AI Readiness & Opportunity Assessment
Review of existing data, systems, and workflows for AI fit, with an honest flag on where the business is not ready yet.
Use Case Identification & Prioritisation
Candidate use cases scored against feasibility and value, ranked rather than left as a wish list.
Build-vs-Buy Recommendations
Evaluation of existing tools versus a custom build, including total cost of ownership and vendor lock-in.
Risk & Data-Privacy Review
Data sensitivity mapped against proposed use cases, with regulatory and vendor risk flagged early.
Our optimization framework
Discover
We review current workflows, systems, and data to understand what is actually available to work with.
Identify Use Cases
Candidate problems get surfaced from across the business, not just the ones already assumed to be AI-shaped.
Score Feasibility
Each candidate is scored against how feasible it actually is given your data and systems, and what it would be worth if it worked.
Analyze Build-vs-Buy
For the use cases worth pursuing, we assess whether an existing tool fits or a custom build is warranted.
Review Risk
Data privacy, compliance, and vendor risk get checked before anything is recommended, not after.
Deliver Roadmap
A phased adoption plan is handed over with a clear first step, not a menu of equally-weighted options.
What actually changes
| Before | After |
|---|---|
| A tool gets purchased first, and a use case is found for it afterward | Use cases are identified and scored before any tool is chosen |
| Whatever gets the most internal attention goes first | Priority order is explicit and value-driven |
| Readiness gaps surface mid-build, when they are expensive to fix | Data and system readiness is checked up front |
| An open-ended mandate to 'use AI more' | A short, ranked list of prioritised use cases |
What we report on
Use case prioritisation
Each candidate scored against feasibility and value, reported as a ranked list rather than a wish list.
Readiness gaps identified
Data, system, and workflow gaps documented before any build commitment is made.
Build-vs-buy clarity
A documented recommendation with reasoning for each priority use case, including total cost of ownership.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
How AI Strategy connects with AI search
AI Strategy ├── AI Consulting ├── Generative AI Solutions ├── AI-Powered Applications └── Business Automation
AI Consulting
Broader organisational and technology guidance that sits alongside a focused strategy engagement.
Generative AI Solutions
One category of use case strategy work often prioritises, once feasibility and value are confirmed.
AI-Powered Applications
The implementation stage that follows once a use case has been prioritised and scoped here.
Business Automation
Often the highest-feasibility use case category strategy work surfaces first, before more complex builds.
Related AI Solutions services
Get a Second Opinion Before You Commit Budget
Independent AI advisory for teams evaluating vendors, tools, or an in-house build.
Generative AI applied to a specific, measurable outcome
Generative AI implementation for content, code, or creative workflows, scoped to a specific business outcome.
AI That Does Real Work Inside Your Product, Not Beside It
AI application development where the model is core to the product, not a chatbot bolted onto the side.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
Typically two to four weeks, depending on how many parts of the business are in scope and how much discovery is needed to understand current systems and data. It is bounded by assessment work, not by build time.
Where a build-vs-buy decision favours an existing tool, yes - we recommend based on fit to your specific requirements, not a fixed preferred-vendor list. We are not paid by any vendor for a recommendation.
No - a use case being feasible and valuable on paper does not guarantee execution goes smoothly, and outcomes depend on decisions made throughout the build, not just the initial scoping. What we guarantee is a rigorous, honest assessment of feasibility and value before you commit budget, which meaningfully reduces the odds of building the wrong thing.
Then that's exactly what we'll tell you, along with what would need to change first - usually a data or systems gap. Recommending a delay costs us a build engagement, but recommending a build that is likely to fail costs you more.
No - strategy is scoped as its own engagement, separate from generative AI or application builds. Once a use case is prioritised, implementation is scoped separately, either with us or with whoever you choose to build it.
General digital strategy covers channels, positioning, and growth more broadly. AI strategy is narrower and more technical - it's specifically about which AI applications are feasible given your data and systems, and in what order they're worth pursuing.
Not sure where to even start with AI?
We'll assess what's actually feasible with your data and systems before recommending a single use case.
Ready to get started?
We usually reply within 24 hours.
AI Strategy, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Readiness Assessment | A written review of data, systems, and workflow fit for AI adoption |
| Use Case Ranking | Prioritised list of candidate use cases scored against feasibility and value |
| Build-vs-Buy Recommendation | A documented recommendation for each priority use case, with reasoning |
| Adoption Roadmap | A phased plan for what to pursue first, second, and later |
How this compares
| Strategy-First AI Adoption | Tool-First AI Adoption |
|---|---|
| Use cases are identified and scored before any tool is chosen | A tool gets purchased first, and a use case is found for it afterward |
| Priority order is explicit and value-driven | Whatever gets the most internal attention goes first |
| Data and system readiness is checked up front | Readiness gaps surface mid-build, when they are expensive to fix |
Tool-first adoption isn't always wrong - it just shifts the risk of a mismatched use case later into the project, where it costs more to unwind.