AI strategy examples: what a good strategy contains

You have been asked to write an AI strategy, and you want to see a good one before you start. Most published examples are national policy papers or slide decks full of ambition. Neither shows you what to write when the board asks which use case comes first, whose data it runs on, who owns it and how you will know it worked.

An AI strategy example is a worked document that shows which AI use cases an organisation will pursue, in what order, on what data, under which governance, with named owners and agreed metrics. A good example reads as a plan the builders can deliver, not a statement of ambition. Its sections can be copied; its choices cannot.

This guide is for the person who has been handed the AI mandate: a head of data, digital or innovation, a director of technology, or a chief executive in a smaller organisation. We build data and AI systems, and we write strategies with the people who then deliver them.

The guide sets out the six sections a defensible AI strategy contains, then works through two examples, both tagged Illustrative. Each example shows the same organisation twice: once as a strategy deck, and once as a plan the builders can deliver. The difference between the two is the point of the guide.

What an AI strategy is for

An AI strategy exists to make decisions that would otherwise be made one tool at a time. It says what the organisation will build with AI, what it will not build yet, and what has to be true before each piece of work starts. Without those decisions, teams buy licences, run pilots and produce demonstrations that never reach production, because no one agreed what success meant or whose data the work would use.

National AI strategies set broad aims, such as making AI people centred, trusted and ethical. Those aims suit a government. An organisation needs something narrower: a small set of choices it can fund, staff and measure. The test of a strategy is whether the people who will build the first use case can read it and start work.

An AI strategy also depends on your data strategy. The data strategy decides how data is acquired, governed and used across the organisation. The AI strategy decides which AI work rests on that data and in what order. If you do not have one yet, read our guide to writing a data strategy alongside this one.

A strategy that names the data, the owner and the metric is a plan. A strategy that does not is a deck.

The sections every AI strategy needs

Every defensible AI strategy covers the same six areas. The content changes from one organisation to the next. The headings do not. You can copy this structure directly.

Use-case priority

List the candidate use cases and score each one on business value, feasibility and how ready its data is. Then choose one to go first. The first use case should be small enough to ship, visible enough to matter and close enough to existing data that it does not wait on a long foundation programme. A first win that pays for the second matters more than a long list of ideas.

Data readiness

For each priority use case, state which systems hold the data, how complete and consistent it is, who owns it and whether an AI system can reach it in a governed way. Be specific. "Customer records sit in three systems that disagree on addresses" is a finding. "Data quality needs improvement" is not. This section usually decides the timeline, because AI amplifies whatever it sits on. Over strong foundations it amplifies gains. Over weak foundations it accelerates failure and exposes risk.

Governance

State who approves a new use case, which risks are reviewed before go-live, how outputs are checked once the system is live, and how personal data is handled under UK GDPR. Keep the controls proportionate. A tool that drafts internal meeting summaries needs lighter review than one that changes a customer's account or informs a credit decision.

Ownership

Name a business owner for each use case, a technical owner for the build and a named team that runs it after go-live. A strategy that says "the business" or "IT" owns something has not made a decision. Ownership after go-live is the line most often left blank, and it is the reason many working systems quietly stop being used.

Metrics

Agree one measure per use case before the build starts, and record the current baseline. Good measures describe an outcome: hours of admin time per week, tickets resolved without escalation, forecast error, time to first response. If the outcome cannot be measured today, setting up the measurement becomes part of the first piece of work.

Sequencing

Put the work in order and show the dependencies. Foundation tasks come first, then the access controls and business rules that let a model reach governed data, then the use case itself. Show what later use cases will reuse from the first, so each build makes the next one quicker.

Illustrative example one: scattered data

Illustrative. A mid-market wholesale distributor holds its orders in an ERP system, its customer service emails in shared inboxes and its product information in spreadsheets maintained by three different teams. The board has asked for an AI strategy within the quarter.

Deck version: four lines of ambition

The first draft has four lines: become an AI-first business, launch a customer chatbot, use predictive analytics to optimise stock, and upskill staff. None of these is wrong. None can be built either, because the draft names no data, no owner, no metric and no order of work.

Plan version: email routing first

• Use-case priority: classify and route inbound customer service emails first. The emails already exist, the task is repetitive and the result is visible to the whole service team.
• Data readiness: the inboxes hold a usable history of emails. The service team labels a sample to give the model examples and a test set. Product data is not ready, because the three spreadsheets disagree.
• Governance: the model routes and tags emails but does not reply to customers. A person reviews any email the model is unsure about.
• Ownership: the customer service manager owns the outcome. A named engineer owns the build. The service team runs it after go-live, with a monthly review.
• Metrics: time from an email arriving to its first response, with a baseline measured before the build starts.
• Sequencing: in parallel, product data is brought into a cloud data warehouse with one agreed definition of each product. The chatbot comes second, on that clean product data. Stock forecasting comes third, on the same warehouse.

The plan says not yet to two things the deck promised. The chatbot waits because a chatbot answering from three conflicting spreadsheets would give confident, wrong answers to customers. That decision is the most valuable line in the document.

Illustrative example two: a stalled pilot

Illustrative. A business services firm already has a cloud data warehouse and a small data team. Last year it built a pilot assistant that answered staff questions from internal policy documents. Staff used it for a few weeks, then stopped. The pilot is now described internally as a failed experiment.

Deck version: scale the pilot

The draft strategy proposes to scale the assistant across the organisation, add more AI tools and appoint an AI champion in each department. It does not ask why staff stopped using the pilot.

Plan version: diagnose, then relaunch

The plan starts with a short diagnosis. The assistant gave out-of-date answers because it read from a copy of the documents taken at launch. It could see documents that some users should not have seen, so the team restricted it by hand. No one owned it after the pilot team moved on, and no test set existed to show whether answers were right.

• Use-case priority: relaunch the policy assistant for one team before adding anything new, because the organisation needs a working example more than a longer list.
• Data readiness: the assistant reads from the live document store, not a copy, and each document carries its owner and review date.
• Governance: access runs through a governed connection using Model Context Protocol (MCP), so the assistant only returns documents the person asking is allowed to see, and every answer can be traced to its source.
• Ownership: the head of operations owns the assistant. The data team owns the connection and the evaluation set.
• Metrics: the share of questions answered correctly against an agreed test set, and weekly use by the pilot team.
• Sequencing: fix the document source and permissions, build the test set, relaunch to one team, then extend to other teams that use the same documents.

This firm is further along than the first, and its strategy reflects that. It does not need a new warehouse. It needs the connective layer between the warehouse and the model: access rules, business logic and lineage. If you are unsure which position your organisation is in, an AI maturity model helps you place it before you write anything.

Where AI strategy and governance meet

AI strategy and governance are often written by different people at different times. A strategy without governance approves use cases nobody has checked for risk. A governance policy without a strategy reviews tools one at a time, with no view of which ones matter.

The two meet at a small number of decisions that belong in the strategy itself:

• Which risk level each priority use case sits at, and therefore how much review it needs.
• Who signs off a use case before it is built, and who signs off before it goes live.
• How the organisation checks outputs after launch, including how errors are reported and fixed.
• Where personal data is involved, and whether a data protection impact assessment is needed.
• Which records the organisation keeps so it can explain how an answer was produced.

The detailed rules live in a separate document. Our guide to building an AI governance framework covers ownership, standards and risk review in full. The strategy only needs to state which rules apply to which use case, and make sure the build plan includes the time to meet them.

Governance written after the build is a list of reasons to stop. Governance written into the plan is a reason to trust the result.

Common gaps in enterprise AI strategy

Enterprise AI strategies tend to fall short in a few predictable ways. Most of them are visible on the page before any money is spent.

• No data section. The strategy lists use cases and benefits but never says where the data lives or what state it is in.
• Too many priorities. Every department gets a use case, so nothing has enough attention to reach production.
• No owner after go-live. The pilot team moves on and the system stops being maintained.
• Activity metrics. The strategy counts pilots, licences or staff trained, which says nothing about outcomes.
• Governance added later. Risk review happens after the build, when changes are most expensive.
• Written by people who will not build it. The document is handed to a delivery team that was never asked whether it is feasible.

Each of these gaps has the same cause: the strategy was treated as a document to approve rather than a plan to deliver. Our guide to why AI projects fail sets out the recurring causes in more detail, with the checks that predict them.

Turning the strategy into a roadmap

A strategy decides what and why. A roadmap decides when and in what order. The roadmap should follow directly from the sequencing section, and each stage should end with something a person can use or a decision the organisation can make.

• Assess. Score readiness across strategy, people, data and change, and confirm the first use case.
• Prepare the foundation. Bring the data the first use case needs into one place, cleaned in stages, with agreed definitions.
• Connect it safely. Put the access controls, business rules and lineage in place so a model can reach the data without losing control of it.
• Build and measure. Ship the first use case to a small group, measure it against the baseline and fix what the measurement shows.
• Extend. Start the second use case on the same foundation, reusing what the first one built.

Keep the document short. A strategy of a few pages that people read and argue about is worth more than a long report that sits in a shared drive. The roadmap can be a single page. Our guide to AI implementation strategy covers how the first use case moves from plan to live, and the AI readiness checklist lists the questions to answer before the first build.

The people who write the strategy should include the people who will build it. A business sponsor sets the priorities. Data and engineering leads test whether each choice is feasible. Someone accountable for risk checks the governance section. If you want a starting point before that conversation, the free AI readiness assessment scores strategy, people, data and change on the spot.

Where this leaves you

A good AI strategy example is not a template to fill in. It is a set of choices made in a fixed structure: which use case comes first, what data it needs, how it is governed, who owns it, how it is measured and what comes next. You can copy the structure. The choices have to come from your own data and your own priorities.

If your current draft reads like the deck versions above, it does not need more ambition. It needs a data section, named owners, one metric per use case and the willingness to say not yet. Those are the lines that let the builders start.

Our AI strategy service provides a readiness and maturity assessment, a sequenced roadmap and a business case, designed by the team that delivers it. We start with your data foundation, scope the first use case honestly and stay past go-live. If the right answer is that you are not ready yet, we will say so. Talk to us if you want a strategy your builders can deliver.

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