What Should an AI Strategy Actually Include?

26 March 2026

The Problem with Most AI Strategies

We review AI strategy documents regularly. The most common failures:

• Too focused on technology, not enough on business problems. Pages about large language models and neural networks, but nothing about which specific processes they will improve or how success will be measured.
• No honest assessment of current capability. The strategy assumes the business has clean data, willing staff, and adequate infrastructure. It usually does not.
• All vision, no roadmap. Grand statements about "becoming an AI-first organisation" with no concrete steps, timelines, or accountability.
• Written once and forgotten. AI moves too fast for a static document. A strategy written in January 2026 needs updating by June.

An AI strategy should be a working document that drives decisions, not a document written to impress the board.

The Five Essential Components

1. Business Case and Problem Definition

Start here, not with technology. Your AI strategy must answer:

• What specific business problems are we trying to solve?
• What does success look like, in numbers?
• How much is the current problem costing us?
• Who benefits from solving it?

"We want to use AI to improve Customer service" is not a business case. "We want to reduce average customer query resolution time from 4 hours to 30 minutes, saving approximately 120 staff hours per month" is.

Be specific. Be measurable. If you cannot attach numbers to the problem, you cannot measure whether AI solved it.

2. Data Readiness Assessment

AI runs on data. Your strategy must honestly answer:

• What data do we have that is relevant to our target use cases?
• Where does it live? Is it accessible?
• How clean, complete, and current is it?
• What data do we need but do not have?
• What are the privacy and compliance implications of using this data?

This is where most strategies fall apart. The honest answer is usually "our data is messy, siloed, and incomplete." That is fine. But your strategy needs to acknowledge it and include a plan to address it. Deploying AI on bad data produces bad results, expensively.

3. Technology and Vendor Plan

Now you can talk about technology, but in the context of solving your specific problems:

• Which AI approaches fit your use cases? (Not every problem needs a large language model.)
• Build, buy, or integrate? For most businesses, the answer is "integrate" - using AI capabilities embedded in existing tools or accessed through APIs.
• Which vendors and platforms are you evaluating? On what criteria?
• What are the infrastructure requirements? Do you need Cloud computing, on-premises hardware, or a hybrid approach?
• How will you avoid vendor lock-in?

Be honest about your technical capability. If you do not have ML engineers on staff, a strategy that relies on building custom models from scratch is not realistic.

4. Governance and Risk Framework

This section is increasingly non-negotiable, particularly with the EU AI Act's August 2026 compliance deadline approaching:

• Who owns AI decisions in your organisation?
• What is your acceptable use policy?
• How will you handle AI-generated errors or bias?
• What are your data privacy and security protocols?
• How will you ensure regulatory compliance?
• What is your incident response plan when AI systems fail?

Governance is not bureaucracy for the sake of it. It is how you manage the very real risks that come with deploying AI in business. The organisations that skip this step are the ones that end up in the news for the wrong reasons.

5. Phased Implementation Roadmap

This is where strategy becomes action:

• Phase 1 (Months 1 to 3): Pilot. Pick one or two high-impact, low-risk use cases. Deploy, measure, learn. Keep the team small and focused.
• Phase 2 (Months 3 to 6): Expand. Apply learnings to additional use cases. Begin building internal capability and governance structures.
• Phase 3 (Months 6 to 12): Scale. Embed AI into core business processes. Establish monitoring, optimisation, and continuous improvement cycles.
• Phase 4 (Ongoing): Optimise. Review and update the strategy quarterly. Retire what is not working. Double down on what is.

Each phase should have clear milestones, owners, budgets, and success criteria. If a phase does not deliver expected results, the roadmap should include decision points: continue, pivot, or stop.

What Your AI Strategy Should NOT Include

Just as important as what goes in is what stays out:

• Technology for its own sake. "We will implement a transformer-based architecture" means nothing if it is not tied to a business outcome.
• Unrealistic timelines. Full AI transformation in six months is fantasy for most businesses. Be honest about how long things take.
• Vague ROI projections. "AI will save us millions" without methodology or evidence is not a forecast. It is a guess.
• Promises about replacing staff. This is a sensitive topic and rarely plays out as simply as strategy documents suggest. Focus on augmentation and productivity, not headcount reduction.

How to Tell If Your Strategy Is Working

A good AI strategy produces measurable results within its first phase. If you are six months in and cannot point to specific improvements in specific processes, something is wrong. Either the strategy is too vague, the implementation is off track, or the use cases were wrong to begin with.

Review your strategy quarterly. AI capabilities and Pricing change fast enough that a strategy written more than six months ago may contain outdated assumptions. Treat it as a living document, not a one-time exercise.

Is This Right for You?

Not every business needs a formal AI strategy. If you are a small team using ChatGPT for email drafting and meeting notes, you do not need a 40-page document. A lightweight acceptable use policy and some team guidelines are enough.

A formal AI strategy becomes important when:

• You are spending (or planning to spend) more than 10,000 pounds on AI tools and services
• AI will touch customer data or regulated processes
• Multiple teams or departments are involved
• You need board or investor buy-in for AI investment

If none of these apply, save the strategy document and focus on practical experimentation instead.

Frequently Asked Questions

How long should an AI strategy document be?

Between 10 and 20 pages for most mid-sized businesses. Anything shorter risks being too vague. Anything longer risks being unread. Focus on clarity and actionability over length.

Who should write the AI strategy?

A cross-functional team including business leadership, IT, and representatives from the departments that will use AI. Avoid making it purely an IT exercise. The business case must drive the technology decisions, not the other way around.

How often should an AI strategy be updated?

At minimum, quarterly. AI capabilities, pricing, and regulations change rapidly. A strategy written more than six months ago likely contains outdated assumptions about what is possible and what it costs.

Do small businesses need an AI strategy?

Not necessarily a formal one. If you are a small team using off-the-shelf AI tools, a lightweight acceptable use policy and some team guidelines are sufficient. Formal strategies become important when AI spending exceeds 10,000 pounds or touches customer data.