What is AI strategy?
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Definition of AI strategy
AI strategy refers to a company’s vision for how AI will be deployed to help achieve the company’s business goals. It should be closely linked to your data strategy and hence to your business objectives. It provides a roadmap for executing your business’s AI priorities.
Executive view
Setting up an AI strategy helps businesses:
to diagnose which business goals can be solved with AI.
adjust the culture and mindset in the business to be accepting and ready for AI.
set clear, achievable plans and goals for their AI initiatives.
Business function leader view
AI strategy helps align teams towards a common goal and ensures the right foundation and culture have been laid for success.
This matters most if:
your data strategy doesn’t have a clear section dedicated to AI for improving data quality and data governance.
you have knowledge or skills gaps in your team that need addressing.
you have identified an AI use case but do not know how or have been unable to execute it.
If you want to measure the performance of your AI strategy, you should set KPIs that are in line with your data and business strategy and associated KPIs. Your AI strategy should also help you achieve your business priorities.
Technical view
The AI strategy of your organisation will identify opportunities for automation, AI product development and AI integration into products. In order to successfully follow the strategy to meet business goals, technical teams need to lead the way in data culture and ensure that data is appropriately collected and stored to drive the AI initiatives.
AI strategy helps deliver:
increased automation.
more successful AI initiatives.
increased adoption of AI in product development.
a more positive attitude towards data and AI from the whole organisation.
Worth revisiting your AI strategy if you encounter:
a lack of enthusiasm for AI initiatives.
uncertainties as to which potential AI initiatives are aligned to business goals.
difficulties deploying AI products.
Key criteria to consider are:
What tech stacks will be required to deliver AI initiatives?
Are there skill gaps in your team that need to be filled in order to follow the AI strategy?
Will you need to make changes to the way that your data is stored and accessed in order to deliver AI initiatives?
The most important strategic decision: where AI belongs, and where it doesn’t
A mature AI strategy isn’t just a list of places to bolt AI onto a product — it includes an explicit, disciplined answer to when not to use AI. This is the part that’s easiest to skip and most consequential to get wrong.
The reasoning is straightforward: AI (especially machine learning and large language models) is the right tool for problems with genuinely unstructured inputs, where the “correct” output can’t be expressed as a deterministic rule — reading a scanned document in an unknown layout, understanding free-text customer queries, recognising an object in a photograph. It is often the wrong tool for problems that have a well-defined, checkable correct answer and a search space that classical algorithms can explore directly — a lot of scheduling, routing, and combinatorial optimisation problems fall into this category.
A concrete example from manufacturing software: reading a mill certificate (unstructured document, inconsistent formatting across suppliers) is a strong AI use case, because there’s no deterministic rule that covers every layout. But working out the optimal way to cut a batch of stock bar to fulfil a set of orders with minimum waste — cutting optimisation — has a precisely defined objective (minimise waste, fulfil every order, respect grade and length constraints) that a genetic algorithm or other combinatorial optimisation technique can search directly, without needing to learn from historical data at all. Using AI for the second problem wouldn’t add capability; it would trade away the deterministic, explainable, always-reproducible result a classical algorithm gives you, in exchange for the training data requirements, unpredictability, and opacity that come with a learned model — with no corresponding upside.
An AI strategy that only asks “where can we add AI?” without also asking “where would a classical, deterministic approach serve us better?” tends to end up with AI features that are harder to explain, harder to validate, and no more effective than the simpler alternative would have been. Building that second question into the strategy from the start is what separates a genuinely useful AI roadmap from a checklist of AI-branded features.