How Businesses Should Approach AI Strategy

A useful AI strategy starts with the business rather than with a particular AI technology.

The process should begin by understanding business objectives, identifying problems and opportunities, assessing potential value, and then determining where technology—including AI—could help.

The objective is not to introduce as much AI as possible. It is to identify where technology can create meaningful improvement and determine what approach makes the most sense.

AI strategy should connect to something the business is actually trying to achieve.

That could include improving customer response times, reducing administrative effort, increasing capacity, improving access to information, reducing errors, supporting employees, or creating a better digital experience.

Without a clear business objective, it becomes difficult to determine whether an AI initiative is producing meaningful value.

Once an objective or problem has been identified, the next step is understanding how the work happens today.

This means looking at the people involved, the systems they use, the information they handle, the frequency of the work, and the points where delays, errors or unnecessary manual effort occur.

This process understanding is often more valuable than immediately discussing AI tools.

Opportunities can emerge from many parts of a business. They are not limited to obvious AI use cases such as chatbots or content generation.

• Repetitive administrative work
• Customer and employee enquiries
• Lead and enquiry handling
• Information retrieval and knowledge access
• Document and data processing
• Manual workflows between systems
• Website and digital customer experiences
• Processes involving large amounts of unstructured information

Not every opportunity deserves investment. A useful strategy therefore considers the potential value before deciding how the technology should work.

Questions might include how frequently the process occurs, how much human effort is involved, what happens when it is improved, and whether the improvement would affect revenue, capacity, customer experience, cost or operational risk.

The purpose at this stage is not to produce false precision. It is to establish whether an opportunity is meaningful enough to investigate further.

A potentially valuable opportunity may still be difficult or inappropriate to implement.

A broader assessment should consider data availability, system integration, process complexity, security, privacy, regulatory requirements, reliability and the level of human judgement involved.

AI also introduces different failure modes from traditional software, so technical feasibility should not be treated as the same thing as business suitability.

Only after understanding the problem, process, value and constraints should the technology decision be made.

Depending on the situation, the right answer might be traditional automation, AI automation, an AI assistant, an AI agent, a website improvement, a system integration, a process change—or no technology change at all.

AI strategy should not necessarily mean one large transformation programme.

A business may have several opportunities with different levels of value, feasibility, risk and time to value. These can be assessed and prioritised rather than treating every potential use case as equally important.

DEEPS approaches AI strategy from the business problem backwards.

The starting point is understanding what the business is trying to improve, how the relevant work happens today, where friction exists, and where meaningful opportunity may exist.

From there, potential approaches can be assessed and the appropriate technology direction determined.

The DEEPS AI Opportunity Finder provides an initial way to explore potential opportunities. It is a starting point for further discussion rather than a replacement for detailed business discovery.