Building an AI Strategy Around What Your Business Actually Needs

By Connor Whitehouse, Senior Technical Consultant

There is no shortage of AI tools for businesses to invest in. The harder question is which ones are actually worth it.

AI is now built into productivity platforms, business applications, cloud services and specialist software. Existing suppliers are adding new capabilities while new products appear constantly, often promising to solve similar problems in slightly different ways.

That makes it very easy to accumulate technology without ever making a deliberate decision about where AI fits into the business.

A team finds a useful tool, a supplier demonstrates a new feature or somebody identifies a process that could be automated. Each decision might make sense on its own. Taken together, they can leave the business paying for overlapping tools, moving data between more systems and adding complexity that nobody intended to create.

A technology strategy gives those decisions some direction.

Start with the problem

One of the easiest mistakes to make with AI is starting with the technology.

A new tool appears and the conversation becomes about where the business could use it. A better starting point is the problem you are trying to solve.

Is a process taking too long? Are people spending hours on repetitive work? Is useful information difficult to access? Is there a genuine opportunity to improve a service or reduce cost?

Only then does it make sense to ask whether AI is the right answer.

Not every problem needs it. Sometimes an existing system can already do the job. Sometimes the process itself needs fixing first. And sometimes the benefit simply does not justify the cost or complexity involved.

A strategy helps the business make that distinction before time and money have already been committed.

Look at what you already have

Before buying another AI product, it is worth understanding what is already available within the technology the business pays for.

This matters more as established suppliers add AI capabilities to their platforms. A business considering a standalone product may already have something similar available through software it owns.

That does not automatically make the existing option the right one, but it should be part of the decision.

Every additional system brings more than another licence fee. There is another supplier to assess, another place where business data may be held, potentially another integration to maintain and another product somebody needs to support.

One additional tool may make very little difference. Ten different teams making the same decision independently can create a very different technology environment.
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"The question is not where can we use AI. It is where does using AI actually make sense?”

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Understand what sits behind it

The demonstration is usually the easy part.

A supplier can show what an AI product does and how much time it might save. What is less obvious is what needs to sit behind it for the tool to work properly.

What data does it need access to? Where does that data come from? Does it need to connect to other systems? Where is information processed and stored? Who will manage the product once it is in use?

There is also the quality of the underlying data to consider. AI cannot compensate for information that is incomplete, inconsistent or spread across systems nobody trusts. In some cases, the work needed to prepare the business for an AI project can be more significant than implementing the AI itself.

Those questions need to be understood before a promising demonstration becomes a technology decision.

Decide what is worth pursuing

Most businesses will have more potential AI ideas than they have the time, budget or people to deliver.

That makes prioritisation important.

A relatively simple use of AI that removes hours of repetitive work every week may be more valuable than a much larger project with an impressive demonstration but no clear return.

The commercial case also needs to go beyond the initial licence price. Implementation, integration, support, training and increases in future licensing all form part of the real cost.

There is also the question of dependency. If an important business process starts relying on a particular AI product, what happens if the supplier changes its pricing, removes a feature or takes the product in a different direction?

None of those questions means the business should avoid the technology. They simply help separate something that is interesting from something worth investing in.

Build a roadmap, not a shopping list

An AI strategy does not need to be a long document containing every possible use of the technology.

It should give the business a clear view of what it wants to achieve, which opportunities are worth pursuing and what needs to happen first.

Some projects will depend on better data. Others may require changes to existing systems or stronger security controls. Some may be worth testing on a small scale before the business commits further.

Putting those things into a roadmap makes the dependencies visible.

It also makes it easier to say no, or at least not yet, when another product appears. Without that direction, every new AI tool can look like an opportunity. With it, the question becomes whether that tool helps deliver something the business has already decided matters.

That is a much more useful test.

Keep the strategy moving

The difficult thing about creating an AI strategy now is that the technology will not stand still while you do it.

Products will change, suppliers will add functionality and capabilities that currently require specialist software may become standard features of platforms the business already owns.

The strategy therefore cannot be something written once and revisited in three years.

The roadmap needs to be reviewed as the technology changes. Investments that made sense twelve months ago may need to be reconsidered. New capabilities may remove the need for something the business was planning to buy. An experiment may prove valuable enough to become part of a core process.

The direction should remain tied to what the business is trying to achieve, even when the technology underneath it changes.

Making deliberate decisions

A sustainable AI strategy is ultimately about being more deliberate with technology decisions.

It means understanding the problem before looking for a product, checking what the business already owns before buying something else and considering what a new tool will add to the wider technology environment.

Some AI opportunities will justify investment. Others will not. The important thing is having a consistent way to tell the difference.

That becomes more important as AI becomes less of a separate category of technology and more of a standard feature of the systems businesses use every day.

The businesses that manage that well will not necessarily be the ones with the most AI tools. They will be the ones that know why they have the ones they do.

For some businesses, that direction can be set internally. For others, bringing in independent technology leadership can provide the experience needed to assess opportunities, challenge investment decisions and build a technology roadmap around where AI can genuinely add value.

Speak to our IT and digital transformation team about reviewing your current technology landscape, identifying practical AI opportunities and building a roadmap aligned to your business goals.

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This article has been prepared for information purposes only. Formal professional advice is strongly recommended before making decisions on the topics discussed in this release. No responsibility for any loss to any person acting, or not acting, as a result of this release can be accepted by us, or any person affiliated with us.

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