AI Is Not a Strategy

“We want to add AI.” It is a sentence I hear a lot from founders. Sometimes followed by a concrete problem where AI does indeed solve something cheaper and faster. More often followed by a vague intention that mostly looks good on the pitch deck.

AI is not a strategy. AI is a means. The way a database is not a strategy. The way a mobile app is not a strategy. Which problem it solves, how often, for whom, with what reliability and at what cost, that is the strategy.

This article looks at when AI adds value inside a product and when it mostly introduces complexity, maintenance and dependency.

AI as means, not goal

The question “should we add AI?” is almost never the right starting question. The right starting question is: which specific problem do I want to solve, and is AI the cheapest, fastest or most reliable way to solve it?

A summarisation tool, a classifier for incoming mail, pattern detection in raw data, those are concrete problems where AI often adds value. “We use AI” as a marketing message rarely creates something a customer will pay for.

If you cannot describe the problem without using the word AI, that is a signal the idea needs more time.

When AI adds value

Three traits make a problem well-suited for AI:

• The input is messy or variable, free text, images, user behaviour, raw data, and a hand-written rule cannot keep up.
• A human can solve the problem, just not fast or cheap enough to do it at scale.
• A reasonable approximation is good enough. You do not need 100% accuracy, you need to be better than the alternative (often: doing nothing or letting a human check).

Examples: classifying support tickets by priority, summarising long documents, recognising whether an image belongs to a product category, flagging suspicious transactions for manual review.

In all of these cases, AI is not the strategy. The strategy is that you have a workflow where this task comes up often and is currently too slow or expensive. AI is then a fitting acceleration.

When AI distracts

Founders often get fascinated by AI at a moment when they still do not know which problem they are solving. That is understandable, the technology is impressive and the demos are compelling. But it is an expensive distraction.

Building AI into an early product introduces maintenance, costs, dependency on models that change and unexpected behaviour that is hard to debug. None of that is a problem when the value is clear. It is a problem when you do not yet know whether customers will pay for the underlying product at all.

The pattern I see often: a founder builds AI into v1 because it “makes the difference”. Three months later it turns out customers were not using v1 for a completely different reason, onboarding, pricing, distribution, that had nothing to do with the AI. The AI investment was wasted work.

Testing the real decision first prevents AI from becoming an expensive way to discover what should have been tested.

Process problem vs AI problem

Sometimes a founder wants to deploy AI because a process is not working. For half of those cases, the answer is not AI. It is rethinking the process.

Example: a team wants AI to summarise sales calls because sales people spend too much time on it. Before adding AI: are those summaries even needed? For whom? What happens with them? Often the process around them can be cut rather than accelerated.

If the process disappears, the AI problem disappears with it. Cheaper and more durable than building another piece of technology.

Cost and maintenance

AI is not free. It costs money per use (tokens, API calls, model hosting), it takes time to integrate and monitor, and it costs money to keep up as models change.

Before you start, estimate:

• What is the cost per user per month at realistic usage?
• What happens if usage doubles? Tenfold?
• Who monitors whether the output is still correct?
• What do you do if the supplier changes pricing or updates the model?

An AI component that costs €40 per customer per month inside a €19 product is not a bug, it is a fundamental miscalculation. Better to find that out beforehand.

Reliability

AI models give answers with confidence, even when those answers are wrong. For some applications that is acceptable, a suggestion the user can verify. For others it is expensive.

Decide upfront: what happens when the model is wrong? How do you detect it? How often do you need a human check? How do you communicate to the user that the output is a suggestion, not a fact?

A product that adds AI without thinking through this question discovers it at the worst moment: with a customer who trusted the output.

Privacy and data

Which data are you sending to the model? Who sees that data outside your organisation? What are the supplier’s terms on training, storage and use?

For B2B customers in regulated industries, finance, healthcare, government, this is not a side question. It is the first question their procurement team asks. An AI feature that matters to your product but is unacceptable to a buyer can cost you deals.

Questions to answer before adding AI

Before building AI into your product, force yourself to answer these:

• Which specific problem does this solve?
• What is the alternative, what does doing nothing cost?
• How do you measure whether it works?
• What do you do when it occasionally gets it wrong?
• What is the cost per user per month at realistic usage?
• Which data goes to the supplier, and is that acceptable to your customers?
• Who is monitoring this in six months?

Vague answers are not a reason to skip AI. They are a reason to work on the answers first, before you build a feature that has to come back out.

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