Strategy Is a Multiplication Exercise. Your AI Tools Probably Aren't.
A tool produces an excellent audience insight. Another generates three plausible positioning territories. A third turns the chosen direction into a polished creative brief.
Everything looks good. Until someone notices that the brief has almost nothing to do with the original insight.
This is the problem with judging AI tools for strategy one task at a time. Strategy needs a connected argument. Independently impressive documents can still leave the team with contradictory directions.
Strategy behaves more like multiplication than addition
Think of strategic development as a multiplication exercise. Each important stage affects what the next stage can achieve. If the problem definition is wrong, excellent research may investigate the wrong question. If positioning ignores the evidence, a beautifully written campaign strategy can still lead the brand in the wrong direction.
The analogy isn't literal mathematics. Some stages matter more than others, and experienced strategists can work with incomplete evidence. But a serious failure at one critical point can undermine the whole result.
That is why a collection of good AI modules does not automatically produce a good strategy.
Richard Rumelt’s account of coherent strategy connects diagnosis, guiding policy and action. It offers a useful standard for reviewing the argument that survives between stages.
Start with the job the tool needs to do
Most agencies don't begin by searching for an 'AI strategy platform'. They want to interrogate a messy client brief, develop an audience understanding, explore positioning options, improve a campaign strategy or produce a creative brief their team can use.
For an isolated task, ChatGPT, Claude or Gemini may be entirely sufficient. A capable senior strategist who understands prompting, has the relevant client context and knows how to judge the result can get useful work from a general-purpose model.
The challenge changes when an agency wants to connect several tasks reliably, involve people with different levels of experience and carry decisions from one stage into the next. This is the difference between a chatbot and a process.
That is where it becomes useful to distinguish three approaches.
An AI tool for strategy helps with a particular task. An AI-assisted workflow connects tasks in a repeatable sequence. An AI strategy platform supports a wider strategic development process, preserving client context, connecting decisions between stages and giving people ways to evaluate and approve the direction as it develops.
That is what an AI strategy platform actually means in this context. The labels are less important than what the system actually does.
Test from the brief to the final direction
If an agency is evaluating a dedicated strategy system, a polished demo of one module tells it very little.
Use a real brief, preferably one that is ambiguous or contradictory. See whether the system helps clarify the business problem in the incoming brief before producing answers. Follow the work through research, audience understanding, positioning, campaign strategy and creative briefing, as far as those stages are relevant to the assignment.
Then inspect the connections.
Does the audience insight influence the positioning choice? Does the campaign strategy reflect the chosen positioning? Does the creative brief explain the direction in a way creatives can act on? When a human rejects an assumption, do later stages change accordingly?
A system that produces excellent isolated outputs but loses the logic between them leaves the agency with the most difficult integration work.
Keep consequential choices open to judgement
Good strategy involves choices. It involves sacrifice. A system can help reveal options, challenge assumptions and explain consequences. It should not quietly make consequential strategic decisions and present them as settled facts.
Test what happens when the strategist disagrees. Can they reject a weak insight? Can they revisit an earlier choice? Can they see what is evidence, what is interpretation and what is a recommendation?
A tool that always sounds certain may feel reassuring in a demo. It is less reassuring when the brief is genuinely difficult.
When not to buy a dedicated platform
There are agencies for which a general-purpose AI tool is the sensible choice. A small team of highly experienced strategists may already have a strong shared process. They may have time to think, use AI mainly as a sounding board and be perfectly capable of carrying context between stages themselves.
An agency whose strategic work happens predominantly in live workshops may get more value from tools that prepare, capture and organise those sessions than from an end-to-end development platform.
And not every agency means the same thing by strategy. Performance-first and social-first businesses may prioritise rapid testing, channel optimisation or cultural responsiveness over the kind of connected brand and campaign strategy process described here. A platform built around one philosophy of strategy should not pretend to fit every other philosophy.
Ask whether the builders understand the work
Domain expertise matters. The people designing a strategic system need to understand how agencies handle ambiguous briefs, internal disagreements, client politics, creative handovers and difficult choices.
That expertise has to show up in the product, whatever the founder's CV says.
The final test is simple to state and surprisingly difficult to pass: does the system help an agency produce one coherent strategy, or does it merely produce several convincing pieces of strategic-looking work?
An agency evaluating Mirel’s strategic workflow should use the same test and inspect the connections on its own assignment.