Why Your AI Strategy Is Actually a Vendor Strategy

Read the strategy document

There’s a simple diagnostic for whether an organisation has an AI strategy or a vendor strategy: read the strategy document. If the first section describes what the vendor’s platform can do rather than what problem the organisation is trying to solve, it’s a vendor strategy wearing an AI strategy’s clothes.

We reviewed 11 enterprise AI strategy documents across financial services, healthcare, and resources. Nine followed the same structure: a market overview citing analyst predictions about AI spending, a description of the chosen vendor platform’s capabilities, a roadmap of features to deploy, and a set of KPIs tied to adoption rates. Conspicuously absent in most: a clear articulation of the organisational problems AI was meant to solve.

How this happens

It’s not that organisations lack strategic thinking. It’s that the procurement process for AI technology creates its own gravity. The sequence typically runs:

1. Leadership declares AI a strategic priority
2. A team evaluates vendor platforms
3. A vendor is selected based on capability, cost, and reference cases
4. The “AI strategy” is written to justify and operationalise the purchase
5. The organisation works backward from what the platform can do to find things to do with it

By step 4, the strategy has been captured by the procurement decision. The vendor’s capability map becomes the organisation’s roadmap. The strategy answers “how do we use what we bought?” rather than “what do we need to achieve?”

A strategy that starts with the solution and works backward to find problems is not a strategy. It’s a post-rationalisation.

The three costs

1. Misallocated effort

When the strategy is vendor-driven, the first use cases are determined by what the platform does best — not what the organisation needs most. We saw a financial services firm deploy AI-powered document summarisation across legal contracts because their platform excelled at it. The capability worked well. But the organisation’s actual strategic problem — inconsistent risk classification across three business lines — wasn’t addressed because it didn’t map neatly to a vendor feature.

2. Dependency without leverage

Vendor-driven strategies create deep dependency without building internal capability. The organisation becomes increasingly reliant on the vendor’s roadmap for its own strategic progress. When the vendor pivots (as they all do), the organisation’s “AI strategy” pivots with it — whether or not the new direction serves the organisation’s needs.

3. Strategic incoherence

Different parts of the organisation adopt different vendor capabilities, each solving a local problem. Without an organisational strategy connecting them, these deployments become islands — AI in marketing doing one thing, AI in operations doing another, AI in risk doing a third, with no coherent connection between them. The organisation ends up with multiple AI deployments and no AI strategy.

What an AI strategy actually looks like

An AI strategy worth the name starts with three questions:

What organisational problems are we trying to solve? Not “what can AI do?” but “what’s broken, expensive, or too slow, and would AI-class capabilities change that?”

What structural conditions need to exist for AI to solve them? This includes data quality, process clarity, cross-functional alignment, and feedback loops — the organisational infrastructure that determines whether any AI deployment succeeds or stalls.

How will we know it’s working? Not adoption metrics (how many users, how many queries) but outcome metrics (did the problem get better, and can we attribute the improvement to the AI deployment?).

These questions are harder to answer than “what does the vendor offer?” They require understanding the organisation’s actual structural landscape rather than its technology ambitions. But they’re the difference between a strategy that drives outcomes and a purchase order dressed as a strategy.