Fordel StudiosSomeone at your company attended a conference, watched a demo, and came back with slides. The slides had impressive numbers — productivity gains, cost reductions, capability benchmarks. The slides were produced by the vendor whose product was being demoed. Your board approved a budget. Your CTO wrote a strategy document. The strategy document says almost exactly what the vendor's website says.
This is not an AI strategy. This is a purchase decision with a PowerPoint attached.
What an Actual AI Strategy Looks Like
A real AI strategy starts with a specific, painful bottleneck in your business. Not "we want to use AI" but "customer support tickets take 4 days to resolve and 60% of them are answering the same five questions." The tool choice follows from the problem, not the other way around. The success metric is defined before the tool is selected. The failure mode is identified before a line of code is written.
What most companies have instead is a collection of use cases that map neatly onto whatever the vendor demonstrated. "We will use AI for document summarisation. We will use AI for code generation. We will use AI for customer chat." None of these map to a specific business outcome. None of them have a defined threshold for success or failure. None of them have a timeline that is not "ongoing."
“A strategy that could apply equally well to your competitor, your vendor's other customers, and a company in a completely different industry is not a strategy. It is a hope.”
The Vendor Incentive Problem
OpenAI wants you to use GPT-4o for everything. Anthropic wants you to use Claude for everything. Google wants you on Gemini. Each of them will produce case studies showing their product solving your type of problem. Each of them will have a sales engineer who understands your industry well enough to make the demo feel relevant. None of them have any incentive to tell you that your problem does not require a frontier model, or that a fine-tuned open source model would cost 95% less, or that the actual bottleneck in your workflow is a data quality issue that no language model can fix.
The engineer who builds your internal AI tooling without a vendor relationship has different incentives. They want the system to work. They have to maintain it. They will tell you when the LLM is the wrong tool. That voice is usually the quietest one in the room when the vendor's sales team is presenting.
One Question That Cuts Through It
Before approving any AI initiative, ask: what is the specific, measurable outcome this changes, and how will we know in 90 days whether it is working? If the answer involves vague language about "transformation," "capability building," or "positioning for the future," the initiative is not ready. Strategy that cannot be falsified in 90 days is not strategy. It is optimism with a budget.
Your competitors with real AI strategies are not running more pilots. They are running fewer, shipping faster, and measuring more precisely. The gap between them and the companies doing AI theatre is growing every quarter.