Stop Calling It an AI Strategy. It’s a Business Strategy
Somewhere right now, a leadership team is building an “AI strategy.” There is a slide deck. There is a list of tools to evaluate. There is probably a new job title. And there is a very good chance the whole thing will produce nothing useful.
The problem is not the technology. The problem is the framing. When companies treat AI as its own strategy, separate from what the business is actually trying to achieve, they end up with projects that are impressive in demos and irrelevant to results.
The Evidence Is Already In
The failure rates are hard to ignore. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs, unclear business value or weak risk controls. Separately, it predicts organizations will abandon 60% of AI projects that are not supported by AI ready data.
Look at the reasons. Unclear business value. Costs nobody planned for. Data nobody prepared. None of those are technology problems. They are business problems that appear when AI is treated as a goal instead of a tool.
How the “AI Strategy” Trap Works
It usually starts with pressure. A board asks what the company is doing about AI. A competitor announces an AI feature. An investor mentions it on a call. Suddenly the company needs to be seen doing something.
So a team goes looking for places to use AI. They find plenty. Every department can come up with a use case. Pilots launch. Demos impress. Then months pass, and nobody can point to a meaningful change in revenue, costs or customer satisfaction.
The mistake was starting with the tool and looking for problems. It should have been the other way around.
What a Real Strategy Looks Like
A business strategy starts with a few questions every founder already knows:
- Where are we losing customers?
- What is slowing our team down?
- Which costs are growing faster than revenue?
- What do customers wish we did better?
- What would let us win in our market?
Only after answering those questions does AI enter the conversation, and then only where it is the best way to solve a problem that already matters. Sometimes it is. Sometimes a simpler fix, a process change or a hire is better. A good strategy is willing to reach that conclusion.
A Simple Test for Any AI Project
Before approving any AI initiative, I would ask three questions:
Which business number will this move? Revenue, costs, conversion, retention, response time. If nobody can name one, stop.
How will we know within 90 days? A project without a measurable checkpoint tends to run forever.
What would we do if AI did not exist? If the answer is “nothing, this problem does not matter,” then the AI project does not matter either.
The Counterargument
Some leaders argue that AI is so transformative that companies need dedicated strategies just to understand it, and that waiting for clear business cases means falling behind. There is truth there. Experimentation has value, and teams need space to learn what the technology can do.
But experimentation and strategy are different things. Give teams a small, clearly bounded budget to explore. Keep the real strategy anchored to business outcomes. The companies that confuse the two end up with expensive experiments presented as strategy.
What I See in Practice
In my own work with marketing and SEO clients, the AI projects that pay off are almost always boring. Faster research. Better first drafts. Automated reporting. Quicker responses to routine questions. Each one tied to something the business already cared about: delivering more for clients in less time.
[NEEDS INPUT: one short, specific example from your own team or clients, such as a workflow where AI cut hours from a task that directly affected client delivery.]
The projects that fail tend to be ambitious and vague. “Transform our customer experience with AI.” “Build an AI powered platform.” Big promises with no specific number attached.
What Founders Should Do Instead
Write down your top three business goals for the year. Not AI goals. Business goals.
For each one, identify the biggest obstacle.
Ask whether AI is the best way to remove that obstacle. Compare it honestly with other options.For agencies, this also means remembering that long term growth depends on building stronger agency relationships, not just adopting new technology.
Run small, measured pilots only where AI is a strong fit, with clear success criteria.
Scale what works. Kill what does not. Quickly and without sentiment.
That is an AI strategy in the only sense that matters: a business strategy that uses AI where it helps.
The Bottom Line
Nobody has an “electricity strategy” or an “internet strategy” anymore. They have business strategies that use electricity and the internet. AI will go the same way, and the companies that get there first will be the ones that stopped treating it as a separate project.
Start with the business. The right uses of AI will follow.
Abdul Shakoor is the founder of FounderFeat.