What is AI strategy advisory?
AI strategy advisory is outside guidance on where to invest in AI, in what order, and how to govern it once it is live. A good advisor sells no product. The advice stays tied to the business, not a platform. The output is usually a short list of top use cases, a spending plan across people, data, and tech, and rules that would hold up under audit.
Why do most AI strategies fail?
Most start with the tech instead of the outcome. MIT's State of AI in Business 2025 report found that 95% of generative AI pilots fail to show a real return. Only 5% of custom AI tools make it past the pilot stage into daily use. The pattern repeats: teams pick a tool first, then hunt for a problem it can solve. Start from 3 business outcomes instead. Work backward to the tech. That trap disappears.
How much should a company spend on strategy versus tech?
Less on the algorithm than most budgets assume. BCG's 2025 research on AI value found that top companies use a 10-20-70 split: 10% on algorithms, 20% on data and tech, and 70% on people, process, and change. That same research found only 25% of leaders report real value from AI so far. The top group runs 3.5 use cases on average, against 6.1 for everyone else. They also expect 2.1 times the return.
What is the difference between AI strategy and AI implementation?
Strategy picks which problems to solve, in what order, and how to measure success. Implementation builds or buys the tool and puts it to work. Skip the strategy step, and you get a pile of pilots with no shared logic. That is most companies today. Get the strategy right first. Then implementation becomes a plan, not a guess.
Who should own AI strategy inside a company?
The board sets the direction and how much risk to take. A small executive group, usually the CEO with the CFO and either the CTO or Chief AI Officer, owns the order and the budget. Business unit leaders own the use cases inside their own team. They are the ones who can tell you if a pilot actually changed how work gets done. AI strategy that lives only inside IT rarely gets the power to redesign a workflow. That redesign is the part that makes the money.
How do you check if an AI strategy is working?
Check business outcomes, not busy work. The count of AI pilots running is a vanity metric. The numbers that matter are revenue, cost, time, and quality, tracked against the 3 outcomes the strategy named at the start. If none of those numbers move after 2 quarters, the strategy needs a hard look. It does not need another pilot.
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