AI strategy
What is AI strategy?
The choices that decide how your organisation creates value with AI: where to focus, what to build versus buy, which foundations to fund first. A good one starts from business goals, not from the technology. It says no to as much as it says yes to, so effort lands where the payback is real.
Why it matters
The gap between companies that get value from AI and those that just burn budget on it usually traces back to whether anyone wrote a strategy down. Without one, spending scatters across whatever tool a team saw in a demo, and none of it adds up. A strategy forces the harder conversation first: which problems are worth solving, what only your company is placed to do, where a competitor already has a lead you cannot close. It also sets the risk appetite, so a bank and a game studio do not end up copying the same playbook. The work is less about picking technology and more about the leadership team agreeing on what actually counts as a win.
In practice
A retailer might decide its edge is demand forecasting, not customer chatbots, and put its best data and people there while buying an off-the-shelf tool for everything else. A rival with weaker data makes the opposite call. Same market, different strategy, because each started from its own strengths and constraints rather than from a list of trending use cases.
