How to build an AI strategy for an established business
An AI strategy should answer where AI will create value, what the business will build, how risk will be managed and how the organisation will get better at deploying useful AI over time.
01
Build the strategy around the operating model
The strategy should start with customers, workflows, systems, data and leadership priorities rather than with a preferred model.
AI becomes commercially useful when it is attached to the economics of the work.
02
Choose a portfolio, not one giant project
Most established businesses have a portfolio of opportunities across functions. Some are simple productivity improvements while others require integration and workflow redesign.
Prioritise the portfolio and deliberately choose the first few deployments that can create value and reusable implementation capability.
03
Make adoption and governance part of the strategy
A strategy that stops at technology selection is incomplete. It should define how human accountability, access, testing, ownership, training and measurement work.
The goal is an organisation that gets faster at deploying useful AI each quarter.
Frequently asked questions
Direct answers.
01
What should an AI strategy contain?
Leadership priorities, opportunity portfolio, roadmap, business cases, technology principles, data and governance requirements, adoption approach and success measures.
02
How many AI projects should a business start with?
There is no universal number. Start with a small portfolio that is commercially meaningful, feasible and capable of teaching the organisation reusable deployment patterns.
03
Should AI strategy be owned by IT?
Technology leadership is essential, but AI transformation crosses commercial priorities, workflows, people, data, risk and operating design. Ownership usually needs to be cross functional.
