Why it matters for your business
Without a strategy, AI adoption tends to happen as scattered experiments: staff pasting data into consumer chat apps, overlapping tool subscriptions, and pilots that never reach production. A short, practical strategy prevents that by choosing a few use cases with clear value, setting rules for which data can go where, and sequencing work so early projects build reusable foundations such as clean data and integrations. It also gives leadership a basis for budget decisions. A strategy should fit on a few pages and be revisited as models and costs change.
Example
A regional accounting firm drafts a generative AI strategy after staff start using chat tools on client files. The plan approves one enterprise AI assistant with a data agreement, bans client data in personal accounts, and ranks three projects: client document collection, engagement letter drafting and time entry suggestions. Each project has an owner, a budget and a success measure.