What is an AI strategy?
An AI strategy is a company’s plan for using artificial intelligence to meet its business goals. It states where AI should create value, in what order, with which resources, and under what rules. A useful strategy is short enough for department heads to read and specific enough to fund. It usually covers six elements.
- Goals. The plan names two or three business outcomes, each with a metric and a baseline.
- Use cases. The plan ranks a short list of use cases through use case prioritization and assigns each one an owner.
- Sourcing. For each workload, the company decides whether to buy a product, rent a hosted model, or run its own.
- Data. The plan identifies the data platform work the use cases depend on, such as a lakehouse or a master data cleanup.
- Governance. Policies, an AI inventory, risk review, and approval steps are in place before any agent touches a production system.
- People. The plan covers training, team structure, and the change management that adoption requires.
AI strategy and private AI
The sourcing decision shapes contracts, security reviews, and costs for years. Renting hosted models is quick to start and suits experiments and low volumes. Owning the capability means running open-weight models on infrastructure the company controls. It suits sensitive data and steady volume, and it keeps model versions and data handling in the company’s hands. In Kirk Tech Solutions engagements, the choice is made workload by workload. In either case, any agent that can change a system of record gets identity checks, audit logging, and a human approval step.
AI strategy in practice
A mid-sized pump manufacturer sets two goals: faster quote turnaround and lower warranty cost. The strategy funds a quoting assistant that drafts quotes from customer requests and past jobs, and an analysis that links warranty returns to production lots. Customer drawings arrive under nondisclosure agreements, so the models run on a server inside the company’s own environment. Quality records and enterprise resource planning (ERP) data move into one data lakehouse. Estimators approve every quote, and two of them join the delivery team. The plan fits on a few pages and is reviewed every quarter.
What to watch for
- A strategy organized around tools instead of business outcomes tends to produce scattered experiments with no measurable result.
- Tie each item in the plan to a budget line and a named owner, so that progress can be reviewed.
- Set sourcing and data handling rules early, because every later vendor contract and security review depends on them.
- Review the plan at least twice a year, since models, prices, and regulations change faster than most planning cycles.