Artificial Intelligence (AI) Strategy
DataCamp
Last updated August 17, 2026
Artificial Intelligence (AI) Strategy is a course on how business, data, and AI strategies fit together — and which comes first. It explains how these strategies combine into a coherent framework for a data-driven organization, and the role an AI strategist plays in driving change that genuinely supports business goals. A key early lesson is telling AI apart from traditional software, so you can judge whether AI is even the right tool for a given problem. From there it covers setting realistic goals, defining the metrics that mark success, weighing whether a project justifies its investment, and the components needed to scale AI across an organization. It is conceptual, with no coding or data-science background required. You can sample the opening chapter before subscribing; the full course and its Statement of Accomplishment are part of DataCamp Premium.
What you'll learn
- How business, data, and AI strategies connect and sequence
- The role of an AI strategist
- Telling AI apart from traditional software (and when AI fits)
- Setting realistic goals and success metrics
- Judging whether a project justifies its investment
- Components of a scalable AI strategy
Frequently asked questions about Artificial Intelligence (AI) Strategy
Who is Artificial Intelligence (AI) Strategy for?
Leaders, managers, and aspiring AI strategists who need to decide where AI fits in an organization — and where it doesn't.
Is Artificial Intelligence (AI) Strategy free?
No — Artificial Intelligence (AI) Strategy is a paid course. The opening chapter is free to sample; the full course and its Statement of Accomplishment need DataCamp Premium.
What are the prerequisites for Artificial Intelligence (AI) Strategy?
None.
Does Artificial Intelligence (AI) Strategy offer a certificate?
Yes. DataCamp Statement of Accomplishment on completion (requires DataCamp Premium).
Why we suggest this course
A clear primer for leaders, managers, and aspiring AI strategists who need to make good decisions about where AI fits — and where it doesn't. Its most useful move is teaching you to question whether AI is the right fit at all, rather than assuming it, and to attach honest metrics and return-on-investment thinking to AI projects. That discipline is what separates a real strategy from enthusiasm.