Somewhere between a successful pilot and a company-wide rollout, most AI initiatives lose steam. Teams call it a technology gap, but it's rarely that. Across hundreds of machine learning projects, we’ve watched the same organizational gaps show up again and again, and they’re what actually determine whether AI reaches production.

This playbook lays out the five patterns that close those gaps. Successful organizations approach their data, their use cases, their operating model, and how they measure results differently than everyone else. You’ll see real examples of companies that applied these patterns and what changed as a result.

Read the playbook to learn:

• How to locate where your data already lives and build AI around it, instead of moving data to fit new tools
• What a well-defined AI use case looks like, and the three benefits that come with getting it right
• The four things every effective AI operating model needs to do
• Which metrics matter most for building trust in AI outputs, and why measurement needs to be built in from day one