Introducing AI
Phase 1 In Build, Framework Proposed & Approved, Phased Rollout Plan.
Defined Azuga's AI integration framework from scratch, identifying where AI solves existing problems in existing workflows, not where it looks impressive on a roadmap
Every team wanted AI. Nobody knew what that meant beyond a chatbot. Adding AI on top of existing workflows as a separate window would create one more thing users had to learn, solving little they were already struggling with.
AI doesn't need new infrastructure to be valuable, it needs the right problem. The highest-value AI moments were already hiding inside workflows users were abandoning, data they couldn't interpret, and decisions they were taking too long to make.
Audited existing workflows with all teams to identify where low-hanging fruit were:
Defined a framework for AI integration — high-value use cases ranked by user impact and technical feasibility
Designed AI to live inside existing dashboards, Livemap, reports, and workflows not as a separate layer
Recommended which features to build first and why , giving engineering a clear, defensible starting point
Mar, 2026
How We'll Measure AI Success
AI is hard to measure in isolation — so we don't. We measure it through the workflows it lives in:
AI adoption rate - how often users click on AI-generated insights vs. ignore them
Time-to-decision - are users reaching conclusions faster after AI is introduced?
Return rate - do users come back to AI features or use them once and abandon?
Override rate - how often do users disagree with AI suggestions? High override = low trust