Data Tech 2026

Automation is only as effective as the work we automate. In AI initiatives, it’s easy to skip or shortcut confirming that we’re automating the right work in the first place. This talk argues that workflow capture, an updated and expanded take on “process mapping,” is an overlooked make-or-break prerequisite for AI readiness. It creates shared understanding and reduces rework, mis-scoped builds, and adoption surprises. We’ll cover why formal, heavyweight mapping is often unnecessary and counterproductive, and how “lightweight but rigorous” capture can look in practice.

You’ll leave with a practical recipe for efficient, modern workflow capture, plus a checklist to stress-test your own approach, based on ongoing field testing and refinement. The recipe supports the data and analytics needs of AI adoption initiatives and scales through a mix of expert facilitation and self-service input from the broader organization.

You’ll also leave with clarity on why analytics and data science professionals should advocate early for effective workflow capture, so strong technical work translates into real-world impact.