Lisa Vanderlip
AI Value Realization Leader | Creator of the AI VALUE Framework | Founder, WyseMinds Advisors
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Lisa Vanderlip is an AI value realization leader who helps organizations prove whether their AI was worth what it cost. Over twelve years at Microsoft she co-founded FastTrack's at-scale adoption approach, built two commercial M365 business-value offers Microsoft sold as products, and served as a global SME on Microsoft's Responsible AI team. She is Founder & Managing Principal of WyseMinds Advisors, helping Fortune 500 and other clients attain key outcomes from their AI investments, creator of the AI VALUE Framework, and publisher of the AI Value Report.
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Measuring Real AI Value in the Consumption Era: Why the Old Metrics Just Broke
What: A practical framework for measuring the true cost and value of enterprise AI now that consumption pricing has changed the economics. I introduce a four-layer total-cost-of-ownership model — licenses, readiness, management, and consumption — and the shift from measuring usage to measuring value.
Why now: As organizations move from AI experiments to production, "people are using it" no longer answers the question leaders actually face: is it worth it? Consumption-priced agents stack variable costs on top of licenses, and most success metrics track only the cheapest, most visible layer. This is the gap between AI ambition and proven value.
How: A real-world walkthrough mapping an enterprise AI scenario across all four cost layers, plus the Knowing→Doing→Value→Spend chain — evidence-based, vendor-agnostic, and grounded in over a decade modeling enterprise technology cost and value at Microsoft.
Takeaways: Attendees leave able to retire broken usage metrics, apply a four-layer TCO model to any AI investment, and build a value narrative a CFO will actually act on.
Getting the Most from Your Copilot Investment: Prioritize, Measure, and Prove AI Value
Microsoft 365 Copilot, Copilot Cowork, and Copilot Studio are unlocking real productivity — but with Cowork and Studio metered in Copilot Credits on top of the Copilot license, getting full value means being intentional about where and how you apply them. The goal isn't to use AI less; it's to use it smarter — pointing it at the work that delivers the most impact for the effort and spend.
Drawing on twelve years at Microsoft — co-founding FastTrack's at-scale adoption approach and creating and managing four enterprise AI scenarios at Microsoft scale — Lisa Vanderlip shares a practical playbook built on her VALUE Framework for moving AI from pilot to proven ROI. You'll learn to prioritize AI use cases with a business-requirements approach that maps effort vs. impact to surface high-impact, low-effort wins; then measure the outcomes that actually count — not just usage — so you can prove the value you're creating and reinvest with confidence. Leave able to point Copilot, Cowork, and Studio at the work that matters most — and show the results.
Champions That Count: Turning AI Champion Programs into Measured ROI
The champion program is the standard-issue play in every technology rollout — and the one part of adoption nobody measures. We run enthusiasm-driven events, count prompts, celebrate usage, then struggle to answer the only question that matters: did any of it change how the business performs? In the AI era that gap gets expensive — with Copilot Cowork and Copilot Studio metered in Copilot Credits, unmeasured champion activity isn't just hard to justify, it carries real cost.
Drawing on twelve years at Microsoft — co-founding FastTrack's at-scale adoption approach and creating and managing four enterprise AI scenarios at Microsoft scale — and a career running champion and adoption programs, Lisa Vanderlip shares a model that makes champion impact measurable and owned. Using her VALUE Framework, you'll learn a three-role operating model (Champion, Business Owner, Tech Owner) that ties every champion activity to a business outcome, plus a practical shift from prompt-a-thons to scenario-a-thons that compete on real business impact. Leave able to redesign your AI champion program around outcomes you can actually measure.
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