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If your SAP leadership team identifies 10 excellent AI use cases but your budget can fund only 3, which three would you choose? Would you prioritize the most impressive AI demo or the initiative with the strongest executive sponsor? Finding AI opportunities is becoming easier, but deciding which ones deserve your organization's money, people, data, and attention is much harder. In this video, we break down how SAP leaders can move beyond AI wish lists and build a defensible investment portfolio. You'll see how factors such as business value, business pain, data readiness, SAP fit, feasibility, time to value, adoption, and risk can shape AI investment decisions. Because a strong SAP AI strategy isn't just about deciding what to fund, it's also about deciding what not to fund yet.
Chapters:
00:00 - Introduction: The Strategic Dilemma of Funding SAP AI Use Cases
00:40 - The Common Mistake: Quantity vs. Prioritization in AI Strategy
01:13 - Flawed Selection Criteria vs. Multi-Dimensional Evaluation
02:07 - Practical Scenario: Flashy AI Agent vs. High-Volume Procurement Solution
02:56 - Balancing Short-Term Value with Long-Term Capability in an AI Portfolio
03:34 - The SAP AI Use Case Scoring Sheet & Decision Framework
04:18 - SAP AI Leadership Mastery Program Overview with Devraj Bardon
In this video, you’ll learn:
Why having 25 or 40 SAP AI use cases does not automatically mean you have an AI strategy.
Why prioritization is a leadership responsibility when budget and organizational capacity are limited.
The risks of choosing AI initiatives based only on the loudest sponsor, best demo, newest technology, or competitor activity.
The key criteria for evaluating SAP AI use cases, including business value, business pain, data readiness, SAP fit, feasibility, time to value, adoption, and risk.
How a sophisticated autonomous AI agent can look impressive while still having low data readiness, unclear ROI, governance gaps, and high complexity.
Why a less exciting procurement exception use case can sometimes have clearer business value and measurable financial impact.
How to compare hype versus evidence when evaluating enterprise AI investments.
How an SAP AI Use-Case Scoring Sheet can create a common framework for leadership discussions.
How weighted criteria such as Value, Pain, Data, SAP Fit, Feasibility, Time to Value, Adoption, and Risk can be used to evaluate competing initiatives.
How prioritized AI use cases can eventually connect to vendor strategy, governance, ROI measurement, and 90-day or 12-month roadmaps.
Finding AI ideas is becoming easy. Choosing which ones deserve investment is the leadership skill. The goal isn't to fund the most exciting AI, it is to build a portfolio that leadership can defend, govern, measure, and scale.
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