Session
When Dashboards Aren't Enough, Build Around the Decision
One use case, one recommendation and one accountable human choice
PAWorld26 San Francisco · 23–24 September 2026 · San Francisco
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– · Work Design
About this session
NetApp's analytics rationalisation solved fragmentation but not the work required to reach a decision. Manisha Bajaj's team is experimenting with a different product model: start with one bounded choice, identify the internal data, external information, policies and practical constraints that matter, produce a recommendation and leave an accountable human able to accept, reject or challenge it. The principal example is a meeting-location adviser that combines employee locations, travel distance and cost, office suitability and company policy. The value is what the team is learning about decision-ready intelligence, not the chatbot itself.
Learning outcomes
- See how to move from data availability to decision usability.
- Learn how to design an AI adviser around one bounded decision.
- Apply a test for whether a recommendation is useful enough to scale.
This session will explore
- Why dashboard consolidation did not remove the user's synthesis burden.
- The bounded decision and inputs behind the meeting-location adviser.
- Where the human accepts, rejects or challenges the recommendation.
- How the team plans to test use, quality and value before expanding.
Context
A practitioner product case. Future hiring, skills and organisation-design examples remain roadmap.
Speakers and contributors
Manisha BajajPresenterDirector, Workforce AnalyticsNetApp