When AI reclaims a researcher’s time, what's worth spending it on?

AI is not shrinking research teams. It is raising their ambition, and forcing a harder question: what do you do with the capacity it gives back? In this session, design and research leaders from Verizon, Nationwide, Newell Brands, Datadog, and Oracle shared how they are drawing the line between what AI takes over and what stays human-led, and where they are redirecting the reclaimed time to create net new value.

Hosted in partnership with Dscout, the conversation featured Gordon Ching (Design Executive Council), Anshuman Kumar (Datadog), Richard Dalton (Verizon), Brian Rice (Newell Brands), Jennifer Darmour (Oracle Health), and Brian Greene (Nationwide).

This session is Track 2 of Modernizing Research with AI: Customer Centricity at Scale, a four-part series with Dscout and the Design Executive Council. It builds directly on Track 1's foundation and turns to a more operational question: as AI takes on more of the execution, where is the reclaimed capacity actually going, and how do you know it is being spent well?

What we covered in the webinar

1. Automating low-value work: Which research activities have leaders permanently automated, how they decide what stays human-led versus AI-assisted, and where "good enough" has been acceptable versus where they have held the line.
2. Harnessing reclaimed capacity: Where reclaimed time and human effort are being redirected, how leaders are measuring whether that reinvestment is creating strategic value, and what has changed about the quality bar.
3. Cross-functional use cases: With organizations becoming equipped with AI-native research capabilities, which cross-functional use cases have been most promising, and how stakeholders are responding.

Key takeaways

AI is not simply helping research teams work faster. It is raising their ambition and reshaping where they create value.

• Upstreaming research: Reclaimed time is going upstream, toward deeper problem framing, longitudinal research, and the kind of customer understanding that changes strategy, not just confirms it.
• Cross-functional collaboration: From new talent models to faster concept visualization, teams are using time savings from AI to improve how teams work together.
• Rising role accountability: The role is reshaping toward greater human accountability, oversight, and stewardship as AI systems can amplify risk and teams no longer control every decision directly.

Path forward

The panelists returned to one point again and again: reclaimed capacity is not the win. What you do with it is. Teams that treat AI as a way to make more stuff faster miss the real opportunity. Teams that use it to stay in the problem space longer, ask better questions, and drive alignment across the organization are the ones creating strategic value.

The shift is not just about drawing a line between human and AI work. It is about moving research upstream, where framing the right problem matters more than solving the wrong one faster. That means investing in business acumen, deepening cross-functional partnerships, and building the judgment to know where rigor still has to hold. 

Continue the Series

This is Track 2 of a 4-part series. Each track examines a different dimension of the shift: 

• Track 1: Building the research team of tomorrow
  How leaders are rebuilding teams, redefining performance, and steering change
  
  
• Track 3: Retaining customer-centricity when everyone builds
  Protecting user truth when research is democratized
  
  
• Track 4: Scaling quality with AI evaluation models
  Building the evaluation and governance layers that keep quality high at speed