What does it actually take to go beyond AI pilots and turn enterprise ambition into real productivity gains? That question was central to my conversation with Olivia NottebohmChief Operating Officer at Boxand it’s one that every boardroom seems to be grappling with at the moment.
AI conversations have matured quickly. The early excitement has given way to more difficult questions about returns, trust and what changes when software stops supporting the work and starts trading on it.
Olivia brings a rare perspective to that discussion, shaped by leadership roles at Google, Dropbox, Notion and now Box, where she oversees global go-to-market, customer success and partnerships at a time when AI is becoming embedded in everyday business operations.
We discussed why early adopters are already seeing a productivity increase of about thirty-seven percent, while others remain stuck experimenting. The difference, as Olivia explains, rarely lies in the model itself. Strategy is more important.
Teams that view AI as an opportunity to rethink the way work flows through the organization are pulling away from teams that simply layer automation on top of broken processes. This is where unstructured content, often described as ‘dark data’, becomes a competitive advantage rather than a disadvantage. When that information is compiled, authorized, and ready for agents to use, entire workflows start to look very different.
Much of our discussion focused on AI agents and why 2026 will be the year they go from novelty to necessity. Agents are already joining the workforce and taking on tasks that previously required multiple handoffs between teams. This shift brings speed and autonomy, but also raises new questions about trust.
Olivia shared why governance has become one of the biggest blind spots in enterprise AI, especially when agents act independently or communicate across platforms. Her perspective was clear. Without strong security, consent, and oversight, the risks grow faster than the rewards.
We also explored why companies that use a mix of models and agents tend to achieve higher returns, and how Box approaches this with a neutral, customer choice-based philosophy, while maintaining consistent governance.
From the five stages of enterprise AI maturity to the idea of ​​a future agent manager role, this conversation provides an informed look at what AI at scale actually demands from leadership, culture, and business models.
So as investment accelerates and AI becomes part of the job, the real question is this. Are organizations ready to redesign the way they work around agents, data and trust, or will they continue to experiment while others lead the way, and what do you think is the difference between the two?
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