Nathan Labenz: commitment

Transcript context

…Sure. And there, the main bulk of the data is the survey data. This was something that has been in the works for months and months. We fielded it December and January 2025, 2026. And I think what's novel about how we collected the survey data, so it's 6,000 knowledge workers, 3,000 in the US, and then 1.5K in each of the UK and Australia. It was developed in partnership with our eight founding members of the Work AI Institute, which is our internal research center at Glean. And what's special is each one of these experts, these eight experts comes at the AI conversation a little bit differently. Some are very much focused on the psychology, the mindset around AI. Others are more focused on the technology, you know, digital transformation. Others are more focused on org design. You know, how do we think about this from a systematic, you know, org design perspective? We thought it would be important, given the transformative nature of the technology to have a pulse into all of these different dimensions. So that's the survey component. A lot of conversations, you know, we're talking with our customers at Glean as well as, you know, organizations broadly in terms of what works, what doesn't work. And so that informed a lot of the narrative. And then the aggregated anonymous Glean telemetry data is something that, is very exciting because it's objective. And, triangulating the data in terms of both the subjective survey data, which is, inherently biased for many different reasons, pairing that with something that is objective, we thought was important. And in particular, looking at how we're seeing adoption happen on the Glean platform. You know, the importance of cross-functional adoption, the importance of, you know, whether your manager adopts or a team member or cross-functional team member adopts, the network effects associated with this technology are massive. And when we think of change management, overwhelmingly right now we're seeing top-down change happen, mandates, memos telling employees to use AIRLs. Well, the best organizations, the most effective ones, they're having a bifurcated strategy. Yes, top-down change is important. We absolutely need a policy. We absolutely need principles. We absolutely need to see the CEOs and executives using the technology. But bottom-up change is just as important and finding these AI influencers or champions within your organization is so essential to activate and activate meaningful change and not just the symbolic change because the CEO has told us to use the technology. Moment to just take a beat on Glean and what Glean does. I think our audience is generally AI obsessed. I think that's the one commonality that we all share. So everybody has heard of Glean at least. I will confess though, as an individual operator, I'm obviously not in the sweet spot of the target market. And so I've never actually used it. And I don't know too much about what the experience is like, although I certainly have a sense. And I also don't know how you go to market, whether it's a sort of enterprise sales model versus, a kind of product-led bottoms up, or maybe it's a hybrid. But maybe give us like the double click on Glean so people can understand, in a little bit more of a functional procedural way what that, how that data is being generated. Sure. So Glean is a work AI platform. Historically, we started in enterprise AI search, solving the problem of how do we find the right relevant information within an organization. This was pre-AI, pre-mainstream AI. Now our platform has an intelligent assistant as well. So every employee has that intelligent teammate that deeply understands not only how they work, but also how the organization works. So starting to give you recommendations in terms of what you should be prioritizing each day, as well as when you ask it questions, knowing enough about you and your job function to provide intelligent answers. And then agents are a big part of the platform as well. So automated workflows But again, have that organizational context and able to streamline tasks and cross-functional tasks across the organization. And the real, I think, bread and butter of the platform is context. There's a lot of conversations right now around the importance of the context graph. Well, our data model enables us to surface that context and feed off of it in a really exciting way when AI is able to truly understand your work, your team's work, and the organization's work, well then you avoid generic answers and you start to get into this really exciting territory of predictive AI and proactive AI telling you what matters right now in the moment of your day-to-day work.…

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