Rebecca Hinds: recommendation

Transcript context

…Makes sense. I might have a couple little follow-up questions regarding your expectations as we go. But Tell me about the methodology. I mean, everything we're going to discuss here in terms of findings is downstream of basically 2 main data sources, as I understand it. One is a big survey, which you can tell us more about. And then the other that caught my eye in the report is aggregated telemetry from the Glean platform. And I'm interested in what that actually looks like and understanding a little bit better the substance of that data as well. 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.…

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