Knowledge Cast by Enterprise Knowledge
Oct 1, 2026 · 13 min · 11 segments
Enterprise Knowledge’s Lulit Tesfaye, VP of Knowledge & Data Services, speaks with Rosanna Stephens, Senior Knowledge Manager of Employee & Workplace Experience Transformation at Adobe and speaker at…
Lulit TesfayeHostRosanna StevensGuest
It's interesting you called out that experience with an understanding of library science, how much it translates to providing context, and I really appreciate how you made context tangible, 'cause there's a lot of noise on the market about context right now.
And to play back to you the distinction between context for content and then context within the content is ...
That's such a great way of putting it, Rosanna, and I know in our work we've learned a lot as well.
So in many organizations, especially for those that are listening in and are thinking about these two types of context, for instance, and especially in light of the semantic layer topic that we're talking about, the context for content, many have huge amounts of frontline knowledge or hidden knowledge across the organization, probably siloed or distributed.
What are some of the things that you would tell them to start doing today? I think you started touching on what needs to be addressed before you can provide context for AI, but would love to double down a little bit on that and hear your perspective on where would you start from what you've learned.
I think we were fortunate that we adopted knowledge-centered success as a methodology many years ago.
And what that is is you're not necessarily building a centralized knowledge team that writes all the knowledge and governs all of it.
It's really about democratizing and enabling the organization to capture what they know in the flow of what they're doing, and to understand why that's important to do that.
So you have to extract it first out of people's heads, and then you can structure it.
And just, like, helping them with upfront, "Here's some simple templates for you, how to do it within your flow," making sure that we're capturing, because knowledge is dynamic and changes every day.
So you're not gonna code everything and structure it and then just leave it alone for two years now.
So I think that's the most important thing, is you need to have it to begin with.
It's interesting you called out that experience with an understanding of library science, how much it translates to providing context, and I really appreciate how you made context tangible, 'cause there's a lot of noise on the market about context right now.
And to play back to you the distinction between context for content and then context within the content is ...
That's such a great way of putting it, Rosanna, and I know in our work we've learned a lot as well.
So in many organizations, especially for those that are listening in and are thinking about these two types of context, for instance, and especially in light of the semantic layer topic that we're talking about, the context for content, many have huge amounts of frontline knowledge or hidden knowledge across the organization, probably siloed or distributed.
What are some of the things that you would tell them to start doing today? I think you started touching on what needs to be addressed before you can provide context for AI, but would love to double down a little bit on that and hear your perspective on where would you start from what you've learned.
I think we were fortunate that we adopted knowledge-centered success as a methodology many years ago.
And what that is is you're not necessarily building a centralized knowledge team that writes all the knowledge and governs all of it.
It's really about democratizing and enabling the organization to capture what they know in the flow of what they're doing, and to understand why that's important to do that.
So you have to extract it first out of people's heads, and then you can structure it.
And just, like, helping them with upfront, "Here's some simple templates for you, how to do it within your flow," making sure that we're capturing, because knowledge is dynamic and changes every day.
So you're not gonna code everything and structure it and then just leave it alone for two years now.
So I think that's the most important thing, is you need to have it to begin with.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
