رونوشت 🔗
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Hello, and welcome to another episode of 600 Seconds in
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HR Tech. We are at HR Tech in Las Vegas,
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and today I'm joined by Ike Penyon. Hi. Hi.
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So good to see you again.
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Me too. So can you tell our listeners a little
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bit about you, your role at Visior, and Visior?
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Yeah, absolutely. So my name is Ike Fenyon. Great to
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meet you all virtually and then hopefully in person soon.
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So I've been a visitor for the last four years.
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I head up our product management team for our agents,
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which we'll talk about quite a bit. So our agent
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solutions for talent management, also for our platform that ingests
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and creates context. Again, another item that we'll get into
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as well as workforce planning. So how do you go
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from not just the budget level down to the layers
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below so that you can orchestrate on a position level?
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on a work, how do you shape work as it
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translates and disrupts into the future, all that sort of stuff.
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So in my past lives, I worked at Cornerstone on demand,
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overseeing portfolio strategy for Saba as well as Cornerstone Solutions,
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and then HireVue and Structure. So quite a journey through
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the HR tech landscape. So it's always been good to
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interact with you, Red Thread, as well as Danny and Stacia. Yeah, likewise.
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Very cool. So I know Vizzer does a lot, but
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today I want to talk about something that you all
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recently released. So this past September, you all released a
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connector with Microsoft Viva Glint. So can you tell our
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listeners a little bit about what that connector is, what
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it does, and why Glint? Why not any other platform?
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Yeah, it's an excellent question. Engagement data is one of
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that first underpinnings of everything else in the insight world.
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It's one of those things that most employers do with
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their customer base. And so it's the question of what
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do you actually do with it then? So great. We
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have the insights back. We see where people feel on
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certain topics, their overall sentiment, but it doesn't really point
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to what is the actual action that you should take.
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So we were very fortunate to work with Glint and
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they started to build an API to get that data out.
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And we're looking at how do they tell that value
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story to the broader market? We are a partner with Microsoft.
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And so we found a really great opportunity to help
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take that Glint engagement data, wrap it with other data
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and context from the enterprise ecosystem, then help managers to
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understand not just what was happening on a more granular level,
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but then what you should do about it. And so
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Microsoft was excited about that part to integrate Glint data
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for the first time into the broader ecosystem. We were
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excited to be a founding partner on that API, but
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then also to help our customers who had been asking
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for this type of integration to make it more streamlined.
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So you can get that insight more rapidly, more on demand.
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on a regular basis so that your managers know what
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to do with it. Yeah.
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So, okay. Can you help our listeners maybe understand this
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better by giving us an example? What do those insights
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actually look like? Where do they land?
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And who owns them at the end of the day?
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So it's a great question. And that's what Vizier ultimately
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does is one of our strengths is that we were
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built for the people space to reconcile data from across
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the ecosystems. So from your HRI system with Workday, you
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might use it for your record keeping. You don't use Pecan.
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You use Microsoft Glint. You use Greenhouse as an ATS.
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You have iSolve for benefits, et cetera, et cetera. There's
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16 on average. I think that's even maybe on the
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low side solutions that an HR department uses. And within
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each one of those is a little bit of data
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that helps to tell the story of what was done
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by who
Load full transcript
to drive the workforce as it is today.
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And HR has really struggled to actually tell the final
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story of what's the outcome to the top line, what's
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going on with the business. That's what a lot of
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practitioners are constantly gunning for. And the reason is, is
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that you don't have even all the actions into one
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place to let alone then tell the story of what
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impact those actions made. So when you take engagement data,
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for instance, now we can look at why a certain
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department is seeing much lower engagement in this most recent
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quarter of surveying, whereas the quarter before everything seemed fine.
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Turns out a driver of that might be manager retention
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and instability. So you had a prominent manager leave, high
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value talent leave. And oh, that's probably a big driver
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and reason that wouldn't show up in a question that
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you could ask your workforce. You don't ask like, are
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you unhappy because of manager instability? It comes from all
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the other data that enriches your employee engagement data to
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then point to, oh, we probably need to spend some
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time with managers, make sure everyone's happy, keep them in seat.
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So therefore, we keep engagement high and retention high in
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our workforce. And that insight only comes from creating the
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connective view, the high rich context across the enterprise data
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ecosystem that you can't do with Glint alone. You can't
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do with Workday alone.
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Right, yeah. So I know you explained to our leaders
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why bringing this data is more important now with this
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context there. But I want to specifically talk about what's
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happening now. Like, why is this so much more important
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in this moment?
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You go to any booth here. And you'll see the
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word agents, AI, take action, that sort of thing. And
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you know, my boss loves to use the phrase, all
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roads will take you to the destination if you don't
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have one. And so where are you now is an
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important point to figure out where you need to go.
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And now that we're automating and we're using AI that
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may not have the knowledge that you and I have
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about working in our individual workspaces, context becomes really important
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to make AI reliable. to make it dependable and high
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impact use cases, and also to make it governed and
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secure so that I as a manager know that the
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data that I'm using is actually high quality, it is
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the right data, and I have the right tools to
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then execute on it. And so I think the moment
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is now to make sure that enterprises not only have
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the building blocks to create context, but an ontology that
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is dependable, that is the gap for HR that just
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hasn't existed to this point. Visier, with the partnership of
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our customers, 85,000 accounts around the world, have built that
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gap for HR. And so now there is a rich
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context that AI can be more dependable, governed, and high
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value as they operate to automate decisions and to assist
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managers in the workforce.
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Yeah, so critical right now. Okay, I want to wrap
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up our conversation today by asking you about what you're
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generally seeing here. I know you've been coming to these
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kind of events.
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Long time.
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So what is it something or tell us about something
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that you're seeing different this time than you have before?
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I think that there's a new reality when it comes
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to AI. I think for a second, and it still
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pervades a little bit, right? Which everyone believes that they
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can build everything. Which on an individual perspective, if I
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want a budgeting tool just for myself or a tool
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just for my team, sure. I think that world exists.
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I think that we're a lot more sober now to
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the fact that to get the data foundation for AI together,
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to get the context for AI together, governance, security, is
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not a
trivial task and it's not one that should
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be trivial for enterprises, I think that the world is
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starting to wake up to that point a little bit more.
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So how do you use that foundation of good governance, security,
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data foundations, and build really powerful artificial intelligence and you're
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using the benefits of both? I think that's different. I
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think last year everyone was just like AI crazed. We
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had the stars in our eyes that it was going
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to fix everything. I think the tone is a little
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bit more sober, but still really optimistic in what AI
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can do.
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Yeah.
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Great.
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This has been really wonderful, Ike. Thank you so much
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for joining us in this conversation today. And thank you
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for listening in to this episode. We'll see you at
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the next one.
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So that was my conversation with Ike Bennion, Vice President
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of Product, Talent, Agents, Planning, and Platform at Vizier. I
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want to spend just a few minutes talking about what
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I took away from that conversation. And here are the
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three things that stood out to me. First, let's talk
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about the Glint connector. So Vizio released a connector with
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Microsoft Viva Glint in September. I said, scores show how
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people feel, but do not say what to do. So
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in the example that he shared in the video, a
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department's engagement scores drop. The cause may be a manager
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who left and no survey questions asks about that. Only
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data from other systems show that and so it's hard
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to know what is the right action to take. This
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fits what we see across the market. In our 2026
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People Analytics technology report, we found that 91% of vendors
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say they help leaders act on insights. Insights from survey
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data are based on limited data collected in that survey.
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So when combined with other types of data, such as
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HRIS data or ATS data or data from other systems,
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users can get much more richer insights. But vendors now
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compete with each other on action and not on insights anymore.
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So what I'd be interested in seeing is whether a
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system tracks if an action was actually taken based on
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the recommended action that was provided. And then once that
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action was taken, what happened? I'd also be interested in
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seeing if other survey providers follow this route like Lindt
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and build similar partnerships as having a holistic view of
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the data becomes more and more critical within organizations. This
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other thing that I want to talk about is context.
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Ike said HR uses about 16 tools on average and
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that he said is probably a lower number across a
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given organization. And each tool holds part of the story.
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He said, Vizier built a shared structure for HR data
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and ontology, which makes AI reliable. I agree that AI
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needs this, this holistic context that is built by connecting
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together all the different data within the organization. AI on
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unconnected people data gives answers that read well but make
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little sense. This is where human review, judgment, and expertise
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becomes critical. Humans need to have the expertise and the
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knowledge necessary to know whether an AI output is actually
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reliable or just looks good on the surface but is
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in fact inaccurate. So as a buyer, I would want
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to know where does a human check the output before
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someone acts on it. I'd also want to know how
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a system that integrates data from other systems handles when
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two systems disagree. The third thing that I want to
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talk about is the change in mood that Ike mentioned
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about the conference. Ike said that last year, many people
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believed that they could build everything with AI for a
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personal tool that still holds. But for an enterprise, the
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data foundation, context, governance and security are hard work. He
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said buyers now see this and he called the tone
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more sober and still optimistic. This speaks to the build
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versus buy debate that we've seen resurfaced over the recent past.
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When does that sell data foundation gain when buyers see
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what building actually costs in reality? It also suggests that
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the answer is not one or the other. It's not
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whether you just build or you just buy. We're now
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seeing companies buy the technology for its capabilities, but build
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their own experience layer that is added to the top
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because the experience and agent layers are now cheaper to build.
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So the question is becoming more about which layer do
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you need to control? Is it the data, the business rules,
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the employee experience, or the AI agents? So that's what
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stood out to me from our conversation with Vizier. If
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the conversation was interesting to you and you would like
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such similar insights, do check out our research. And if
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you found this useful, share it with a friend and
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share your thoughts with us. Thank you so much for listening.