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Most organizations measure AI rollouts by seats activated and prompts per week, while their most thoughtful people quietly check out. That gap between activity and real outcomes is what keeps AI initiatives stuck, long before the tool itself is ever the problem.
Mindy Honcoop, founder of Agile in HR and co-founder of AltHR Hive, breaks down why AI adoption stalls, and why the cause is almost never resistance. She introduces the SIGNAL Loop, her practical framework for spotting real friction, testing small changes, and turning that friction into forward progress. Jay from Teamflect then demonstrates Teamflect's AI Agent in action inside Microsoft Teams, from summarizing goals and feedback to answering policy questions. An audience Q&A closes the session, covering AI governance, data access boundaries, and how to build an employee-centered AI policy.
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Emre Ok [0:04]
And everyone who is coming in — and everyone's AI note-takers — it's really nice to have all of you here. Welcome to "How to Lead Your AI Rollout: HR's Role in Removing Friction." Just killing like 3 to 4 minutes as everyone keeps rolling in, but it is so nice to have you all on board with us. And I think while we wait for everyone to roll in, Mindy, we need to address some of the controversy about this webinar. We have been getting some backlash, especially from some people who are not very pro-AI in the workplace. And I just want to say: every single concern those people have about AI in the workplace, we share. And that's why we're doing this.
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Mindy Honcoop [0:41]
Yes, exactly. And I always reframe it — rather than seeing it as a problem or something to be fearful of, how can we shift that to wonder? How do we be curious about it? Because often underlying those comments and fears are some really powerful questions that we do need to be thinking about and keeping top of mind.
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Emre Ok [1:30]
Exactly. And just to clarify, Mindy — is this going to be a webinar where we just say, "Oh, you've got to go all in on AI, bro. AI is the future. You've got to AI yourself, you've got to AI your grandmother, you've got to AI your work"? Is this going to be one of those?
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Mindy Honcoop [1:38]
No! Absolutely not. This is about the most important thing we need right now: humanity at work and human skills. How do we build that muscle back into the conversation? How do we make sure that humans are actually steering the wheel of the AI loops that we're creating?
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Emre Ok [2:12]
Exactly. I love it. And while we wait, I'd also love to introduce our very own Jay Sharris to the crowd. Jay will actually show you a practical way AI can be implemented in the performance management and HR process. Jay, is there anything you'd like to say before we start?
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Jay Sharris [2:39]
No, thanks, Emre. Just looking forward to showing how to use it, where it can be configured, and some different use cases.
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Emre Ok [2:48]
Amazing. Well, I'm so glad to have you here with us as well. A lot of people are coming in and the room is slowly starting to fill up. The chat function is up and running, so if there's anything you'd like to talk about before we start, just hit us up in the chat. We also have the Q&A function up and running, so feel free to ask your questions right there. As everyone starts to trickle in, I can move us slowly along into the agenda. So — hello, this is the welcome and housekeeping section.
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Mindy Honcoop [3:35]
Yay!
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Emre Ok [3:40]
If you have any questions throughout the entire webinar, just ask them in the Q&A section and we will answer them at the end. If it's something we can answer quickly, we'll also respond within the Q&A in real time. And if you run into any technical difficulties, our very own Casey is right there with you in the chat — she'll be interacting with you throughout the entire webinar and responding to some of your questions. If you run into technical difficulties, you can reach her at [email protected]. With all of that out of the way, I am very excited to pass the mic along to my trauma-bonding buddy —
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Mindy Honcoop [4:20]
All right!
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Emre Ok [4:35]
The founder of Agile in HR and AltHr Hive, Mindy Honcoop. She is the bubbliest, most amazing person you can find in the HR tech space. However, Mindy and I did record an entire episode of a podcast about trauma in the workplace, so that is also something to take note of.
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Mindy Honcoop [4:51]
Yes, great topics! And honestly, when I keep reflecting on this conversation, I think it builds on everything we talked about before. So I highly suggest people go back and find that podcast. If you guys have the link for that, you might want to drop it in the chat — it was a really good one.
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Emre Ok [4:58]
We just might do that. However, I think we have quite the crowd now and I wouldn't want to keep anyone waiting. Mindy, the floor is yours.
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Mindy Honcoop [5:18]
Let's go! Amazing. Thank you, Emre.
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Mindy Honcoop [5:34]
I'm Mindy Honcoop. I'm based in Austin, Texas, and I am a recovering chief people officer. I've been in the HR profession for 26 years — longer than I'd like to admit. I founded Agile in HR a couple of years ago, and it's slowly evolved from fractional executive HR into executive coaching and advising of leaders and HR professionals in the world of work. I love meeting people in the heart of change. I am one of those rare birds who absolutely loves the gray space of change. It is hard — it is not easy — but I love being able to equip and elevate people through change to really rethink how we're aligning with process and technology within the workplace, and making sure that we're always thinking through the lens of the art of the possible.
So we're going to dive into why you all came to join us. You didn't come here to hear about me — we came here to hear about AI adoption, and how the focus on humans is so important, and the conversation of humanity in change. One moment as we get these slides.
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Emre Ok [7:06]
Mindy, I love your approach to AI rollouts — treating it less as an IT process and more as an actual human-centered change management process, because that is exactly what it is.
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Mindy Honcoop [7:18]
Emre, you just synthesized that so well. And I think it's the missing piece. It's where we as an HR function — along with IT — are one of the few functions that understands the flow of how work gets done at the ecosystem level within an organization, because we work across the different functions and the humans within that ecosystem. It's really the opportunity to think about how humans within the flow of work are being able to utilize tools in the best way at the best moment to accomplish business outcomes, and how we're thinking about using technology — including AI — as a way to augment ourselves. Yes, jobs will evolve and change, but there are so many net new roles that will exist that don't exist today, and we're already starting to see that. For us to be successful and step up, we are perfectly positioned to be having these conversations with the IT team, because change management is all about humans and behaviors, and the human skills needed for change to be successful.
That's what we're going to dive into today. The change management and human piece is where we get to offer, I think, a missing element to the puzzle — in the conversations I'm having and in the clients I'm working with. So I'm just super curious before we jump in — and Emre, always feel free to move me along or keep me on guardrails, because you know how much I love to talk and how passionate I get. But I wanted to start with a poll, if that's okay. I'm curious: how is your organization measuring AI adoption right now? Pick one of the following.
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Emre Ok [9:41]
Mindy, the poll has been running since the second you mentioned it, and we already have answers. Before this webinar, during the dry run, we had a betting pool going on which one would be the most picked — and at a whopping 50%, "We're not really measuring it" is leading the entire poll.
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Mindy Honcoop [10:04]
Not surprised. Those individuals are not alone. I would say the second most common, when organizations are measuring it, is often activity and transactions — like token consumption, training completed, prompts per week, seats activated. And the issue is that these are activities and transactions that are often not tied to business outcomes. When you see this kind of dashboard — I'd love to see emojis from those who are measuring: is this similar to what you're seeing?
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Emre Ok [11:06]
Yeah, a thumb did go up and was followed by another.
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Mindy Honcoop [11:10]
Usage is fine, but it's not the same as adoption. And adoption is not an outcome. That's where we start to see the pattern: organizations on this AI journey that have transactional measures are counting activity because it's easy to count, and it feels like progress. But is it really? That's the question. The cycle I usually see goes like this: they start with a mandate, they count usage if they're measuring it, the usage looks okay, but they're not seeing positive business outcomes. Where's the ROI? We see the usage, but why aren't we seeing greater business outcomes? Then the conversation starts to get problem-focused — we start talking about friction as resistance, the uncertainty of why we're not seeing something more. We do more of the same, or we replace one transaction number with another. And then we just have silence, or the same conversation over and over again. That's where people are getting frustrated — we're lost in this loop. So how do we make sure we don't get into it, or if we're already in it, how do we get out? If you're resonating with this right now, I'd love to hear in the chat: where is your organization stuck?
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Emre Ok [13:36]
The poll just went out and right now, honestly, I'm looking at it and trying to figure out where I actually land on this myself.
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Mindy Honcoop [13:39]
What are you sensing?
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Emre Ok [13:51]
Currently the numbers are rolling in, and it's quite an even spread — but "counting usage" and "pushing harder" are actually taking the lead right now.
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Mindy Honcoop [14:05]
Counting usage and pushing harder. Yes. And Emre, you had an interesting story about that.
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Emre Ok [14:07]
I do, though I'm not sure I should get into it because this is the webinar for the company I actually work for. But I did get called out: "Hey Emre, are you possibly using too many tokens?" However, I will say that conversation evolved into an entire workshop on how to use these AI tools more efficiently.
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Mindy Honcoop [14:43]
I love that, because you're shifting from a problem to a possibility — which is exactly what we want. But that requires a level of self-awareness, and we're going to talk about how to make that shift. I've also seen usage go the other way — where it's not enough usage, and it starts to become a performance management conversation. And when we push harder, it never feels great. We keep pushing against a wall and just see the same problems resurfacing.
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Emre Ok [15:19]
Mmm.
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Mindy Honcoop [15:29]
This isn't just a slow loop — it's a loop with no learning in it. And that's where the frustration comes from. A learning conversation sounds like: "Here's what we didn't know before." We had a clear outcome we were measuring, a clear hypothesis — "If we do this with AI, we expect this" — which is much different from just measuring usage or training completions. And then we're able to reflect and say, "Here's what we learned." Has anyone on this call had the opportunity to get to this level of conversation about their AI rollout?
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Emre Ok [16:24]
Casey should be putting this live poll in the chat as well. Honestly, for myself — during our AI rollout discussions about the tools we were using, we did discover so many different features and workflows in our organization that I didn't even know about. As we were having these conversations about AI and how these tools could assist people in their workflows, I actually found out things about how the product team works or how the customer success team works that I had no idea about.
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Mindy Honcoop [16:54]
And that's so important. That level of human dialogue and communication — you were able to get to the team level, to cross-functional conversation, which is so powerful. But what are we hearing from people here? Are they at this level of conversation?
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Emre Ok [17:38]
The poll just went out, so we'll see. And I want to say to all 80-plus people on this call — if there's an answer you want to give that doesn't fit a binary poll, feel free to use the chat.
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Mindy Honcoop [18:04]
Oh my gosh, so good, Emre. And just to take a step back — there are a lot of signals in what you're talking about. There's a lot of conversation happening in organizations and a lot of underlying emotions beneath it. Those signals are often seen as friction, as resistance, as complaints. I've heard many of these statements from clients, and it's framed as "why are people resistant and complaining?" When really, for me, these are just signals. They're people sharing thoughts — data points. But what was coming up for you and your team when you started your AI rollout? What was the loudest signal you were hearing?
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Emre Ok [19:08]
I'll tell you the loudest signal I was giving. My initial role when all these AI tools started becoming prominent was as a writer. So my initial response was absolute terror — "I am fully irrelevant now."
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Mindy Honcoop [19:11]
Fear. Yeah. And how we create the safe space for individuals to share their thoughts and have these conversations without feeling judged — judged as being resistant or complaining — is so important. Because these are the very data signals we need to start the two-way conversation. When we have friction in an organization, it generates heat, and that's energy. And if we lean into it with curiosity and wonder — as your organization did, Emre — to have cross-functional conversations and lean in to learn more, that's when we harness that heat as a catalyst for change. But if we don't have the space to have these conversations, we create fire — fire that works against the very change we're trying to make progress on. So it's really important not to jump to "heat is bad" — just see it as energy. How we enter these conversations, if it's from a place of seeking to understand without judgment and holding space at the holistic level, we can move down very different pathways. We can operate from a possibility and catalyst mindset, or we can be firefighting — which already starts narrowing our viewpoint and causes us to miss a lot of possible pathways.
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Emre Ok [21:25]
Before you proceed, Mindy — we just got some great anecdotes from the chat. Lorna Jackson said: "Feedback from our team is that there are a few early adopters who still want to stick with ChatGPT and aren't interested in trying tools like Claude or Copilot." So they're interested in AI — just resistant to learning something new. And Kayla is saying not knowing how to start, and thinking it's more complex than these tools actually are — which yes, with all the jargon in the industry, like "agentic AI" and "MCPs," they do sound more complex than they actually are. And Fredalyn Lape — Fredalyn, it's been a long time, so good to hear from you — she's saying: "Being perceived as relying on AI without actually applying critical thinking."
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Mindy Honcoop [22:17]
Yes! And what you just hit on there — all of these things point back to what's underlying them: curiosity, critical thinking, communication, innovation, ideation, the ability to learn. They're all human skills. They're the muscle and capability we need to continue to practice and grow. And also — when was the last time we looked at the behaviors in our organization that support our values and culture? How are those behaviors supporting play, curiosity, and the ability to ask questions without fear of repercussions? These are all human skills. And with leaders modeling and holding space for these conversations — those skills aren't birthed overnight. In organizations where we've been moving so quickly and going so lean, the very focus on these human muscles has atrophied. How are we making sure we're spending time to first check in with ourselves — reconnect, and even be able to answer the question of who we are as an organization — to help create that individual self-awareness, to connect with self so that we can then connect with others and enter into play? We've often lost the art of play. But what I love is that this is the foundation of everything we're going to look at. What are your thoughts, Emre?
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Emre Ok [24:56]
I am just blown away. Every time you go on one of these long and passionate monologues, all I want to say is amen. But I would also love to get into — what are these signals?
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Mindy Honcoop [25:09]
The signal loop! Here we are. Humans driving change. I think a lot of people are overthinking this. None of us know what's coming, and leaders have this feeling that they need to know — but none of us do. So how do we lead knowing we don't know? That's what I love about the signal loop that Marty and I created together from all these conversations. How do we simplify, in a moment of change, how we help an individual through this practice?
It's a six-step practice and you can enter it at any point — it's not linear, which is why we put it into a loop and a circle. But let's say we start with S — Spotting. We're spotting the friction in the organization. We're staying in "seeking to understand" rather than jumping to assumptions. First, we recognize where the friction is showing up. Then we get Curious — Investigating. We're not labeling it as bad or as a problem. We're putting on a magnifying glass, asking powerful coaching questions of individuals involved in this friction, having two-way dialogue, asking what the data is saying — or isn't saying — and what more we need to know.
In order to then Generate a hypothesis — a list of opportunities, all the possible pathways forward to harness that friction into a catalytic converter of change. Once we've looked at all the opportunities, we generate a hypothesis for one of them: "If we do this, we believe this to be true." That helps us because we're uncertain, but we're making an educated guess about what we believe about one small change — one step forward. Not transactions and activities, but an actual outcome.
And that's where the Nudge comes in. How are we going to test this in a small way? Not a three-month project or program — a small test. And then the most important step is Aligning: how are we letting people know we're going to be testing this? Why are we testing it? How does it work? How are they involved? What is a new behavior we're asking them to put on, to unlearn and relearn? And being very clear about what success looks like. Once we're aligned, we've created a Feedback loop — because we've been hearing from them throughout. And once we've done the test and given it time, we can Learn: what went well, what didn't, what do we leave behind, what do we move forward with, or what new uncertainty surfaced that we want to test next?
That's the learning loop, and that's why the signal loop practice is so powerful. I've even seen people use it to improve their one-on-ones — to try just one new thing that moves it from a status update to something more meaningful. Emre, what was landing for you there?
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Emre Ok [29:39]
What really clicks with me — and I've been having conversations with other professionals about this too — is always the idea of spotting the heat, spotting those points of friction. Because more often than not, in the HR ecosystem, when we look at these processes, we're so accustomed to seeing the steps: roll out the AI tool, it starts getting used, we measure adoption. But we actually forget about all these little bits in between — all the seams that connect the steps, and all the crossings of information in between them.
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Mindy Honcoop [30:24]
So true. It's that holistic view. Unfortunately, HR has often become the kitchen sink of the organization — everything no one else wanted to do. And we get so far in the trees and in the weeds that there's not a lot of opportunity to take that holistic step back that we so need to take to spot all the things you just described. The signal loop helps build the practice. Once again, this is a muscle — a way of speaking with ourselves, but also with our teams, our leadership teams, and our executives across the organization. It gives us the practice of how to make sure we're always holding the human in the same conversation as process and technology.
So where do we start? How do you run your first loop? I love a good practical takeaway. First: just pick one thing. Where is the heat in your organization? If you're not sure, take a step back and just observe where you're seeing it.
If you have a dashboard that looks very transactional, like what we looked at earlier, another place to start is to put one of your current AI rollout metrics on trial. Is it tied to an outcome or not? If not, what would it look like to drop it? What would a metric tied to an outcome look like?
Also — surfacing how each person is using AI. Emre, you already modeled this. It's building a rhythm around it as a team. How do we start talking about how we're each using AI, and then take some of those individual approaches that are working well and start using them collectively? From there, how do we tie that conversation back from transactional time saved as an individual to true greater business outcomes?
And if you want to learn more about the signal loop, we have an e-workbook and a complimentary worksheet that helps you learn how to run your own loops. Really, it starts with: where is the heat in your organization around your AI rollout? What keeps showing up? Where would you like to start? And if you feel comfortable sharing that, go ahead and drop it in the chat — we'll have Q&A time at the end, Emre, and we might have time to talk through some of those.
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Emre Ok [34:06]
Yes — and I've been taking note of all the questions and discussion points being posted. In the chat, people have been asking: how do we form an AI policy while maintaining our company culture and staying employee-centric without compromising quality outcomes? And there are questions about the safety of some of these AI tools. I've noted all of these and we will go over them in the Q&A section towards the end.
But I'll also ask everyone watching right now to drop in the chat: what is currently the strongest point of friction in your AI rollout? What is the heat, as Mindy puts it? And we will actually ask Mindy about how to tackle those points.
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Mindy Honcoop [35:19]
Awesome. Thanks, Emre. And if you do want to learn more, you can scan the QR code — and I'm always happy to chat with people on LinkedIn. So thank you so much, Emre, for having me. It's such a pleasure to be here.
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Emre Ok [35:43]
Amazing! Now we are moving on to a part of this event that I am very excited about. As we transition, I'll remind you all that Mindy is here to stay — so if you have any questions about how to put these frameworks about spotting signals into practice in your AI rollout, just let us know in the chat. Now let's take a look at the practical side of rolling out AI in the HR space. As many of you know, Teamflect is a performance management and HR software for the Microsoft 365 ecosystem — the ideal performance management solution for those who live in Microsoft Teams. We also have a powerful AI Agent HR assistant built into the app. To show you how you can use that assistant to impact almost every one of your HR workflows without losing the human side that we cannot afford to lose, I am passing the floor to Jay Sharris.
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Jay Sharris [37:18]
Great, thanks, Emre. Hi everyone — I'm Jay. What I'm going to do today is show you different areas within Teamflect AI — specifically Teamflect AI as well as the AI Agent. I want you to think about three things: one, where to access it; two, some use cases of how you can use it; and three, configuration — how you can set controls and really manage what is coming from Teamflect AI Agent.
So first — we're now in the Teamflect app. What you'll notice on the right-hand side of the screen is our AI Agent. This Agent can be accessed from pretty much every screen within the app. On the right-hand side, you'll see some quick suggestions — questions it will prompt you with, really guiding your usage. You can always ask it anything. What I did beforehand, in the interest of time, was click "Summarize progress, gaps, and next steps for my active goals." If I click Chat History, that will pull up my response — and that's another great thing, you can always go back and look at past questions. What Teamflect AI Agent has done here is pulled up responses based off what the system has been compiling on team members — giving you a breakdown by individual, highlights, progress, and actionable next steps. One would be creating a follow-up task, which gives you something like this. All I need to do is quickly review it, click Create, and it's published.
Another area: it's not just about looking at employee data. Maybe it's a company policy question. I don't want to bog down my HR department with something like "what are our company observed holidays?" Teamflect AI Agent will go back and look into whatever information is stored in the admin center on that topic and give you that information very quickly, along with a reference link. A third use case: maybe I need to learn a function within the software and I don't want to contact my customer success manager. I've asked it: "How do I create user attributes within the Teamflect admin portal?" And it's giving me step-by-step instructions on how to do that in Teamflect, even citing an article in our help center, along with actionable next steps.
So that's one way to use the AI Agent. But let's talk about other ways to use AI within Teamflect. Now I'm in a manager view. You'll see that Ask AI Agent is still right on the right-hand side. One of the other things AI will do is not just give you responses, but summarize and analyze data. There's a feedback module within Teamflect — if I scroll down past the BI reports, you'll see "Generate AI Summary or Ask Questions Regarding Feedback Detail." If I click Generate AI Summary, it starts analyzing that data and summarizing it in the way your company would like to see it. This summary can be visible across all the different dashboards within the reporting module. It gives me a great summary: what's changed, attention points, key takeaways.
Another theme I want to keep top of mind: AI is literally scattered throughout the product. What I'm hovering over now are what we call quick action triggers. Maybe I'm a manager tracking employees' goals or OKRs, and I don't want to review every individual one — I want it to summarize goal progress in the way I'd like to see it. I click the quick action trigger, and on the right-hand side it gives me that progress, summarized in a way that's relevant to my day-to-day. I can see all my team members and their goal progress broken down by areas of improvement, highlights, and areas of concern. And I can utilize a quick action next step — for example, preparing a task for Q2 follow-up. It analyzes, pulls up the task, and all I have to do is hit Publish.
One more use case: let's say we're in Reviews and I've been assigned my yearly annual performance review. It can be tough to go back and recall what you've achieved throughout the year. We have quick action triggers to help you fill out a performance review. If I hover over "Help me draft my self-review highlights for this review period," it goes ahead and gives me great content to put into my self-review. And if I'm questioning something I've put in, we have an Enhance with AI function — it gives me options: regenerate the response, refine the tone, make it more casual. I select one, and it's done. It's helped me draft a lot of my responses in my self-review, highlighting the source of the data so I know I can reference it in a discussion.
Now let's move to the admin center. I referenced this earlier as the super-user access of the system where you can control many different modules — including the AI feature. Here I have a lot of control. I can disable the AI feature, disable the Teamflect AI Agent, or go in and curate the information that's shaping responses. In this knowledge base, I can store resources from our employee intranet — like tuition reimbursement policies, PTO policies, our employee handbook — or upload our own company documentation for AI to reference when generating responses. I can also control user access, limit who has it, and set a personalized message letting people know they have access to the tool and how to use it.
Finally, there's a Fine-Tune AI feature where you can give the AI more context about your organization: which industry you're in, your company size, your organization's mission and vision — and you can add more context beyond that if you'd like.
And as we close out — one of the things we're always curious about is usage. There are pre-built reports within Teamflect in the AI Agent module. I can analyze usage summary, adoption rate, weekly usage by department, by job title, by office location, by seniority level — really see who's getting the most out of this. That's all I had, Emre. I'll go ahead and turn it back over to you.
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Emre Ok [49:19]
Amazing. Jay, thank you so very much. I have noted there have been some questions throughout your presentation, along with some discussion topics from Mindy's section. I've added them to the slide deck. I'll start with the direct questions for Jay, and then we'll move into those broader conversation points. But first — my favorite thing about Teamflect Agent is the fact that it cites its sources. Everyone knows where the information is coming from, and it's not generic AI gibberish like "well, I agree with you, isn't it nice that this happened." So, Jay — Angela asks: from a manager perspective, does it work only manager to direct report, or could it support, for example, a head of function reviewing multiple managers and different teams?
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Jay Sharris [50:32]
So it really depends on hierarchy in the organization. However your employees are mapped to different managers in Teamflect or in your Microsoft 365 directory, that's the parameter it uses. If that manager only sees those employees, it's only going to generate data on those employees. But if it's someone higher up with access to multiple departments, it will allow you to see all those different employees across those different departments.
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Emre Ok [51:12]
Exactly. So the ethics of information are still intact — I can't ask Teamflect Agent about a colleague's goals or information I'm not permitted to see as an employee. But if it falls under your jurisdiction as a head of function, as Angela asked, then yes, you will be able to see that information. Now, Olena asks a wonderful question: for the resources, does it only work with uploaded files, or is there a possibility to add a Confluence page or SharePoint, etc.?
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Jay Sharris [51:56]
I might have to turn this one over to you, Emre — I haven't been asked about Confluence specifically, so I'm not sure, but I can circle back on that.
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Emre Ok [52:09]
Through Teamflect's integration with Entra ID — since Teamflect also draws user data from Entra ID — it will pull attributes and information from there. And Teamflect does integrate with live documents like Excel sheets and SharePoint files. For example, if you have a goal and you want the progress to be drawn from a live document, Teamflect can integrate with that. On the Teamflect Agent side specifically, Jay will get back to us to make sure we're not misdirecting Olena. And then, Jay — one last question: what if we have different policies for different countries and functions? Will the Agent handle it, or will it go to the employee's attributes?
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Jay Sharris [53:20]
You kind of answered your own question there. It'll go through the employee's attributes and understand where that employee resides.
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Emre Ok [53:33]
Amazing. And one more — Fredalyn just asked: can we create or add more than one agent within Teamflect?
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Jay Sharris [53:50]
I'll find that response for you quickly — I'll circle back on that one.
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Emre Ok [53:58]
Fredalyn, it breaks my heart that you haven't read last month's newsletter because I worked really hard on it! All jokes aside, Fredalyn is a user and I've had the chance to chat with her — love her to bits. Say hi to Andrew from me. And Fredalyn, we do have AI subskills coming out very soon as part of Teamflect Agent. In the upcoming months, when those features are live, you will be able to assign different jobs and tasks to each individual skill. And Jay, did you get an answer on the Confluence side?
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Jay Sharris [54:57]
Yeah, I'll add that to the chat.
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Emre Ok [55:02]
All right. And while you do that, I'm going to say the most stereotypically HR sentence in the history of HR — I would like to circle back to Mindy.
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Mindy Honcoop [55:18]
Ha!
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Emre Ok [55:18]
I can't believe I got to use "circle back" on a live webinar. I'm thrilled. Now — these were a few discussion points that came up. I want to start with the middle one: someone said they're having trouble thinking through AI policies so that when using AI tools, they're enhancing capacity, analysis, and company culture — whereas teams are getting lazy and the AI output doesn't meet company standards or employee-centric culture. What are your thoughts on that, Mindy?
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Mindy Honcoop [55:54]
There are several questions within that question. And I love that the person is really thoughtfully wanting to build an AI policy that's more like guardrails and guidelines — one that ensures the organization stays human-centered. That it creates clarity around what AI tools are allowed, how we should use them, and how we make sure that as we use them, we're not just outsourcing our thinking. That we're thinking through how we're using AI to augment our work, and how we're continuing to build our skill set and evolve our roles as humans.
And does our AI policy use words that anchor into the behaviors of our organization, that reinforce who we are as a company and how we want to show up? I think in all of this, it's about putting the human at the center of the policy — thinking through whether this is just a compliance exercise or a living, breathing document that becomes a decision matrix for how we as humans come together and have the conversations, like the signal loop, to constantly revisit: how are we using AI? What are our success measures? What are the outcomes? It's about how we elevate how humans show up — not about replacing humans. I almost see this as a living, breathing decision matrix — almost like a team norm for how we decide what to say yes or no to. That's how I would write it. Those are the ingredients.
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Emre Ok [58:31]
Wonderful. And someone in the chat also responded saying they actually recommend using AI to produce the policy itself.
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Mindy Honcoop [58:44]
Yes! It could help — as long as you're anchoring it in human-centeredness, in who your company is, in behaviors, and in this being more of a decision matrix and guardrails. And making sure we're covering security too — not opening ourselves up to risk. How do we think through before introducing a technology? How do we have that conversation with vendors? How do we make sure we understand how they're responsible for auditing their work? Who's responsible for, for example, whether I can use this tool with employees in China? China has very different AI regulations and different rules around where Chinese employee data is housed. Different countries have different laws — and sometimes vendors are putting the onus on the employer to make sure they're meeting all the laws where their employees are and where their employee data is stored. Being able to have those important conversations with vendors before bringing them into the workplace ecosystem is so important.
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Emre Ok [1:00:23]
Exactly. And I just want to add to the question about how safe and secure AI is across platforms — it's very difficult for us to answer for other platforms, but our co-founder and CEO Bora very recently wrote an article on the Forbes Tech Council about how security standards in HR tech are shifting. Because policy questions and security checks are just becoming so central. We added, in our latest Teamflect Enterprise plan, a dedicated cloud infrastructure for your specific data, customer-managed encryption keys, and the ability to control where your data actually resides — because we know just how crucial it is to keep all this information safe. Your people data and people strategy is one of the most crucial pieces of information that has to be protected. That is a huge responsibility and we take it very seriously.
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Mindy Honcoop [1:02:01]
Yeah, and that's so important. I think we're often missing those types of questions.
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Emre Ok [1:02:14]
Exactly. We do get lost a little too much in the weeds of "oh, can we do this?" and people stop asking the right security questions. Now I can see in the chat that Jay responded to the Confluence question, so we're good there. If there aren't any more questions, I'll move us along to the end. Oh, I see I'm still getting calls even while hosting a webinar — wonderful. But hey — thank you all so much for attending. You can contact Jay Sharris and Mindy Honcoop by scanning the QR codes and hitting them up on LinkedIn. If you think of the perfect question after the webinar ends, you can reach them right there. This has been a great ride — thank you all so much for sticking around till the very end.
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Mindy Honcoop [1:03:39]
I know — we still have 37 people at the 1-hour mark!
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Emre Ok [1:03:47]
Amazing. Go enjoy your lunch — or early lunch, late breakfast, depending on where in the world you are.
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Mindy Honcoop [1:03:49]
Exactly — depending on where in the world you are!
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Emre Ok [1:03:57]
Well, thank you all so much for joining us. This has been our webinar on HR's role in leading the AI rollout. Mindy, you've been wonderful. Jay, pleasure as always. Casey, thank you so much for holding everything together in the chat and on the technical side. I've been Emre. We have so many more amazing webinars coming up — stay tuned for updates in the coming weeks. Thank you all so much for coming. Bye everyone — have a good one!
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Mindy Honcoop [1:04:30]
Such a joy. Bye!
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[End of transcript]
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