Name: Human Skills in the Age of AI: Critical Thinking, Judgment, and Decision Quality
Uploaded: 2026-04-02T16:31:39.136Z
Duration: 55 min 35 s
Description: Human Skills in the Age of AI: Critical Thinking, Judgment, and Decision Quality

Transcript for "Human Skills in the Age of AI: Critical Thinking, Judgment, and Decision Quality": Hello, and welcome, everyone. My name is Jenny-Kate, and I'm on Cornerstone's field marketing team. Thank you for being here with us today and joining our topic that is very top of mind for many. Today, we'll be talking about the human skills in the age of AI, critical thinking, judgment, and decision quality. And this topic is our third session in our learning break room series. To see more from this series, you can click the Learn More button at the top of your screen, and there's also a link in the Docs section. But the learning breakroom series is meant to be a break from your day for you to recharge. You can grab a cup of coffee or a tea or a snack and join us for conversations around topics like AI, modern content strategies, and tips for measuring return on investment. When you join this series, you'll be in good company with peers. So we encourage you all to engage in the chat, to hear from each other and gain some fresh perspectives. And hopefully, you walk away with some ready to use tactics that can make your L and D programs smarter, more engaging, and impactful. As I mentioned, today's topic will be human skills in the age of AI. And before we get to the good stuff, I'm gonna just take you through a little bit of housekeeping. So there's various widgets that we use in the Goldcast platform. You can resize them and move them around to make the most out of your experience today. Some networks can cause slides to advance a little bit more slowly, so it is recommended to log off of a VPN. If your slides are lagging a little bit, you can press f five on your keyboard to refresh the page. We'll have some dedicated time at the end for Q and A, so please put your questions into that Q and A window. And we'll also be asking a couple questions to the audience throughout today's session, and we'll encourage you to respond to those questions in our chat. And with that, you'll receive the recording from this webinar in the next couple of business days. And we have a lot to get to today, so I'm going to go ahead and hand it over to Beth to kick things off. Thanks, Jenny-Kate. Welcome, everyone, and hope you've had a great week. Hard to believe that it is the April. And we are back with our series. This is the third one that Natalie and I have participated in and hope you all have enjoyed it. Glad you're back today. And looking forward to a really great conversation. Pamela Conway from one of our partners, Intellezy, is going to join us. She'll join here in a few minutes. But just by way of introduction, again, my name is Beth Noble. I lead the Cornerstone content business, and I'm based out of Seattle where it actually looks like it might be a sunny day today, so different than maybe a few of you are experiencing. So, Natalie, do you wanna intro? Sure. I'm Natalie Augustine. I'm part of the content team here at Cornerstone. I was a practitioner prior to joining Cornerstone about seven years ago. So spent thirty years on Wall Street running large training and development organizations. So I come at this with a perspective of knowing your pain and sitting in your seat. So, here to help, and, and I'm gonna help answer questions and and text throughout. So keep them, coming in. the q and a. Thanks, Natalie. So the agenda for today, it the this is a little bit different format for those of you that participated in any of our other webinars so far this year, and that, really, we're gonna be focusing the time on a conversation with Pam. But to get us started, I wanted to just sort of level set in this discussion of, around what are the skills that are really needed right now. So to get everybody a little bit engaged and and leaning in, I wanted to propose this question, and you can add any of your thoughts or comments in the chat. Natalie gonna, keep an eye on those. But when you think about what skills or roles do you think will become or are becoming more valuable as AI continues to handle more execution work, what are those roles? Like, what are the the ones that maybe aren't as as obvious that, again, as AI is taking on more of the tasks that are, you know, kind of the repetitive work, the maybe data analysis, etcetera, what are the roles that are really gonna become more important? And I already saw Robbie's response of critical thinking. I think you saw ahead to some of our some of our slides as well. So while you all are, again, responding to that question, and and I think you're probably likely to highlight some of the things that I might mention here as well. Again, we always look to other external data sources that, many of you are likely looking at. So whether that's, data from a a Gartner or Forrester, etcetera, or World Economic Forum is one that we often look at just because of our organization's participation in that event. But, again, they continue to highlight as you look at the core skills. Everyone's talking, obviously, about the importance of the technology aspect, right, the integration of AI. I know Natalie, she's gonna talk about it here in a second. She does participate in a SHRM event talking about this intersection of artificial intelligence and human intelligence. And one of the highlights there was this you know, what are the skills that are really needed today? Who what percentage of people actually feel that they have been thoroughly trained on the use of AI? And it's a very low percentage, less than 10%. And I think, obviously, we're gonna continue to see that grow. But it's also looking at the balance of what are the other skills that are now so important and even perhaps more important, as we are looking at having this now sort of, again, a coworker that is an agent, and how do we then change the way that we work and the skills that we need to have in order to best partner with them and be able to leverage that resource. One of the other comments that Natalie had highlighted from her time at the SHRM conference was this idea of cointelligence. And so that was really, again, an interesting way to look at, again, that partnership with AI, as well as the question that comes up around trust. How do we trust the results that are coming from AI? And maybe are we scrutinizing it in the same way that we would have, a a colleague or a coworker? And, Natalie, I don't know if there's any good comments coming from the chat that you wanted to share or any feedback from your time at that SHRM event. Yeah. It's interesting because a lot of the things that people were sharing, problem framing, critical thinking, all called pulls in pulls into that idea of, what the culture is in the organization and what how people are are looking at it. What's interesting is, what one of the things I heard was that 90% of the organizations are still transitioning to a mature AI model. We know that. Right? So we're all on different paths with this with this kind of effort. I think one of the things is is what does it look like and what does it mean? And we're all in this you know, last year, we all felt like, yep, we should be doing AI. Now I. feel like there's pressure from management saying, hey. Why isn't this working yet? Why aren't we there yet? And because of the efforts around culture and different roles and different responsibilities, that people are having, it's it's not as easy as, you know, just making this switch to happen. So I think and. I think that's what was really came through in the in the discussions that we had at the at the SHRM AIHI conference. That's great. I also I like the comment that, Monroe made about cultivating relationships. Right? Mhmm. Can't really cultivate many relationships maybe with AI. So I think. reinforcing the importance of that with, Yeah. our human colleagues is even more important. And and one of? the things too that really stood. out, Beth, it was this idea that a lot of entry level roles are changing or going away. And so the question becomes is how do we get those mid level roles with the experience level? Right? So as you used to kind of travel up the the career path, for different roles in an organization, you came and you built that experience level, that judgment level, that critical thinking level, problem solving level, all those things. But now with a lot of those tasks being done by AI, how are we going to build that middle level now. with the same experience base? Right. Exactly. These are all, I think, challenges that organizations. are are tackling right now, right, of trying to really unpack and and how they proceed going forward. Again, I mentioned that Pam from Intellezy is joining us today. They they have also been acknowledging some of these same data points that we've just been talking about. They released an ebook that highlights the top 10 red flags, your AI adoption will fail. And one of those is really talking about a lot of the comments in the chat that you all have shared, you know, ignoring the change that this brings to soft skills and the human judgment that needs to be brought into the equation. And we'll share that ebook, as one of the takeaways from, this this webinar today. So with that, I wanna pivot, and invite Pam to the conversation, as well. And while she is sharing her introduction and we get the conversation started, I did wanna add just another question into the chat, for folks to think about, what are some of the the risks or the challenges that your organization faces with AI involvement and decision making, and maybe how is that being acknowledged so far? Because, again, that that's a lot of what I think we're gonna end up talking about today. So while you all, again, think about that and add your comments there into the chat, Pam, love it if you could start off and and just provide an introduction to your role, not only in Intellezy, but your background in the in the space. Yeah, thank you Beth and thanks for having me on the webinar today. Really appreciate it. So I am entering my thirty third year in corporate learning and development. I was very fortunate when I came out of college. I wasn't exactly sure what I wanted to do with with my liberal arts degree, but I had an opportunity to start writing software training manuals. And it turned out I had a knack for software and a knack for education and a mentor who was willing to groom me, who I owe a lot to. So shout out to Peg Grimes for for spotting, hopefully, talent in me and allowing me to have this really fruitful and fun career. And I I think I'm getting your Seattle weather. I'm up here North of Boston, Boston, and it is 34 with a cold, depressing rain right now. Oh, goodness. Well and, I love actually what you mentioned there about, mentors. Right? And and maybe, again, that's something that comes up in our conversation today is how do mentors who played such a critical role probably for everyone that's on this webinar going forward in the world of AI, how does that piece change as well? But we'll get to that question later. Maybe to get started, and we'll go again, stop sharing there is, you know, what do you see just, Pam, to get us sort of started as the the biggest gaps as you're talking with organizations and and and thinking about decision quality and that critical thinking aspect that so many people commented on? How do you see that affecting organizations today? Yeah. It's really an interesting challenge we face, because the gap really isn't about intelligence within our organizations. Right? We have a lot of smart people that, that we work with. It's really more about how organizations are designed right now. They're not really designed in the best way to get the best output from AI. Mhmm. So, specifically, what I mean about that is organizations are really optimized for speed and productivity. Right? Speed. and output. Speed isn't necessarily always a friend of quality decision making. Yeah. Right? It requires pausing, questioning, thinking, and we're not really wired for that. And that comes down to the way a lot of corporate cultures reward staff, right, or reward business units. We're rewarded for the speed at which we can do things. Right? Mhmm. And where and the individuals who oftentimes get, rewarded in meetings are the ones that can speak with great confidence. Right? The ones who question more, who pause, who are the devil's advocates, they're not the ones typically who are rewarded for that behavior. Mhmm. So, you know, some of the ways in which we have our organization structured are are are not structured in a way that help us with AI. And that you know, really what we're boiling down to here is critical thinking. Right? And the issue with critical thinking is we assume critical thinking exists within the organization. We assume it exists within ourselves, but critical thinking isn't really taught anymore the way it used to be. And this preceded. AI. So it's not this is not AI's fault. Really, you know, we think about our big focus and our emphasis. Right? Skills, skills, skills, skill building. I remember when I, decided to go to college, I I, I really wanted to study English literature. So I have a a liberal arts background. My father was very clear. He thought that was a terrible idea, that I should go into some sort of profession that was a noun. Right? So I I humored him by saying, well, dad, I'm gonna use that to to become a lawyer. He said, okay. Fine. Go forth and do your liberal arts degree. Never went to law school. But, anyway, I I did get to do my liberal arts background. But what that liberal arts background taught me, what I learned was how to learn, how to debate. I learned rhetoric. I learned logic. These are things that used to be part of our educational system, and they're not anymore. But. we still assume that we have gotten the critical thinking from it, and we kind of haven't. So, you know, lack of decision feedback loops are an issue. We don't take the time often to reflect after we've made a decision. Was it good? Was it bad? What could we do different? Mhmm. So, really, in this age, answers are so easy. Right? I mean, AI helps us get answers. fast, but the understanding is harder. Yep. I I you had mentioned that in one of our prior conversations, and I think that's such a salient point. And it reminds me a lot, honestly, of how I feel like we talk to our kids is sort of really trying to push them more to be thinking about the critical thinking and ask those questions of why, you know, the application, the understanding, as you said. So thinking about those aspects, like, why is that even more important now in the midst of AI that we are developing and intentionally leaning in on these skills around critical thinking, decision making, and and others that you mentioned. Yeah. I think with AI comes a big paradox. Right? We have more answers, but in many cases, less understanding. Mhmm. And AI you know, the outputs are are plausible. Right? I mean, they usually they sound fantastic, but it's not guaranteed that they're true or accurate. So the plausibility sometimes can hide a deception or or, you know, an incomplete answer, if you will. So critical thinking is really about, in the age of AI, not so much producing an answer, which is what critical thinking in the past has done, help us to come up with the answer. Now critical thinking is about how do we evaluate an answer. Mhmm. So it's using our critical thinking skills to interrogate the outputs that we get from AI. And some of you on the webinar may have heard of the Dunning Kruger effect, which I think AI is highlighting a lot right now. So if you're not familiar with that effect, basically, it's an individual who has low knowledge of a subject overestimates their competency within that subject area, whereas somebody usually with high knowledge in a subject area underestimates their competence. because they know how much is there to actually understand. And that is we're seeing more of the Dunning Kruger effect spreading because AI can make us think we're highly competent in areas where we actually have pretty low knowledge. I think the other thing is human accountability. Right? AI can't own its consequences. And what I have, actually heard it twice this week. I heard someone say, well, the model said, as if somehow or another, you know, the the AI was accountable for the misinformation that that they brought forward. No. It's still on us. We have to apply the context, the trade offs, the ethics, Mhmm. all of that is still we're accountable for that, which means we have to pause and consider that. I think the shift in the way that we work, now it's, certainly, we're solving problems. But when we use AI to help solve those problems, what becomes critical? And I think I actually saw someone say this in the answer to one of your earlier questions, Mhmm. was now we have to get better at how we frame our problems. We have to get better at identifying what is the. actual problem we are trying to solve. Because if we put the wrong problem into AI, it's gonna give us great answers back. And then if we act on those answers, we'll potentially be solving a problem that doesn't exist, and our actual problem is over there getting worse. Right. So I think that's a big part of the of the sort. of, like, knowing the right question to ask? Yep. Mhmm. Exactly. Mhmm. And that. really comes long before you sit down in front of your AI tool of choice and start prompting it. Mhmm. It's about critically looking at the work environment, talking to somebody else talked about about making relationships. Right? Cross collaboration within your organization to figure out what exactly is the wider problem. All of that has to happen before you go to AI. Mhmm. And, again, that's where that speed issue comes into play. We think we know the problem. We're gonna immediately go try to get an answer, and then we act on that answer without really pausing to make sure, is it the right problem? Have we completely expressed the problem, Yeah. which is another point. Sometimes we only express part of the problem, and then we only get part of the answer. Mhmm. Right. All of those things are, I think, are critically important. I think the other thing that's important is with AI, context matters. Right? You get better outputs from your AI prompts if you've given it enough context. Mhmm. AI lacks organizational nuance, so it is up to us to understand the nuance and add that as a contextual layer. If we're not doing that, again, we're not going to get the best answer because that those contextual matters weren't taken into. account by the AI. So I I I think those are those are some of the big things that I say. I mean, I could I could keep going on ad nauseam about it. But those are those are some of the high points, I think. Right. Well and even to that last point again, the, yeah, context. Like and when we think about, I mentioned the data Natalie had shared with me after the STREAM conference, which was I think it was, like, seven and a half percent of people felt like they had been thoroughly trained on the use of AI. Like, getting into some nuances of, like, how do you add in that context? How do you and, again, it's about asking the right question. And then how do you question it? You know? Again, that sort of idea of do we actually just, trust the answer that's coming back? But I think we're gonna get into that a little bit more, further in our conversation. Actually, before you ask your next question, one thing that I was thinking about with this is that it's this weird thing that is coming up now called automation complacency, where there are studies that are now showing that people are more likely to accept incorrect automated advice than correct human. advice in some contexts. I that's why, again, mhmm. like that critical thinking part becomes so much more important Just because an answer sounds good and. it came from a technology we trust Mhmm. doesn't mean it's correct. Right. Yes. So, again, we've identified a lot of the pitfalls or sort of challenges, and there are obviously benefits that AI brings to decision making. So I just wonder what what are some of the things that, you know, again, we balance the the good with the bad, right, or the good with the challenges that that we face. So what are some things you would highlight that are positives around how we can, you know, have better decision making as an organization with the use and partnership with AI? Yeah. Great question. And I'm glad you asked that now because while some of the answers I have may sound like I'm critical about AI, that is not true at all. I'm actually a huge AI champion. I think, the benefits, that it can bring us are enormous, if we're using it correctly. And so that's really what we're talking about. here. Right. So I think some of the huge things, some of the reasons to embrace this and what it improves in terms of our decision making specifically are, we really have more expansive insight into our evidence. Meaning, we know AI is brilliant at working with enormous datasets, and it can recognize patterns almost instantaneously. So our time to insight is massively compressed now. We can you know, what would have taken, in some cases, months or in the case of, you know, examining medical data can take years and years to find the patterns. We can now do it in minutes or days. So, I mean, that in and of itself is, is worth the price of admission. Right. Certainly can help speed our real time decisions. And in a world where I feel like we're now we're almost in, like, continuous decision making mode, Really helpful because it can help again, with this compressed insight time, it can generate information for us quicker to help inform our decisions. And. I think that's a key thing to say. Inform our decisions, not make our decisions. And I also think what's brilliant about it is it kind of democratizes expertise. Mhmm. So when we talk about cross collaboration now, I use myself as an example. I'm not a developer. I have, over the years, had to work with development teams. Usually, I feel like I am, running a marathon with a bunch of sprinters, and, you know, they're way ahead of me, and I'm going frantically to keep up. But with AI now, I'm able to, you know, to, ask questions on my ad literally while I'm in meetings to help me. understand better. Yep. That knowledge of what they're talking about allows me to bring bring my expertise into the development cycle in a way I couldn't before, Yeah. which makes the outputs of our development team even stronger. Mhmm. So I think that democratization of expertise is such a huge a huge area of improvement for us with AI. Yeah. I've I've noticed in, meetings I've been even in this past week where the patterns have really changed in the way that we all we don't just sort of launch into a meeting. We first of all sort of level set and using AI as a way to, hey. Has everybody sort of read the summary in advance so that we are all starting with the same sort of knowledge or understanding? And it's all again, the speed of being able to produce those types of summaries that you can spend three minutes at the beginning of a meeting makes it much more effective to when we are having the interpersonal type of discussion. So, again, patterns are really changing all over the organization. So I wonder from an L and D perspective, knowing that, again, we're role and I imagine the folks that are part of this call are in a role where we're really here to help enable our organizations to use these tools in a way and think about decision making differently. How can how can we, collectively as a community, be helping to embed this in our organization and get the right level of sort of critical thinking and decision making and and utilizing AI, but with the right of balance? How can we go about supporting that? Yeah. I love this question. And honestly, I could spend an entire hour talking just about this, because now we're really sort of getting tactical about what can we do to help improve our thinking skills. So, I'm I'm sure a lot of, folks on the webinar are familiar with the term of metacognition. It's been hot in in L and D for a while, and it's, it should be smoking on fire right now with AI. So with metacognition, we're shifting from really helping people understand what to know to shifting how to think and. to think about their thinking. So what that means is really trying to make our thinking both visible and teachable. So what do. I mean by that? Well, think back to, when you were in, you know, grammar school, middle school, high school, and your mathematics teacher would tell you to show your work. Right? Maybe you could come up with the answer. Right? The answer say the answer is 12. But. the you would have to show your work, prove how you arrived at twelve. And that's what we should be doing now in the way that we're thinking with AI. Right? So if we have come up with a decision that was made from AI input, Mhmm. we need to be able to show our reasoning. Why is this the correct answer? What is the reasoning behind. this? We can build that kind of training into the into the the learning modules, the learning events that we craft. Mhmm. So really what we should be doing is I mean, obviously, there's things that we do now. Right? Case based learning, simulations, real scenarios. All of those are critically important. But I invite all of us to look back, like, literally thousands of years at the ways in which humans have developed critical thinking over time. Right? So we can begin to employ things like the Socratic method into what we. do, where we start to teach people to ask the questions, what do you mean? How do you know? What follows from that? We can look back to the medieval period with the rise of rhetoric around examining opposing viewpoints. There's this idea now of creating things called red teams, sort of like a group of a devil's advocate. So when you're trying to solve a problem, you have a group of people that you assigned the responsibility to them to pull holes in everything everybody else is saying. Idea. That's a? great idea. Yes. So we're. forcing that. Yeah. Oh, I love that. What in our family, one of the questions we often ask is somebody will just ask a general question. So what do you think? Like, instead of just trying to get everybody's sort of looking for quick answers, you know, a lot of these things, again, we know ourselves, or we just need to, again, use use your own brainpower critical thinking to be able to come up to the to the answer. So I love what you're going through there. Yeah. Exactly. And, know, I mean, there's there's obviously, there's there's way more than just those two instances. I mean, think another really important one to pull forward is that the renaissance brought us the rise of humanism, Mhmm. which with that, we're now questioning authority. And that's what we really wanna do with AI. We wanna question what it's returning to us. So, you know, all of these things that that are are foundational knowledge, human knowledge, I think we can employ tools from these disciplines into the training that we're building now. And. I think it'll help a lot. The a lot of the you were talking about getting different methodologies, kind of the way to think about it. And I wonder, there certain sort of modalities then that we can be leaning in on as a best practice, sort of a way for people to practice this in in the workplace or as part of our organizations? I mean, I would think it's something sort of hands on. Yeah. Like I mentioned before, the case based learning simulations, but, Yeah. you know, we're using a lot of that now. That, I would encourage that to be part of everything. Mhmm. Scenario based decision practices. And and then what what I also wanna say is that I sometimes you know, I see a lot of simulations, scenario based learning, where there are scenarios, they are decision based. But in a lot of cases, the answer is really obvious. We need to make our simulations and our practices harder, Yeah. where the answers. are more obscure, Mhmm. where they have to Yeah. think more critically about the information they've been given, but also the information they haven't been given. Right? So that then they have to think more deeply, maybe even go out, do research before they can come back and answer that question. Mhmm. So I think we can up the ante in the tools we're already using, making them harder. I also think it's Cornerstone, like us, we build a lot of asynchronous learning that is core and fundamental and critical. I want to invite people to use that to allow people to gain critical knowledge, then gather people in peer groups to do practice case study work around it so that you get the social learning and you get the peer discussion where you have the opportunity to express exposing viewpoints. Come. to understand what does a blind spot look like when you when you encounter. it. How do we overcome that blind spot? And that doesn't happen when you're learning in isolation. That. isolated learning is critical. It helps us gain foundational knowledge and core skills, but then we need that extra level where we come together as groups to do a lot of this practice work. Then the other thing that I think is super critical is promoting and fostering reflection practices. It should become standard process that you have decision reviews and that you not only have postmortems but premortems as well so that you are having this reflective period and everybody knows, what how how to engage in those reflective times. That it's not just the rah rah, hey. Look at us. We're great. Right? I mean, of course, we love those moments. But, really, these are about figuring out what could we do better next time. Mhmm. And I think that's that's really, really important. Right. I can't remember the last time that we actually took a pause to look back and say, let's do that, as you said, the the pre or postmortem, on a decision. And especially when you think about all of the, you know, effort involvement across an organization to, you know, make some of these critical decisions. The fact we don't do that maybe enough, is really highlighting a great opportunity. One other thing you mentioned too when I'm talking about sort of the modalities. Within Cornerstone OnDemand, you know, we also obviously go through internal, development and recently did a role play exercise. It was an AI generated role play exercise that we have within Galaxy, and it was hard. I mean, I I think I went to my team and I said, hey. I I didn't pass. Like, it was a dialogue, and you're sort of supposed to get past a certain gate, and I'd gone through the training. But it stuck with me and as something of, like, you know, some of those you feel like you kinda coast through a little bit, and this was one that it stuck with me because it was hard. It was challenging. It made me think. It, it you know, I couldn't sort of breeze through, and I had to go back and did some other, you know, review and went back to try again. It still was challenging. But, again, I think you're highlighting something that's really important. We do need them to be more challenging. Like, that that is what's going to make all of us, again, improve our skills and do better at these things. The the new areas that we're we're highlighting are so important. As educators, we all know that we learn more from our failures than our successes. And when you have to struggle through something, it creates new neural networks in your brain. I mean, it literally rewires your brain. The problem is that, of course, when we get it right, that feels so good. Right? And, Dopamine mean, that's that of. course, what yeah. Exactly. Exactly. So, I mean, obviously, we wanna still have some dopamine hits in there, but we don't want everybody OD ing on that. We want people to struggle. I mean, when you learning should be should have some amount of struggle in it because then when you overcome those hurdles, you're smarter and wiser for the next problem that you that you approach. Right. And and I I think that's just critically important. And that also means that we need to train people to be more comfortable with failure, and that failure isn't a negative. Failure is an opportunity to learn. Failure is an opportunity to never do something that way again, to learn that, and that's hugely important. And that's a cultural shift in a lot of companies, right, to give people permission to take risks, to explore things, to get things wrong, but then do that postmortem to make sure we get it right next time. Yep. You mentioned culture, and I think, again, that's been highlighted. I we heard it at the SHRM conference. We've seen it from World Economic Forum. Like, all of those, the elements of how we as an organization are supporting and creating a safe place, as you said, for people to fail. It's not part of our culture today as a society. Right? I mean, go into our we can have a whole discussion, I'm sure, about our education institution and why my kids are getting straight a's, and they shouldn't be. But but, again, same thing. Right? You learn the most from when you do fail and whatever. that may look like. We talked about judgment and, again, from a culture perspective, ingraining what is good judgment. And I just wonder, as we look at before we incorporating an AI, what does good judgment look like? Like, how would you define that for an organization so that as they're looking at how we develop those skills, what are we aspiring for? Yeah. I think one aspect of good judgment is healthy skepticism. And I think healthy skepticism isn't always rewarded in companies. The naysayer, the hole poker, the devil's advocate, whatever you want call them. I think we've we've all, you know, we all know someone like that. In my world, it's a gentleman by the name of Joe, and I hear people sometimes always say, oh, god of Joe. Here he goes again. And I have I'm here to tell you, every day, all day long, I want Joe on my team. Yeah. Because someone like a Joe is gonna help make sure we are not overlooking an obvious blind spot, that we are pressure testing everything. Mhmm. And I think those individuals deserve more credit than I think we oftentimes give them in our organizations, and that we should look for people like the Joes who can help us see around corners that we're not peeking around. Yeah. I also think that good judgment looks like people asking questions such as, does this make sense? What assumptions might we be missing? Or what assumptions exist? What might be missing from a contextual perspective here? So when we that's a signal to me when I hear those questions that people are exercising good judgment. Mhmm. I think when people when in meetings or or even in ourselves, we have that sense to ask people to slow down. Wait a minute. Let's take two steps back. When I hear that, that to me is a sign of good judgment. Mhmm. In myself, I always I I jokingly refer to it as spidey sense if you're you know, those of you. who aren't familiar Spider Man. Right? Like, so that's just that little hair on the back of your neck like, oh, I don't know. Something about this isn't quite right. And. you slow yourself down. So when you can see signals like that in individuals or in teams, Mhmm. that's hugely important. And that goes directly then to maintaining the human ownership and accountability for the outputs. So. when you hear people say things like, oh, well, the model the model is Yeah. the model gave me that answer. Okay. That's not acceptable to me. Like and that shouldn't be acceptable to our group. That should be a signal of bad judgment. Mhmm. So when someone says things like, this is the this is the answer the model gave me. This is the reasoning behind it. And by the way, you don't if you aren't doing this or don't know, you can ask your model how did it come to that answer. You. can ask show you its reasoning. Mhmm. So you can bring the reasoning of your AI output to the team as well. So when I see things like that, again, those are signals to me of good judgment. Because at. the end of the day, what we want to marry together are the strengths of AI with our human. insights. And two things together are force multipliers. and I think, what that leads into is really that aspect of for, you know, a culture in an organization, how do we redefine and look at trust? And so it's sort of, again, in the process of good decision making, what is the how do we sort of establish trust with AI, trust with, you know, each other, with colleagues? And I think it probably weighs in a lot of the themes that you've brought up. But maybe just to lean in on that topic a little bit of of of looking at trust within a certain organization. Yeah. I mean, with AI in humans, we each have our strengths and our limitations. And I think trust starts with acknowledging and recognizing what each has in terms of a strength and a limitation. And then I think what we arrive at is something like a calibrated trust. Right? So we're not trying to get to blind trust, nor do we want to live in a world of full skepticism. We wanna get to this calibrated level of trust, Mhmm. and that comes at recognizing the the strengths. You know, AI is great at scale and at recognizing patterns and at doing it quickly. But we also need to understand that data is only as good as what what is inputted. Right? So it's the old bromide of garbage in, garbage out. Yeah. So we need to recognize that the the strength of AI is only recognized and fulfilled when we get good at giving it the right inputs. Mhmm. And that as humans, we have the ability to bring context to the AI that it can't know. Right? It's Mhmm. it it is not going to it's not gonna make assumptions on things like that. We have to give it the context. But it's also equally important to know that as humans, sometimes context is complicated for us as well, and we can sometimes have biases, I is. why human strength comes from group collaboration. That's that's really one of the core strengths of humans is we can leverage the brainpower of those around us to to help, improve context, limit bias. Mhmm. And, I think when we're doing that, when we're working collaboratively, when we're recognizing what AI is good at and what it's not good at, we can create what's called a productive tension between the sources. Mhmm. And when we arrive there, I think we're in a better place of being able to, trust AI, trust the use of AI in the work that we do. And also, I think part of that trust is increasing transparency. So when I have brought information forward to a team and, you know, it looks and it sounds brilliant, I'm not gonna lie. There's a piece in the back of my mind that's like, would love to present this as fully and wholly my own work. But. I'm very clear that I collaborated with AI to create this output. Mhmm. And, so I'm very transparent about when and where I'm using it and how I've used it, and I think that's important. That definitely, helps Yeah. create trust. And I also think normalizing validation of what could be wrong. Right? So normalizing the asking of that question is huge. And I think when. we do that, can arrive at a good place where we all feel comfortable using AI and what comes out of it. Absolutely. I I see in the chat a number of things in there, which is great to see the activity. But Monica mentioned that word, again, healthy skepticism and highlighting that's a great expression to kinda have as a takeaway today. You know, just from a wrap up perspective, I we could talk all day, I think, about this. And before we pivot into, any questions that those who have joined us might have, feel free to add those into the q and a section, and Natalie Augustin can, review those. But just maybe as, like, some sort of closing thoughts of what are some takeaways that everybody who's joined us today can really, you know, focus on to bring the light within their own organization. I know just for myself, one thing is just really your encouragement of make sure you've got the centers. Have those people that are gonna challenge the question and welcome it. So from a cultural perspective, whether it is the culture of a team or within the, you know, division that you're in or the organization as a whole, wherever you can impact that, really, you, have hit home on that idea of how important it is to have that sense of, again, critical thinking that from a human perspective. Right? How can we really challenge these ideas and not just take them as fact? So with that, what what are some other key takeaways you'd wanna share with everyone before we switch to the q and a? Yeah, I think one of them is that we should treat decision quality as a core capability. So making that a core capability that we want to highlight in our teams and grow within our teams is the ability to know how to not only make good decisions, but recognize good decisions. I think that is really, really important. I also think that it's important to make sure people understand that AI is tool is a tool and not the expert. Right? It's. up to us as humans to still apply that in the role of the expert. So I think it's easy to think of AI as an expert, and it's not. It's merely one more tool in our toolkit. It's a powerful tool, Mhmm. but it that's all it is. And I think, by maintaining it it's difficult because it can sound like a person talking to you and working with you. Right. And I think we wanna do everything we can to, shift people's mindset away from that because we're more apt to believe it blindly if we anthropomorphize it and make it like a person. But if we continue to talk about it as a tool, we're less likely to blindly trust it. So I think from a cultural perspective and the way we talk about it, it's super important, Mhmm. in that regard. And then I also think, I also think what's been really helpful is being able to highlight for the teams real examples where AI was wrong or incomplete. We do a great job highlighting all the places where it's been a huge success and has worked wonderfully. But I don't think we do as great of a job of highlighting where it might have not worked as well. And that's critically. important because we want people to recognize that. So how are they gonna recognize it unless we highlight it or or, you know, shift that up into everybody's line of sight so they can see it? So those are a couple of things that I can think of immediately off of the top of my head. Yeah. That's great. Again, such a dynamic conversation. I appreciate you joining today, and sharing some of your experience and what you're seeing in the space. I don't I haven't been able to monitor the chat. Wasn't sure if there was any, comments, Natalie, that you saw in there you wanted to highlight, or anything from think it's coming today. a. Yeah. A lot of good comments and and, you know, agreeing with a lot of what Pam Pamela was saying. So one of the things, when we think about critical thinking, we talk about, Diane mentioned the red hat, six thinking hats, and and love that correlation between that kind of, you know, methodology, and it's a good one to think about when you're when you're training. I think, a lot of people agreeing with the same things, around really like the healthy skepticism comment. I mean, that was a really a good one. And then I love this one. Diane said because the model said so. You know, just. because it said it doesn't mean it's true, Mhmm. which we all have been there before. So, in terms of questions, we didn't have any, you know, totally on there, but lots of great comments. I think one of the things that I heard as as I was listening in is when we think about AI, we're moving past AI today. Right? We're moving into agentic AI. And so. what is that new complexity going to bring to organizations, which I think is something that organizations who haven't gotten their hands around AI as AI yet, and now we're thinking about agents actually running, potentially functions within organizations or managing agents just like you would team members and and what some of those challenges are. And that's, I think, what's coming. So, we. do have And that one thing about that is that certainly is that I think that highlights another skill that as L and D professionals, we should have our eye on, which is, understanding, how to evaluate and build workflows. So because that's really what AI is what agents are doing is they're automating a workflow. Mhmm. Well, if your workflow is not optimized, if it's not a good workflow and you apply an agent to it, guess what? All you've done is is automated a terrible process. So that's really you know, that's not. gonna get you where you wanna go. So it's it's the same thing as trying to use AI to to output and help you make decisions. If you want to help it improve your workflows, that is up to a human first to. map out what the workflow should be, find the optimal path, figure out what inefficiencies already exist in the human process that you have, Mhmm. iron those out before you attempt to automate it. And. what I see so many people doing is rushing immediately to automation, Mhmm. and they're simply automating things that are already in inefficient. Yeah. Yep. Absolutely. We do have a good question from Mark. Have you seen successful enterprise wide approaches to some of the applied learning and training in practice? Any ideas on how to mobilize case studies, simulations, workshops? Yeah, that's a great question. And I would love to say that I'm seeing it all over the place, and I really haven't yet. What I've seen more is more like glimmers of it. Right? So good examples of using the Socratic method in some elearning modules. I've seen a couple of that where they're asking questions and enforcing people to have these pause points in their learning. What I've not yet seen is is any organization and and there probably is someone doing it. I just haven't seen it yet. Somebody really taking a step back and reflecting on how to seed critical thinking across their entire learning eco ecosystem. Mhmm. That is certainly something that I am noodling around with and and, thinking about what that could look like and doing some writing on that and working with a couple of mentors that I had from my master's program who are also doing some thinking in that space. I think we're gonna see a lot of exploration over the next couple of years with that. And then, yeah, I'm really curious. I'm gonna be at ATD this year, and I'm I'm most interested to see if we're gonna see any case studies up there of someone really attempting to do this at scale. But I'd love you to to stay in contact with me because if you if you see somebody that's doing it great, I wanna know because I wanna glimpse at it. One, and the things that if if I can, Beth, one the. things we that we took I took away from when we when I was at SHRM is they talked about kind of four different categories of kind of AI adoption. You've got AI resistors, AI followers, AI forward, AI first, and AI natives. And so those AI natives and AI first organizations so for example, Salesforce would probably be sitting in that AI first. They start with AI as a practice first. We're probably the bulk of people in in on this call and in organizations are sitting in that AI followers. Right? They're waiting to prove those best practices, Mhmm. and they're typically focused on on those common kind of, task type of work looking at AI, whereas those first and natives are looking at AI to do the work versus. support the work. So it's interesting when you start to look and and see how AI is starting to come into your organization. I bet you it's being it it's actually being done pretty extensively in certain small parts of your organization. So that enterprise wide, gets challenging. But look around the organizations you're working you're working in because you're gonna see it, and some organizations are gonna be using it much more than others. Yeah. And I think of the tricky things that widespread AI adoption is, the tools themselves are they're relatively easy to use. Right? I mean, you can put in a pretty basic prompt, and AI is gonna spit you out an answer, and you're gonna feel like, woo hoo. I know how to use this tool. The problem is when you start to think about how am I going to apply AI to the work that I do day in and day out, that is not as immediately apparent to everyone. And one of. the. cool things that, that we've started to do within my organization is, is we will perform, test kitchens where we will have we'll, you know, we'll reach out to all of the business units in the company, and we'll ask the managers to find one or two examples of innovative or creative use of AI. And then we put them all together in a little montage video, and that becomes one of the required training elements that the company the entire company has to go through. a great idea. Mhmm. And. then what they're doing is they're seeing what their peers are doing, Right. and that has generated a lot of buzz and a lot of expanded creativity about where and how people can use AI. Absolutely. That's a great idea, and I imagine this all kinda started with Mark's question. Mark, if we, you know, circle back even in just ninety days, I suspect we'll have even more stories to share. Right? Things are moving so quickly. So certainly look forward to continuing the conversation. Pam, thank you again for joining us today. I really appreciate your insight on all these topics and always, Natalie, for, again, joining and adding to the discussion and experience that you had, particularly at that recent SHRM conference. Just from a wrap up perspective, I wanted to share a few updates from Cornerstone. We had a huge release of Galaxy in March, which incorporate not only for any of you that are on the Cornerstone platform with the single architecture, and also from a content perspective, number of new AI adjacent related content. There's a new series that, AI focused series from the glossary, programs. If you're familiar with those, those are produced in house within Cornerstone, really have very broad adoption. Those are a great one to look at when you're trying to just infuse more ongoing enablement, and those are, again, short microlearnings. So that's all been created. There's some new Microsoft Copilot training, some AI powered role plays. So we're starting to see that going back to, Mark, your question. We're seeing some role plays that are integrated in some of our the courses from our partners. So Biz Library, in particular, had some courses that had role plays, built in as well as a whole host of, you know, new human skills courses that have been released. So encourage any of you that have Cornerstone content to be able to explore those. We will have some resources that we'll follow-up on, as Jenny-Kate mentioned, the link to the Intellezy, ebook that we will provide access to. We also got a a highlight reel that showcases some of the new Grovo series that I was mentioning. So we'll get all that out to you. From an event perspective, earlier this week, we hosted a LinkedIn Live, and I believe you can get the recording to this. And it's really showing the AI powered, you know, new updates that are coming from Cornerstone, again, with the single architecture and how we're infusing AI across the platform. So I believe you all can get access to the recording of that if you were not able to join us. That was just on Tuesday of this week. And then coming up, in May is the Cornerstone Connect. So this is an in person event. It is in New York. It is a broad topic, not just specific to content, but, really all of the innovation that Cornerstone is doing. And so welcome any of you that are either in the area or want to travel to join us for that. It's one that we've been working on for some time and are very excited to showcase what's what, again, is sort of latest and greatest in what's coming in the next in the next year from Cornerstone. And then finally, again, for this webinar series, we do not have the next one, locked down yet. We will be targeting it for the beginning of summer, so late May, early June, just so that we can kind of wrap up the four in the series before we take a little break this summer. So more information to come on that, but you can, again, subscribe to the series so that you will be first in the know, once we do get that scheduled. But, again, we really appreciate, not only Pam and Natalie joining me today, but all of you who attended and hope that you've got some great takeaways, some things that you can take action on within your organization. So again, thank you for joining us, and we look forward to seeing you at our next webinar.