Design Training That Builds Real Business Tools with Rose Benedicks

Most training is built to transfer knowledge. That’s not enough anymore. If we want learning and development to survive budget cuts and earn real influence, we have to build training that produces workplace outputs people actually use. Jay Johnson sits down with talent development leader Rose Benedicks to unpack a practical model: design learning around the deliverable, not the content.

We walk through concrete examples that make this click fast, like onboarding technical facilitators by having them create a facilitator guide as the outcome, or building risk management plans, business continuity plans, performance plans, sales follow-up strategies, and even full marketing campaigns during the learning process. Rose explains why this is harder than traditional training, how it forces you to embed the real way of working into the program, and why evaluating the actual work product is the cleanest measurement you can ask for.

Then we take the same thinking into AI transformation. What does it mean to work with a digital coworker? Where should humans stay in the loop? How do future-built organizations move beyond AI for productivity and into AI that creates business value? We also get specific about AI risk and reliability, including hallucinations, model drift, governance, picking the right model for the task, and how individuals can “train” their AI with source rules, citation checks, and named research protocols.

If you’re serious about training ROI, performance improvement, and using AI without multiplying chaos, this one will give you a clear next step. Subscribe to Talent Forge, share this with your L&D or ops team, and leave a review with the workplace output you want your next training to produce.

About Rose Benedicks: Rose Benedicks works in the people-plus-operations space, most recently as a VP of Ops. Her background in corporate L&D led to her most inspired work improving the top line for all kinds of organizations, from Fortune 50 to startups. She has also taught in UT, Knoxville's graduate ID program and is currently earning an executive education certificate in AI Transformation and Leadership at Chicago Booth. Rose also has a lifelong passion for mental health as it applies to workplace relationships.

Connect with her: https://www.linkedin.com/in/rosebenedicks/ 

Interested in being a guest on The Talent Forge? Contact our producer, Madison Bennett, via email: madison@coeuscreativegroup.com.

Meet the Host
Jay Johnson works with people and organizations to empower teams, grow profits, and elevate leadership. He is a Co-Founder of Behavioral Elements®, a two-time TEDx speaker, and a designated Master Trainer by the Association for Talent Development. With a focus on behavioral intelligence, Jay has delivered transformational workshops to accelerate high-performance teams and cultures in more than 30 countries across four continents. For inquiries, contact jay@behavioralelements.com or connect below!

LinkedIn - https://www.linkedin.com/in/jayjohnsonccg/
Instagram - https://www.instagram.com/jayjohnsonccg/
Speaker Website - https://jayjohnsonspeaks.com

Transcript

Welcome And Meet Rose

Jay Johnson 0:01

Welcome to this episode of the Talent Forge, where together we are shaping workforce behavior. I am excited to bring in a fellow ATD member. For those of you that don't know, the ATD is the Association for Talent Development. And I got to meet Rose at a conference not all that long ago. So I am excited to bring her in here to have some beautiful conversations. Rose and I have a lot of shared knowledge in some different spaces, and I am excited for you to learn from her today. So welcome to the show, Rose. Glad to have you.

Rose Benedicks 0:34

Thank you very much, Jay. I am really excited to have an open conversation with you and see what interesting things we get into.

Jay Johnson 0:40

Yes, definitely. So, Rose, tell me first, how did you get into this space of talent development? Like what was your interest? What brought you in there? And sort of what excites you about it?

Rose Benedicks 0:52

Well, the the easier question is what excites me about it. And honestly, uh, it's twofold. I absolutely love that you just get to learn random stuff. You never know what kind of skills you're going to be developing, what kind of behaviors you're going to be developing. And it leads you down these paths of having to learn fairly quickly all kinds of interesting things from gosh, um, physics lessons and pneumatic lessons

How Rose Became A Trainer

Rose Benedicks 1:17

for Marines driving expeditionary fighting vehicles to uh sales negotiation and consulting skills and relationship building. So you never know where it's gonna go and what you're gonna get to learn. And that is fantastic. As far as how I got into it, I am one of those rare birds who always wanted to do it. When I was a kid, I used to come home from school, redesign the lessons, and teach them to my stuffed animals. So I was always gonna do this.

Jay Johnson 1:44

I love that. And so often we hear on the show, oh, I fell into it accidentally. I did this, and then I got there. So it's really interesting to hear the perspective of wanting to get into this space. And I'm gonna say I do echo one of my favorite pieces is this week I'm working with engineers, next week I'm working with doctors, the week after. So I get to play a little of all of those on TV. That's actually one of the parts that really fascinates me too. So tell me more about this, though. You come home and redesign the lessons. Was it because there was like a sense of like I can do this better? Or was it one of those of like that didn't land for how how did how did your brain quantify wanting to do that? What did that look like?

Rose Benedicks 2:30

Gosh, well, my nine-year-old brain, I think, just was excited about having learned something and was turning it over in my head, going, oh, here's a way it makes sense. Let's let's try this. Here's a way for me to share my knowledge. I was a pretty precocious child, and I um half of my family are professors. So, you know, that that wanting to share knowledge and and, you know, quite frankly, probably talk about it more than I should, uh, was always in there, always driven by example. So I think it was just my way of sort of making sense and sharing, which has always been my MO for learning something. If I can share it with somebody, I'll know it better.

Jay Johnson 3:10

I love that. And it's so true because one of the things that I have found, and it is well documented amongst the research and learning, is when you start teaching something or when you start uh sharing that knowledge with somebody else, whether it's the the drive to bond that sort of like creates that shared connection, or that's the actual, hey, if I have to teach this, I better know it a little bit, whatever that aspect is, we do tend to remember, recall, and also retain more of the information. So for those of you out there that are learning something, go teach somebody immediately afterwards. You are highly likely to actually retain it, be able to recall it, and be able to use it. So, Rose, you have a lot of knowledge and experience that you're able to retain, recall, and use. Talk to us a little bit, and and I want to kind of start. You know, one of the things so long ago, when I it wasn't all that long ago, but so long ago when I got to see you speak at the ATD conference, you had such a great presentation. I thought it was so fascinating, and it was a very novel way to do it. It connected with me immediately with some of my past experiences. Um, do you recall which one I'm talking about? I don't want to put you on the spot here.

Rose Benedicks 4:27

I'm pretty sure it was on workplace outputs as the goal of training.

Jay Johnson 4:33

Yes. Can you speak to that concept? Because I think that the way that you framed that was probably one of my favorite talks in a long time because it was all about outputs, getting people to actually have

Training Designed Around Outputs

Jay Johnson 4:50

a return on investment for the dollars that they're putting into training, which is something that is so important and something I've been a big advocate for. So the way you framed that, the way you shape that, I thought was really powerful. And there should have been a hundred CEOs sitting in that room listening to this and saying, this is where we should be putting our dollars. Talk to us a little bit about that.

Rose Benedicks 5:14

Well, gosh, that's high praise. So let me see if I can talk as eloquently as your compliments. Um let me give an example first, because you know, I can talk about all the benefits and talk about it in these highfalutin terms, but let's let's land the plane to begin with. A really simple example would be uh, let's say you're onboarding technical trainers, facilitators who are going to teach people how to maintain HVAC systems, for example. Onboarding might traditionally look like here are the tools, here are the systems, go explore the equipment, try some troubleshooting. But what if for those uh trainers, onboarding, the output of onboarding is you have created your facilitator guide. Right.

Jay Johnson 6:02

Gosh, I love that. And and the the funny thing is, is when you say it, people are like, huh. Well, that seems like that could be logical. You're actually creating something that's going to be a business value beyond just training somebody, but actually going through the structure and building that. But nobody does this. Help help us understand why why is it that most trainings, most trainings, most talent development does not come into place with creating something like an output? Like, here's the facilitator guide for future use and use cases or future onboarding, et cetera. Why what is it that stops us from getting to that level?

Rose Benedicks 6:46

Well, it's really hard, quite frankly. Uh so you know, when we think about learning and performance, we're often pinpointing a performance issue or a specific need. We people are are X and we need them to be able to do or produce Y. And you know, it's easy to default to knowledge, we all know that. And then it's really, you know, we have this great hype around scenarios and story-based learning. Let's actually have them practice in a safe space. But I think it's a lot harder to say, let's practice in the real space. And by the way, it's not practice, you're actually doing it. So a great example for those who are maybe more executive-minded would be this concept of human in the loop when using AI. So let's say you've got an AI agent that will flag potential fraud for a credit card company. The human in the loop says before they freeze that account, somebody has to review it. Right? So you've got to train all these people in the workplace now to work with AI. So, what does that look like? How do I come up with my plan for reviewing that flag? And if the if instead of training on here's how you prompt the AI, here's how you um make a decision, here's the algorithm for it, here's the decision tree. Instead, you train people on having to make the correct decision. The output is the correct decision. Now, what's cool about this and why it's so hard is because you have to get into the way of doing work, right? So you're not giving people a program on how to interact with AI, you're building interacting with AI into creating that output. So you're flipping it on its end. So if you are in a company where people need to collaborate, like let's say you're creating a high-volume uh patient plan for a um ER or emergency department, if you're creating that or updating that, you've got to talk to a lot of people. You've got to talk to the nurse manager, you've got to talk to the hospital admin, you've got to talk to data analysts. Um, there's lots of steps and whatnot. Instead of training somebody on here's who you talk to, here's what the plan needs to include, you create the plan. And the blended learning includes who do I talk to about this? When do I talk to them? What inputs do I need? And you're actually then imbuing the way of working into getting that output.

Jay Johnson 9:28

I love that phrase, the way of working. And and I think about this because traditional training, traditional talent development. I go to a conference room, I sit there, I listen to somebody tell me what I should do, how I should do it, and why maybe, maybe, maybe I'm gonna say why I should be doing it that way. And I say maybe because we would think that that would be a part of it at this point in time, but it's not always. So we get often the how and the what. We don't always get the why, sometimes we do. And then we leave there, and then the expectation is just that we can just, you know, monkey see monkey do. I just learned it, so now I can go do it and actually the way of working, I think, is such the novel concept. And for those of you listening in, I really want you to think about this because the outputs that come from these trainings are often the return on investment for spending money on trainings. And I've seen way too many LD departments getting cut, getting slashed, people losing positions because, well, things are uncertain, economies are uncertain. And one of the first things to go is talent development. And that's really sad. If you want to elevate your status as a strategic business partner and not a luxury item inside of your organization, you've got to figure out how to impact the way of working and the outputs that are coming, that is going to be I put $1,000 in and I save myself $1,500 later. If we're not thinking like that, more than likely your talent development, your change management programs are going to get cut. Is that a fair statement, Rose? Is that your experience? What are you looking at when you're implementing some of this type of training?

Rose Benedicks 11:23

Yeah, absolutely. I think you said something really smart there, Jay. So this concept of ROI, and yes, as an industry, we have been talking about it forever and ever and ever. But here's something really interesting. If the outcome of your training is an actual value-added output, workplace output for the company, two things happened in terms of ROI. One is you can be a little more confident in your correlation. That training, the output of it, was a work product. We can therefore better correlate that that training resulted in something of value, right? That disconnect is it gets a little gets a little shorter. You can follow the chain of evidence more confidently. And then there's the obvious one, which is yes, you have a return on investment because you created something of value for the organization, right? You didn't create knowledge, you created an actual thing that's going to be used upline or downline, and you created it in a way that reinforces how you want your company to work that built fundamental skills along the way, like how to interact with AI, how to work across departments, how to break down silos. So there's, you know, there's a really big advantage here in terms of getting the bang for your buck.

Jay Johnson 12:39

And I think that's huge. And we're going to step back into this AI in just a moment, but can you give some other examples of maybe some of the outputs? Like broaden the audience's mind. You you had mentioned, you know, obviously the HVAC one, but what are some other examples of outputs that could be created through the learning experience or training experience that would be a value proposition to any organization? What are your thoughts on that? Even if it's just a kind of a short list.

Rose Benedicks 13:09

Gosh,

ROI Through Real Work Products

Rose Benedicks 13:10

I think, you know, risk management plans, right? Creating a risk management plan, keeping it updated, business continuity plans, absolutely. Uh performance management plans, right? So the output would be you actually have your plan for how you're going to manage performance. You know, that's aligned with how the organization wants to do it. Uh, sales enablement is a big one. So that could be anything from um, you know, better using you know AI to better predict which leads are likely to close, and coming up with you know, my plan for how I'm gonna follow up on that, right? Actually, have my plan for making those database decisions, for making those predictive decisions, um, creating marketing campaigns. How do I you know the output of that might be the actual campaign itself? It might be the the um agent, the agentic workflow in the CRM is the output, right? And you're and you're actually creating the output through the training. And the tricky part then is you design the training to create the output, not just step one, get some data. Step two, make a decision, but it's you know, step one is you have to get the data. Here's how to look at the data. Go talk to this person who's going to help, you know, understand this part if that kind of collaboration is important to your organization. And then you have natural checkpoints along the way, right? Because we're not just talking about the workplace output full stop, we're talking about the workplace output to a specific set of standards and requirements, right? And there's no better way to evaluate somebody's work than by looking at the real product of it. The biggest barrier, I know I'm kind of going on a path here, but the biggest barrier I see is clients will tell me people don't have time to look at other people's work. And I say that is a big fat lie. Because they're either gonna look at it or they're gonna redo it, and that's the same amount of time.

Jay Johnson 15:08

Yep. Or they're gonna look at it under crisis, right? And you can either do this now, and and it's funny you say that, like one of the, and I didn't know what this was called and or or even the concept of it, but with one of the very early trainings that I developed. So part of my educational background was in crisis management and crisis communications. And one of my very early trainings was essentially the output was here's your crisis communication plan and here's the different strings. But it was teaching them like, okay, this is how people think in this stage of the crisis. What would we need to do? How would we need to do this? And then we would literally develop like, here's the first uh communication, here's the first communication template that we're gonna send out. It's gonna do these four things. Let's put the information in there. And by the time that we would finish the program, they literally would have a start to front, like open the book, step one, here's where we need to be. Step two, this is who we need to contact. Step three, this is how we get everybody on the same page. Step four, this is how we send out the first piece. So the training was teaching them not only the psychological mindsets of themselves, the organization, the audiences, et cetera, but it was also about them building something that in the event of a crisis, they've got a plan. And yeah, so you know, when you were sharing, uh when you were sharing your presentation, I was just like, it was one of those things where I was like, this is so brilliant. I accidentally, and like after your session, I'm like, here's the nine things that I would do differently now that I've learned, uh, to kind of form uh make that a little bit more um formatted and even, you know, sort of uh, I'm gonna call it uh uh a little bit more polished, a little bit more toned. But uh I thought it was such a cool concept because I accidentally kind of stumbled into that just because I felt everyone should have a crisis plan. Almost nobody did. So it was one of those things where it's like, all right, this is what you're gonna get. But putting the value on that, and it's funny because I have had clients come back and say, we had X happen, we had this crisis plan, no one panicked, and it was and and it worked except for usually it was a except for this one part. And it's like, okay, well, we can train to that part now, but you're out the plan. So I I just really loved that. Um, so thank you for sharing that with the ATD, and thank you for sharing it with the audience here today. Audience, be thinking about this. When you're enacting some level of talent development, what is the output? How are you going to actually make a long-term organizational difference? How are you going to help your teams to have something? This is a really cool model. And Rose will share her uh contact if you ever want to reach out to her about some of these different things later on in this episode. I want to go back to AI. I know you've been doing some pretty cool uh study and work in this space. Tell us a little bit about what is your experience and you know, sort of what has you excited about that?

Rose Benedicks 18:22

Oh, well, there are again, I'm gonna go with the with the two answers. From an LD perspective, I think LD is positioned in a really interesting space to help figure out what working with a digital coworker looks like. So LND has long grappled with sort of elusive capabilities.

AI As A Digital Coworker

Rose Benedicks 18:45

What is critical thinking? What is resilience, right? What are those things? What is agility? How do we pin that down? What does that look like in action? And now we're going to be faced with taking some of those more elusive behaviors and pinning them down, creating capabilities. What does it look like to work with an AI or digital coworker? Uh, what does it look like to be a human in the loop? That's very different. And L and D can get into a space where they're helping to find that because we do have those, we do have those set of competencies as an industry. The other part about AI. So right now I'm in an executive ed program for AI transformation and leadership. And what I'm finding most fascinating is how future-built companies are really doing it. It's a co-ownership model between the business and IT. There's FinOps at play, you've got to get the data structure right, you've, you know, you've got to get an interoperable tech stack. There's a lot of base requirements. And everything that I am learning, all of the research, everything from the big four consulting firms, all says your talent strategy better be in there.

Jay Johnson 20:02

Yeah. I I can see that that would, and unfortunately, I mean what is your experience? And I'm gonna ask this. I've I've got at least some experience. What is your experience with organizations and or businesses being ahead of the curve when it comes to AI?

Rose Benedicks 20:25

Oh, what a great question. So there is a framework where you look at organizations in terms of their AI adoption from stagnant, lagging, emerging, and future-built. And if you look at those future-built organizations, they have an AI operating model. They're not using AI for productivity, which I think is where a lot of people get stymied. So using it for productivity might look like you know, generating content, segmenting your customers more quickly. But when I say using it to create value, I'm talking about a return that shows up for shareholders. Now, what those companies are doing differently is they're not they're starting with the fundamental strategy, a clear strategy and vision. Co-ownership, like I said, between the business and IT, uh, they have an actual investment plan with ROI clearly stated for how they're going to structure AI. They mandate near-term gains when you're talking about going from your central infrastructure out into departments and how they're using it. Near term, they're mandating near term gains. Um, and then, like I said, the talent strategy that seems scary, I think, to a lot of small and mid companies because the investment seems so high to get the fundamental platform, hybrid cloud, whatever you want to call it, ego. Systems set up. But when we talk about value, uh, think about a supply chain and agent-to-agent ecosystems, which is where one agent can talk to another agent and do stuff on its own, essentially. So think about supply chain. Something breaks down in supply chain, a supplier's not going to fall, come through. Your AI agent can go talk to other suppliers' agents, get, you know, negotiate an alternate uh avenue, an alternate supplier, figure it out, get it all done, and by the time your people come in, they have an email that says this supplier wasn't able to meet demand for whatever reason. We negotiated, it's now coming from these people, it'll be here in this time. Right? That's what we talk about with value that shows up for the shareholders.

Jay Johnson 22:49

All while somebody's sleeping. Now, take the human in the loop concept, because I know that that would be I know that there are some people, whether it's a manager or a leader out there that's listening, that's going, that's terrifying. What if it hallucinates? What if it makes this mistake? What if it, you know, uh, you know, I I still want to rely on my purchasing team to be doing the negotiations. Like, what's what's the answer to that? Because I there are some legitimate fears out there. Don't get me wrong. Uh, you know, I just recently asked uh ChatGPT, and it, you know, I was having a little bit of fun with it. I was like, what about this song by this band? And it came back and it was like, that song doesn't exist by this band. I'm like, I'm literally looking at my iTunes and I'm like, that song, that band. I'm like, you're hallucinating. Please look again. And of course it comes back. So we've all experienced something, whether it's a small hiccup or whether it sourced some information from Reddit and shouldn't have happened. How do how do we get sort of the attitudinal change? And that's really where I think that there is some aspects of that attitude can come from fear, that attitude can come from this is taking away as opposed to uh leveraging growth, et cetera. How do we get past that attitude barrier that may be holding some companies back?

Rose Benedicks 24:12

Well, that's a big barrier. Uh, first of all, let me apologize for putting everyone to sleep, and I'm gonna try to make this a bit more interesting. So, you know, there's a concept that, you know, generative AI, predictive AI, agentic AI is this big thing and it's trained on everything. But if you're gonna do it well for your organization, you're gonna pick the precise model for the tasks at hand. So when you're having that kind of precision strike, you do get, you know, you can get a better handle on hallucinations. And if you have a virtuous cycle of training that particular model in its

Preventing Hallucinations With Governance

Rose Benedicks 24:50

specific use case, then you also can avoid model drift, right? Um, where your training becomes sort of outdated. And I'm talking about the AI training data, it becomes outdated, and so your model drifts. And those two are the big ones AI hallucination and and model drift. Now that's a really key concept. You deploy the precise amount of compute power needed for each task, and you have strict governance, so you're not kind of going out there into the gooey mass of everything. You have a fundamental, you know, AI infrastructure that has data and whatnot, but then you go out into the departments and the specific tasks and use cases get the computing power they need. Just doesn't go big and get everything. And in doing that, first of all, it's a huge cost savings. And second of all, the governance becomes a lot easier to put into place.

Jay Johnson 25:47

And I I want to piggyback on that because it was actually interesting to me. And this is audience. If you're using AI and not training your AI, you're making a mistake. And and and what do I mean by training that training the AI? And I thought that this was um, and it's really interesting because I kind of I've I've seen AI used where it's a search engine. All right. It's basically the new Google. I've seen it used at some very advanced levels. One of my colleagues, one of our certified guides and behavioral elements, is actually literally building out some incredible models, uh, spent hundreds of hours of time training it uh to actually do like personalized coaching towards uh managing health outcomes with uh pre-diabetes and things like that. So like really advanced things, really cool stuff. Um the AI, teaching the AI all of the knowledge related to different medications, to different pieces uh to be looking for, inputs, outputs, all of it. Really, really cool. I think in some cases, one of the fastest pieces that people can start doing is teaching its AI what it's allowed to look at and what it's not. So, audience, here's a quick thing. And and again, just did a talk on this um when in the ATD behavioral science thing. And it was interesting because I was like, how many of you are actually telling your AI, you are only allowed to look at these resources from Harvard business, from this, from this, from this. You are not allowed to look at Reddit. You are not allowed to look at this, you are not allowed to look at this. Now help me come up with a strategy or a plan for X, Y, and Z. And they were like, you can do that. And it was just like, okay, yes, this is what you should be doing. That training piece, I think, is really fascinating because people don't realize like AI is not a one-way communication model. It should be a two-way where it's iterative and back and forth. So, are do you have any tips on training AI? You know, from the individual level, not even necessarily the corporate or the enterprise level. Are there things that you have done to make sure that your AI is more dialed in than say just the regular, I've gone to chat and opened up a new, uh, you know, opened up a new new talk? What what are your thoughts on that?

Rose Benedicks 28:14

Yes. So, you know, I do some of what you said, don't look at this. Right. Um, I've also had a conversation with uh my AI of choice, uh, which is Claude, um, about the kinds of sources that I view as credible and not. And then I've had it sort of save that search and recall it, right? So maybe in a later prompt, I'm telling it, hey, remember that chat we had, put that into play here. Um I also will ask it to, if it ever cites anything, give me a link so I can go verify it myself. And then, you know, that's when I'm using a broad um gen AI model, right? But there are specific models that even individuals can use now. So for example, a quad cowork, which is sort of a productivity tool that um integrates with your personal computer, and it's not out there trained on everything that a generative AI large language model is trained on, right? It's looking at your data. It's going, what's spam, what's not, what kind of emails do you want to respond to first thing in the morning? What should I bubble up? How do I, you know, tell you, you know, hey, you're gonna need some time to prepare for this meeting based on the notes from your from your last call, these are your action items, right? That's not out there being trained on everything. That's actually a really fun space to play around because it's smaller and you can start telling it, this is important to me, this isn't. This is valuable, and you're right about this, but you're not. So getting in and playing around with those kinds of models is really helpful versus trying to tackle wrangling in a huge model like Chat GPT or Claude or whichever one you want to use.

Jay Johnson 30:09

I love it. And I I'm gonna I'm gonna share this. And we've had enough conversations, you probably figured out that I'm a little bit of a nerd when it comes to some of the tech, some of the science, and particularly behavioral science, but there is a mixture between feeling like a, you know, an adoration for Tony Stark, uh Iron Man. Yes. So I have taught my AI different protocols. So I have, you know, something like Research Protocol One, which is the highest level research protocol. It knows it is only allowed to look at peer-reviewed journals, particularly I have an entire list of them if it's doing this type of thing. Research protocol two is like you're allowed to now dip into things like Forbes, Wall Street Journal. Research protocol three is you're allowed to look into something like psychology today or some of the things where it's not all peer-reviewed. There's articles, there's guest contributions, et cetera. So I've set those up. I do also have Christmas protocol, um, which is like, hey, I need to actually do my Christmas shopping. So I've got a number of different protocols, but I've trained it to know when we dip into a particular protocol, here's the parameters that you're allowed to do, here's the things I don't want you to do. And I just have little names for them so that way it makes sense for me that I can say, hey, we're doing research protocol one, jump in there, here's what I'm looking for, find me 15 articles that say X. And then I've got, you know, a nice list or let's synthesize this. Let's find what are the patterns that we're seeking. But it knows how to look at those things because the first several times it didn't work. So then it was like, okay, you did this, that was smart. I want you to continue to do this. I want you to stop doing this, I want you to uh expand on this type of thing. We don't think about training the machine, we think about the machine training us too often. And I think it's really important that general users start really looking at it as that two-way.

Rose Benedicks 32:14

Yeah, and this goes right back to our primary topic of workplace outputs. Um that when you're creating something at work, and we're all going to be using AI to do it, right? So, what you just talked about, how do I make the AI better? And not just the large language model that that our company has adopted, but in this workflow, when it has told me, hey, you need to, you know, this is a human

Training Your AI With Protocols

Rose Benedicks 32:45

in the loop moment. Um are you able to make it better? Are you able right? So in in even if you're creating a marketing campaign, there's a moment where you have to learn through creating that output to make the AI better at giving you what you need. And that becomes part of the training that gets you to that output. So instead of attacking it and going, you need to learn how to make your AI work better for you and have these ideas for you know, workflow design and whatever, it's now part of the training to get to that output.

Jay Johnson 33:21

Love that. So, with this, with this executive ed program you're you're working in in the AI and leadership space, how do you see yourself working with organizations? Like if you were to say, hey, my you know, my ideal here is I want to help organizations do what? What does that look like for you? Because I know that your internal is the teacher. So you're gonna be teaching this and and and moving that forward. What does that look like in that space?

Rose Benedicks 33:52

I really want to join an organization where my enjoyment of creating a framework or an approach out of complexity and chaos is gonna come into play. Where my desire and sort of natural inclination to create a team that's quite resourceful and share some enthusiasm to get that done comes into play. So it's more about the again back to ways of working and the way my brain works. And I want to work in a space where that's valuable, but also where I'm gonna learn, which is the whole reason why I'm doing this AI program. It's not because I'm out there going, I need to be this, you know, AI great person, otherwise we're you know, I'm never gonna get a job. It's like, wow, I'm very curious. And AI is such a systems concept, so it kind of works with my my desire to see systems, to improve the internal logic of a system to make it better. Um, so I'm leaning a little bit towards the operations space, more strategic operations. Um, and you know, AI is just gonna be a part of that moving forward. Now, having said all of this, let me be very clear. I am not a technical person. So, big disclaimer for anything I've said. Um, I just give really good face when I'm with tech geniuses.

Jay Johnson 35:28

No, I I love that, Rose. And the best part is is so the first time that I spoke on AI, I was uh I did an international conference all on AI. I'm the only non-tech person there, literally the only non-tech person there. Um, I know enough to be dangerous, but I am certainly I'm not a coder. I'm not somebody who is somebody that can build something. I understand some of the background, but not all of it. Uh, but I understand people. And in order to get the technology to work, you have to have the people component and that human in the loop. So usually when I'm brought in on something, I'm with you. I'm not a tech person. I dabble, I like it, it's fun. Um, but that human side, and and the thing that I think really resonated with me, with what you said, is the tech is a tool. And if you don't have a system in place to manage the tool and the interactions with it, all you're going to do is create more chaos. And I see a lot of companies doing that now by not creating the systems for how do we interact with AI? How do we create this as a future program or future opportunity? So I'm going to ask you kind of one question before we close out our conversation. First of many, I hope. Um, but I the question that I would have is if if if I'm in the audience and you were to say, I want to start, you know, and I was to say, I want to start moving to sort of that futuring of AI. What is maybe one of the first things that I should be thinking about, acting on, or doing to move me more from the I'm using it as a search engine or I'm using it for efficiency, as you said, to that long-term. What is something that I should be thinking about doing differently or even just starting in order to get there?

Rose Benedicks 37:29

Yeah, so wow, gosh, there are so many starting points. I would say look at your workflow end to end, not a single part, but end to end. And what are the big questions? So, for example, in HR, well, let's go L and D. That's our audience, right? L and D and HR. So you're looking at yeah, you're looking at the life cycle of an employee. So you start with how do we attract the talent? And how do we bring them in? How do we get them

Start By Mapping Workflows

Rose Benedicks 38:05

going? How do we keep them going? How do we make them happy? So you look at that entire workflow end-to-end and go, how can we reimagine this? If if we want to meet a person at every step of the way and have them feel a certain way or experience a certain thing, how do we reimagine that workflow? That's where you start. Because when you get into, hey, how do we make our hiring decisions faster? You're gonna have all of these little different AI-inspired projects and pilots and experiments. Your resources get spread too thin, it's wasted money, and nothing is coalescing to create actual value. You've just created little pockets of efficiency.

Jay Johnson 38:52

Okay. I'm gonna selfishly, I'm gonna say I love that because we just started that process in uh our behavioral elements team of looking at the entire workflow of uh recruiting new guides, um, onboarding them, helping them reach their first, you know, first sets of uh training success and coaching success to building out their enterprise and everything else. And we're looking end to end on this one. And we've been utilizing some AI in there to help us. What are our gaps? Where is somebody going to get a hiccup? What is their experience? So even looking at like attitude and looking at enjoyment and frustration models. So you said this and I was like, we're doing it right. So yeah, no, that's very cool. Rose, if our audience wanted to get in touch with you and tap into the wealth of knowledge and experience that you have in this space, how would they re how would they do so?

Rose Benedicks 39:52

Well, I'm on LinkedIn and I'm pretty good at checking it. However, if you really want to slide up into my messages, uh I would just use Rose Benedict's at gmail.com. Uh that's uh last name is B-E-N-E-D-I-C-K-S.

Jay Johnson 40:08

Perfect. We will make sure that that's in the show notes. Rose, thank you for joining us today. Like I said, when I had first seen you at that ATD conference, I knew I wanted to have a deeper conversation with you about the

How To Contact Rose

Jay Johnson 40:20

outputs about being able to build a training program that's actually creating business value. I would I was so enthused and excited about the talk. Um, still am, obviously. Uh, and I'm hoping that the audience can see the value of like, wow, we can do actually more than just knowledge transfer, but we can actually build tools, systems, and experiences that are going to build our thing forward. And I think with the knowledge you're sharing today and the excitement that you share it with, uh, it becomes much more real. So thank you for joining us.

Rose Benedicks 40:53

Well, Jay, thank you so much. And I got to tell you, um, you know, I only learn things from other people. That's my MO. So I am really interested in people contacting me because they have something to share. I spoke a lot, but I also want to learn.

Jay Johnson 41:08

That's amazing. So, audience, don't hesitate to reach out to Rose. And I'm sure that we're going to have you back because there's a lot more to talk about. But thank you, audience, for tuning into this episode of the Talent Forge, where together we are shaping workforce behavior. Thanks again, Rose.

Rose Benedicks 41:24

Thank you.