The Digital Revolution with Jim Kunkle

AI That Delivers: Measurable Outcomes with Keith Metcalfe

• Jim Kunkle • Season 3 • Episode 23

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AI has officially entered its accountability era, and that’s good news for anyone tired of big promises and vague roadmaps. We sit down with Keith Metcalf, President of Acorn, to talk about what AI can deliver right now and how to prove it with metrics you can defend. If you work in regulated environments, asset-heavy industries, field service, or any operation where cycle time, compliance, and technician productivity matter, this conversation is built for you.

We get into why so many organizations stay stuck in AI experimentation mode. Keith shares what Acorn sees in the market: development plans that aren’t specific to the role, coaching that isn’t consistent, and AI rollout plans that lack the basic infrastructure to make new tools meaningful. The result is an “AI capability assessment” gap where executives believe expectations are clear, but managers and individual contributors don’t feel it at all. We break down a more practical approach: define roles in a human way using four to five capabilities, agree on what “good to great” looks like, and use that shared language to turn learning into behavior change.

We also talk about KPIs and why measurement can backfire when people fear it’s tied to pay or job security. Done well, performance metrics become a trust-building tool that links training, mentoring, and real operational outcomes. We close with Keith’s advice for leaders starting their AI journey: keep it fun, start small with one problem, and treat AI like the powerful tool it is rather than pretending it’s a person.

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Download the Acorn report: "The 2026 State Of Learning For AI Fluency" here: https://info.acorn.works/hubfs/Gated%20Assets/Acorn-Learning-for-AI-Fluency-Report-2026.pdf?utm_campaign=42891088-Report%202026&utm_source=report-view 

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Cutting Through AI Noise

Jim

You're listening to the digital revolution with Jim Kunkel. And today we're cutting through the noise. For years, AI's lived in the land of promise. Bold claims, flashy demos, and endless talk about potential. But in 2026, the conversation has shifted. Leaders aren't asking what AI could do someday. They're asking a far more important question: what can AI deliver right now? And how do we measure it? And that's why today's episode matters. We're joined by Keith Metcalf, president of Acorn, a company that has built its reputation not on hype, but on outcomes, real ones, the kind you can quantify, defend, and scale. Acorn sits at the intersection of data, workflow, and operational reality, and Keith brings a perspective that every executive, every technician, and every digital transformation leader needs to hear. In this conversation, we dig into the shift from AI experimentation to AI execution. We explore how organizations move from pilots to production, from dashboards to decisions, from AI features to AI that actually changes the business. And most importantly, we talk about the metrics that matter: productivity, cycle time, compliance, technician enablement, and how Acorn helps comp uh companies provide ROI with clarity and confidence. If you're tired of AI theater and ready for AI that works, this episode's for you. Let's get

How Acorn Evolved Beyond Completions

Jim

into it. Keith, welcome.

Keith Metcalfe

Hey, how you doing, Jim?

Jim

I'm doing great. And I greatly appreciate you joining me here on The Revolution. You know, for those who might not know who you are and uh Acorn, could you provide a background about yourself and also too about Acorn?

Speaker 1

Yeah, no problem. Yeah, Acorn started about uh 11 years ago, and I came on about three years ago to the to the organization, and uh it really started in the world of learning, like really complex, compliance required learning, getting through those learnings to prove to some auditory body that um we had covered off what we need to in order to make sure we're doing something safely. That's that's largely where it came from. During the journey, uh our founder, uh uh Blake, he actually came to the conclusion that learning by itself, just completions, wasn't really changing behavior. And we sort of stumbled upon this finding. And the story is quite funny. Blake, um, you know, he's in a in a town where he was walking through and we do a lot of government work, and he had a lot of employees who are um working for those agencies saying, you know, hey, I use your software. And he's like, Well, do you like it? And he'd say, No, no, we don't like it. Like we've we're using this LMS and we hate taking those courses. And and he really dug into why, and he started to learn that there was a fascinating thing going on where people were doing what was required, but not actually onboarding. And and conceptually, it's quite we see it all the time, Jim, where people learn something, but they don't deploy the learning and then it doesn't become behavioral. And he really started attacking that. Um, I came on three years ago um to help him with that and and really sort of get the message out. So largely Acorn's been trying to get the message out that there's a better way to think about learning and behavioral change than just taking courses and getting completions.

Jim

Yeah, we've really seen out there a shift from the hype related to AI to getting into more of the accountability related to AI.

Speaker 1

Yeah.

Jim

Especially in a lot of the different industries out there. Um, when we look at anything from you know asset-heavy uh sectors, you know, they're demanding more now proof and not promises anymore. So let me ask you about Acorn's philosophy.

Quantifiable Value Over Flashy Demos

Jim

Um, from doing research on Acorn, uh, I see that your philosophy is that AI must produce quantifiable business value. What does that mean?

Speaker 1

Yeah, well, good question. I'll I'll get to that and I'll sort of tie in my background a little bit. I I've been in tech for 30 years. Um, sort of a weird thing to say now, but I remember the first dot-com boom, right? Like all these incredible promises. Um and ultimately a lot of those promises came true, but the the cycle of which the expectations were um was ahead of itself, right? And we we saw that adjust. And I think we're seeing something very similar. It reminds me of walking around with 9,800 baud rate modems and talking to businesses saying, hey, this is going to make a difference in your world, and them going, I don't get it, right? I think AI is sort of going through a very similar cycle where the promises of what it could do for people are incredible. And it is doing some incredible things for people, but the expectations aren't aligned to what's actually occurring internally. And that's why we commissioned this report that we do annually, and we really focused it this year because we're so focused on learning and how it links to performance and behavior. We really honed the report this year and the survey in on that topic area. So I think that's how we ended up on the podcast here today.

Jim

Yeah, definitely. Let's talk a little bit about the Acorn, the platform,

Turning Jobs Into Capabilities

Jim

and also uh the tech and the and the value. Um, you know, you did provide some background about AI-driven solutions that come from Acorn. You know, how does the platform, how do they, how do you integrate uh data, the workflows out there, and then bubble that up into decision support?

Speaker 1

Yeah, I think one of the challenges and what you'll see in the report is that many organizations have tried something to define what jobs actually are. So simply put, Jim, like in your role, what are the four to five things you need to be good at? And what does good to great look like? Now, in the learning industry, we've wrapped a whole bunch of things around that. We've wrapped the idea of competencies and skills and proficiencies, but rolling that out to the business so a leader and a learner can have a conversation regularly using that nomenclature has been really, really hard for people. So the platform we've worked on is how do we take what traditionally is done heavy consulting, right? You'd have a consultant come in, meet with hundreds of managers and leaders, try and create some sort of matrix or um uh skills or competencies kind of model that you could map roles to and then get them to leaders and learners. Now, the neat thing is AI has sort of entered our world in what we are already working on that and made that much more achievable without the consulting hours. But we're still dealing with a tremendous amount of behavioral change in individuals where we're trying to get towards how do we actually get them to talk to each other in a language which reinforces the learning and behavioral change that we're going for. So much of our platform is all centered around that. Historically, we've done LMS and we do it well. We believe that learning and content has a place. But just like if you were to be, you know, my 15-year-old taking a driving test right now, if he goes and takes that exam, he's not ready to drive a car and he still needs to practice and implement the behavior. That's the capabilities part of the product that we work on quite a bit.

Jim

Yeah, and on the on the benefits and the value side of it, what I really see when it comes to like, you know, real life type of examples, the first comes into mind is a reduction and operational friction. Um, I think that is important to be able to deal with that on the operation side, but also um faster decision cycles and also uh an improvement for uh compliance, uh documentation, uh, and then ultimately I think enhanced uh technical or uh technician um productivity when you're looking at the users out there as well. Are there any other examples of uh measurable outcomes and value that I might have missed on that?

Speaker 1

No, it's a good summary. I I would say it's not necessarily different than what you said, but I think one of the things as a technology company we sort of look at all the time is how do we take technology and software and enable a more real conversation? So I think software companies historically have been really good at marketing concepts. Conceptually, here's something you could do. And you did a great summary where we sort of focus on outcomes at the beginning at Acorn. The simplest outcome that we try to drive towards is an interface for a leader and a learner to talk regularly about their job and what good to great looks like, and then to provide resources to actually improve and change that behavior. Now, the interesting thing is we're humans and we don't change behavior easily, right? And taking a course isn't necessarily going to do that. In regulated and safety required environments, this is one of the biggest struggles of the organizations we talk to, right? There, they say, hey, we did a safety training course. Yeah, look at the number of violations, look at the number of days safe, look at that. It doesn't seem to be making a dent in it. And that's where we need this direct mentoring, direct conversation, where people are demonstrating the behaviors we want to see, and then the ones we don't want to see. And then how do you link that to organizational objectives where you can say, hey, we did some training, we did some learning, the leaders were talking about where they saw people doing it or not, and it changed the metrics in the business somehow. That's what we're really honed in on. And I think AI is just right now, it's like a brand new tool in everybody's toolbox. So it's really accentuating the importance of getting this right.

Jim

Yeah. Let's go ahead and talk about the AI maturity curve, you know,

Behavior Change In Regulated Work

Jim

especially specifically where companies are collectively right now. Earlier when I opened up the podcast, I talked about companies want to move away from the experiments into execution and deliverables and things like that. What in your opinion, you know, why are most companies, the majority of companies, why are they still stuck in that AI experimentation mode?

Speaker 1

Yeah, we uh and the report that we did, we actually commissioned this one. We commissioned it every year. It's not our customers, it's a general market report over like 1,200 people with various organization sizes, sort of from a thousand to 10,000. And we broke that question into three things that we wanted to understand from the community. One, the concept of broken development plan promises. Like we saw 58% of organizations report their development plans are either somewhat effective or not. So they've said to employees, we're gonna help you develop. The biggest challenges from the survey we saw at GM were that one, the development plans weren't specific to their role, two, they weren't consistent, three, they weren't actually regular communications, and they didn't know what great, good to great looks like. So that's the first chapter. And there are a ton of like stats to sort of back up this concept that we've broken this promise of development plans with employees. And the other big one in that, in that finding of that chapter is that if you say to somebody we're gonna give you development plans and some training to get better, and then we're gonna talk about it, but there's no tie into their career direct trajectory, that behavior doesn't get cemented. So that's the first chapter. Then we go into AI adoption without infrastructure. So that's the next chapter. And that one's really about people rolling out a tool, and that's what it is. It's it's a very powerful tool to employees. And if they haven't perfected the development plan and piece, that new tool doesn't have a place to be relevant in the role. And then the last chapter is this concept of AI capability assessment voids. So there's just no, there's no assessment tool that allows people to say, hey, AI is working well or not. So when you get to why are people sort of promising outcomes with AI, and you can really see it at the executive level, a huge divergence between what executives are thinking the organization feels around this new tool, and a massive divergence as you go to the manager and individual contributor level.

Jim

Interesting, interesting. So another question I have for you is why is there still a large gap between the digital ambition and then the digital execution?

Speaker 1

Yeah, I mean, I think you have to look at organizational structure to really peel that one apart. I think there's a tremendous amount of pressure and acknowledgement, just like I mentioned in my history with the dot-com revolution, right? It was real, and we saw the full effect of that over decades, really. Um, when it happened, there were a lot of people stymied on like what is going on? What does the future look like? And if you really simply tear it back and you look at a company, we've been built on a paradigm of most companies over a couple of decades at least, maybe since that last revolution, um, where people look to their leaders to teach them what they need to know. And most of those leaders have come up through the ranks in some way, having done it for a long time, with a great deal of authority and confidence built on that experience. When you throw a new tool in for those leaders who are

Why Most AI Efforts Stall

Speaker 1

at the same level as the people on their team, I think it pushes much more to maybe your earlier podcast days around purpose and leadership. You know, I was intrigued about your history on that because I think leaders are being challenged, and we see it in the survey results, to lead humbly with curiosity, and they don't have that authority that they once had. Um, and it's changing fast.

Jim

Yeah, it is definitely changing fast. So let's segue into how Acorn, you know, how you help companies move from those pilots, from those ideas, those dreams into full production.

Speaker 1

Yeah, great question. I like to keep it really simple with companies. Um, we have like a five-scale evolution kind of concept. Um, I think what's interesting with companies is if we walk in and we say, we're gonna give you a development plan for every employee, and your leaders and learners are gonna talk a language they've never talked before, and it's gonna be fantastic. Companies are ecosystems when you talk about these kind of topics. There may be some departments who are already trying to do that. Acorn's not the only tool in the world, right? In fact, the irony is we find one of the biggest challenges for companies is they have four or five tools trying to do this, and oftentimes there's golden spreadsheets involved somehow, right? Where somebody's trying to talk to somebody and capture their job and you know the conversations. Um, so we're not the only tool, but I think one of the interesting things out there is that you'll have a department that has leadership that's already pushing down this path. Um, we can see that. Um, and then you'll have another department that is not. So you almost have to approach an organization like a group that has different groups within it, and you have to evaluate some levels of maturity. So, in our maturity model, what we talk about is the first thing, and I think you work in regulated, regulated industry. There's just a thing where you have to train people about safety. Like for whatever industry you're in, that is a requirement. You can't sort of get ethereal about it and say, let's talk about your development and your career path if you're not actually safe. So there is that sort of some departments need to get there first. And what we would do is we would get them through the learning side of that journey. But once that learning side sort of becomes in place, if you want to start moving up towards this concept of capability development and behavior change, you have to start articling the job and the learning towards the job in a way that the learner can actually say, Hey, I understand how learning this will make me better if I change the behavior and implement the learnings. So that would just crawl your way up that scale.

Jim

Yeah. Recently there has been so much in the news about downsizing and adjustments with companies. And a lot of people have been throwing out there, oh, AI is taking away the jobs. And when you really read into the depth of everything, you're we're really not seeing what everyone is so fearful out there or using as an excuse out there. So let's talk about the human side. AI is a force multiplier for workers, and in your experience and what you're seeing out there right now, why is AI not replacing humans and why is it amplifying those workers out there?

Speaker 1

Yeah, and the report we call this the AI capability assessment void. Um, so if thinking back to like the rest of the conversation and referring to it, if you if you don't have jobs articled in like some way that people can understand, and by the way, skills is really hard. Like the report came back and said some jobs and most jobs on average have between 11 and 20 skills. So just if you take technology and apply it at the interaction level, Jim, if I'm sitting with you and I'm every three months saying, okay, let's talk about skill number 20, now 19, now it that's not a human way of interacting. So we've done that down into what we would call more skills groupings, like capabilities. So it's four to five things. That's what people can consume. They can't consume much more. Then you need to have some very common definition of what good to great looks like in that discussion. Once you get there, then you can start evaluating a person's development in their role. AI, what we're seeing some companies

Leadership Gaps And Execution Reality

Speaker 1

do with our platform now is say, it's fine to say, you know, as a podcaster, you should use AI more, Jim. Okay, well, if that's you know, if that's what your leader's guidance is, what do you do with it? What does success look like? What we like now is that people are taking roles and saying, okay, I want to actually say, here's what good to great looks like for use of AI in your role. And that's where this the void goes away. Now, when you don't do that, the the data is very clear. We had 78% of executives saying AI competency expectations are clearly communicated. If you go down to the manager level, that halves, and when you get to the individual contributors, it's 19%. So if you throw a powerful tool at somebody and begin with not having a clear way of talking about a person's development, how do they have any hope of saying that a person's getting better with that tool in their role?

Jim

Very good point. Very good point. So when it comes to Acorn, you know, how do you design tools that fit real world work and not, you know, idolize workflows?

Speaker 1

Yeah, I mean, I'd be interested in what you're thinking on workflows. So why don't we start there? Because I'd have an answer to the first part of your question. Um, but what do you what do you see when you say workflows? Because we hear different things on that.

Jim

Yeah, so when I think of workflows, is companies will have legacy types of processes and procedures. And what they're looking to do, and and I encounter this even with my current employer, where they're looking to try to can let's say bake in the AI, but they really don't have a full grasp or understanding of the current workflows. Um, they've existed for time, um, they work, obviously they work, um, but are they the right workflows? And then how can AI be leveraged in there to uh again complement the human part of it to amplify it, but also to maybe take it to a new next level?

Speaker 1

Yeah, I mean it's okay. So that's a big question. I think if I were to make it really simple, if without obviously taking you through the whole product and a demo, all that kind of stuff, right? I would say this. I would say foundationally, if your organization um could tell you, Jim, here's your four to five things in your role you need to be good at. One of them is AI, and we've given thought to what that looks like. And then if you do it and demonstrate that behavior, here's what good to great looks like, that will get your attention. That'll get your attention much more than a leader who does also doesn't know what to do with AI because it's so new, saying to you, Jim, do more with AI, right? And I think it's

A Practical Maturity Path To Production

Speaker 1

fundamental, Jim. Like what we see is without that fundamental focus, it really is this up in the air on who's doing well and who's not with AI. It's sort of like I don't know, I actually don't understand how organizations are even making these decisions. And then how do you capture that conversation? So, first off, if you don't article the jobs in a competency manner and you don't say to people how they're developing in those, how are they determining whether people are using AI well? But even more worrisome, Jim, I think is that how are they making decisions on redundancies? I I don't know. I don't know the answer to that. Um, and I don't think a lot of organizations do. And again, in the survey, that's very clear for us is that they don't. If they don't have that fundamental tie of AI to role and then expectations within it, you don't have any data to tell you whether or not people are being effective with it or whether you need more or less people. Um, we do see some companies doing that, and and clearly they're ahead. There's just no question.

Jim

Yeah, uh, you really kind of set in my next thought process and our conversation is related to the measuring success. We talk about in business about key performance indicators, KPIs, and those are really important to not only show the health of a business or an organization, but also to measuring what success. What's it like you said, you define it. Here's what good, here's what better looks like, and how do you measure against that? What's success look like when it comes Acorn, what are some of the KPIs that actually really matter related to AI?

Speaker 1

I think the KPIs fit very well into development plans and skills and competencies. And what we've done recently, we're we're sort of pushing organizations around on this one. Um, from the from the report, it was really clear like 82% of executives are excited about AI as a topic. But when you get into the individual contributors, like 28% of them are scared or disillusioned, and 54%, 54% of them are just like, I'm out. Like you know, so it's so if you're in that state and then you throw a KPI or a metric in on it, like you lose people. And in fact, it's fascinating because this is where we have focused on this link between performance and learning, right? Because as soon as we start throwing KPIs in, people are thinking about their jobs, they're thinking about pay, they're thinking about their mortgage, their kids' schooling tuition. Like, so when you have an honest development conversation and you throw a KPI in the mix without any context, there's a danger that that conversation is not going to result in any development. And the exciting part about if you do article development plans and job, four to five things in their job that they need to be good to great at, and then you attach a KPI, what it does is it forces the leader to have to be a lot more thoughtful about what they're actually measuring in regards to somebody's development. So it sort of breaks that chain of distrust.

Jim

So let's talk about the future now. Based on your professional background, your experience, and also the experience of Acorn, you know, what's the future? Where's AI taking us?

Speaker 1

Yeah. I mean, I'm very excited about it personally. I think back to those 98 hit 100 bog rate, uh, the bottom days, and you know, the eyes, and all that kind of stuff. Like one of the most beautiful things about that time,

AI As A Tool That Amplifies People

Speaker 1

and I entered software in my in my uh in my early 20s, I was really fortunate too. Everybody was equal. Like we all came from diverse backgrounds, and nobody had experience because it was all new, right? And I think we're sort of in a similar time now where AI is creating possibilities that we've never seen before at the job level. It is for individual contributors who are curious, who aren't scared of making mistakes, and love playing. Like that's what we did. We played, right? And and when you played with the possibilities, your mind really opened to things. And I think we're sort of in that spot. One of the funny things on development plans that we see is um, even from a behavioral dynamic standpoint and teams, you know, oftentimes companies have a brilliant jerk, right? Somebody who just like has that experience and knowledge to make somebody else not feel as knowledgeable. AI is sort of eliminating that now. You can sort of take those kind of behaviors and say, well, they're not okay, and maybe they're not that informed, and maybe we are a lot more close to equal than we ever were. And I think that's exciting. And I think for the companies and individuals that look at it that way, uh, I can even tell you in our organization, we have a ton of people who are just playing and the things they're doing um with the results of that curiosity, almost like childlike curiosity, and um, you know, just sort of uh the toying around. You see people like late here at night, or when I'm coming in the next morning, be like, you wouldn't believe what I did, right? And I think that is where AI is is bringing us back to. And frankly, I think it's inspiring. I think it's it's a fun time to be a part of. I tell a lot of the team members, you're so lucky. I wish I was your age again. Um, because uh this is gonna be fun. And and that's sort of what I think um the people who think that way with this next turn um with AI shouldn't be scared. They should be excited. Uh, that's my belief.

Jim

Yeah, we are definitely in exciting times right now. And the bee at the beginning of this is just absolutely amazing. So let's close out this uh this episode. And Keith, I've got to ask you, you know, what advice do you have for leaders who are really starting to take that AI journey with their companies?

Speaker 1

Yeah, I think um I'll do it in two respects because you know, everybody's got their own um company they're working at their own mandate with their own team of individuals on it. Um have fun. Like go try something, find a problem. Don't find too big a problem because I've done that. I I was like, I'm gonna I'm gonna be able to replace this role. Anyone who's actually played with AI, anybody who's created skills and clawed or like projects or anything, they know that that's not where we are. We're not replacing a person, we're not saying to it, hey, we can there we go, we have a new agent. In fact, and we just actually on our support side changed our AI agent because it was impersonating a person that was supposed to be like a person, and and customers are getting confused. We said, no, this is a tool, that's what it is. And our customer uh deflection rate on and satisfaction just jumped like almost double because we weren't trying to make it a person. So I think if you look at it as a tool and you go after it hard and you find simple problems, start there, then you can start using other tools like you know, um uh N8N and other tools to stitch things together, but just go find one problem and then you will hit any one of the different LLMs out there. Really, they're all different, and we have opinions on which one's better and best right now. I did a post on LinkedIn on that recently, but have fun solving that problem and then the rest will start to unfold. Um, so that would be my advice. Um, and then apply that at your individual level from an acorn perspective. Very simple. Every employee deserves a development plan day one, and they deserve regular conversations that are captured from an accountability standpoint with their leader. And if you aren't able to do that today, for after everything we just talked about, your team will not advance at the or uh at the rate that other organizations do.

Jim

Great advice, great advice. And

KPIs That Build Trust And Closing

Jim

everyone, I will definitely put in the uh description a link to Acorn's uh site, but also to uh the survey and article that have been written uh that Keith was uh referencing. I'll make sure that I include that as well so everyone can dig into it and take a look at that. And also to please make sure you reach out to Keith and Acorn. Uh, if you are uh you know interested in furthering the discussion on these topics and also to to see exactly where you could go and where Acorn can help out. Um I appreciate everyone who is commenting on the episodes but also sharing them and also promoting uh the podcast itself. Keith, um anything that uh anything that I might have missed that you want to cover or any last messages for the listeners?

Speaker 1

I have a quick question for you. I wanted to see how good AI was because this was so focused on it. Um, what was your Myers Briggs?

Jim

Uh I'd have to pull it out. I apologize.

Speaker 1

I have you as ENFJ, and uh I took it I took a guess based on like everything that you've done there. So I thought that was sort of fun from a development standpoint, and then I was pushing it a little further and I took a guess, it took a guess at your favorite song. So um let's see, what did it come back with? Journey. Don't stop believing. Is it any good?

Jim

It's definitely in my top 10.

Speaker 1

Is it okay?

Jim

Well, it's not bad. All right.

Speaker 1

I I had that question, I wanted to see for her.

Jim

In fact, uh, yesterday uh my wife, one of her uh local girlfriends came over and she was going through, she was going through, not my wife, but her girlfriend was going through some of her old stuff from when she was a teenager. She found a journey, a ticket to see journey concert for like ten dollars.

Speaker 1

No, really?

Jim

That's awesome, Jim.

Speaker 1

I love it.

Jim

All right, everyone. Thank you so much. We'll see you in the next episode.

Speaker 1

Thanks a lot, Jim. Talk to you later.

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Jim Kunkle

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