Shadow AI Is a Permission Problem, Not a Tool Problem
Episode Summary
Most enterprises did not decide to adopt AI. It happened to them, one Copilot license at a time. Bob Mitton, co-founder of Expera Consulting and the founding member of VMware’s marketing AI council, has a diagnostic that cuts through the confusion: ask employees how many of them use AI, then ask IT how many use the company tool. At one healthcare client the answers were 80 percent and 10 percent. His argument is that the gap is not a tool problem or a training problem but a permission problem, and that the fix is intentionality, saying plainly what people are expected to do and enabling them to do it before measuring whether they did.
Key takeaways
The clearest test of whether adoption is working: survey employees on AI use, then ask IT how many use the company tool. At one healthcare client staff reported 75 to 80 percent while IT measured 10 percent. The tool was fine. The difference was permission, and the rest was shadow AI
Measure business impact, not activity. Not logins or prompts run, but whether persona development got faster, whether more A/B testing happened, whether sales call prep dropped from two days to an hour
You cannot measure what you do not enable. Raising the expectation without upgrading the enablement function produces a KPI nobody can hit
Adoption usually starts at the mid-manager level, with someone solving one problem and telling colleagues. That is how VMware’s AI council began. The executive job is air cover and sponsorship, not dictating by fiat
Guardrails not gates. Enablement goes past the half-day prompt-writing class: prompt-a-thons judged Shark Tank style, prompt of the day, leaderboards, office hours for the reluctant
A Wharton study puts intentional adoption at roughly 40 percent, up from the tens. That still leaves 60 to 70 percent of enterprises without a strategy, including Fortune 100 technology companies
About Bob Mitton
Bob Mitton is co-founder and Managing Partner of Expera Consulting, which helps enterprises move from scattered AI experiments to governed, measurable programs. He was the founding member of VMware’s award-winning marketing AI council, a model he has since rebuilt for other organizations, and his clients range from Fortune 100 companies such as Home Depot to industry bodies like the American Marketing Association and venture studios including Mucker Capital. His background spans the full marketing spectrum, from market analysis to go-to-market strategy to executive communication, and his work centers on translating generative AI into frameworks, tools and workflows that teams actually use.
In this episode
| 00:42 | Welcome and guest introduction |
| 02:54 | What ethical and intentional adoption actually means |
| 02:59 | Augment and amplify, not replace |
| 03:35 | Investing in skills rather than tools |
| 04:01 | Optimization is 10 percent thinking, innovation is 10X |
| 04:31 | How a CEO should measure whether adoption is working |
| 04:52 | Why logins and prompt counts are the wrong metric |
| 05:23 | Measuring impact instead: A/B tests, focus groups, sales call prep |
| 05:47 | Measuring whether the adoption itself is responsible |
| 06:13 | Assessed skill level as a measure of elevating a team |
| 06:40 | KPIs that hold the human and the agent together |
| 07:29 | Throwing spaghetti at the wall |
| 07:43 | Copilot licenses for everybody, and nobody using them |
| 08:26 | The healthcare client: 80 percent of staff, 10 percent of the tool |
| 08:56 | Permission structure, not tool quality |
| 09:21 | Where shadow AI comes from |
| 09:43 | Expectations showing up in annual reviews |
| 10:01 | You cannot measure what you do not enable |
| 10:49 | Nobody’s to-do list ever gets empty |
| 11:09 | Innovators, the people to elevate, and the laggards |
| 11:58 | A five-step plan for starting |
| 12:15 | Finding the hand raisers |
| 12:35 | Why it starts with mid-managers |
| 13:04 | How the VMware AI council became a company framework |
| 13:25 | Air cover, permission and sponsorship |
| 13:40 | The pattern on the AI Realized Summit panel |
| 14:38 | Why you cannot appoint this by fiat |
| 14:58 | Legal and IT on the council from day one |
| 15:41 | Sheila Jordan at Honeywell, policies before rollout |
| 16:26 | Guardrails not gates |
| 16:31 | Prompt-a-thons and Shark Tank judging |
| 17:10 | Prompt of the day, prompt of the week, office hours |
| 17:48 | What if the supply chain were a dating app |
| 18:44 | Thumbprint, the five-day sprint |
| 19:29 | A condensed version of a three-month engagement |
| 19:58 | What has changed in enterprise adoption in a year |
| 20:18 | The Wharton numbers, and the 60 to 70 percent without a strategy |
| 21:26 | Where AI transforms the enterprise next |
| 21:42 | Why laying off your knowledge base is shortsighted |
| 22:06 | Nobody measures the ROI on the internet |
| 22:29 | The new roles that did not exist before |
| 22:53 | Guidance for executives early in adoption |
| 23:00 | Pick your most annoying time-consuming task |
| 23:45 | Resources |
| 24:29 | The one thing to remember |
| 25:06 | Leadership: the defining edge |
| 25:45 | Not a marketing project, not an IT project |
| 26:15 | Wrap-up |
In Bob’s words
“We turned on Copilot licenses for everybody and nobody’s using it. But there’s an intentionality of how are you using AI within your company.”
— Bob Mitton (07:43)
“The quality of the tool is not bad, so I’m not gonna say it’s that. But it’s the permission structure. Otherwise, they were doing it on their own and you had shadow AI.”
— Bob Mitton (08:56)
“You cannot measure what you do not enable.”
— Bob Mitton (10:01)
“Nobody measures the ROI on the internet anymore. You don’t have an internet strategy. It’s just there. It’s baseline. And I think that AI is going to be that.”
— Bob Mitton (22:06)
“You only need one small win to start a movement.”
— Bob Mitton (24:52)
“This is not a marketing project. It’s not an IT project. This is a leadership exercise.”
— Bob Mitton (25:45)
Resources
Bob Mitton and Expera Consulting
• Bob Mitton on LinkedIn: linkedin.com/in/bobmitton
• Expera Consulting: experaconsulting.com. AI strategy work for regulated industries, and the home of the Thumbprint sprint
What he points listeners to
• Marketing AI Institute: marketingaiinstitute.com. Paul Roetzer’s organization, and the source of the optimization versus innovation line he quotes
• The Artificial Intelligence Show: marketingaiinstitute.com. Paul Roetzer’s podcast, named on air
• Christopher Penn: trustinsights.ai. Named on air as a podcast he follows. He is also the guest on episode 41
• Ethan Mollick: oneusefulthing.org. The Wharton professor he calls a phenomenal writer on adoption and new tools
Frameworks and ideas discussed
• Thumbprint: Expera’s five-day sprint: goals on Monday, data on Tuesday, narrative on Wednesday, then testing for clarity and credibility, then turning it into a system. A condensed version of a three to four month change management engagement
• Permission structure: His term for stating plainly what employees are expected to do with AI, and what they are not. The absence of one is what produces shadow AI
• Guardrails not gates: Stay in the lanes and you can go fast
• Prompt-a-thons: Teams build a use case and present it Shark Tank style, with the winning tools published across the company
• VMware marketing AI council: The cross-functional group he founded, with legal and IT involved from the beginning alongside marketing, operations, procurement and sales
People and events named on air
• Sheila Jordan, Honeywell: Chief Digital Technology Officer. His example of the top-down mirror image: employee, adoption, governance and compliance policies written with legal before any rollout
• Mandy Elliott, Red Hat, and Sarah Rich, CarGurus: The AI Realized Summit organization and management panel. Both fit the grassroots pattern he describes, and both have since been promoted
Related AI Realized episodes and events
• Agentic AI and Revenue Work: What Actually Pays Off: Christopher Penn of Trust Insights, whose podcast Bob recommends on air, on measurement and proving lift.
• AI Governance as Code: From PDF Policies to Pipelines: Ken Johnston and Bob Rapp on making the intent enforceable instead of aspirational.
• Cognitive Capital: The Advantage Nobody Is Protecting: John Sviokla of GAI Insights on why the knowledge base you are tempted to cut is the asset.
Frequently Asked Questions
-
Employees skip the company tool when nobody has told them they are allowed to use it. Bob Mitton of Expera Consulting surveyed a large healthcare client and found 75 to 80 percent of staff using AI, while the IT department measured about 10 percent using the company’s own tool. He is explicit that tool quality was not the problem. What was missing was a permission structure, and without one people carry on privately, which is what shadow AI is.
-
A permission structure is a plain statement of what employees are expected to do with AI and what they are not. It is not a policy document or an acceptable-use agreement but an explicit expectation, in Bob Mitton’s words "here is what we want you to do, and here is what we don’t." He argues it is the missing piece in most enterprises: without it, handing someone a license is not the same as giving them permission, and the gap between the two is where shadow AI lives.
-
Measure business outcomes, not activity. Counting daily logins or prompts run tells you nothing about whether AI is working, and Bob Mitton dismisses both as the wrong question. The measures that count are things like whether persona development got faster, whether more A/B testing happened, whether a virtual focus group tested an ad campaign before it ran, and whether sales call preparation dropped from two days to an hour.
-
You measure it two ways: the assessed skill level of your people, and KPIs that name the AI contribution explicitly. The skill measure shows whether AI is elevating the team or displacing it. The KPI measure holds the human and the agent together, so the output target stays the same and what changes is whether the person is using the agent to reach it. On this episode Christina Ellwood and Bob Mitton arrive at both, and Mitton ties them back to stating the expectation before measuring against it.
-
Enable them before you expect anything of them. Bob Mitton’s rule is that you cannot measure what you do not enable: show people how to use the tool and how you want it used, demonstrate that it is five times faster, and only then raise the expectation. Turning on licenses without that step is what he calls throwing spaghetti against the wall, and his example is a company that gave Copilot to everybody and found nobody using it.
-
Both, but it usually starts bottom-up and needs top-down cover to survive. Bob Mitton has seen adoption begin at the mid-manager level four or five times: someone solves one problem, tells colleagues, a coalition forms, and that group becomes the framework presented upward. It is how VMware’s marketing AI council began. The executive contribution is air cover, permission and sponsorship, and he is direct that you cannot appoint the people who run it by fiat or by title.
-
Run it as a competition rather than a class. Bob Mitton’s enablement goes well past the half-day session on writing a prompt: prompt-a-thons where teams build a use case and pitch it Shark Tank style to the C-suite, with winning tools published across the company, plus leaderboards, awards, a prompt of the day to keep people interacting daily, a prompt of the week run as a contest, and office hours for anyone reluctant. His framing for the guardrails around all of it is stay in the lanes and you can go fast.
-
Both, in the same pattern the internet followed. Bob Mitton argues that cutting headcount is shortsighted because the people being cut are the knowledge base the new opportunities get built from. His evidence is the last comparable shift: the internet created roles that did not previously exist, SEO managers and entire social media divisions, and it stopped being something anyone measured ROI on or wrote a strategy for. He expects AI to become baseline in the same way, and to create the same kind of jobs on its way there.
-
[00:42] Christina Ellwood: Welcome to AI Realized, the podcast for enterprise executives leading AI deployments. From tackling security, data, and operational challenges to navigating organizational transformation, AI deployment offers a unique opportunity to redesign our organizations from the inside out. I'm Christina Ellwood, your host for today's episode, and we are talking today with Bob Mitton, the co-founder of Expera Consulting. He's helping some of the world's leading companies turn generative AI into real measurable impact. He and his co-founder guide enterprises through the intentional and ethical adoption of AI, and we'll dig in on what they mean by intentional and ethical adoption. They work with Fortune 100 companies like Home Depot, and industry institutions like the American Marketing Association, and top venture studios like Mucker Capital. So everything from the very large to the very small. So he's a founding member-- he was the founding member of VMware's award-winning marketing AI council, and we'll talk a bit about that too, 'cause it's a b- it's a sort of model that many organizations not only can adopt, have adopted, and maybe he can a-advise us a bit on how his thinking about the marketing AI council concept has evolved over the last couple of years. So his expertise is the full marketing spectrum, from market analysis to go-to-market strategy to executive communication. And what sets him apart is his ability to translate generative AI into practical application frameworks, tools, workflows, and we'll talk a bit about some of the frameworks that he's using so that you all can benefit from them as executives a-adopting AI inside your organizations. So with that, let's welcome Bob Mitton to the show.
[02:32] Bob Mitton: Thank you, Christina. Pleasure to be here.
[02:34] Christina Ellwood: We're very happy to have you. I'm super excited 'cause we do lots of work together, and I'm, I really would like people to have a good, deep understanding of how you help companies adopt AI. As for AI Realized, we help, we foster the adoption of responsible AI in the enterprise, and you've added in the, into that notional idea ethical and intentional. So let's start with what do you mean by the ethical intentional adoption of AI?
[02:59] Bob Mitton: So what we're tr-really trying to get across here is not that AI is out to replace people as much as it's there to augment and amplify. And so we're trying to show Companies, enterprises, executives have a story about elevating people, about transforming your teams from order takers to strategic thinkers to keep up with the endless list of requests of to-dos that you have, and moving from being a, a content vending machine, for example, to being a strategic thinker. And the idea there is investing in the skills, not just the tools, training your people to, to use agents and workflows and AI, and to celebrate the learnings and, and the f- wins and, and to learn from the failures. And really looking at more of innovation thinking. Paul Roetzer has a quote, "Optimization is 10% thinking. Innovation is 10X thinking." So really aiming toward how do you stop chasing the less and chase the better? And the better is your people. They're the people that-- They're the resources that you're gonna have to get better and to do better in the market. And so that's really where we're aiming at, is being intentional about how you adopt AI and being ethical with, uh, your people and with your customers and with your partners and the ecosystem about how it's used.
[04:31] Christina Ellwood: Great. And if I were the CEO of a company you were working with, how would you advise I measure whether we are in fact ethical in how we're working with our people and are intentional about how we are adopting AI? What are some of the useful metrics for an executive?
[04:52] Bob Mitton: So people try to, uh, measure the ROI by saying, for example, "How many people are logging into the tool d- every day? How many prompts are getting run?" and things like that. And it really, that's not really the right question to ask. You wanna measure what matters. It's measuring the business impact, right? Not necessarily how many times I issued a prompt, but did I get the persona development done faster? Did I get more AB testing done? Did I create a virtual focus group and test out our new ad campaigns, or did I put together a, a tool that allows the sales rep to do sales call prep in an hour instead of two days? So measuring the impact of what the tools do as opposed to how many times did you measure, log in, and how much money did I pay for this tool?
[05:47] Christina Ellwood: I love that you're measuring impact. I-- obviously, that is the most critical way to determine the impact on the business, too, because all of those impact measures you just described are KPIs in the business. But I'm actually asking a little different question, which is, we need a way to measure whether we are, in fact, implementing responsible AI or ethical AI or intentional AI or all of the above. And one hypothesis that I, I'm hearing from some organizations is that they are using the development of their people. What is the assessed skill level as one measure of whether they are, a- as you put it, I think really nicely, elevating, right, their team. And then in terms of the, the intentional having plans where the AI contribution is called out. So having KPIs that include both the human and the AI in the KPI, and having a way to measure the agents that we're using against the human, uh, like i- in combination with the human. So Christina can produce this much output as you, like you were talking about content creation, for example. Christina can create this much content by herself. But with an agent, Christina is able to do 10X that number of, uh, that amount of content. So we need Christina to be really good at using the AI to get that 10X content. And so we're not gonna change the KPI, the, what we're measuring her on. We're gonna change how we measure her. Is she using the agent to get the increased output? So I don't know if that-- I'm sharing a few things I have heard. I don't know if they reflect, in any way, your practical experience. I'd love to hear about that.
[07:29] Bob Mitton: Oh, absolutely. Absolutely. And in fact, it goes back to intentionality when you first start out. So one of the first things that we do is, a- and so we've seen a lot of enterprises just basically throwing spaghetti against the wall and hoping it sticks. And the classic example there is We turned on Copilot licenses for everybody and nobody's using it. Yeah But there's an intentionality of how are you using AI within your company. And one of the things there is a permission structure for your employees on how you are expecting them to use AI. Mm. It becomes part of- Have
[08:10] Christina Ellwood: intention, make real. Give you the tool called Copilot, I expect you to use it. My intention is that you will use it. The other side of that is, if I'm not using it, is it because I didn't buy into the intention? Is it because there's a better tool I'm using instead?
[08:26] Bob Mitton: That's both, right? So, so there's a... We just did a, we have a very large healthcare co- customer, client, and they have their own internal tool. And so we sent out a pre-engagement survey and asked how many of the people in the group we're working with are using AI, and it came back around 75, 80%. And then we went to the IT department and said, "How many people are actually using your tool?" And they, they came back about 10%. So there's this huge disconnect. So two things we found there. One, you talk about the quality of the tool. The quality of the tool is not bad, so I'm not gonna say it's that. But it's the permission structure. Hmm. Here's what we want you to do, and here's what you don't, we don't want you to do. And to be able to say to people and be very clear, intentionality, clarity of, "Here's what we want you to do." Gives them now the permission to use it. Otherwise, they were doing it on their own and you had shadow AI.
[09:27] Christina Ellwood: Interesting too that, and when I think about this, you aren't really saying that you're required to use it. So maybe part of our management shift needs to be that we articulate our intention on, in everything, not just in the use of AI, because we don't normally do that.
[09:43] Bob Mitton: We've started to s- we've started to see it show up in annual reviews.
[09:48] Christina Ellwood: Interesting. We are ex- Like in an annual review. That's a very good point. It's,
[09:51] Bob Mitton: it's part of your KPI. So we're expecting you to use this tool for this much. We're expecting that you are going to do this and show us the results of what you have done. It's also, because you're using this tool, and you're, we're provi- And here's another one, is you have to en- you cannot measure what you do not enable. You have to enable them. You have to teach them how to use it, and how you want them to use it. So your enablement function needs to be upgraded as well. So the idea is that we've now shown you how to use this, and it's shown that it's five times faster to do it this way. We're going to expect that you are going to get that much more work done. Or we're going to put this many more things on the list of, for the QBRs or for your annual review, we're gonna see, we want you to work on these new things, right? So I don't know of an executive, I don't know a person, whoever has got a to-do list that gets empty. Right? So it's always about, okay, can we give you more work to do because we've given you tools to do it faster? Mm. And we're expecting you to do that. Now, granted, crossing the chasm is li- is real. There, there are people that are going to be out in front, the innovators that are gonna be out there raising their hand and doing everything. There's the people that you want to elevate. There's going to be the laggards. There are gonna people that just don't want to use AI. Okay. Maybe they're gonna look for a different job. I don't know. But it's going to be an expectation now from the company that you will be using AI, this tool that we give you to do the work.
[11:30] Christina Ellwood: So are-
[11:31] Bob Mitton: In the same-
[11:31] Christina Ellwood: Do you have... Well, when you go into work with a company to start with, you mentioned, for example, that you do a global survey to find out what people are currently doing today. Do you have a framework for the leadership to help them understand what this change management process you're gonna run looks like, or what their end game looks like, or what their ongoing AI work should look like? Is there some kind of framework set that you're using?
[11:58] Bob Mitton: Hey, yeah. Actually, there's a We put together a five-step plan essentially for when you start. So obviously in change management, you gotta measure the current state, and then you've gotta have a plan for what the future state is. So the very first step is, okay, what's your plan? What are you trying to do? Where are you trying to get to? And then the first thing is to start to, to form, find those innovators, find the hand raisers, find the people that are out in front that wanna do that. And in a lot of cases, we've seen this four or five times now, maybe even more, especially in larger companies where it actually starts at a grassroots level. It starts at a sort of the mid-manager level of, um, I have this one problem that I wanna solve, and they figure it out, and then they say, "Hey, everybody, this, I did this." And you start to see a coalition of people start to say, "Hey, I got something similar," or, "I wanna do that." They create the central, and that's how VMware started the AI council, was everybody that was interested started to, to converse, and we started to share ex- learnings and wins and stuff like that. And then that became the framework that then became up to the executive level, showing them, here's what AI is good for. Here's how we can use it. Here's how we can be that much more productive with it. This is the new things we can do. And that became a framework that we used throughout the company. So it spread from there. So that first step was start to form those coalitions, start to, to give it from an executive point of view, you have to give air cover and permission for it to happen, right?
[13:36] Christina Ellwood: Yeah.
[13:36] Bob Mitton: And maybe even- Reality, right ... maybe even sponsorship. Yes.
[13:40] Christina Ellwood: Yeah, yeah. Right. So I love that, and I'm sure you picked up on the fact that at AI Realized Summit we had two people on the organization and management panel, Mandy Elliott from Red Hat and Sarah Rich from Cars, CarGurus. They both fit the description of what you just described. They were, I don't know, the mid-level was necessarily with the way would-- they would be described inside their organization, but they weren't in the executive suite. Right. And they were very well respected by their peers and started to adopt AI and share with their peers, and that led to the management team, the leadership team recognizing that willingness to, to be at the leading edge of adoption within the organization was precious, and they started to support them and to value what they were doing and invest in the more of that, kinda like you described for your AI marketing council at VMware. And since then, they have both been promoted.
[14:38] Bob Mitton: Exactly. And from the executive point of view, it's sponsoring that, but knowing that the people that are going to run that aren't necessarily something you can, you can dictate by fiat. You don't-- It's not IT.
[14:55] Christina Ellwood: Or recognize my
[14:55] Bob Mitton: title. It is...
[14:55] Christina Ellwood: So
[14:57] Bob Mitton: it's led to open this up. So we actually do a, a whole thing about let's put together enablement and make it fun, maybe make it engaging, put together communication channels within the company, and then watch the ad hoc groups form, and you'll start to see some really interesting... So our AI council, we had IT and we had legal on it from the very beginning. And it's just not where you would expect AI to start, the legal department, but we wanted to make sure that, A, we were staying within the guidelines, but we were mitigating our risk. We had marketing, we had operations, we had procurement, we had legal, we had sales. It, it, it comes from everywhere, and that is actually really beneficial.
[15:41] Christina Ellwood: You know, well, you also did that very successfully. Now, you triggered a, a recollection here from GAI World in 2024. Do you remember Sheila? Jordan from Honeywell
[15:52] Bob Mitton: Hmm. Okay
[15:53] Christina Ellwood: She was doing a top-down adoption of AI at Honeywell, and she partnered with her legal counterpart to start by developing the policies, employee policies, the adoption policies, the governance policies, and the compliance policies, so those four before they ever rolled it out. So that's similar to what you're saying you do, so there must be some-
[16:14] Bob Mitton: Right ...
[16:14] Christina Ellwood: reproducible value there in- Right ... bringing in legal early. I'm intrigued by your idea that you have some ways to make it fun. You wanna share some of those thoughts with us? How do you make it fun?
[16:26] Bob Mitton: Yeah. So once, we talked about guardrails not gates, right? So it's stay w- stay within the, in the lanes, and you can go really fast. And, and then we get to putting together enablement. We can go beyond the half-day class of how to write a prompt to doing things like, oh, prompt-a-thons, right, where you actually can put together teams, or you assign the teams, and they come up with a use case. And then you have, like, Shark Tank-style judging of who did the best use case, and maybe they present to the C-suite or to the board. Their tools get published throughout the company. Maybe they get some recognition. You can have leaderboards. We can have awards. There's alsot- all kinds of fun stuff you can do. The-- we also do, like, prompt of the day or prompt of the week, where prompt of the day is just send out a pre-written prompt and have people cut and paste it. And the idea there is just to make sure they're interacting with the AI every day, give them something new to, to spark their curiosity and make them think about, "Hey, that was cool. Maybe if I did it this way," and get them into experimentation. Then you can have prompt of the week, 'cause maybe that's more of a contest, right? Who had the best prompt this week? Or you send out, we need something for this particular use case. Write a prompt for it, and then it comes back as a competition. Who had the best one? Creating virtual focus groups. How would you do that? And then hand-holding for people that are a little more, I don't know, reluctant or don't have the time, office hours, that type of thing. We also start to challenge people to think of different ways to think about how to use AI. So one of the, one of the ones that we had the most fun with is what is, what if the supply chain were a dating app? I love it. So yeah, it's the profiles are the, the descriptions of the suppliers, and then you've got... So you, you can get this whole You know, interesting thing, the warehouse is sort of the chat phase, and then there's last mile delivery and lots of anticipation and high stakes. It's make or break. It's, you can get the, uh, the whole thing going, but it gets people thinking about how do I actually interact with AI?
[18:36] Christina Ellwood: Yeah, I like it. You also mentioned to me that you have something that's like more like a sprint version of what you do. T-talk about- Right ... the sprint version.
[18:44] Bob Mitton: So we have a thing called, we call Thumbprint, which is a, it's a five-day framework where we actually put this whole thing, the steps together. On Monday it's, it's analyzing what your goals are. On Tuesday it's analyzing all of the data that you have. On Wednesday it's shaping the narrative and putting together what are you, what are you trying to do in, in the case of, say, content creation. Testing it and for clarity and credibility, and then turning it into a system, right? So it's really walking you through the change management steps that you would have, but we're, we do it in a very accelerated way so that you walk out with, "Oh, I understand what I'm trying to do now," and you can go back and hopefully implement that. I- if you need help, you can always call me, but it's really a, it's a condensed short Reader's Digest version of change management that we usually do over three months or four months.
[19:41] Christina Ellwood: As a leadership team, the AI Realized leadership team is looking to both adopt more AI in our own processes, but also to provide opportunities for the team to gain skills. Maybe we should run Thumbprint for AI Realized leadership team.
[19:57] Bob Mitton: Oh, that'd be fun.
[19:58] Christina Ellwood: It would be fun. I would love that. So all very interesting and valuable. Where do you see the differences in enterprise adoption today over what you were seeing a year ago? How has it changed?
[20:13] Bob Mitton: So it's going now from, like I said, the kind of the random acts. We're starting to see much more intentional adoption, and I think the Wharton study recently showed that. You-- That we've moved from in the tens to now maybe the forty percent of adoption of who they've surveyed. It still shows that you got sixty, seventy percent of enterprises still haven't ha- don't have a, a good strategy for how they're going to put this together. And in fact, I've seen that in interviews with Fortune One Hundred companies, very large, in effect, tech companies that don't have a strategy yet. They don't know how they're gonna implement it. But we're starting to see it come around. We're starting to see some people focus on it, understand that AI is going, not going away. Very, very much like the internet. It's here to stay. It's gonna be a tool. You're going to be expected to use it. It's going to help create new jobs and new opportunities and new places that you can sell. But it's definitely something that has shown up on people's radar, and now they're asking the questions. And so that's really helpful for me as a consultant to be able to go out and provide them the answers for that.
[21:26] Christina Ellwood: Where do you see AI transforming the enterprise in the next couple years?
[21:31] Bob Mitton: You know, I see a lot of negative press about AI replacing people and laying-- you're gonna run companies with just AI, and I think that's shortsighted. I think what, if you're laying off people, they're the knowledge base that you really want to be building from. So what I'd like to see and what we try to talk about is i-in just the same way that the internet transformed enterprises and now it's an assumption, right? Nobody measures the ROI on the internet anymore. You don't have an internet strategy. It's just there. It's baseline. And I think that AI is going to be that. But what the internet did was it created whole new businesses. It created whole new opportunities within e-existing businesses. You didn't used to have SEO managers. Now you do, and there's a whole division devot-devoted to social media. That's gonna happen again. There's gonna be new stuff. There's going to be new opportunities, and the people that are your knowledge base are the ones that you're going to wanna tap to be able to use that.
[22:41] Christina Ellwood: In the next couple years, you think we're gonna have it become more baseline, and we're gonna have new titles and some new business opportunities tied to AI. Is that a good summary?
[22:50] Bob Mitton: Abso-absolutely, yes.
[22:53] Christina Ellwood: Okay. So what guidance do you have for executives who are early in their AI adoption journey?
[23:00] Bob Mitton: The very first one for, especially if you're not adept at and constantly using it, is pick one, one issue, the most annoying issue that you have, the thing that wastes the most amount of time, the thing that, uh, bothers you the most, and sit down and figure out, "How can I use AI to solve that time-consuming task?" And watch that, find that. And in, in the process of doing that, figuring that out, you will learn how much AI can transform your business.
[23:33] Christina Ellwood: Yeah, I think that's really good advice. I do. And what resources do you recommend to listeners who wanna learn more about taking that first step and about you? How do they learn more about you and your work?
[23:45] Bob Mitton: Well, obviously, experaconsulting.com is the website. You can see a lot of about us there and about what we offer and how we, how we, you know, interact with our clients. As likewise, I do a lot of podcasts. Obviously, AI Realized podcast, the Marketing AI Institute. Paul Roetzer has a podcast. Chris Penn has a podcast. And Ethan Mollick, phenomenal writer and Wharton professor, does a lot of good stuff around the adoption and what the new tools are doing. So all great places to start, and if you wanna go from there, I have a whole list of other places to talk to as well. So-
[24:24] Christina Ellwood: Well, great. Maybe- Contact- -share it out with us, and we'll put it, we'll put it in the show notes for your, for the listeners, that'd be great. If listeners remember only one thing from today, what should it be and why?
[24:34] Bob Mitton: One thing to remember from the whole conversation is to get started, right? Lean into your squad, commit to some OKRs, and go try it. Make sure that you share everything, your successes and your failures, and find the rest of the people that in the company that want to do this. Find the hand raisers. Find the innovators. Find the leading edge people, and get started 'cause you only need one small win to start a movement, and that's what you really wanna get to.
[25:06] Christina Ellwood: Great. That's awesome. So in the AI revolution, different leadership skills are needed 'cause this is not only a fundamentally different technology, it's a cultural shift within our organizations. So in this AI revolution, what's your defining edge when you're working with AI leaders?
[25:26] Bob Mitton: The thing that I bring to the table is I've been there. I've done that before, and I can do it again. I know what is needed. I know what the questions are going to be, and I've seen several different ways to answer those and frameworks in order to get it done. This is not a, it's not a marketing project. It's not an IT project. This is a, it's a leadership exercise. It's change management, and your competitive advantage is not necessarily knowing everything about AI, but it's surrounding yourself with the people with the right questions and tapping into their collective knowledge. This is a team sport, and you just-- everything starts with engagement and intentionality, and that's when everything starts to stick.
[26:15] Christina Ellwood: Okay. Thank you so much, Bob Mitton, co-founder of Expera Consulting, for joining us today on AI Realized.