In this episode of Executive Connect, Melissa Aarskaug sits down with Kristi Pihl to talk about what really keeps enterprise AI from reaching production. Kristi explains why most breakdowns are not technical, but organizational. She points to weak stakeholder alignment, fuzzy decision rights, poor governance, and a lack of trust across teams as the real blockers. She also breaks down how leaders should think about judgment, guardrails, human oversight, customer trust, and why speed without clarity creates fragility instead of progress.

This episode is for executives, operators, founders, and advisors trying to make AI useful inside real businesses, not just impressive in a demo. Press play before your next AI initiative becomes another pilot that never leaves the sandbox.

Chapters:

(0:33) Why most AI pilots quietly stall

(1:45) Where AI projects actually break down

(3:00) How to evaluate whether AI is serving users

(5:08) Why AI changes the way leaders should think about workflows

(8:55) Human judgment, algorithms, and generative AI

(13:32) What business strategy has to do with customer experience

(17:00) The governance enterprises actually need

(21:02) Why pilot cycles need to move faster and smarter

(27:01) How speed without judgment creates fragility

(30:27) The shift from operator to advisor

(38:46) The biggest leadership mistake executives are making with AI

(44:12) Kristi’s final warning on judgment atrophy

(46:43) Where to connect with Kristi

Kristi

(0:00) I just think that as much as we’re excited about the technology and the opportunities and I’m there, we need to be more excited and proud of these beautiful brains that live between our ears and the collective experiences that have built them over time. (0:16) As a long-time builder in this arena, building in public, you know, I think one of the most other important things is this is a wave that’s washing over all of us, right? (0:24) So the more that we share our stories, the more we connect with each other, you know, the more we can move away from some of the gatekeeping of expertise, etc.

(0:31) I think the better.

Melissa

(0:33) Every company seems to have an AI pilot right now and most of them quietly stall before they ever reach production. (0:41) Not because the model failed, but because the organization wasn’t ready for production. (0:48) They didn’t trust it, they didn’t organize it, and the decisions were made quickly.

(0:53) Today’s guest has seen this from every single angle. (0:57) Christy Peel has spent nearly two decades advising Fortune 500 leaders on AI transformation, shaping evaluations through buy and sell side technology diligence from private equity and helping build institutionally backed frontier products. (1:14) She now runs an independent advisory practice and writes Systems and Spines, focusing on how AI reshapes enterprises and what real leadership looks like when systems start learning.

(1:28) This conversation isn’t about tools, it’s about judgment, governance, and why enterprise AI lives or dies at the leadership level. (1:37) Welcome to the Executive Connect podcast, Christy.

Kristi

(1:41) Thank you, Melissa, for having me. (1:44) It’s a pleasure to be here.

Melissa

(1:45) Now, you’ve said most AI pilots don’t fail for technical reasons. (1:51) Where do they actually break down?

Kristi

(1:55) Well, I’m going to go ahead and go straight for the simple and most common answer that may not make everyone in my audience thrilled, but it is reality and it’s true, and that comes down to stakeholder alignment. (2:10) It’s really, at the end of the day, (2:13) one of the things that people misunderstand about AI is that if you have any disagreements (2:20) in your definitions, in your workflow, in your strategy, you know, if your EVP of sales is (2:26) arguing with your CFO over what revenue means, AI is going to amplify that, or it’s going to get (2:34) stuck in pilot mode because we’re not going to get to yes from the executive team to actually (2:40) push it into production.

Melissa

(2:43) Spot on. (2:44) As one of those EVP of sales, I’ve done my fair share of arguing about revenue with a CFO. (2:51) So, you know, how do you truly evaluate whether a product is actually serving its users?

Kristi

(3:00) How do you evaluate whether it’s serving its users? (3:03) I mean, this is where, at the end of the day, it has to come to outcomes, right? (3:08) I mean, we talk a lot about efficiency because that’s the easiest one to measure, right?

(3:15) So, if the users are doing their job more effectively, more efficiently, that one is there. (3:21) But oftentimes, the bigger ROI and the bigger, you know, if you want to get to different exponential benefits, we have to talk about judgment. (3:31) We have to talk about strategy.

(3:32) We have to talk about how are these technologies informing the decisions that the users are making, right? (3:41) Whether that’s customers making decisions about buying, retaining, continuing to use a product or service, or it’s internal users that are making decisions about, you know, how they go through their workflows or through their day. (3:55) And if we only focus on a small subset in that efficiency piece or those automation pieces, we miss out on the much bigger potential that comes when you start to really influence judgment and you influence, I like to call it the point of decision.

(4:11) When you’re really getting this technology to align with those points of decision, that’s when the users are going to start doing things that weren’t on any one strategy plan, but have the potential to move the meter.

Melissa

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(4:37) Don’t just watch, act. (4:40) Yeah, it seems so rudimentary, but you know, everybody’s working in their own silos, and there’s not really an organized decision. (4:51) It’s hard for, you know, any pilot, whether it’s AI or a technology pilot to move forward, we’re all just kind of in our own places.

(4:58) So you talk a lot about evaluating AI based on these decisions to make improvements. (5:03) Walk us through what that thinking looks like for those that are in these kind of pilots.

Kristi

(5:08) Yeah, the way that I think about it is the first thing I want to do is you have to step back and think about what this technology is versus how we’ve all been trained to think over the last, you know, 20, 30 years in SaaS. (5:23) We have gotten very good at what I would call algorithmic thinking, right? (5:27) So, or where users, we talk about use cases, we talk about problems, we talk about, you know, prediction models and those types of things.

(5:36) What those are all doing, though, is making the assumption that a human decides what this feature needs to be, what data it needs to pull, what the logic is, what the user experience needs to look like, what the workflow needs to look like. (5:54) And then the technologists and the technology build that. (5:57) And they build it to those specs.

(5:59) And then we deliver on those promises. (6:02) AI takes that table and just flips it completely upside down. (6:06) Because AI doesn’t always do what it’s told.

(6:10) AI is not just an executor, right? (6:13) So I think so many pilots I see where you’re trying to force the AI to just repeat a process over and over again and to do it faster. (6:22) And that misses one of the fundamental understandings and building blocks of what AI actually is, which is another intelligence, form of intelligence that you’re inserting within your systems and with your organization.

(6:36) So you have to think about it more in the context of what decisions are we comfortable with this form of intelligence making versus what decisions do we need to keep that human in the loop or set up a guardrail or set up, you know, something, a harness that can control that agent’s behavior? (6:55) Because that’s really where the work is to get it from pilot to production. (7:00) The work is all around the harness, right?

(7:02) The orchestration layers. (7:04) How are we making sure that this new form of technology isn’t just, you know, doing what we tell it to do, but we have the correct boundaries around it. (7:16) We have the correct safety mechanisms in place.

(7:19) And that’s all that very, you know, unsexy word, but I’ll say it, governance, right? (7:24) At the end of the day, it’s decision rights, it’s governance. (7:27) If you can get that right and you can have, you know, the right specs, the right ground truth, you can train, you can do all those pieces effectively, then going to production is a very simple exercise.

(7:41) But if you haven’t done that governance work and that alignment work to get, you know, what is the profit margin that we’re really targeting, right? (7:50) So let me give you an example, you know, if we’re looking, you know, credit lending, right? (7:55) So a really common use case for an agent, you know, if we want an agent to be able to approve or disapprove loans the way that an employee does, we first as a leadership team have to understand what are our guardrails around what, how would we bound that for an analyst?

(8:13) How do we do that in code, right? (8:15) How do we set, what is our acceptable threshold? (8:18) What are, you know, the credit scores that we’re comfortable with?

(8:21) How do we encode that? (8:23) Because that is a layer that we didn’t do in SaaS because in the SaaS world, if, you know, we have, we built protection mechanisms, right? (8:34) We’ve built fail stops.

(8:35) We told the system to error out if it didn’t know what to do. (8:38) In AI, it’s just going to go off and the agent is going to start approving or disapproving loans in that example I just gave. (8:47) And if we don’t, haven’t done the rigor to build the harness correctly, that’s where we’re going to get ourselves into trouble.

Melissa

(8:55) Now, I agree. (8:57) I think governance, I do love that word. (8:59) I do.

Kristi

(9:00) It’s so, maybe I’ve been a consultant for too many years. (9:03) That’s governance. (9:04) Can you, when you say a word too many times, it starts to take on new meaning, but yeah, but it’s true.

Melissa

(9:10) It’s so true. (9:11) It’s so important. (9:12) And so, so how should leaders think about when to use the human, I keep saying, no, we say the human judgment, not like we’re not human anymore, but you know, the human judgment, the algorithms, or the generative AI, like when, when should leaders think about these pieces?

(9:33) Because there’s very kind of three different pieces here.

Kristi

(9:37) Yeah. (9:37) So if you think about, I like to think about it as a trust curve, right? (9:41) So one of the things that I think is too often missing in this dialogue back and forth is there are still users and customers at the root foundation of all of this technology work, right?

(9:54) Where we’re trying to do, have these agents do something in service of, you know, the company, the workflow, the internal user, the customer. (10:03) And so each of those actions, each of those, you know, point targets that we want to point these agents at exists somewhere on a trust curve, right? (10:13) So I think about it this way, you know, a customer service interaction.

(10:17) I don’t know if you’re anything like me, but when I have to, you know, call a customer support because, you know, my, my piece of furniture is stuck somewhere in the supply chain. (10:25) I’ll be honest. (10:27) I’m not really looking for a lot of trust in that interaction, right?

(10:30) I don’t need to know that that representative has my back, is going to go fight with finance to get my credit or whatever is necessary. (10:39) I just need my problem resolved, right? (10:42) That’s simple.

(10:43) That’s an example of a low trust requirement that an artificial intelligence solution is ripe for, because we don’t need the judgment. (10:52) Take that to the other end, you know, we’ll go to, you know, the legal use case. (10:56) I’ll go to the whole entire other extreme, you know, imagine that you are facing, you know, a major custody battle with, for, you know, and you’re working with a lawyer, you’re not paying for that lawyer to just understand the case law and to, you know, know exactly, you know, what the, the information input is to your options and state law, et cetera.

(11:18) You’re looking for a high trust because you know that that person needs to be held accountable and to represent you in the room, right? (11:26) So it’s about that person’s, that lawyer’s judgment and their ability to exercise that judgment at the point in time where it matters. (11:34) And trust is incredibly important.

(11:37) So while the technology might be capable of, you know, being a lawyer or behaving in that way, the customer, the user, the human involved is not going to be there from a trust perspective and they’re not going to turn to it, right? (11:52) So I always like to think about when you’re talking about what forms of intelligence higher the trust requirements for success and for adoption or to get utilization, the harder it’s going to be to replace, I love that you said human, but to replace the human judgment with an artificial solution.

Melissa

(12:14) Yeah. (12:15) And it’s true though, because we’ve all had those calls where we’ve had to call customer service for one thing or another, and we just want the problem solved. (12:23) And it seems to me that, and maybe it’s just me, or maybe it’s just what I’m calling.

(12:28) It seems like it’s so much harder to solve problems. (12:31) I don’t know if it’s like longer, whether it’s a bank problem or a medical problem or a furniture problem, like you, you know, this team has to call that team. (12:41) They don’t know what’s going on with the supply chain.

(12:44) And so it’s just difficult. (12:46) And then you try to solve your problem on the website and use any of these, you know, bots that are communicating with us and they don’t understand what we’re talking about. (12:55) And then they tell us to phone someone.

(12:57) And, you know, I know I felt the same way. (13:00) I felt like I’m getting the runaround and it’s, you know, two hours later and I don’t have what I need resolved and I’m frustrated. (13:06) And so, you know, I guess the question is in these scenarios, what types of decisions (13:14) should be delegated versus, you know, going to that furniture stores website, you know, (13:19) maybe they have a bot that can, you know, check where the furniture is or what the malfunction is (13:24) versus actually having to call and sort through your, you know, your understanding of when you’re (13:30) going to get what you ordered and paid for.

Kristi

(13:32) Yeah. (13:33) I mean, this is where this, this might be an uncomfortable statement, but I think this is where the rubber hits the road. (13:40) And having done exactly this kind of work for a long time, the answer to that is actually going to be much more driven by the business strategy than it is going to be by the technical decision-making.

(13:52) Because, you know, for example, you know, we’ve been playing this game for a long time with time on phones and, you know, what it takes. (14:03) Oftentimes those difficult customer service experiences have been strategically designed by the company because, you know, it’s to their financial best interest, right? (14:15) So for example, you know, there are companies, you know, take your telecom providers that they know down, their analytics have been so sophisticated.

(14:25) They know things like how many minutes or how many different buttons have you had to click in order to try to get your issue resolved before you just get, throw your hands up in the air and say, I give up, whatever, just, I’m not going to fight this battle anymore. (14:38) I guess I’ll go buy a replacement somewhere else. (14:41) So we, those leadership teams have been making decisions around what’s acceptable thresholds for a long time.

(14:51) What AI just does is gives us more power on both the customer side and the business side to be intentional about those, right? (15:01) We can all tell the difference between an organization whose business strategy is, I don’t care what the problem is, just issue the credit, we’ll figure out the rest later, right? (15:10) Those experiences as a consumer are the ones that make us feel, oh, great.

(15:14) They just handled it. (15:17) They’re all the way to the other end where you have organizations that put you through the ringer on purpose and it’s a part of their financial model. (15:27) So as much as, you know, we can talk about product, you know, the pros and cons and strengths and weaknesses of different user experiences designed with AI or without AI, at the end of the day, it’s going to come down to the business’s strategy and where does the business make money and what is their philosophy on how they treat customers and how, you know, their go-to-market strategy, right?

(15:52) If having high customer satisfaction, I wish that was the goal. (15:56) I mean, I’m a person. (15:57) I wish that was the goal of every business in the world, but it’s not, right?

(16:03) And I think that’s where I’ll give the real answer, which is that’s the real world. (16:08) AI just, you can either make those workflows more efficient or you can intentionally slow them down. (16:15) And those are, again, I go back to those are controls and governance that the leadership teams and the questions that they’re being faced with right now.

Melissa

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(16:44) And as a listener of the Executive Connect podcast, you can get it completely free. (16:50) Just visit moneyripples.com forward slash secrets and enter the promo code E-X-E-C. (17:00) If companies don’t solve these problems and their customers are getting the runaround, however it may be, they’re going to switch places.

(17:09) Like I just recently switched a doctor’s office over something so simple that it took me five hours to solve. (17:16) I’m not dealing with this anymore. (17:17) I’m just going to go find another doctor.

(17:19) I think that’s the question. (17:22) Let’s go back to this G word. (17:25) When systems start learning, what kind of governance do enterprises actually need to have in place?

Kristi

(17:32) Well, the governance that they need to have in place is one that the human is still making the strategic decisions about exactly what I just talked about. (17:41) I think that’s an example of what I’m talking about is the more sophisticated end where you have companies that are playing different games, like contractual moats with HOAs and different go-to-market strategies. (17:57) A doctor’s office is, I think, a great example of probably very cleanly, you don’t want to have your patients going through a negative experience because it’s high sensitive, highly personal.

(18:10) Again, back to that trust component. (18:12) It’s very high on the trust spectrum. (18:15) The things that they need to put in place are make sure that the AI understands that that’s your guardrail.

(18:23) If time to resolve issues is the most important metric for the practice, then you need to track that metric and the AI needs to have a very strict guardrail that’s been set that if it isn’t resolved within X number of steps, within X number of seconds or minutes, throw an error out or notify a human to get involved. (18:50) Again, those are the way it gets down to understanding your business strategy. (18:55) If it says, hey, if someone’s tried for 10 minutes to talk with this chatbot, or they haven’t been able to find their patient record three times in a row, those are rules that we can put into the harnesses of these systems that are very easy then for the agents to maintain.

(19:13) But if you don’t set those rules and you don’t create an orchestration layer based on a context graph that can actually govern those agents, they’re going to optimize for whatever they see fit. (19:27) They’re going to come up with a strategy that probably doesn’t align with what your goals are as a business.

Melissa

(19:33) Yeah. (19:33) And it’s funny, back to this doctor office visit I had, they were doing a pilot with a specific AI company on integrating tools into their practice, very large medical practice here. (19:51) And they actually stuck a note when you come into the doctor’s office that literally said, we’re doing a pilot, please be patient with us.

(20:03) And I, so of course, me being me, asked the nurses, I’m like, how long have you guys been doing this pilot? (20:09) Just because I was curious. (20:11) Eight months.

(20:12) And I’m like, are your are your patients happy? (20:16) Are they, do you see more efficiencies with check-in, billing, you know, everything that you guys are trying to put to leverage the tool with? (20:26) And they’re like, actually, no, it’s gotten worse.

(20:28) We’re all trying to. (20:30) And so to your point, putting these controls in place, you know, if a patient’s done this, or they’ve done that, and they’re still struggling, these controls have to be put in place. (20:41) Otherwise, you’re going to upset your customers, and they’re going to leave to somewhere else.

(20:47) And so, you know, I feel like, you know, how do enterprises monitor modern drift over time? (20:56) Like, is there a way they can, you know, put, you know, boundaries, checks and balances in place?

Kristi

(21:02) Yeah, first thing I’ll say, and, you know, I don’t know this particular practice, but eight months in the year 2026 is not a pilot anymore. (21:12) You know, one of the fastest things is if you’re not capable of, you know, deploying an agent solution, or, you know, testing out a workflow in the order of hours or days, that’s, then you’re not, that’s, this technology is fast, right? (21:30) I mean, I’ve been a part of, you know, things that used to take, you know, you’d bid out, you need to build an application that takes four weeks, you have to build the back end, you have to build the front end, you have to integrate into the system of record.

(21:43) Those things took engineering time, and designer time, and weeks. (21:47) Teams are capable of doing that in days now, you know, when you really use cloud code. (21:52) So, that’s sort of mistake number one, is if something’s been running in pilot, and you’re still waiting for learnings, that just leads me towards poor strategy.

(22:02) I’ll just be, say it candidly and bluntly, because that just means if you’re not seeing the results, you know, what’s that old saying that we all say, you know, show me once, shame on me, show me twice, shame on you? (22:14) You know, those front end staff that you spoke with have known for many months now that it’s not creating efficiencies, and the process isn’t working for the patients or for the staff. (22:26) But again, you get back to, I think, this is where that damage that’s been done by the public narrative has, I think, been so rotten.

(22:35) So, I say this because, so, I’ve actually been working and doing AI deployments since 2018. (22:40) So, this technology has actually been around for a lot longer than people realize. (22:46) I mean, I was hooking into, you know, workday systems with an AI solution in 2019.

(22:52) What happened, though, when ChatGPT and others burst on the scene, and we got into this insane hype cycle, and I’ll call it that, is that every company all of a sudden had access to it through the consumer application. (23:08) And so, it wasn’t, you know, now all of a sudden, and it was purposely designed for maximum potential seeking, right, and attention. (23:17) And we’ve created such this fear of, if we don’t have a pilot running, or if we, you know, aren’t finding these efficiencies that folks are talking about, we’re six months behind.

(23:31) And so, or we are, our practice economics aren’t going to work anymore compared to, you know, the big practice down the street who has been implementing this AI solution. (23:41) And that’s all just fundamentally, you know, unfortunately, falling victim to the hype narratives and the media narratives, right? (23:49) Because I think the companies that are really doing this well, and I see them all the time, are the ones that have maintained their conviction and what they’re good at, right?

(24:01) You know, the billing office in that practice, there’s a reason that practice has grown and been very, very successful, and they’ve had healthy financials, probably a lot to do with the expertise and the knowledge and the systems they’re using for things like billing. (24:15) So, instead of just, you know, believing that an AI solution is going to be able to, on day one, come in and remedy that, what companies are doing that are, I think, approaching this differently is they’ve just dramatically shortened the windows of test and learn cycles, right? (24:31) So, they’ll point and say, hey, let’s just only in this part of our practice, or let’s pick this one doctor and let’s point the agent just here and see if we can optimize this process.

(24:44) And then they double down, right? (24:47) I think, I even heard a story, you know, Anthropic is releasing features, and this is Anthropic, based on feedback within two weeks, right, on their product and platform, right? (24:58) That’s how fast, you know, I think the build cycles have gone.

(25:02) But the problem is the build cycles, you know, went all the way down to zeros and to small numbers, but the cycles of being able to interpret the results, being able to see, hey, actually, our billing had more inefficiencies, more errors. (25:21) We lost, you know, our folks are taking longer. (25:24) It’s taking more minutes to process than it used to still take just as much time because our patient flow is the same.

(25:31) So, we have to slow down the builds and the strategy roadmaps, and I’m a true technologist. (25:39) I do come from faster. (25:41) It’s hard.

(25:42) It has been a hard evolution for me to go on to change my brain set from faster, faster velocity, more engineering teams, get features out faster, get the pilot in the field faster, grow, grow, grow. (25:56) But this moment, (25:57) I think, calls for all of us to check that wiring a little bit and say, hey, (26:02) we need to make sure that we’re going the pace of our business, the pace of our customers, (26:07) the pace of our people at that, you know, while accelerating it, because if we do that, (26:14) our strategy will stay aligned, and then we’ll get the hockey stick curve down the line once we (26:20) truly understand how this new artificial intelligence makes into these workflows.

Melissa

(26:27) Yeah, and I think it goes back to what you were saying at the beginning with decisions, right? (26:31) And I think, you know, to that doctor’s office, nobody was making a decision, and the people that had to deal with the outcomes, you know, probably said something to the decision makers, and yeah, yeah, we’ll get to that. (26:42) Yeah, yeah, we’ll get to that.

(26:44) But let’s talk a little bit about judgment, speed, you know, the organizational fragility. (26:51) Now, you’ve warned that optimizing for speed without protecting judgment creates fragility. (26:57) What does that look like today and in practice?

Kristi

(27:01) Yeah, so for just so organizations that only optimize for speed, you know, things like tech debt, things like, you know, backlogs of credit failures, those things didn’t just magically go away. (27:19) And so, if you’re only looking at the variable of speed, that’s the SaaS way of thinking about things, right? (27:28) That’s a linear direction.

(27:31) You know, if we go faster, we’ll get to higher volume, we’ll get to higher scale, we’ll do better. (27:35) But what happens when the faster you go, the less sophisticated your strategy is, the less learning, the less rules, the less governance. (27:45) And so, the organizations, I like to say that clarity is the new moat, right?

(27:50) So, I think clarity about, you know, what you won’t do is equally important as what you will do. (27:58) Does that make sense? (27:59) So, when you’re, this is the first technology we’ve done, where, you know, take your continuing with your practice example.

(28:07) This technology fundamentally, I think a lot of folks miss how it works at the kernel and why things like hallucinations happen is because this technology is incapable of not giving you an answer, right? (28:21) It is fundamentally designed to fill in the holes to the best of its ability and find the right pathway to lead you to an answer. (28:31) Whereas in other technologies, I think where folks were used to just letting these pilots run, you know, SaaS and algorithmic logic isn’t going to invent things.

(28:42) It’s not going to, it’s not forcing to an answer. (28:46) It’ll just error out or it’ll just stop or people will just stop using it. (28:49) Agents don’t sleep.

(28:51) Agents don’t just stop running in the background. (28:53) They’re going to continue to optimize towards their strategy as they see fit. (29:00) They’re going to keep learning from those patient workflows.

(29:03) They’re going to keep learning what the feedback is and they’re going to optimize. (29:07) And so, (29:09) by not being hyper clear and having strong clarity about what those controls need to be (29:18) around these solutions, because we move too fast back to the speed and we sort of skip that step (29:25) because we see the potential and people don’t want to talk about, you know, slowing things down, (29:31) you just end up scaling and putting jet fuel on solutions that are going to take you down (29:40) pathways you may not want to go down.

Melissa

(29:44) Yeah, well said. (29:45) And I think part of it is I don’t know, maybe it’s just me, but you know, we’re all kind of conditioned to have everything today or tomorrow, like everything quick, quick, quick, done quick. (29:59) You know, we need something from Amazon.

(30:01) It’s on our doorstep. (30:03) We need delivery services same day. (30:06) So, I think it’s some of this conditioning that, you know, we’re just, we’re developing so quick.

(30:12) We come out of college and we want a six figure job right out of college. (30:15) And so, I think it’s just all these things that are technologies moving faster, the world’s moving faster. (30:21) I want to talk a little bit about you moved from operator to independent advisor.

(30:27) So, what changes and what really doesn’t change?

Kristi

(30:33) Oh, switching gears to the good old operator to advisor transition, which I, having been, I have lived on both sides of it. (30:44) So, I’ve been the operator chair where you’re, you know, responsible for the outcomes and you’re the ticket. (30:51) But I’ve also, I think what maybe is a different value perspective is I’ve worked for a couple of different firms now where we’re very intentional about trying to make sure we balanced our resources between, you know, folks that came from the strategic advisory world.

(31:05) So, the consulting firms, et cetera, and folks that came from operations. (31:09) And so, I can speak to not only what the good parts about it are, but also what some of the biggest hurdles and the struggles are, right? (31:19) So, I think the first thing to say is, as an advisor, you are the product.

(31:27) And that shift from where you are representing the product or you have, you know, say you’re an SVP of products for an organization, you are always, you know, your role, just based on the title itself, carries credentials because your expertise and your day job might be there, but you are the product that you’re building or the product that your teams are building. (31:53) And therefore, every vendor you talk to, every potential employee you talk to, every, you know, podcast that you go on, you are an extension of that company. (32:04) When you are an advisor, it’s you and it’s your brainpower and it’s your collective experiences that that is the product.

(32:13) Does that make sense? (32:14) So, I think one of the biggest things is all of the sudden, your expertise may not have changed. (32:19) You may still be deploying the same methodologies, the same frameworks, have the same knowledge base, but that need to have to credential yourself and sell it and prove it without having, you know, the expertise that’s just connotated with the level of the role is a big hurdle that I’ve seen a lot of folks have to make, right?

(32:41) Because you walk into a room without the expectation that you belong there, right? (32:47) Or, you know, depending on which firm you work for as an independent, but being able to kind of shed and still maintain your level of expertise and learn the tricks for how do you communicate in such a way that you can quickly get to that level of respect and that level of expertise is a tough thing for folks that have never actually jumped from the operator to the advisory side. (33:11) The other big hurdle I always talk about is there’s this thing called control.

(33:17) And, you know, any advisor that’s been doing this craft for a very long time, one of the first things I’ll say is if only my clients had done what I told them to do and, you know, had taken my advice. (33:30) And that’s not coming from a place, you know, advisors often have big egos, but that’s not coming purely from a place of ego. (33:38) It’s coming from, you know, part of why you’re hired as an independent advisor is because you’re bringing outside perspective.

(33:44) You’re bringing what you’ve seen work or not work in various rooms. (33:49) And so when you can see a client or a business, you know, like your doctor’s office example, I would have had a very hard time as an advisor. (33:59) It sounds like you were the same, not just saying, trying to walk up to that doctor and saying, do you understand that you’re destroying patient trust?

(34:07) Like, do you understand that you might not see it in the metrics yet, but there are a non-zero number of patients that are walking out the front door every day going, I’m not dealing with that mess ever again. (34:21) And it’s so true. (34:23) When you’re, when you are, when you’re directly there, you can just tell that to your, you know, those, the people that are working in that practice can just walk up and tell that to their boss or, you know, there there’s some risks to that, but it’s a little bit more, if you’re a powerful executive, you just say those things in the room and people listen.

(34:40) But advisors oftentimes see things that you have to learn the politics and the relationship building side of, okay, what’s the appropriate time, you know, wouldn’t be a good for business development if you’re trying to grow a practice or a firm to just, you know, anger your clients right out of the gate with the first conversation. (35:00) So I would say that, you know, from the transition from operator to advisor, not having direct control, but having your power all come through influence and how good of a communicator you are is, is a big hurdle that folks have to get over. (35:14) And then also, you know, that, that need to not think in terms of this specific client, what will this specific client need?

(35:23) I’m, I’m representing my product, but having that direct separation, those are pretty big hurdles that folks have to overcome.

Melissa

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(36:30) I think it’s communication, right?

Kristi

(36:32) That’s, that’s what I’m saying. (36:33) Like it’s, you know, I like to say that I’ve never been a comms professional, but I’ve spent my entire career honing narrative, right. (36:40) You know, it’s because you have to learn how to, you know, advisory work, you’re going to have 15 minutes, maybe in an entire month to get, to take an executive team to the point of decision.

(36:55) So all the work that has to happen beforehand, all the work that has to happen after it’s all about controlling narrative and making sure that you are doing that in a really fast and effective way. (37:08) Because again, you know, to be a services provider and to be an independent advisor, a successful one, you have to build trust and you have to build influence. (37:18) And those are fundamentally about communication about, you know, how you show up in a different way than, you know, you know, being the performance metrics that you have as an internal operator, which are more tied to your business.

Melissa

(37:32) And it’s the human side of what we do, right? (37:34) We can’t deploy, you know, maybe I that’s, that’s a flat statement, but we can’t, in my opinion, we can’t deploy anything correctly if we don’t factor in the human side or the communication side, the, you know, the emotional side, getting feedback, what’s working, what’s not working, what can we do better? (37:53) And trust is so huge.

(37:55) I read an article and I forget, like, I forget the, who wrote it and what the percentages were, but so I’ll just leave it, you know, non-descriptive, but the amount of, it was polling the Americans. (38:11) There’s a political article that how many Americans trust the US government, trust their employers, you know, the whole article was about trust and the numbers were staggering on how little people trust anymore, you know? (38:27) And so that to your point, trust is really, really key.

(38:32) So when we think about, you know, the, you know, executive side advisory questions in the world we’re living in, what is the single biggest mistake executives make when they’re deploying AI today?

Kristi

(38:46) I would say they, the single biggest mistake that they’re making is they are not creating an environment of trust and safety, you know, this, and I would actually extend that not just to the executives. (39:03) I mean, we’re seeing it, the full marketplace, right? (39:05) As someone who’s a true believer in this technology and because I’ve, you know, (39:11) always been, I loved front-end technology and I just view it as a technology, (39:16) the fast, the speed of this rollout is doing real damage to trust and doing real damage to, (39:23) you know, if we want to get to adoption and we want to truly use this technology to make (39:29) lives better and to improve our businesses and improve experiences, (39:34) the marketing and the PR and the communication and the speed of the rollout matters. (39:39) And we are doing a lot of damage to societal trust in this technology right now.

(39:44) You know, when the CEO of one of the biggest companies goes on and compares, you know, the amount of food, you know, the amount of energy it takes to train a model to how much food someone eats over the last 20 years or what it takes to raise a human being, those things betray trust of human beings because human beings don’t like being, you know, thought of as batteries. (40:08) There was a movie called, I don’t know if you remember, it starred Keanu Reeves that made that point quite clear that we don’t, the Matrix, that we don’t, I’m sorry, that we don’t want to just be batteries, right? (40:19) That’s, we are human beings.

(40:21) We thrive on community. (40:22) We thrive on connection. (40:24) We thrive on ambition.

(40:25) We thrive on joy and working with each other. (40:30) And so I think when I think about executives, the biggest mistake that they’re making is not understanding and really living in the experience that their team members are having when they are being asked to adopt these new tools, right? (40:46) Someone is having the visceral reaction.

(40:49) I’ve seen it play out tons of times. (40:51) When you realize, you know, software engineers were the first to go through this wave, and I work with a lot of software engineers, so I’ve seen this. (40:57) You know, if you’ve spent 20 years learning how to code and then all of a sudden you open up a cloud code and you see what it’s capable of, it is a normal human reaction to be afraid of what that means for your livelihood.

(41:13) You know, if you’re, if you’re supporting a family at home, what does this mean for my identity? (41:19) And so I think on executives, this is a massive leadership challenge because you, you have to understand, I mean, it’s like taking change management the way we used to talk about it on steroids is what’s facing the leadership challenge right now. (41:35) And so I think the leaders that are doing, not just showing, so second biggest mistake, I actually probably would put this at zero, but if you’re not an executive that’s building within these tools already and reteaching yourself, that’s probably actually your first biggest mistake.

(41:50) Because if you’re presenting the slides on what this is supposed to do, and you’re letting the companies come in and, you know, do their workshops about how it should be used, how it should be leveraged, but you’re not also using it and, you know, building your own websites, building your own agents and doing that, you, you’re missing something here because the times have changed. (42:12) So to me, it’s about, you need to be able to speak the speak. (42:16) And if you’re going to understand (42:17) the fear of that software engineer, who’s resisting using this, or you’re going to (42:21) understand the fear of, you know, the customer support reps that are being asked, you know, (42:26) feeling like they’re training their replacement, you need to also feel that fear of, oh, wow, (42:31) this is building a strategy that was faster than maybe I would have historically built that (42:36) strategy. (42:37) And so I think executives, you’ve got to get your hands in the pot and we’ve got to get back to building and then slowing down and having honest conversations that are rooted in trust and, you know, authenticity is probably the second biggest mistake.

Melissa

(42:58) Yeah. (42:58) And I think about how many generations are in the workforce now and how they all leverage technology and AI is different. (43:07) Like my children, super comfortable with technology and AI, my parents, not so much.

(43:13) So when they have to fill out things a certain way medically, it’s, it’s very different for them when they’re talking to bots or other things, they’re not comfortable with it. (43:23) They need that human experience. (43:25) My children, you know, their homework is on a computer, their whole life is on and leveraged around technology.

(43:31) And so I think back to what you’re saying, trust, communication, meeting people where they are. (43:38) My parents, you know, generation is going to need more training, more communication, more understanding on why things are happening, how they’re, you know, having to use this and how it’s going to help them as a customer in different, in different scenarios. (43:54) But so much good information here today, Christie, thank you much for sharing your knowledge and your time with our listeners.

(44:02) I want to get any final thoughts that you want to leave today and then share a little bit about what’s the best way to connect with you and learn more about the good work you’re doing.

Kristi

(44:12) Yeah. (44:14) I would say that the final thought that I want to, to end with is I do have a concern about, you know, judgment atrophy, right. (44:27) And in some of the pace, I’ll tell this in the form of a quick personal story.

(44:32) You know, I’ve been on my own up and down journey as a user, a power user of these AI tools. (44:37) And there are points where you catch yourself offloading judgment or, you know, accepting an answer because it came faster, it came cleaner, that, you know, if I was hearing that from a member of my team or I was hearing it from somewhere else, I would have historically questioned. (44:58) So it is hard to resist the pull of that confidence.

(45:03) And, you know, all of, you know, the reason I’m effective with AI is because I’ve done this work in real, you know, before I had the AI. (45:12) And if we lose that ability and that judgment, that’s the closing thought I always like to end with, because, you know, the costs that we’re paying with our own brains when we use AI is real, right. (45:25) And, you know, the succession plan gaps, and you talk about generations.

(45:30) I mean, there’s a cost that if we don’t teach the junior resources, and, you know, I learned how to do what I do by watching my mentors and managers do it over the course of a 20-year career, right. (45:45) If we hollow that out, we’re going to wake up 10, 15 years from now and not have built those experiences. (45:51) So I always like to end with (45:53) the most important, I think, the top of the pyramid for all of us as individuals, as companies, (45:59) as parents, as leaders, is how are we protecting our own expertise and judgment and making sure (46:06) we’re being intentional about where we’re offloading that, and bring back apprenticeship, (46:13) or how are we thinking about what the plan is going to be to replace that in the future with, (46:21) you know, the junior team members, our kids, etc. (46:25) So I just think that as much as we’re excited about the technology and the opportunities, and I’m there, we need to be more excited and proud of these beautiful brains that live between our ears and the collective experiences that have built them over time.

(46:43) In terms of where to find me, so you can go to systemsandspines.com. (46:48) That’s where you’ll find my website. (46:50) I do publish a weekly newsletter where I, you know, as a longtime builder in this arena, building in public, you know, I think one of the most other important things is this is a wave that’s washing over all of us, right?

(47:02) So the more that we share our stories, the more we connect with each other, you know, the more we can move away from some of the gatekeeping of expertise, etc., I think the better. (47:11) So I try to share openly. (47:13) It’s always free, so no cost to find me there.

(47:16) And, you know, if you’re curious about making the jump into independent advisory work or you are in need of those kinds of services, you can also find my company contact information on that site.

Melissa

(47:27) Thank you so much for being here today, Christy. (47:30) That’s the Executive Connect podcast.