In this episode of Executive Connect, Melissa Aarskaug sits down with Emily Lewis-Pinnell to talk about AI adoption, organizational change, culture, and why most companies are still getting this wrong. Emily draws from years of work across cloud transformation, data strategy, and enterprise change to explain why AI success is less about buying tools and more about aligning teams, workflows, and expectations. She breaks down why smaller companies often have an edge, why leadership behavior matters so much, and how AI creates the most value when it augments people instead of trying to replace them.

 

This conversation also explores ROI, shadow AI, adoption friction, early-career development, communication skills, and the real gap between how fast AI is moving and how slowly most organizations know how to adapt. It is especially useful for leaders, operators, and executives who want a practical lens on how to make AI useful inside an actual business.

Chapters:

(0:27) Meet Emily and why most companies fail at AI for human reasons

(1:23) Why AI adoption breaks down inside organizations

(3:16) What actually signals whether a company is ready

(5:04) Why smaller businesses may have a real advantage right now

(9:59) Why leaders often benefit from AI more than expected

(12:36) Delegation, strategy, and using AI as a thinking partner

(16:17) How leaders can use AI in their personal lives too

(18:51) Why AI adoption still feels slow across much of the economy

(24:03) Which industries are moving first and what that means

(28:52) What AI means for early-career professionals and the next generation

(33:14) The communication skills that may matter most in the future

(37:18) What separates successful AI investments from wasted spend

(51:28) Emily’s one core takeaway for leaders navigating AI transformation

Emily

(0:00) The biggest piece of advice that I give any child I know right now is to find a way that you get up and get comfortable speaking and listening, which was a lesson I took a really long time to learn. (0:10) I very happily went into software development out of college because I didn’t want to talk to anybody. (0:14) And now I’m here talking to you and like doing these things.

(0:17) But if our children can focus on that early and their generation is very much on phones and so forth, it’s a differentiator that you can use to get into the marketplace better.

Melissa

(0:27) Everyone’s talking about AI, new tools, new models, new promises that everything is about to change. (0:36) But the truth is most companies don’t fail at AI because of the technology. (0:41) They fail because people don’t know how to actually use it or worse, they never changed how they work.

(0:48) Today’s guest has spent years helping organizations navigate major technology shifts from cloud transformation to data strategy and now AI adoption. (1:00) Emily Lewis-Pinal works with leaders to cut through the hype and build practical AI strategies that work inside a real organization. (1:10) This episode is about what actually works and why most companies are getting it wrong.

(1:17) Welcome, Emily.

Emily

(1:19) Thank you, Melissa. (1:20) It’s great to be here. (1:21) Thank you for having me.

Melissa

(1:23) You’re welcome. (1:24) Now, you worked on major transformations for companies like Dell, NTT Data. (1:30) From your perspective, why do organizations struggle with AI adoption?

Emily

(1:36) Yeah, over 20 years, I’ve helped large enterprises, small companies of all sizes with various disruptive technology innovations. (1:44) And the technology is always hard, but it’s actually still the easiest part. (1:48) Dealing with how it’s impacting your people, how your culture adapts to working differently to make use of new technology, that’s much more challenging than just implementing the technology itself.

(1:59) That’s something I worked with many executives when I was at NTT, driving the cloud practice, because they were working to try and move to more of a DevOps model within IT. (2:08) And I would come in and they’d be like, we built this system. (2:11) You’ve got to go tell the dev team over there what they’re doing wrong.

(2:13) And then the dev team would tell me, you have to go talk to the apps team about what you’re doing wrong. (2:17) And we actually pulled on that thread and did a bunch of research that showed that the IT teams that invested in culture and up-leveling their people and reframing how they work together were the ones who were getting the most out of the investments they were making in cloud and all the other associated technologies. (2:32) And so, as we started working with companies on piloting AI over the last few years, where I’ve had a lot of technology teams that we’ve brought in to help early adopters and tech forward businesses think through how they could use AI, I started recognizing that is the biggest chasm we have to cross, because if it was a problem just for IT teams 10 years ago with cloud, AI is everybody.

(2:54) So put that on steroids times a thousand of everyone figuring out what it means to them and their jobs. (2:59) And we’re starting to see that a little bit more right now, as you’re seeing some of the big platforms partnering with some of the big consulting teams and trying to figure out how to make this, because I think everyone’s starting to realize that this is the big thing that needs to be solved to make use out of the investments people are putting into AI technology.

Melissa

(3:16) Yeah. (3:17) And I love that you mentioned culture, because I think in my opinion, that’s where a lot of these AI initiatives break down first. (3:25) It’s misalignment.

(3:26) And if we’re not aligned in whatever we’re transforming, it’s hard to make it work. (3:31) So what signals really indicate if an organization is ready to make this transformation?

Emily

(3:38) I think anybody is ready, but the key issue is, are they ready to make the right steps? (3:43) And are they going to make the right decisions? (3:45) Because just plugging in a tool and expecting it to take off and work and be used well is not going to work.

(3:51) You’ve just seen that. (3:52) Encouraging your people and getting them on board and thinking through how is it going to be used? (3:57) What are our new norms going to be?

(3:59) Because these are issues that haven’t come up yet. (4:02) What do we believe is a good way to use AI? (4:04) Let’s all be honest and decide on that together.

(4:06) What does it mean to the future of the workforce? (4:08) How are the people and teams working together? (4:11) And this is where some of the current dialogue going on is actually unhelpful and moving in the wrong direction, because when so many of the headlines are about, we are cutting 5 million jobs right now, and it’s all thanks to AI, which pretty much everyone takes away.

(4:24) No one’s getting that much value out of AI right now. (4:26) These are companies that are just making headcount and throwing AI in because the investors want to see it. (4:30) But you also have then the tech teams, the tech leaders themselves talking about the large percentage that can be cut.

(4:36) That is not a helpful conversation to encourage everyone to say, this is a new empowering technology that we want to make use of. (4:43) And Anthropic just came out with their latest report last week, which is actually showing some of the biggest values coming from augmentation, when people are using AI and working with it to improve their jobs more than just pure automation. (4:54) So we need to be figuring out how do the two work together?

(4:57) And then how does that rewire where processes need to change and things are happening to tap into all the value?

Melissa

(5:04) And I love this. (5:05) And one of the things I love about AI right now is it helps the small to medium-sized businesses punch above their weight class. (5:12) And usually a lot of times, the only people that have access to this was the companies that have the dollars.

(5:18) And so there’s a lot of focus right now around enterprise IT, but smaller businesses have a really unique opportunity right now if they take advantage of it. (5:27) What excites you most about the SMB world and how can they use AI right now?

Emily

(5:33) Yeah, a lot of the early work in the early pilots was with the big companies that were able to pull together the budgets, frankly, that were all getting the same high-level strategic thinking of we need to dive into this. (5:44) And so they really moved the needle in terms of moving things forward. (5:47) When you look at where the tools are right now and how much a company can do with just a professional business subscription to chat to BT or Plod or using their Gemini or whatever else, it’s a lot.

(5:58) And for a small company, it’s not so much then a question of what does Jane’s job look like in the future with AI coming in? (6:06) It is, we don’t have Jane. (6:07) We’ve never been able to afford Jane.

(6:09) We aren’t a 300-person company. (6:11) We are a 10-person company, and we know these certain things very well, but how can we lever that up and go beyond things that we couldn’t do before? (6:19) And the fact that AI is most successful when people understand and are familiar with the domain, but can get into specifics around it that they might not have had.

(6:27) So it’s letting those small teams do more. (6:29) So think about a one-person marketing team being able to dive in and really drive SEO and their website stuff to the same extent that they would have before had to have hired specialists to do that and able to go and build that out. (6:42) And in this case, it’s not a question of, is it as good as the person would have been?

(6:46) It’s a, we couldn’t have done it otherwise. (6:48) So any improvement is good in there. (6:49) And then there’s also the flexibility that smaller companies have to experiment and work a bit more than a larger corporation can do, because they have to think more about the pros and the cons.

(6:58) And that entire piece of thinking about how it works for the workforce, that’s a negative. (7:03) The bigger you are, the harder it is to figure out how, what does this mean for all 50,000 of you? (7:09) How are we all thinking about how we work together?

(7:11) How are the processes that flow amongst many, many people to get one piece of the value stream done? (7:17) How is that adjusting? (7:18) That’s a much simpler question when you’re dealing with a small organization that you’re all talking to each other all the time.

(7:23) So I’m seeing some of the most innovative use coming out right now with smaller companies, not to say that big companies aren’t pushing forward and working on this. (7:31) They just have a bigger challenge to try and figure it out.

Melissa

(7:34) Yeah. (7:35) And I love that. (7:35) And I agree with you on the business subscriptions.

(7:38) I use PowerPoint a lot, make a lot of PowerPoint decks for presentations and either myself or an assistant is doing a lot of these clerical administrative tasks to make it pretty and nice and all the things. (7:51) So now all I do is put the content that I know in these PowerPoints and I spit it in some of these tools. (7:56) And it’s amazing how easy it is to make my PowerPoint, a C-level PowerPoint to an A-level PowerPoint.

(8:05) So there’s a lot of strategic advantages right now to leveraging these tools for 20 bucks a month. (8:10) So I want to get your perspective a little bit about what advantage do you believe smaller companies have right now over these larger enterprises? (8:20) Ready to lead smarter and invest wiser?

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Emily

(8:35) It’s just that it’s how quickly they can adapt and use those new tools as they’re coming in and be smart about it. (8:41) Not just the, can you make a PowerPoint, but can you use the various best practices to come and say, here’s what your PowerPoint should look like. (8:49) I’ll give you an example, which is I started my career building websites, but this was a long time ago.

(8:55) The technology looks a lot different right now. (8:57) And then since then, I’ve been working with my marketing functions across various organizations on what websites look like, but I’ve had layers of people that actually did the work. (9:05) I haven’t touched anything.

(9:06) So one of the first things I did last year was I do my go-to-market for my company. (9:12) What am I going to do? (9:12) So I was able to just get advice from the chat on here’s how Webflow works.

(9:19) This is the step to take. (9:21) And I knew what I wanted it to look like. (9:23) I didn’t know the execution to get there.

(9:24) And that’s an example of, I mean, I knew enough that to control the output and have the judgment piece that people need to bring in. (9:31) So I wasn’t just building a piece of garbage, but I didn’t actually know the right steps. (9:36) And I was able to use the tool to get from there to there.

(9:39) Whereas three years ago, I would have had to go and outsource that to another agency, which frankly, I at the time didn’t have the budget for. (9:46) So I would have been stuck with the minimal web presence as the alternative. (9:49) And by the way, because things keep moving.

(9:51) Now, when I make updates, I actually do it in Cloud and Cloud does it in Webflow because now it connects right to it. (9:56) So that keeps getting easier and easier.

Melissa

(9:59) It’s so true. (10:00) And I think, you know, the one thing I caution people with is, you know, they are tools, right? (10:05) We still need this human oversight and we need to push back with these tools, just like we would in dialogue with our peers or our leadership is, is go back and punch holes.

(10:16) Like I do this a lot with my tools. (10:19) Like, you know, I put the PowerPoint in and ask the tool, you know, here’s my final PowerPoint, you know, where did I miss the mark? (10:27) I’m trying to do this, this, this, and getting my prompts right.

(10:30) And sometimes the beautiful thing about that is sometimes I actually miss things and the tool is letting me know what I missed. (10:39) And so I want to talk a little bit about why leaders can benefit the most from AI. (10:44) And you pointed that out that senior leaders often have more leverage from AI than expected.

(10:51) What is that exactly?

Emily

(10:53) Yeah, it’s definitely showing up in terms of surveys. (10:55) The highest value is coming from the people who are most senior. (10:58) And it gets back to that point I was making about augmentation and like the coordination loop being where some of the highest value is being brought.

(11:06) So if you think about the need for human judgment to be working well with an AI and what AI can bring to the table, it can go and exec, it can, it can go and execute tasks, but might not have the results exactly the way you want. (11:17) So you need to be able to think through how do I get this to what the vision needs to look like without being in the details of actually having to do the hands-on work. (11:24) That sounds a lot like management and leadership.

(11:28) Fundamentally, that’s what it is. (11:30) It is bringing to the table the skills of I know how to delegate to somebody. (11:34) I might not be getting back exactly the way I want it, but I have to make the decision and be able to know when is it good enough, even though it’s not the way I would have done it, versus no, this is what we need to do to get to where we need to be and how to give that feedback.

(11:46) This is things that we have been doing with our human employees forever that now translates over into how we work with these agents or AI tools or queries in our prompts and go through that iterative process. (11:58) And so as we’re thinking through how do we scale the employees of the future, starting at the ground level, this is starting to become skills that people need to know right away. (12:07) And as we think about what does early career look like and what does training (12:09) need to look like, the advice I give to people is to try and figure that out, how to do that, (12:14) because you can’t rely on everyone having the benefit of taking a number of years of leading (12:19) managing teams to learn these things and how to do cross-functional leadership and how to apply (12:24) the judgment and how to get to what that looks like. (12:26) People are going to need to know that right out the gate as they work with being supported by artificial intelligence and agents and so forth and getting the best out of them.

Melissa

(12:36) Yeah. (12:36) And similar, I agree. (12:38) I think using it as just another resource, like another employee.

(12:42) And so how can you leaders use it for AI delegation or something like strategic thinking?

Emily

(12:49) From an AI delegation perspective, it’s the send it off and let it do something. (12:53) From a strategic thinking perspective, that’s when you start getting the thought partner piece about using it to generate ideas and bring them up. (13:00) And again, it sort of comes back into what leaders would do with their teams.

(13:03) I worked for a period at Bell on the corporate strategy team. (13:07) I never worked for a big consulting company. (13:08) I was the only person, I was the first person they hired that hadn’t, but it was similar to like a vein coming in.

(13:14) And when that happens, you also aren’t taking the 24-year-old out of college, doing all the work and generating strategy ideas and letting them actually make the final decisions, but they’re bringing ideas to the table that leadership can be thinking through and working with. (13:27) And that’s a really similar thing you can go through from that perspective when working with your AI tools in terms of doing research, getting data, talking through things, running scenario analysis, and doing more advanced what-if planning. (13:40) It can be the partner to work with you on all of that, and it can also be a creative partner.

(13:44) I do want to call out, because I do get wary when we start talking about like treating it like an employee, because I just said we should, and think about how to manage it, and all those skills translates over. (13:53) But I also think when we’re talking through how to impact workflows and getting back to the point of culture, every time I talk to any company, we start with where could we use AI technology and could it help us, and get really explicit about where do our people need to play a role, because that I think comes back to let’s not panic everybody. (14:11) But it also is helpful to start making the trade-offs of how good is the AI and where is it weak, and where do we need to have the human element in place, whether it’s from a judgment perspective, and how much do we trust the AI to do on its own versus when are people need to be checking it and looking at it in a loop, or just more the human interaction piece.

(14:29) My favorite example is when you deal with (14:31) the sales team, because unless it’s some sort of really commoditized consumer sales, but I go in (14:36) and talk to B2B salespeople, and it starts with like obviously we still need our salespeople, (14:41) because it’s the easiest one to talk to, because everyone knows that clients want to talk to a (14:47) person, and so then our end goal is to how can we get those salespeople who spend more of their time (14:51) in the field talking with their clients, and talking and trying to make connections and drive (14:55) our sales, and less time playing around with running analysis and doing other things. (14:59) What are the things that we really want our people to be doing, and where can their time be redirected away from things that are lower value for them and higher value for automation?

Melissa

(15:09) And I love that you said that, because I’ve seen a lot of AI slop. (15:14) Everybody’s using AI right now, and so we’re putting a prompt in, a really basic prompt, a very wide prompt, and we’re allowing the tool to have full autonomy to build or make or write whatever it’s doing. (15:30) And to your point, the human in the loop, I’ve been past AI work, and I look at it, and I either see in my industry gaming regulatory cases that don’t exist, or stories that aren’t really there, or whatever other things that are happening.

(15:46) So I agree, looking at it as a tool, not an employee, is really the way to look at it. (15:52) And the other thing I think as a leader, I use AI in my personal life a lot. (15:58) And I consider myself a creative mom, but sometimes when the brain’s overloaded at the end of the day, I need something to help me come up with dinner, with the items that I have in the refrigerator, because I don’t want to go to the grocery store.

(16:12) And so how can leaders personally adopt some of these AI tools?

Emily

(16:17) Oh, I think there’s tremendous possibility. (16:20) And as they keep getting built up and easier with the hooks they get in on things, being able to pull it into your personal life. (16:26) I use it to triage my mail, my email on a regular basis, just like I do at work, and stay on top of my personal things.

(16:33) So it’s flagging me to do’s for my kids and things like that. (16:37) One of the first things I used back in the day when JTPT was pretty early and it wasn’t that great yet, is coming up again, because I use it to plan out my Easter egg hunt. (16:46) Instead of looking for the Easter eggs, we do a scavenger hunt where it goes numbers.

(16:51) And my daughter is getting old, but she loved it when she was younger. (16:55) And it’s hard. (16:57) So that was one of the first things that I went ahead and said, JTPT, help me with this, please.

(17:02) But the question then, of course, of trust and how much trust do you give it. (17:05) Personally, I will be thrilled if AI gets to the point where I can have an agent go and start making doctor’s appointments and things like that for me. (17:13) We don’t have that yet, but I would love that because it’s one of those things that I do not want to do it.

(17:17) And I would love to get that off my plate. (17:20) But all of those, whether it’s organizing yourself, whether it’s managing your communications, and for when you’re really busy, for how can you be streamlining things and keeping them teed up, are all good things. (17:30) I actually am involved, in addition to my consulting business, with another thing that’s very personal to me, which is building up AI to help family caregivers use AI to manage the paperwork.

(17:41) So again, the human piece that you want is you want to be spending time with your family member, your parent, or your child, or whatever else you want to be spending time there. (17:48) But no one really wants to be spending time writing letters to the school for the IEP, or to insurance to get moms care approved, or whatever else that’s not fun. (17:58) It’s not really a mind space you want to spend your time in and thinking through, what are all the problems that they’re having that I need to convey here from the past history, and pull that together.

(18:07) And that’s something I can do really well.

Melissa

(18:10) Yeah, and I love that. (18:11) I think the one thing as a mom as well, and a wife, and all the things, and an executive, and a leader, and on, and on, and on. (18:19) I call it tabs.

(18:21) I don’t know, maybe there’s a smarter way to call it, but we have so many tabs open in our brain all the time. (18:26) And as Easter gets closer, or as these things get closer, there’s more pressure to get things done. (18:33) And so to your point, if we can offload some of those mental cycles, and some of that energy off to a tool to help us with the inbox, or remembering all the things that we remember as good moms and executives, it’s really, really helpful.

(18:51) But the other thing is the real pace of AI adoption is really slow in the world, generally speaking. (19:00) But in our tech circles, and the places we work, AI adoption is so fast. (19:07) But when we look at the broader economy, it is so slow.

(19:12) And so I’m seeing a lot of tech slop in the healthcare, through the doctors I visit. (19:18) You know, I’m seeing it in different places now. (19:20) So what are you seeing on the ground as it comes to that?

Emily

(19:25) Yeah. (19:26) When you look at adoption, what’s interesting is not only is there the kind of the, you talked earlier about the headlines that AI leaders like to throw out there of, you know, half the jobs will be gone, and da, da, da, da, and how practical that is. (19:37) Not only do they have a bias to try and say my tool is going to solve the world, but they’re also have a weird perspective, because the software development cycle is ideally suited to be pulling in AI.

(19:51) When you think about it, I mean, everything’s super well documented. (19:54) So it fits really well, but you can hand over like there are rules, it is, it is following things, there’s process that’s all out there, the knowledge is there of how the job is executed. (20:03) And the workflow is really well defined, with handoffs, really well defined.

(20:09) I don’t, I’ve not come across any other business model that is as smoothly ready for things to just pop in as that. (20:16) Every other business I’ve talked to, we have to spend time thinking through. (20:19) So how does this work again?

(20:20) Wait, we need to go pull in Bill and Sue and, you know, everybody and figure out how this is working in the first place, and then think through how we want it to work. (20:28) And there’s a lot of manual handoffs, and there’s a lot of just conversations that happen, and things are not well structured. (20:34) And so that’s why AI adoption generally is not going as quickly as people say it could.

(20:39) It’s because it is a lot of figuring out where does it fit, how do the workflows adjust, and change is not fast, change is slow. (20:47) There’s a lot of inertia in terms of how we’re used to working together. (20:51) So even when the tool’s there, people continue to work together the same way until they figure out how to move over.

(20:56) And that gives us, I think, opportunity to try and make sure we do it right, which is an interesting challenge in and of itself, because the pace of change that’s going on with the AI is part of the pressure that’s coming in here. (21:08) One data point I like to remind people of is if you compare it to previous technologies, how quickly we’re moving here, you look at the internet. (21:15) I think that is pretty foundational.

(21:17) So we’re now a little over three years since Chats with the Tee was launched, so coming up on three and a half. (21:22) I’ll call that like when consumers, when we all first got access to AI, would be late 2022. (21:28) You look three and a half years after people first got like broad access, or not even broad, but you could conceivably go off and get access to the internet when AOL was first launched, right?

(21:40) And if you looked at three and a half years after that was first introduced, you pretty much still just had some people using AOL. (21:47) And like some companies had built up their own mail systems, and that was like the web didn’t exist yet. (21:52) Forget about, you know, social media and all the other things that have come since.

(21:56) It was a much slower adoption curve, partly because the enabling technology wasn’t there. (22:01) People had to buy personal computers and their homes weren’t everywhere yet. (22:05) The internet had to get built out.

(22:07) Like there were all these things that needed to happen, whereas we were kind of perfectly ready for AI to drop in our laps. (22:13) Everyone had a phone that we could install Chats GPT on or cloud on. (22:16) We all have desktops.

(22:17) So everyone’s running around using it really quickly, which is part of the tension that’s coming in of the pressure of can we have our hands on, how quickly this is moving, what the impact is and so forth, versus the fear of missing out, which is real because there’s real competitive disruption. (22:31) So it’s a difficult balance between those two that everyone’s having to manage. (22:35) My view is that you can’t just stop because not everyone’s going to stop unilaterally, whether that’s business competition or if you want to look at more of a global perspective.

(22:44) But we don’t quite have this speed of pressure that you see in the tech industry everywhere else, just because there are natural frictions going on that we have to work through in terms of how it gets adopted in and what it means within other workflows and other teams and how they work together.

Melissa

(23:01) Yeah, and I love that. (23:03) I think it’s so true. (23:04) And I still have one of those Hotmail accounts, although I get made fun of by everybody in my network in tech, including my husband.

(23:14) I have not got rid of my Hotmail. (23:17) I have it still. (23:19) I don’t have AOL, but I do have Hotmail.

Emily

(23:21) I had trouble logging into something the other day, and the lovely lady on the phone, who was clearly younger than I was, she was trying to walk through. (23:28) I was trying every email I’ve had that I could go through, that I could think of. (23:32) I mean, I’ve got eight right now, right?

(23:34) She’s like, I’ve never seen this one. (23:37) And after a little while, it was an Excite.com address. (23:41) And she’s like, I’ve never heard of this.

(23:42) I was like, it was very, very cool to have in 1997. (23:46) I’ll have you in 1996. (23:48) I got that one.

Melissa

(23:51) It’s so true. (23:52) And it’s funny, I think about what you’re saying. (23:55) It’s so true how long there was a lot of hype about it, but there was a lot of lag behind it.

(24:03) And so I’m curious, in my industry, this was last year’s number that I was given that under 5% of the gaming industry had fully adopted AI or has done these kind of digital transformation projects successfully. (24:20) Successfully, key word there. (24:22) But I’m curious to get your perspective on what industries are leading the way versus lagging behind everyone else.

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Emily

(25:03) First of all, the last year has been, the progress made over the last year. (25:08) I’m so wary when I get a data point right now. (25:10) I’m like, when is this from?

(25:11) Because if it was from March of last year, that is too old. (25:14) And we are starting to see positive returns that we weren’t even a year ago. (25:18) So the story has changed there.

(25:20) And it is changing across the board. (25:23) Positive returns tend to happen when people really focus on a workflow and go deep in terms of restructuring it. (25:29) And because of, as I was talking about before, because of kind of how well suited it is, that is why tech is the, in software development in particular, is the industry that is having the most adoption.

(25:41) And I went back into coding for the first time in ages to work on the aforementioned AI tool for family caregivers. (25:52) I had started even as of late 2025 thinking, this just needs to be HIPAA compliant. (25:59) It needs to be very serious.

(26:00) I need to go hire a team. (26:01) You can’t just bad code this thing. (26:03) And I kind of slipped into starting with, well, before I prototyped it and was running it with people.

(26:09) And then started talking, again, big picture thinking with Claude and with JGPT around researching the market, things like that. (26:17) And well, let’s start talking about what the architecture should look like. (26:19) We have to have a HIPAA compliant architecture.

(26:21) Let’s think through, should I apply to Google for their startup program or AWS? (26:25) So we build an architecture and built through, here’s our principles. (26:28) And next thing I know, I’ve Claude, and this is AI, not Claude Code yet, starts generating code for me.

(26:33) It’s like, here’s your, you know, that you can go and do your sign on stuff. (26:37) So I kind of slipped into building this thing. (26:41) I’ve had to audit it third party and so forth because I am very worried about the security and everything.

(26:46) And I ended up switching over to using Claude Code. (26:48) But it was for me a really sobering moment, actually, because it was, I was kind of working on this late last year, early this year, over the holidays when things were kind of quiet. (26:56) I was just sitting there coding away.

(26:59) And it brought home for me that the job I first had is gone. (27:03) And it wasn’t just the AI, by the way, it was also the underlying enablement, kind of like we talked about for consumers, it was all there. (27:09) I used to have to code, you know, SACIT connections to have things talk to each other.

(27:13) We didn’t have GitHub ages ago. (27:16) All of that enabling technology is there so that the AI can just kind of slide in and start doing its job, which changes then what you need to have a good software engineer in terms of still being able to oversee it, make sure that you’re going through the right processes of designing and testing and developing and pieces all coming together and understanding how all of that works. (27:35) So I think that industry is a really early indicator, you know, it’s kind of a canary in the coal mine of what does this mean then for others as this comes along?

(27:45) Because the idea of early college career people, and if my job isn’t there anymore, or not as many, you know, it looks different if you hire someone else. (27:56) How do they get to be the people who can have that good vision? (27:58) And that is something I think we’re going to need to solve holistically.

(28:02) Like, we’re not really set up for individual companies to necessarily figure that out on their own, but we have to. (28:08) Because if the traditional way that we’ve done this from an office work perspective for the last however many years, however many decades, has been you hire young people, they come in, they do rough work, and then they figure out what’s going on. (28:20) That’s certainly how I learned.

(28:21) Like, oh, and you got to think about these principles, and this comes along. (28:24) And I, when I switched over to out of business school, it was I was making those PowerPoint decks, and understanding and listening to and doing the research that the leaders were talking about. (28:33) And that’s how I got to understand their perspective, how they thought about running a business.

(28:36) In both of those cases, you don’t need someone to do what I was doing anymore those first years. (28:42) But you can’t just, where will we be in 20 years if we don’t get people to where they need to be? (28:46) So the reframing of what is early career look like is also extremely important right now.

Melissa

(28:52) And I love that you brought that up. (28:54) Because I, given where we are in the world politically, inflation, salaries, and on and on, I see a lot of people that are either doing side hustles, or they’re, you know, driving an Uber to get some extra cash. (29:11) But I think about my children and our children, and how their lives are going to look.

(29:17) My oldest will drive, my youngest probably won’t be driving. (29:21) And I think about the differences, and just, you know, a 10 year period between the oldest, you know, and the youngest driving. (29:29) And so one of the big questions I’m asking is kind of what you were, you know, leaning into, how are our kids?

(29:36) And they’re, you know, coming out of high school, what does that mean for them as an early career professional, straight out of college? (29:45) And, you know, really, what responsibilities do companies have right now? (29:49) Because I was lucky enough to come out of engineering school with an internship, and I had somebody helping me here, but I don’t hear that as much in the workforce these days, having the internships.

Emily

(30:02) Yeah, I think, not that they’re going to zero, because I am caring about them. (30:05) But having 10% fewer is tremendously impactful, if you’ve got 10%, you know, 10% more people that aren’t figuring out what to do. (30:13) And I do believe this is a collective responsibility.

(30:15) I think the current trend of just kind of waiting for it to go away is problematic. (30:19) And you can see the potential political impact to come back around to that, you know, the movement of stop all data center development for AI. (30:29) And I don’t think that is feasible, as I said, because there’s geopolitics that come in, because other people aren’t stopping.

(30:34) But we have to figure out how to do that without leaving people behind. (30:38) And as I said, not just because what does it look like in 30 years, if you haven’t figured out how to develop those people, which I think is the most important question. (30:45) But as a corollary to that, you also just things show bad things happen, even at high unemployment amongst young people.

(30:52) So we don’t want to get there. (30:53) And I, I see it, you know, the stop AI and concerns about AI is growing amongst young people as a result, they they’re hearing about the data and being concerned about it. (31:03) The most virulently concerned about AI person (31:06) I know is my high school child, who, you know, is not happy with my entire career at the moment, (31:11) is worried about the environment, she’s, I finally got to tell her we shut down Soros, (31:16) so she doesn’t have to accuse me of killing the environment to make dumb videos anymore, (31:19) to which my reaction would be like, no, people are trying to solve can’t cure cancer and other (31:22) things that are more useful to you. (31:24) But now that’s gone.

(31:25) So we can take that off the table. (31:27) But she’s also worried about, you know, stories about deep fakes and bad things that are happening and just, there doesn’t seem to be any control around this right now. (31:33) And it’s the wild west, and it’s scary.

(31:35) And what will my job be? (31:37) And what does that mean? (31:38) And I think we owe it to them to sell all of this more consciously than we are right now.

(31:43) And there’s certainly efforts being made, and you can see it bubbling up. (31:46) And I think it will go there because people are pushing for people concerned about at all ages, losing their jobs are pushing for what is this going to look like people concerned about, you know, we’re just coming off of lessons learned of uncontrolled use of technology with social media. (32:01) And that itself, you know, there’s a lot of debate going on there.

(32:03) But we just have these two big, you know, court cases with the social media companies there, where they were found liable for various things. (32:10) And so they’re raising the questions there, but there’s not a lot of trust. (32:13) And I think that is what’s driving part of that.

(32:15) We need to figure out how to do this ethically and responsibly that’s out there on the flip side. (32:19) I spent a lot of time. (32:20) Oh, go ahead.

Melissa

(32:21) No, no. (32:22) I was just going to say, I love that you brought up the deep fake. (32:25) So with the fast moving kind of world we are with AI, I think back, even, you know, last year on most of the cyber attacks that were happening, they were very technical attacks.

(32:38) And I think about in my industry, and we saw this with Striker recently earlier in March, that these attacks are not as sophisticated, they are communication. (32:51) And so one of the things I focus on for my children is not, yes, they’re proficient in technology, but the communication piece. (32:59) I believe this is a skill that children will really need to learn about moving forward and humans.

(33:06) So I’d love to get your perspective on what other skills will matter most to our children as they move forward into their careers and beyond.

Emily

(33:14) Yeah. (33:14) And I feel for the kids graduating right now because everything they were told is flipping, right? (33:19) Everyone that was like going to STEM becoming, and now all of a sudden, you know, that’s a difficult thing to graduate with.

(33:25) You brought up about slop earlier, and I think that that’s a real thing. (33:30) I remember seeing Gartner calling out a data point on this like two years ago of forecasting and like the amount of content is going to go up this much, which means breaking through is going to go down this much. (33:39) It didn’t get into the slop idea yet.

(33:41) And I’m sad about this because I actually love the written word. (33:45) Like I learn better by reading and I like writing, but I think kids need to learn to talk. (33:51) They need to learn to talk and to listen.

(33:52) Like if I could design education curriculum, I’d be like Socratic method and having all the teachers sit there and ask questions and hear answers, which for many reasons we’ve gone away from. (34:01) But that is the biggest piece of advice that I give any child I know right now is to find a way that you get up and get comfortable speaking and listening, which was a lesson I took a really long time to learn. (34:12) I very happily went into software development out of college because I didn’t want to talk to anybody.

(34:17) And now I’m here talking to you and like doing these things. (34:19) But if our children can focus on that early and their generation is very much on phones and so forth, it’s a differentiator that you can use to get into the marketplace better. (34:31) If you can figure out how to speak eloquently, listen eloquently and frankly show this is true for any of our ages at this point, show that you actually know what is being written because you can know that I have something in my head if I’m sitting in front of you talking versus maybe I just had AI generate the whole thing and posted a beautiful article about something I know nothing.

(34:52) I could write I could publish some great articles on cyber that you might look at and be like, it looks kind of sloppy, but I put it out there and I’m not an expert on cyber. (35:01) If you came and talked to me, I wouldn’t be able to answer any of the questions. (35:04) And that’s that direct communication that starts becoming more of a trust flag and more of an authentication flag and an authority flag.

(35:10) Whereas in the past, we used to be able to trust that if somebody wrote something, it meant that they knew it well and we just can’t do that anymore.

Melissa

(35:18) Yeah, and it’s so interesting. (35:20) So for me, I am not a writer naturally. (35:24) I’m a math and science girl and I always wrote how AI writes.

(35:29) So I write in text and emojis and I laugh because multiple times I’ve had people say, did AI write that? (35:37) I’m like, no, my life is in short sentences, incomplete squirrelly with emojis. (35:43) If you ask my husband, the communication I give him on text, he has no idea what I’m trying to say half the time.

(35:48) So for me, I love AI because that’s how I think in my brain works squirrelly. (35:54) And so, I love that you kind of mentioned the listening part, because I think that’s a really good part of ways for young professionals to stay relevant. (36:06) I hear a lot of young professionals and even my children want to be leaders.

(36:11) They want to manage people and lead projects. (36:14) And to your point, the communication is one thing and dictating what needs to be done, but it’s a whole nother thing to listen to your team when they tell you there’s a problem. (36:25) And I 100% agree with you.

(36:28) And when you say listening, it’s such a huge piece of it. (36:34) And so switching gears back to making the investment in AI that actually delivers the results. (36:40) I think a lot of the big companies I work with or I know, the decision metrics back to what you were saying at the beginning, they’re having meetings to have meetings to figure out things where the smaller companies are nimble and they can make decisions faster.

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Emily

(38:11) Yeah. (38:12) I would say the first thing is to think through what is the return you’re expecting to get and thinking through the different types of expectations of what’s coming out of the use of AI. (38:21) I mean, the first one is, are we trying to drive value?

(38:23) Are we trying to be more efficient? (38:25) And unfortunately too much of the easy capturing of value comes from trying to be more efficient, but then eventually that becomes a death spiral. (38:31) So that’s a temporary thing you can look at.

(38:33) It is the easiest way to show ROI, but it, you know, getting back to what we talked about culture, it’s not great to be leading with that. (38:40) So it’s much better if you can start thinking through what are the business outcomes and actual business metrics that we’re going to drive and see as a result of this and start framing things around that. (38:49) You also get into the fact that there’s kind of two different strategies companies can take, and they have different implications back to measuring ROI.

(38:57) One would be, we are going to get all of our people to start using AI well and getting more into an augmentation methodology. (39:05) And they’re going to start doing their jobs better and more effectively and all the good things and invest in that. (39:12) Give everyone licenses, give everyone training and expect also some innovation to then come kind of from the ground up that we’ll figure out how AI can be used and where it can be used more effectively.

(39:21) That is a great strategy for a lot of ways. (39:24) I think round up brings up more ideas. (39:26) I also, that I actually recommend all companies that they should just get over the hump and do that because it addresses the risk that’s out there of shadow AI, which is a real thing.

(39:36) When companies aren’t enabling their team members to have tools and quickly adequate tools, because sometimes it comes in where they’ve given an enterprise license, but they’ve locked it down so much that people are like, why can’t I do anything here that I can do on my phone? (39:49) And (39:49) then if they don’t have the, they’re using the stuff in their regular lives and aren’t being (39:54) given it to use at work, we’re seeing trends where people even without permission will start (39:58) just putting stuff into their personal AI instances to get their work done, which is (40:02) problematic for so many reasons in terms of, you could have data leakage, you might not have the (40:07) right security and like all these problems. (40:09) So you don’t want to do that. (40:11) For that reason, I encourage everyone to try and go broad with enablement, but enablement and a broad strategy is notoriously hard to measure ROI.

(40:20) People are trying and it’s just to gather the little tiny bits of efficiency everyone might have and figure out where it’s going or improvements they’re making. (40:27) You can make really broad, generalized assumptions around we have macro results like this, and we’re going to attribute part of that to the AI initiatives. (40:37) And you can do that even by teams and these teams are using more, there’s things you can do to try and get at it, but you can’t get a really direct measurement there.

(40:44) And some, depending on what pressures are coming in, in terms of how to do this, sometimes that’s not good enough. (40:51) But I really try and get people to get over the hump of saying, should we invest in spending $20 for everybody or $5 for everybody in my organization to have an enterprise license, which a lot of companies are still stuck on. (41:03) And if they’re stuck there, that’s a problem because of all the things I just said, you need to get over that and you probably aren’t going to be able to measure that ROI.

(41:10) And so the companies I’ve seen that were the most effective in that aspect are the ones that just said, look, it’s the cost of lunch once a month. (41:17) We’re just going to say, we just believe that we’ve got the results and we’re doing it. (41:20) At this point, no one’s debating, do we give everyone an email license?

(41:24) You just need email.

Melissa

(41:27) That’s a good analogy. (41:29) And I love, one of the things is having the leadership buy-in with this. (41:33) I’ve seen a lot of co-pilot implementations happen yet nobody’s using co-pilot.

(41:38) And so we’ve got to kind of have the leadership buy-in and train the people on how to properly use the AI tool. (41:46) I’ve seen this again in my industry, not used really well, putting in player data or PII information or company information. (41:54) And so to your point, getting a company enterprise tool set for 20 bucks a month, it really makes the most sense.

(42:02) And then making sure that people are using it and then asking the employees in your company, putting out a questionnaire or feedback and say, okay, are you using it? (42:11) Yes or no? (42:13) What can we do better and pulling your employees?

(42:17) Because I know when I did this at a previous company, we found that less than half the people were using the co-pilot license and we were paying for the co-pilot license. (42:25) And so we shrunk it down to the people that were using it and saved the money. (42:30) So really to be successful at any of these digital transformations or migrations, or even leveraging the tool, we need to allow it and spend it for it.

(42:42) And then a few months later, let’s ask if they’re using it. (42:45) If they’re not, stop paying for it.

Emily

(42:47) Well, it also gets back to your culture point, right? (42:51) That we started with because just throwing the license out, yeah, not everyone’s going to use it. (42:57) You need to also, not just the training, but the best examples that I’ve seen where people got the most used across their workforce was where they consciously tried to introduce a culture of encouragement.

(43:10) They recognized people that were using things well. (43:12) They were encouraging people to share and rewarding people for sharing. (43:17) Not just that now Emily’s doing her job really well, but how could she help Melissa figure out how to do the same thing.

(43:22) And so sharing in different processes and things like that, and trying to drive all that. (43:26) And again, getting back to it being so new, there’s reasons that aren’t just like, I’m difficult and a bad employee that are holding people back from doing more. (43:33) There is the learning and not everyone is comfortable jumping in and learning.

(43:39) There is the uncertainty about what’s it going to be for me and my job. (43:42) There’s even the uncertainty about what do people think about me if I’m saying that I’m doing this a lot. (43:46) They think I’m less capable and there was research last summer that got some attention because I’m interested to see if this changes.

(43:54) Because again, it’s only three years. (43:56) And I think as we get more used to this technology, I think this will evolve. (43:59) But in a vacuum, early indications are your co-workers think you’re less capable if you talk a lot about the use of AI.

(44:06) And this was the study that I saw was one where the company was explicitly trying to encourage everyone to use it. (44:13) It was within a software development use case. (44:16) So it was like tools were there ready to go.

(44:18) But as they would survey people on what they thought about their various employees based upon whether they said they were using AI or not, there was a noticeable like, I think these people are less capable. (44:27) And by the way, it was even more so if they were older, if they were women. (44:30) So if there was already bias against you, it was even more taken down on their penalty.

(44:33) So that’s a real concern. (44:35) Like if you’re worried that you’re not going to get ahead individually, if people think you’re using this, it’s holding people back. (44:40) And none of that is the tool.

(44:42) All of that is around how are we figuring out how to make our culture of our company think that this is part of what we do and that it was a good thing. (44:51) And that is not going to be a move it on a dime. (44:53) It’s going to be a gradual thing that we have to continuously with effort move towards.

Melissa

(44:58) Yeah. (44:58) And I love that. (44:59) I so agree with that.

(45:00) And the other thing I’ve, I’ve heard from very high executive at a very large investment firm recently, we were having this discussion this week and he was telling me, he’s like, you know, what’s interesting to me is everything in my team’s chat and everything in my chat now, it’s so clear that it’s all AI and people aren’t actually doing their own writing and thinking. (45:25) And so, you know, my, and he is a big leader of very large teams around the world. (45:29) And I was telling him, I’m like, maybe it’s up to you to have that dialogue and communication and discussing with your teams.

(45:38) And I said, you know what I would do is I like anybody that knows me and I’m typing in teams, I hate doing the apostrophe all the time. (45:45) So I’ll often let that go. (45:47) And I won’t even make the apostrophe changes and I’ll continue my team’s chat.

(45:51) You want to make sure your people aren’t losing their ability to read and write with the leverage of the AI tools. (45:59) And you want to make sure that it’s their words too. (46:03) Right?

(46:04) And so I think it’s on the responsibility of the leadership to explain, yes, use the tool, but still be a human and use your own voice in what you’re doing.

Emily

(46:17) Yeah. (46:17) And think through, and again, we have to surface through what is the cultural expectation of where we want to use and where we don’t. (46:25) So authoring thought leadership pieces, I want my voice there, right?

(46:29) Writing a proposal. (46:31) I’ve spent a lot of time working on writing proposals, but I don’t know if any customers actually really appreciated my authentic voice being there. (46:37) And to your, you know, to your point that gets back a little bit to, I tend to write businessy talk anyway.

(46:42) So I’m pretty good at writing proposals, but I’m quite happy to outsource that over and be checking it for accuracy, but not wordsmithing. (46:49) Like how well, you know, how well do I like the emotion of this proposal? (46:53) But those are two different, you know, and again, small companies.

(46:57) So I’m able to be pretty clear on here’s a good use case. (46:59) Here isn’t one. (47:00) I think the conversation going on on LinkedIn right now is itself like a macrocosm of what individual organizations are having to go through is the tension of it’s so clear.

(47:09) So much of the commentary right now is all AI. (47:12) Ethan Mollick, who talks a lot about it and posts about this recently, how you can tell every one of the replies are just structured is like, it’s just auto-generated replies as far as the eye can see. (47:23) And LinkedIn is trying to fight against this because they want their platform to be one that is about connection.

(47:28) I mean, the point isn’t just to be how many things can I offer that, you know, maybe again, maybe I know it, maybe I don’t, and it’s all out there and it’s slop and no one’s paying attention anymore because it’s computers. (47:38) The intent is to be connecting with people and that gets back to the, where do we want the human? (47:43) It might be something that AI could do just as well and generate a bunch of comments, but that’s not the point.

(47:50) The point is to know that Melissa is really interesting and interested in what I said, not that your little automated tool could do it. (47:56) Right. (47:57) And that’s, yeah, those same guidelines need to be in an organizational level.

Melissa

(48:02) Yeah. (48:03) It’s funny that you say that. (48:04) Cause I just actually realized this two weeks ago and I’m embarrassed to even say this cause I’m a LinkedIn person that I could actually do some of my chatting through that tool.

(48:16) And I didn’t realize that until somebody asked me recently, like, how do you keep up with all your posts? (48:23) And I’m like, I don’t know. (48:24) I write them on the weekend and then I schedule them far out.

(48:28) So I don’t have to think about it, but I just learned how to use that. (48:31) So I’m anxious to try to try it for next week’s post. (48:34) We’ll see how it works.

(48:34) Or maybe I won’t use it because I like to talk in my, you know, squirrely voice.

Emily

(48:40) Well, to give them credit, they’re very much trying to make sure it is still people. (48:44) I actually ran into this myself. (48:46) So last year I was making a conscious effort to reconnect with some fellow alumni that I back long enough ago that I was only connected to a few on LinkedIn and for various reasons, wasn’t connected to each other.

(48:58) So I was trying to reach out to different people and it was pretty easy. (49:02) I went and found my page for our class. (49:04) I was like, you know, going through and reaching out to people to say, Hey, and someone had given me this, what I thought was a good piece of advice to when you connect with someone, try and give, you know, it’s polite to give them the context of why they know you.

(49:16) And they’re like, plus, you know, and then it reminds you how you know them. (49:18) It’s all good. (49:18) It’s good advice.

(49:19) I need to make sure I don’t just send off random things. (49:22) So there was one Friday morning where I happened to have nothing like booked on my calendar. (49:26) And so I booked aside, I’m going to work on my LinkedIn connections.

(49:29) And I spent like two hours going ahead and through the same page, clicking on people. (49:35) And I truly was interested in what they were up to. (49:37) So I’d look at their page and they’re like, Oh, now they’re in California.

(49:40) This person’s in Singapore. (49:41) This person’s in Hong Kong. (49:42) It’s so great to see what they’re up to.

(49:43) And then every one of them, I do a, you know, please ask you to connect and WG04. (49:48) They know what it is. (49:50) It means we’re in the same classroom.

(49:51) That’s why I’m reaching out. (49:52) And then I got a email from LinkedIn that they had seen suspicious activity and my account was suspended for the weekend. (50:00) And they’re reminded that it’s against their terms of service to be using automated tools, to which I’m like, Oh my God, I can send you videos.

(50:07) Like this is literally, please don’t turn me off. (50:10) I’m really doing this. (50:11) But at the same time, I was like, well, good.

(50:13) I’m kind of glad that you’re trying to make sure that there isn’t just bots running them up here, you know, kind of putting their thumb in the, in the dike a little bit, but they’re trying and they are trying to make sure it is a human connection place because it is going to lose its value. (50:27) If it’s not, that’s, we want human connection. (50:29) We don’t want to be talking with bots.

Melissa

(50:33) And I love that you said that because I’ve been guilty. (50:35) And one of those people that accept the people that, you know, request me, but I would say, you know, in 20, 20, 26, I’ve done more, you know, cautious on who I’m accepting or why I’m accepting them. (50:50) Cause you know, my profile seems to be a large majority of people trying to sell me something anymore or, you know, whatever it may be.

(50:59) So I’m being very cautious and, you know, we know that AI may be one of the most powerful technologies that we’ve seen in decades. (51:08) But as you reminded us today, successful adoption, isn’t about just chasing the newest tool, but it’s also about helping people changing how they work, making better decision and actually creating value in their workplace, because that’s where really the transformation is going to happen for people personally and professionally. (51:28) And so I want to get kind of in closing, you know, Emily, if leaders want to take away one idea from today’s conversation, what should it be?

Emily

(51:38) It should be that in order to think about what you want from AI adoption, you need to be thinking through truly, what is the value you want to have within your organization of the AI and your people working with it? (51:47) And what does that look like? (51:49) And be really conscious about distilling that throughout the organization so that everyone is moving to the same vision together.

Melissa

(51:56) I love it. (51:57) That’s great. (51:58) Thank you so much for being here today and sharing your knowledge and time with our listeners.

(52:03) And if you enjoyed this episode of the Executive Connect podcast with Emily, make sure you follow and share it with a leader who is navigating AI transformation. (52:13) And for more conversations on leadership technology and the future of work, subscribe to YouTube or your favorite podcast platform. (52:21) That’s the Executive Connect podcast.