Framing AI: Not an IT Problem

Transcript

Ross Dawson: Nikki, it is wonderful to have you on the show.

Nikki Barua: Thanks for inviting me, Ross. I’m thrilled to be here.

Ross Dawson: You focus on reinvention. And I’ve always, always liked the phrase reinvention. I’ve done a lot of board workshops on innovation. And, you know, in a way, sort of all innovation—it’s kind of like a very old word now.

And the thing is, it is about renewal. We always need to continually renew ourselves. We need to continually reinvent what has worked in the past to what can work in the future. So what are you seeing now when you are going out and helping organizations reinvent?

Nikki Barua: Well, first of all, reinvention is no longer optional. I think both of us have spent a large part of our careers helping organizations innovate, transform, and shift from where they were to where they want to be. But a lot of those change management methods are also outdated. You know, they tended to be episodic.

They had a start date and an end date, and changes that were much slower in comparison to what we’re experiencing right now.

The reality is today, change is continuous. The speed and scale of it is pretty massive, and that requires a complete shift in how you respond to that change. It requires complete reinvention in what your business is about, whether your competitive moats still hold or they need to be redefined, and how your people work, how they think, and how they decide. Everything requires a different speed and scale of execution, performance, operating rhythms, and systems.

It’s not just about throwing technology at the problem. It’s fundamentally restating what the problem even is. And that’s why reinvention has become a necessity, and is something that companies have to do not just once, but continuously.

Ross Dawson: There’s always this thing—you need to recognize that need. Now, you know, I always say my clients are self-selecting and that they only come to me if they’re wanting to think future-wise. And I guess, you know, I presume you get leaders who will come and say, “Yes, I recognize we need to reinvent.” But how do you get to that point of recognizing that need? Or, you know, be able to say, “This is the journey we’re on”? I mean, what are you seeing?

Nikki Barua: Well, what we’re seeing more of is not necessarily awareness that they need to reinvent. What we’re seeing a lot of is a lot of pressure to do something. So it’s the common theme—the pressure from boards asking the C-suite executives to figure out what their game plan is, how they plan to leverage AI or respond to adapting to AI.

There is a lot of competitive pressure of seeing your peers in the industry leapfrog ahead, so the fear that we’re going to get left behind. And then, of course, some level of shiny object syndrome—seeing a lot of exciting new tools and technologies and not wanting to get left behind in investing in that.

So somehow, from a variety of sources, there’s a lot of pressure—pressure to do something. What is happening as a result is there’s a little bit of executive inertia. There’s a lot of pressure, but if I’m unclear about exactly what I’m supposed to do, exactly where to focus and what to invest in, I’m not sure how to navigate through that kind of uncertainty and fast pace.

So a lot of the initial conversations actually start from there—where do I even begin? What should I focus on?

Ross Dawson: That’s the state of the world today?

Nikki Barua: Exactly. I mean, well, welcome to era of leadership, right? I mean, there’s no business school or textbook that prepares you for it. You have to lead through uncertainty and the unknown and be more of an explorer than an expert who knows it all.

Ross Dawson: So, I mean, you’re, of course, very human-focused, and we’ll get back to that. But you mentioned AI. And of course, one of the key factors in all of this—what do I do—is AI. So how does this come in when you have leaders who say, “All right, I need to work out what to do, or I need to reinvent myself”? How do you think they should be framing the role of AI in their organization?

Nikki Barua: Well, I’ll tell you two things that they often come stuck with. One is, “Well, we know we need to do something about AI, and we’ve got an IT team.” And to me, that’s mistake number one. If you think this is an IT problem, you’ve already failed. So let’s start with that. That’s the wrong framing of the problem and the wrong responsibility.

This is fundamentally about reinvention of the business and a leadership challenge, because it impacts people and culture and how you work. So don’t delegate it to a department and think you’ve got it taken care of.

The second thing is waiting for the perfect moment where you have total clarity and certainty to take even one step forward. And that’s another huge mistake, because by the time you are ready to act, so much more will have changed.

The only way to think about it is like building muscle—you need the reps. You need to dive in. Don’t be a bystander while the greatest disruption in modern history is happening. Step into the arena, start experimenting, build a culture of exploration, and admit your vulnerabilities.

To go in during this time as any leader at any level and say, “I know it all, I have the perfect game plan,” is like saying you can predict the future. You can’t. The only thing you can do is build a culture where you can experiment together, iterate in short sprints with clear business purpose, and start to figure out what’s working and what’s not.

How can we really unlock grassroots innovation across the board? And when you do that with psychological safety for your teams, and the agility and adaptability with which you respond to this, you’re still going to come out far ahead, even if you don’t have the perfect answer at the goal line.

Ross Dawson: Well, there’s plenty of talk of culture of experimentation and psychological safety and stuff, but it’s a lot easier to say than do.

Nikki Barua: Often they end up being lip service—things that are talked about. But the reality is, there’s no endless budgets and endless appetite for failure, which is why I think one way to do this is to experiment at smaller scale and shorter sprints. You’re putting guardrails around that experimentation.

One example I came across was a very large company, a global brand that invested millions of dollars and over a 100-person team dedicated to AI-led innovation with no real clear purpose. It was sort of like, “Here’s a whole bunch of people and a ton of money and budget associated with that.” A year later, when they failed to come back with anything concrete that was really valuable, it was written off as “the problem is AI,” or “we should not be experimenting.” And that’s the wrong takeaway, because it’s really an ineffective structure for how you might experiment and make it easier for people to build the competency around continuous reinvention and innovation.

Ross Dawson: So are there any examples you’ve seen of organizations that have made a shift to a bit more culture of experimentation than they had in the past? Can you describe some of the things that happened there?

Nikki Barua: Yeah, one of my favorite instances, especially this year, is a pretty large manufacturing company that started from a place of org design, which is really interesting, because they didn’t start with “what’s the technology application,” or “let’s provide AI training and certification to all our people.” They started looking at, “How might we gain speed and empower teams to embody the entrepreneurial spirit?” How do we start looking at org design differently?

One of the things that they did was, instead of the traditional departmental structure with hierarchy and the pyramid model, they created what I would call agile, Navy SEAL-like teams—smaller teams with a very clear purpose, with cross-functional skills, all with a specific problem to solve. With that objective, they gave them the autonomy to experiment.

What came out of that was almost a hackathon-like energy and the ability for these teams to figure out how to solve concrete business problems they were facing across different parts of the business, whether it was marketing, sales, operations, product development, and so forth. Each one was able to innovate that way and really find practical usage of AI within their workflows, but also optimize for efficiency, speed, decision-making, and collaboration in a whole different way.

All of that happened because they operated as these independent units. It’s the difference between the Titanic and 100 speedboats—if they’re all going the same direction, the speed and agility you get is fundamentally different.

But what was interesting to me in that example was that the starting point was actually org design. The starting point was not training or technology.

Ross Dawson: Yeah, well, the org design has to be a piece of it, an important piece of it. But I think what I take away largely from that is you gave them a sample of it, a taster—as in, saying, “All right, well, we can experience what it’s like to try stuff, and to work together, and to come up with new ideas and to put some of those in practice.” And actually, this is quite fun, and I can see that it’s useful. So that’s where you’ve got to have some positive experience in order to shift the way you go about things.

Nikki Barua: Yeah, I think the positive experience piece plays such a key role in building confidence and belief in taking risk, right? So once you have the evidence that this worked, this formula, this approach worked, you’re more likely to do it.

And that’s what goes back to the human side of any of this disruption—we still have to address the things that often hold humans back: the fears, the insecurities, the need for certainty and security and comfort. Unless you address those needs, you cannot shift teams into a different paradigm where the expectations are fundamentally different.

Some of this that we’ve come across is even the identity shift that is necessary, because old identities served a pre-AI work environment, and you cannot go into a post-AI era with the old identities, mindsets, and behaviors. It requires a shift of that kind to really bring out the value and impact.

Ross Dawson: When you were chatting before, you talked about the example of the instance of identity around hard work.

Nikki Barua: Yeah, and that’s one of the examples that really stood out for me in some of the work that we do. It’s a company that had deployed significant investments in technology and a wide variety of tools with the expectation that that would have a dramatic increase in performance and output and throughput and so forth, and yet they were struggling to see the adoption. They could not figure out why.

And by the way, this was a tech company, so it’s not like they’re in a legacy industry. This is a large tech company that was not seeing the ROI from the investments they had made.

In our work with them, one of the things that we start with is the people side. We don’t start with the tech adoption. We start with, “What will it take to help people reinvent how they think and work and approach their responsibilities?” Then we work with teams on the process reinvention and finding the tools.

So in the first part of working with people, some of the things that surfaced in our work was how core values and your conditioning and the beliefs you develop from that actually become an impediment to AI adoption. In this case, this individual said, “When I use AI, I feel like I’m cheating, because my identity is built around hard work and long hours. I’ve spent my entire career being first in, last out. I put in a lot of hours. I hustle and grind really hard, and I put in all this effort, and today, so much of that work is done in minutes because of AI, and I don’t like that because it doesn’t make me feel good at the end of the day, so I’m going to still put in the long hours and do the hard work.”

So this is someone who did not fully adopt the tools available because it made her feel like she was cheating. And that is the kind of thing that, unless the company is aware of those invisible barriers that get in the way, they’re not going to be able to solve it.

By helping that individual and people like them shift their identity to, “How might you define your value and worth with AI, with humans collaborating with machines that actually make you exponentially more capable, as opposed to less valuable?”—that allows for a whole different way of approaching work and collaborating with machines.

Ross Dawson: Yeah, the identity shift is real, and I think, you know, when we’re working with ourselves, all right, let’s say, “All right, I recognize that I need to shift, and I’m fine. Let me think through and work out who I am versus who I was.” But if you are a leader of an organization or an external consulting organization coming in to help, I mean, basically what you’re saying is the job is to come in and you’re going to change their core values, you’re going to change their beliefs, you’re going to change their identity as a person. That is a fairly big challenge.

And I guess, you know, framed in that way, it’s necessary, but it’s almost impossible. But any incremental ways in which you can help people help themselves on that journey is going to be positive. So how is it that you can, from the outside, help someone recognize that there’s value to them in shifting their beliefs and values and identity?

Nikki Barua: Yeah, and that’s why the approach we’ve taken is not someone telling them that they need to do it, but giving them the tools, the frameworks, and the strategies that help them figure it out for themselves. That empowerment is so necessary for them to not feel like they’re being replaced or being told that they’re irrelevant to this new environment, but discovering their true inner zone of genius.

What is that that actually is relevant in this future context, in this new context? What are the parts of yourself that you want to amplify that actually create massive value, versus the parts of how you applied yourself, your energy or time on a daily basis, that you need to let go of? That allows you to shift from lower value tasks to higher value tasks.

When you’re able to shift not only who you are and the identity that actually creates more value in the future, but also what you hold on to, that then allows you to create real leverage in your own personal workflows as well.

You start to rethink, “Okay, every day, how did I do my job? Maybe it was a six-step process that I followed every day—what parts of this can I create unique value versus the parts that machines can do for me now?” Now, it releases more capacity, more mind space, for me to continuously do more.

What we’re finding is that once that capacity gets released, what’s interesting is they might find that they can shed like 40% of the work that they used to do, and machines can do it now. There’s another leap that they have to make that is often invisible to their own supervisors, and that is a question of reallocation. Forty percent capacity got released. How are you reallocating that?

If you don’t do that, even all of the optimization or the efficiencies gained, or the speed or scale you achieved, does not actually convert into actual enterprise-wide results. The only way it does that is if that individual is driving that reallocation.

There was an interesting study done by a large consulting firm, and what they found was all of the efficiencies gained by individual employees of a company because of AI only led to more personal time, not necessarily more productivity and higher performance.

Ross Dawson: So two things I want to dig into there. One is you mentioned frameworks and tools and so on for individuals for their journey. So what do those look like? What are examples of frameworks or tools or processes that you can give to people to help them work this through?

Nikki Barua: Yeah, so the starting point—our methodology is built on a combination of change management methods as well as neuroscience and innovation techniques and all of that. But it’s really more about the delivery mechanism. It’s a persistent tool, similar to Grammarly, that is persistent in your workflow and your browser.

So our agentic system actually is persistent in each individual employee’s workflow. It’s guiding them as they’re doing their work to discover new ways of doing things, or new ways of making decisions and giving recommendations, but also engaging with them as their coworker of sorts. It’s there to guide them into a different way of going through this process.

In addition to that, we take them through a 90-day reinvention lab, where every week they are given specific strategies, not only to learn, but to actually apply in real life that very same week, and start to see the results of that compound.

The reason we do that 90-day reinvention lab is it starts to build evidence—success. It’s not passive learning, it’s active application. When they start to see, “Okay, I can make this change in something that I have an appetite for—it’s not so much change that scares me, but it’s enough change that I can take on this change every week,” and they start to see the results in their productivity, in how they’re valued at work, in shedding the old behavior, adopting the new behavior, and it’s still okay. It actually makes them more relevant.

As they start to see that week after week, and they have an agentic system embedded in their workflow, and they see the results compound, what we have found in our diagnostic measures is we start to see an 80% shift in behavior in just 90 days. So even being able to measure their baseline and to see the shift week after week, and to see that in 90 days, it leads to significant shift in identity, behaviors, habits, and even their workflow—personal workflow redesign that guides them into not only being more efficient, moving to higher value tasks, but also reallocating what that freed capacity goes into. That’s what’s creating results, not just at the individual employee level, but then aggregated starts to end create enterprise impact.

Ross Dawson: That’s very interesting. So, amongst other things, in addition to the AI agent tracking, each week they’re tasked with a “do something, try something” ask?

Nikki Barua: And it starts with—the starting point is always figuring out what’s the business priority. So we don’t just create this open-ended innovation thing of, “Let’s go experiment with this in week one or week two.” It’s always tied to what is that particular group’s objective, what’s the most important business impact they can create.

Think of it almost like a business school case challenge. You have a specific challenge to solve, and now you’re going into it in a cohort, and you are using a series of frameworks to solve that particular challenge in bite-size increments, with AI as your partner, as your co-evolution partner, and you’re applying those tools and those frameworks to solve that challenge, and you start to see the results of that in very concrete terms.

So the end result is you end up with redesigned workflows, adoption of new tools, freed up capacity, and greater capacity to take on more work, and clarity around the value of reinventing yourself instead of the fear and the confusion and the chaos around it.

Ross Dawson: So you mentioned as a cohort. So this is run as a compact cohort, of course, as work so people go on and, okay, that’s great.

Nikki Barua: And what that also does is, because the cohorts are functional teams, it also gives them new ways of establishing team norms, new KPIs, new outcomes, because what they’re doing is essentially reinventing as a team. “How might we do this function differently? If this is our objective, and this is how we’ve historically collaborated, how might we reinvent that from a people, process, and tool standpoint?”

It gives them a new kind of language, a new vocabulary, new team norms that make them more agile and more confident that no matter how much uncertainty there is in the future, they’ll be able to figure out a way.

Ross Dawson: Great! so I want to look back to the capacity reallocation. So as I understood what you were saying, somebody uses AI to free up time from what they have been doing before, and so they have now capacity. So they, as individuals, come up with the ideas of how it is they reallocate their own capacity. How does that fit into an enterprise context where everyone’s coming up with new ideas for other things that you can do.

Nikki Barua: That’s why it all goes back to first establishing what the key priority is before the cohort even begins. So when they know that is the ultimate business objective that they have, now that you have freed up capacity, in what way can you align that freed up capacity to the greatest value creation? Being able to identify that—whether it is, “Hey, I can take on more projects,” or “I can handle more customer accounts,” or “Here’s something that I can do in shorter cycles”—being able to correlate new ideas for the freed up capacity directly back to the primary business objective makes it more concrete.

Part of the process is also where it goes back to aligning that to what the teams are agreeing upon, what the leaders approve. Those interventions are part of it. So it’s never done in isolation, with just an individual coming up with new ideas to use up their free capacity, or worse still, coming up with their own tech stack—which is one of the dangers. I’ve seen that with companies where it’s sort of a free-for-all of “let’s all experiment with AI with no direction,” and then everyone ends up with a personal tech stack that is completely violating every sort of security guideline or coming up with ideas that don’t correlate to actual business results.

Ross Dawson: Fantastic. So just tell me a story. Just give me an example which illustrates some of the things we’ve been talking about around this shift in identity and, you know, as part of a journey of organizational reinvention.

Nikki Barua: Yeah, well, one of the companies that we worked with—what was interesting was that it was actually the engineering team. My initial hypothesis was that it might be a lot of non-technical folks that are uncomfortable with this new way and are perhaps resisting the change or slower to adopt. In this case, it was a large tech company and its engineering division, the core engineering team that went through this entire experience.

So much of what was uncovered, of the fears that they had, was that they now would be completely replaced, because so much of development can be done by AI, and as a result, the unwillingness to adopt components in their engineering and development process that would give them the efficiencies as well as what they valued themselves for.

I remember a few of these individuals in particular that were actually the engineering team leaders. They themselves, and middle management—it’s a really interesting situation, because they are not the makers themselves anymore, but so much of what they’re having to manage is there’s a resistance to, “Now, am I really going to manage a team of humans plus agents? What does that look like for me as a leader? How do I lead differently?” Even though these are advanced techies, there was underlying fear around their own ability to adapt, their relevancy and value, and being replaceable.

They knew that at some point, if we don’t have more need, the capacity is going to shrink. So in these cohort conversations, so much of what came about was deeply human needs and fears. It had nothing to do with the technical proficiencies or their efficiencies or productivity gains. It all came down to what connected the sense of worth and identity to this new environment.

Being able to unpack that, not just at the individual contributor level, but understanding how it’s impacting team leaders—from worrying about not knowing how to lead, “What does this mean for me as a manager or leader, and how am I supposed to lead when I myself am terrified and I’m not sure I’m going to have a job, because you’re not going to need that many team managers and leaders?”—and being able to help them identify new ways of creating value that they discovered for themselves and started to see that they could actually create a whole different way of working.

Within 90 days, to see that transformation—to see them from going, when we did the diagnostic at the baseline level, this was a group that scored something like 30% on the scale, and within 90 days to see them at like 85%—to get there in terms of their confidence around adoption, their confidence about value creation, their own shift in identity and discovering that, yes, there’s a lot of new things that we have to learn, and there’s a lot of old things that we have to shed, but we now see that we can get there.

To see that engineering team—ultimately, the objective that they had was to increase throughput. The goal for the enterprise was, really, “How can we increase our engineering throughput so that we can ship product faster?” But we’re not going to be able to do that if they’re resisting reimagining how they do their work. And interestingly, more than the engineers, it was the team leaders that was the bottleneck in this case.

Ross Dawson: Yeah, I think that’s not an uncommon story. So just before we wrap up, just to go big picture—next few years, what do you see in how organizations shift? Are they going to do it? Are they going to reinvent? What’s the path? How’s humans plus AI going to play out in the enterprise?

Nikki Barua: I think the ones that truly embrace reinvention will win disproportionately compared to the ones that are slower to step in or adopt very superficial ways of adopting AI. When it’s just about, “Oh, we’ve got an enterprise ChatGPT license,” well, that’s not going to change your business. Blockbuster would have never become Netflix just by throwing more tech licenses at it. Netflix emerged by complete reinvention of the business that they were in, and that’s what it’s going to take.

We’re certainly seeing examples of companies that are truly at the leading edge in terms of how they’re thinking about the future and what possibilities they see. We are going to see more monopolistic businesses emerge that will grow bigger and bigger exponentially, and others that will start to diminish in size, and many that will perish as well.

I see a rise of entrepreneurship, partly by choice, partly forced into it because of shrinking workforces and potentially an evolution of the org design from pyramid models to diamond models, because a lot of the entry-level work historically has been a lot of grunt work that machines can do better, faster, and cheaper than entry-level workers ever can.

That, to me, is a scary future, because I don’t know what that means in terms of college students stepping into their first job and not having as many jobs or opportunities, and more importantly, the loss of apprenticeship and skill building that helps them become the workforce of the future.

So those are some of the macro-level things that I think we’ll see—changes at all levels, from what leadership looks like, what org design needs to be, how incentives change, how talent pipelines are built, which will require policy change as well. I think governments around the world are going to have to think about what does that mean in terms of how policy shifts to support this new reality. And then academic institutions—my gosh, they’re in for a world of reinvention if they have to stay relevant and actually still contribute value in society.

Ross Dawson: Absolutely. So, Nikki, where can people go to find out more about your work?

Nikki Barua: I would love to connect with folks on LinkedIn or through my website at nikkibarua.com and through my company website at flipwork.ai.

Ross Dawson: Fantastic. Thanks so much for sharing your insights today. It’s been a wonderful conversation.

Nikki Barua: Thanks for having me, Ross.