Fixing the Pilot‑to‑Production Gap in Enterprise AI - with Lawrence Whittle of HTEC Group

Summary

The AI in Business Podcast
Episode: Fixing the Pilot‑to‑Production Gap in Enterprise AI
Guest: Lawrence Whittle, Chief Strategy Officer, HTEC Group
Host: Daniel Faggella
Release Date: May 11, 2026

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Episode Overview

This episode tackles one of the most pervasive challenges in enterprise AI: the inability to bridge the gap between promising pilot projects and production-scale deployments that yield measurable business value. Host Daniel Faggella interviews Lawrence Whittle, CSO of HTEC Group, a global engineering firm specializing in AI-driven solutions for regulated and complex industries. The conversation focuses on why so many AI pilots stall, how to realign efforts toward business value, and what culture and strategy shifts companies need to realize ROI from AI initiatives.

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Key Discussion Points & Insights

1. The “Pilot-to-Production” Gap—Why Most AI Pilots Fail to Scale

• Hype vs. Reality

  • Whittle’s History Lesson ([02:29]): The mainstreaming of AI in 2024 led to a rush of pilots, particularly with the arrival of generative AI and agentic technologies. Most early projects centered on proving technology, not delivering value.
  • Quote: “There was a lot of pilots in 2025 that were focused on use cases where it wasn’t necessarily easy to identify value. So the pilots were ticking technically, but not necessarily focused on value.” — Lawrence Whittle [04:50]

• Survey Discordance

  • Survey headlines (e.g., "95% of GenAI pilots deliver zero measurable ROI") cause boardroom anxiety, but often miss rapid market evolution and differing organizational realities.
  • “It’s almost confusing because whenever the survey is published, you have to look at when was the actual survey taken, because it moves so quickly.” — Whittle [04:31]

2. Use Case vs. User vs. End-to-End Workflows

• User Adoption is Not Business Impact

  • Whittle distinguishes between users (individuals experimenting with tools), use cases (specific business problems), and true end-to-end workflows (connected processes delivering measurable ROI).
  • “A use case is where there is a specific business process... end to end use cases... is where you’ve got a sequence of use cases, a sequence of workflows that is driving an end to end value.” — Whittle [06:42]

• Regulated Industries: A Double-Edged Sword

  • While banks and health sciences face extra hurdles, regulation can also drive innovation—especially where adjacent, less-regulated processes (like customer support) can serve as AI sandboxes. [08:31]

3. Organizational Barriers: Skill Gaps and DNA

• The Enterprise DNA Problem

  • Traditional enterprises are hampered by long planning cycles, siloed teams, and a separation between product ideation and implementation. AI requires tight integration and a new “builder” culture.
  • “It’s a DNA issue, it’s a skill set and a DNA issue... Even large enterprise software companies are struggling because of the cycle times and the fact [that] it’s way more of an integrated process.” — Whittle [12:10]

• The Skills Gap

  • Few professionals inhabit the intersection of subject matter expertise, business process, and AI technical skill.
  • “There are very few people in the world... that can actually do all three of those.” — Whittle [11:08]

4. Rethinking “Pilots”: Shift to Small, Real Deployments

• Stop Calling It a Pilot

  • Whittle argues for moving beyond noncommittal “pilots” to small-scale, real deployments where success or failure can be quickly assessed and iterated. “Put an X through the term pilot... you need to do more smaller deployments where it is a real deployment... and then you start to get momentum.” — Whittle [13:37, 14:09]

• Momentum Through Quick Wins

  • Quick, tangible returns build organizational appetite for further AI adoption. The focus should be on tight investment-to-value ratios and learning through rapid iteration.
  • “Even if it’s small, it’s the ratio that matters... learning that way sort of fuels itself within the company.” — Interviewer [14:50]

5. Strategy for Culture Shift—Top Down and Bottom Up

• AI Adoption Across the Org Chart

  • Leadership needs to set the example; “If the CEO is not embracing it personally, then maybe that’s going to be a battle for the whole organization.” — Whittle [16:13]
  • The company must “enable the organization to understand what is AI... so that you don’t leave anyone behind.” — Whittle [16:37]

• Departmental Initiatives & Avoiding Siloes

  • Encourage departments to experiment within strategic guardrails, validating hypotheses with external help when needed. Create forums for sharing learnings and avoid duplicated efforts. ([16:50–18:28])

• Carrots, Not Sticks

  • Use encouragement and rewards—not mandatory quotas—to drive adoption.
  • “If you lay out very strict KPIs and monitor those very religiously... you’re likely to get very indifferent adoption levels. People work better to be motivated with a carrot versus a stick.” — Whittle [19:03]

• Incentives and Recognition

  • Celebrate small achievements (e.g., through newsletters, competitions, internal recognition) to drive participation and create a positive competitive dynamic.
  • “We have a weekly AI newsletter... it’s an awareness mechanism... highlighting really maybe small, small achievements and then you get momentum.” — Whittle [24:15]

6. Think Like Engineers and Builders

• The Engineering Mindset

  • Shift from long, abstract planning to hands-on building and rapid deployment. “Encourage people to think about building things rather than thinking about things too much because rapidly deploying... use either internal expertise or external expertise to validate... encourage everyone to be involved in the building and get that building done fast.” — Whittle [27:13]

• Thinkers and Doers—Now, Both

  • Enterprises need people with both strategic (“thinker”) and implementation (“doer”) skills—ideally, hybrid talent who can do both.
  • “Historically you’d have a lot of thinkers run a project... now you need people who can think and do concurrently.” — Whittle [34:35]

7. Curiosity and Culture as the Ultimate Accelerators

• Cultivating Curiosity

  • Curious employees drive technology adoption fastest. “The more curious people will typically go faster... encourage people that aren’t necessarily the traditional people who are involved... to actually be part of it.” — Whittle [30:15–30:48]

• Job Expansion, Not Just Job Loss

  • AI delivers “force multiplier” effects—doing more with existing teams, not just cutting headcount.
  • “Now what I say to people, hey, you’ve got 20 people. Maybe they can do the work of 30 people.” — Whittle [31:52]

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Notable Quotes & Moments

• On the dangers of endless pilots:
  “The problem with pilots is that people aren’t necessarily committed. They’re investing a small amount of money with not necessarily an expectation of an outcome.” — Lawrence Whittle [13:37]

• On where responsibility should sit:
  “You need to establish internally a baseline understanding that you don’t want to leave anyone behind. Whether it’s the janitor or the CEO, there should be some base level understanding.” — Lawrence Whittle [16:20]

• On changing the organizational DNA:
  “I think you really want to encourage people to all be engineers. Like think about everyone being an engineer, because what do engineers do... they build things.” — Lawrence Whittle [27:13]

• On culture and practical adoption:
  “It’s a very human story... it’s giving people the platform to showcase where they’ve received results. Encourage, reward, highlight. If you fall short on either... you’re probably not going to get the end result.” — Interviewer [25:56]

• On curiosity as a driver:
  “The more that you can encourage people that aren’t necessarily curious to see the people that are curious, you’ll actually see... an effect where, wow, if I’m not, like, actively being curious around this stuff...” — Lawrence Whittle [30:15]

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Key Timestamps

• 01:37: Introduction of the scaling problem and boardroom “credibility wobble.”
• 02:29–05:58: Lawrence Whittle recounts the recent history of AI’s adoption and the pilot boom.
• 06:42: Explains “user vs. use case vs. end-to-end workflow” and why this differentiation matters.
• 09:36–12:43: The realities of enterprise talent, organizational structure, and the skills gap.
• 13:37: The argument for eliminating “pilot” thinking in favor of committed, small-scale deployments.
• 16:06–18:28: Practical advice on embedding AI across the org, leadership roles, and department-level experiments.
• 19:03: Reward frameworks vs. mandatory KPIs for driving AI usage.
• 23:07: How to distinguish scalable solutions from “shiny decks.”
• 27:13: Mindset shift—everyone should be thinking like a builder/engineer.
• 30:15: Final advice on curiosity and embracing both thinking and doing.
• 34:35: Wrapping up: “Thinkers and doers” must become “thinkers who do.”

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Takeaways for Business Leaders

• Replace pilots with small, accountable deployments tied to business value.
• Drive AI adoption through a blend of top-down strategy and grassroots experimentation.
• Cultivate a “builder” culture and foster curiosity across all roles—not just technical staff.
• Motivate with encouragement and incremental rewards; highlight and share every win.
• Shift the organization’s mindset from delayed, large-scale projects to fast, iterative sprints.
• Ensure that AI adoption strategies consider both talent gaps and cultural readiness.
• Realize that AI is a force multiplier—not just a cost-cutting tool—enabling teams to achieve more.

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For executives seeking practical, culture-centric strategies to traverse the pilot-to-production chasm, this episode offers a roadmap of actionable insights, vivid metaphors, and culture-first tactics straight from the frontlines of enterprise AI adoption.

Transcript

[Podcast Host]

Welcome everyone to the Emerge AI in Business podcast. Today's guest is Lawrence Whittle, Chief Strategy Officer at htech. Htech is a global engineering firm focused on AI centric software and hardware development. It works across financial services, medic, automotive,

[Interviewer]

telecom and enterprise software, helping large companies

[Podcast Host]

solve complex engineering problems. The team is distributed across more than 20 engineering centers. Lawrence joins us on today's show to confront a problem many enterprises now face. Pilots that look promising in the lab but fail to deliver when scaled. He draws a clear line between individual users, isolated use cases and the end to end sequences that actually create business impact. He also surfaces the shift in culture and cadence required for AI to work in the real world, from smaller real deployments to tighter cycles. And a builder's mindset that replaces long planning horizons with continuous delivery. Today's episode is sponsored by HTEC for our solutions partners. Position your brand alongside the Fortune 500 leaders defining the enterprise AI roadmap for the opportunity to showcase your solution to the executives currently funding and scaling global initiatives. Partner with Emerge. Secure your [e-mail] partner. That's go.emerj.com partner. Now the conversation with Lawrence.

[Interviewer]

Lawrence, welcome to the show.

[Lawrence Whittle]

Thank you very much. I'm looking forward to this discussion.

[Interviewer]

Yes. So I want to start at something that's been bothering me for a while. Every time I open my inbox lately, you've probably seen the MIT Nanda number still doing the rounds. 95% of enterprises Gen I pilots delivering zero measurable ROI. There's really a credibility wobble happening in the boardrooms. Everybody's saying AI, AI, AI. And in pilots in the lab they're working perfectly. And then when they try to scale them and deploy them within the enterprise, that's where they start seeing that all the work done in the lab is just not returning the way they were hoping and expecting to. So especially across financial services, when you get to regulated industries, there's just another bunch of add ons that makes it more difficult. What's the gap between companies, what they expect and then what actually happens when they start deploying?

[Lawrence Whittle]

Yeah, I think it's a great question and I sort of like to give a little bit of a modern history lesson. So if you sort of step back to 2024, which I think was the first time when the AI word came into everyone's vernacular, I think it was obviously established. AI is not late breaking news. It's actually the technology that I've been involved in is I was doing AI before it was called AI 20 years ago. The early branches of AI being machine learning were established a long time ago. But if you sort of go to 2024, you started to hear a lot about AI. You started to hear about OpenAI, ChatGPT. And so there started to be a lot of sort of interest and I would say not so much pilots yet, but sort of maybe discussions and, you know, sort of evaluations around tools. As you got into late 24 and early 25, that sort of momentum around pilot started to pick up. And then sometime around Q1 of 2025, we started to hear this term agentic.

[Interviewer]

Yes. And everybody had it on their lips. That's all we heard about.

[Lawrence Whittle]

That's right. So if you think about what was happening natural, it's the natural adoption of new technologies. I've been around Silicon Valley for 25 years. There's sort of the early adopters, then there's the early majority. Then there's the majority and they go through various cycles. There's this well understood term, the Gartner hype cycle. So I think what happened is that 2024 people were starting to do evaluations at very early pilots. And then Q1 AgentIQ came in and people said, oh, I need to have the AgentIQ version. So then we to some extent started that pilot process again with AgentIQ. And that started to pervade, I think in Q2 of last year and Q3, there's been a number of surveys that have been published, whether it's mit, whether it's Gartner. And it's almost confusing because whenever the survey is published, you have to look at when was the actual survey taken,

[Interviewer]

because it moves so quickly.

[Lawrence Whittle]

Because it's moved so quickly. So what I think I like to describe is what I call the anthropic effect, which if you look at anthropic Claude a year ago, only experts had really heard about Claude or anthropic. But if you look at their growth from the middle of last year, where they middle of 2025, where they crossed a billion dollars, which is huge. They started the year at 100 million. By the end of 2025 they were 15 billion, of which the vast majority of that was in the enterprise. So what I think happened is there was a lot of pilots in 2025 that were focused on use cases where it wasn't necessarily easy to identify value. So the pilots were ticking technically, but not necessarily focused on value. Then what you saw is late 2025 and early 2026. I think executives started to say, I want people to Adopt AI at scale, but not necessarily at use case scale, more at user scale because they saw enough early signs but not enough pure value. So I think the reality is the timeline is a little bit confusing. I think what happened in the majority of 2020, because a lot of pilots were to prove out the concept of AI versus proving out the value. And then towards the end of 2025 and into 2026, people started to move towards adoption and now I think there's a lot more focus on value. So it's really about a time series and depends on when you actually interview people and when you published.

[Interviewer]

And then everybody's in a different part of the race. Some people are still not at agentic, they're still trying to get there. And especially when it comes to regulated industries because it lags behind. I'm saying lag behind, but really it's, that's not, that's not completely true because sometimes regulation actually drives the technology, which means especially in banks, sometimes they can be further ahead and then you've got health sciences because it's life and death that, that obviously lag slightly behind. So what was interesting to me, what you just said was use case versus user and we still hear a lot of executives talking about use cases. Can you explain to me even deeper what that means on, on the executive level? How do you look at this differently, this use case versus user scenario?

[Lawrence Whittle]

Yeah, so I think there's, there's actually a third dimension which is end to end use cases. So users is someone that has a license to Claude code or you know, GitHub or ChatGPT and you know, they are using it from a personal perspective. So maybe they're using it as a sort of an advanced query or advanced search. I think a lot of advanced users are using it for deep research. So once you understand as an individual how to prompt. But it's basically for my own use. A use case is where there is a specific business process where there may be multiple users that are going to utilize the particular AI. For example, it could be a next generation chatbot that is going to support incoming support requests. When I think about the end to end use cases, which I think is where people aren't yet, that's where you've got a sequence of use use cases, a sequence of workflows that is driving an end to end value where you can actually start to see, you know, I spend X dollars, pounds, euros, I got Y return and the return is based on either, you know, increased sales, reduce costs or increase productivity.

[Interviewer]

So the business the, the bottom line, that's, that's.

[Lawrence Whittle]

Yeah, either top line or bottom line. I think there's certain use cases that can really drive increased conversions, hopefully at a lower cost, but not necessarily always at a lower cost. It may just be a higher velocity. And I think that's where in financial services and regulated industries they have an interesting challenge and also an interesting opportunity because yes, you have the regulatory framework, but they have a lot of data.

[Interviewer]

Yes, historically.

[Lawrence Whittle]

And they can delineate elements of the data and associate that with use cases that aren't necessarily regulated use cases, particularly like customer support use cases or top of the funnel customer acquisition where that process, whilst it's regulated, is not regulated to the same extent as a loan application or a credit card or an underwriting process. So I think you sort of need to break down the subjects and to summarize the question, a lot of early sort of proof of concepts, some early pilots then grab mass usage and now think our people are back to use cases and the early signs of end to end workloads.

[Interviewer]

And where do you think they're getting stuck? Is it a matter of they just don't have the teams to. They've got the ideas and they've got the use cases, they've got the users, but now bringing that all together and getting a solution, is it that they stuck in that process? They need, I don't want to say assistance, but they need somebody coming in in and actually helping them work through getting that value. Because you said concept to value, do they already have the concept? They need the help to get to the value.

[Lawrence Whittle]

So it's an interesting question that I ask a lot of my clients on a global basis, on a frequent basis. I don't see any patterns yet regarding the why. What I do see is two categories when I say I don't see any patterns. I don't see some sort of financial services having issues here versus retail not having issues here. I think the challenge very much is understanding where you are up to in that sort of maturity curve. So I think that, you know, high tech companies are duty bound to cannibalize themselves with AI so they have a clear view of the what I should build and the how I should build. So what is the what is the use case? What is the business case? So it's a use case and the business case, what is it I want to achieve, how much would it cost, what I expect to get out of it and what tools it should I use when you get into the more what I would call just Non tech, you could say traditional, but I think just maybe say tech versus non tech. I think that the majority of companies have some hypothesis on maybe where they should apply AI, but are not yet at the point of really understanding what, what they should build. And so I think there's sort of a categorization of hypothesis. But the actual and the challenge for this is that whatever people think about how powerful AI is, when you're thinking about at the enterprise level, this is not plug and play because you've now got the need to understand both the subject matter, banking, the use case, a particular domain use case, as well as the technology. And so there are very few people in the world that have a volume of people that can actually do all three of those. And I think even the technology market historically has delivered product management separate from product build and product implementation. Whereas now you have to have this really integrated. And I think one of the reasons why my company htec has actually really accelerated during this period is that we, we never got into the business of building products for long term implementations. We have very much had this agile approach, whereas most of enterprises had this momentum where you spend, you know, six months deciding what to do and then one to two to three years to deploy and then you see what, and

[Interviewer]

everything has changed by that time. It's all changed and it's changed and

[Lawrence Whittle]

it's a DNA issue, It's a skill set and a DNA issue. Even large enterprise software companies are struggling because of the cycle times and the fact is way more of an integrated process. So I think it's tech and non tech have different challenges but I think in the enterprise you've got this DNA and skill set gap that is new and I think people are starting to discover that whilst this can be really rocket fuel, these AI solutions, you still need an expertise which is not that visible in most companies I think, and

[Interviewer]

we, we hear it across industries the skills gap that is really becoming a problem. The skills gap is, it's widening and it's, it's already been big for a while and it's definitely widening which is great for people that want to come in and fill those gaps, but the enterprise is struggling with that. If I want to play a bit of a devil's advocate it obviously we've, we've heard it so many times like pilots are just not scaling but then we've, we do have quite a big part of the audience that's saying but we have to fail forward and failing is a way of learning. So how do you balance that between Learning internally and failing, but also getting to the value. To me, it seems that we've sort of bridged that gap. And though you can, let's say on the back end, still have sandboxes where you're learning, you do still need those projects that are driving value. Otherwise the company's going to lose their appetite for technology and AI and they'll just fall behind when it comes to their competitors.

[Lawrence Whittle]

Yeah, so I think it's sometimes a semantic discussion, but I think we need to sort of put an X through the term pilot because the problem with pilots is that people aren't necessarily committed. They're investing a relatively small amount of money with not necessarily an expectation of an outcome. I think what you need to do is to look at more smaller deployments where it is a real deployment, where maybe the value, the cost and the value is not significant, but it is

[Interviewer]

really, it's the momentum.

[Lawrence Whittle]

Momentum. Because what I think you need to get is momentum. So once you start to see, actually I invested X dollars for Y time period and I saw X return, even though the absolute return may not be massive, it's clearly cause and effect. And then you say to people, where do we have any other analogous areas where we could deploy in this? And then you start to get momentum and that momentum then really accelerate. So think about that again, it's a, it's a different approach from the past where you would have a, you know, a multi year program with work streams that literally go forever, whereas now you need to sort of iterate through it like what's technically called sprints. But you need to sort of sprint in a little sense in terms of business.

[Interviewer]

And I think that ties back to what you said to concept and value. It's. The concept must be tight enough to clearly see what the value could be because that's where you'll get the momentum. And I like the fact that you say that ratio needs to be very tight between your investment versus what the value can be. And even if it's small, it's the ratio that matters. It's that you can get a quick, quick return on it and then start building that momentum. And I think learning that way, sort of it feels itself within the company because when you see what has worked, and it's probably more the approach than that, the actual solution that's important is it, it's approaching it in the same way, no matter what the concept or what, what the idea, what this, what you're trying to solve. So if, if we're discussing this in a sense of practicality, what are companies, what are enterprises having to look at investing in at the moment? Is it taking the time to really define those cases where you can get that quick ratio that the return on investment or how would you approach that and where in the company should that be sitting? Is it on a department level that everybody in the company should be trying something small, tasting the success and then taking it further? Or is it more a top down situation where it's one deciding on one within the enterprise?

[Lawrence Whittle]

Yeah. So if you think about again the term I used earlier on the anthropic effect, the growth of anthropic was somewhat organic. It was bottoms up and what I encourage CEOs. I was actually talking to one this morning and I asked him, are you personally using AI as him personally using AI? And he said I'm embarrassed to say no. And I said you shouldn't be embarrassed, but if the CEO is not embracing it personally, then maybe that's going to be a battle for the whole organization. So I think you need to sort of establish internally a baseline understanding that you don't want to leave anyone behind. Whether it's the janitor or the CEO, there should be some base level understanding. So I think enabling the organization to understand what is AI, to get everyone to a baseline or maybe baseline plus one is super important. So that's sort of a top down initiative, everyone to be familiar. I want to have a strategy where there's a framework of what tools can be used and how they can be used so that you don't leave anyone behind. I think that's super important as a top down. But then maybe not bottoms up in terms of individuals, but certainly at the department level you need to sort of set some top down objectives that I want each department to spend a significant amount of their time to identify. Maybe with external help from companies like HTech around, you've got a hypothesis. Now validate those hypothesis, come up with a roadmap of deployments and communicate that systematically from an organization perspective. So you don't have duplications of effort, but also you actually have a forum for shared experience. So top down from a encouraging people janitor to the CEO to adopt AI in their own life, obviously within the framework of what's approved, a top down sort of strategy to say at a department level or a region level, encourage people to validate hypothesis use cases and set objectives around. I want you to deploy X number of use cases over Y period. But have a framework where they're not rogue out doing stuff themselves. So It's a little bit complex, it's multi dimensional. I think it's partly top down, it's encouraging bottoms up and then having a reconciliation in the middle.

[Interviewer]

And this is actually extremely practical. As business leaders, you really, this, this is a strategy, this is a mind shift. And looking at this very intentionally and strategically to a sense. And if you say motivating, let's look at the top down that motivate vision. Does it look like KPIs? Are you telling people that there's a certain level of AI usage you need to get to that could become difficult to manage? I guess as well you've, you've been sitting with companies where it does work. We, we've spoken about where it doesn't work, where it does work. What is that difference that you've seen it?

[Lawrence Whittle]

You know, it's a sort of a carrot versus stick discussion. If you, if you lay out very strict KPIs and monitor those very religiously, depending on the size of the organization, you're likely to get very indifferent adoption levels because on average people work better to be motivated with a carrot versus a stick. So I think if I'm sort of telling people, you know, don't over rotate towards, you must do this, but do create a framework where you are setting a vision for the company that by a date we need everyone to be adopting at scale. And the reason I said it is that we as a company made it this. I mean we've been doing AI, you know, we're not a huge enterprise, still pretty big, you know, two and a half thousand people. And you know, we recognized that we've been doing AI for a decade, but not everyone was doing AI. And so 2024, we made a very clear decision that we wanted everyone in the company to be certified to some level of proficiency and it was optional. And then what we started to see is that different people would prioritize. So then we made it not mandatory, but we started to have some characteristics that you would be rewarded if you adopted things earlier. Ultimately you could turn it around and say you're penalized if you don't sort

[Interviewer]

of the reward sounds better.

[Lawrence Whittle]

Yeah, that's right. So you start with a framework to encourage people. Then you see who's adopting, who's not adopting. So you come up with a rewards framework. And then over time, so I think you need to sort of recognize the, the DNA of your company and the JIT and the demographic now, your culture and the culture and the geography because geographies can be different as well. But I do think you need to reward people and encourage. So you start with encouragement, then you go to reward and ultimately you maybe need to push people because for large enterprises it's just not the way they've operated even for, you know, if you think about large systems integrations, amazing companies like Accenture or Cognizant that are hundreds of thousands of people, enterprises haven't worked on this level of velocity. They're more sequential programs as opposed to rapid deployments. And I think that's what the, that's why I think the DNA opportunity and issue is is that people are used to multi year projects and the results you get at the end of the yeah, you get milestones along the way but the results are sort of two to three years. Whereas now you've got to figure out where can I work on something where the results could be in 90 days, then another result 90 days. So I think you start off with a framework to encourage, then you add reward and then ultimately you may add pressure based on the speed of your organization, your own culture.

[Interviewer]

And I think if I were an enterprise leader I would want to tap in. I'm not going to put you on the spot right now, but I would be very interested in what that process look like. It looks like I've heard in some other companies they've got what would you call excellence, what do you call it? Enterprise excellence unit where it's very much driven through and they weekly or monthly meetings where they literally got rewards in the sense of you win tickets to this, you win tickets to that because of your usage. And that sounds simple but we all human you are incentivized by being rewarded and I guess there could be some real great strategies that you've seen before when it comes to reward and then working it through until pushing it. So I would say like a lot of enterprises who do not have, they've got that skills gap and they now need to consider bringing somebody in a vendor. But they all come in with use cases that look like shiny decks and they've proven it. How do enterprises distinguish between is this going to scale and deploy within my company or is this just a great idea?

[Lawrence Whittle]

I think it goes back to the basics of speed and identifying things that you can achieve in a rapid amount of sense. Because you're absolutely right, the consulting world, we're not a consulting company, but the consulting world has got an incredible reputation for delivering amazing content. Yeah, you deliver the 50 PowerPoint slides and nothing really happens. I think you have to just be a lot more PRAGMATIC I think you have to treat this as a, you know, as a mission that we're all going on, but not like a program that is going to be treated in the same way. So I think the thing I've seen works so well in larger organizations is when they think about this differently, it's not like a. It's not another IT product, not another IT project. It's about enabling the organization to become more efficient. And you typically find that asking people to put their hand up who wants to be part of this and then showing and telling, because I think the shared experiences are what we are seeing, really accelerate the momentum. Because if you have these massive organizations that have massive projects with the name of the project, whereas if you create this environment where rapidly iterate, show and tell, then you get momentum, the more people raise their hand. So I think it's more of an approach, obviously show me the money is always going to get all levels of attention. And typically there are some use cases which are so huge in terms of ROI, but our point of view is that 10 use cases that generate a million dollars versus one that generates $10 million, you're probably going to get way more momentum initially, especially initially. And then once it starts, then you get momentum. So I think the net net is find a way to get momentum and those organizations that encourage reward and then highlight. It's interesting, we have a weekly AI newsletter and one of our massive clients, I'm talking, it's a Fortune 100 company, we were talking to them, they asked about us, we said, oh, you read this weekly newsletter? And they said, could we use, can we see it? And then they said, would you mind us? We repurpose it. And it's really just an awareness mechanism because, you know, and highlighting, you know, really maybe small, small achievements and then you get momentum, then people start to be competitive, et cetera. So I think it's a lot of it is culture and approaching it in a different way. Not the normal, hey, let's get the normal people involved, the normal partners involved, external partners. Let's go on a long journey as opposed to let's go on lots and lots of things to get them at momentum.

[Interviewer]

And I like that because as much as we're speaking about technology, it seems like a very human story. It's something we've seen forever. It's giving people the platform to showcase where they've received results. And I especially like your encourage rewards highlight. And if you fall short on either of these, you're probably not going to get the end result that you're hoping for. So it's very much what I'm hearing is do an evaluation of your culture, at least know your culture, understand what's going to be the driving forces and probably you'll be able to already have an idea of the people that could drive this, that are usually good at motivating the other employees or the rest of the company and ensure that you get them on a platform where they are able to show and tell. And I think I like the idea of the small, smaller projects because what that means is the wins you make in each of those, those iterations probably inform all of the other use cases because even though it might be extremely different from CX to sales to lab work, whatever it may be, there are some universal truths where if you learn within a company this worked within this specific use case, you could probably repurpose the win there in a certain way to gain faster. And that will, as we spoke about earlier, that momentum is really what makes the difference.

[Lawrence Whittle]

That's right. And maybe one other thing which I sort of touched upon it a little bit, but I may be a little bit more explicit. The world of technology for the last 25 years has been sort of dominated by I'm going to buy a piece of software and then I'm going to get a professional services company to own the implementation. And I think what you need to look at as an organization is think about this almost like a. It's like a manufacturing project. I'm actually. I don't need someone to advise me and come up. I need to build something. So you've got to also get into this mentality of do, don't talk about it. So I think you've got to also internally, externally, so involving the same cast of characters that you've worked with on IT projects for the last 20 years. Whilst there's always needing some level of technical expertise and some level of project management. I think you really want to encourage people to all be engineers. Like think about everyone being an engineer because what do engineers do? Whether it's a software engineer. They build things. Yeah. They don't like spend 90% of their time staring, thinking what? No, they build things. Philosophizing mentality is get everyone to think as though they're. Whether they're a software engineer, but that they're an engineer because about building things, not a program. And I think that's really what's different. So encourage people to think about building things rather than thinking about things too much because rapidly deploying most people have a Pretty good hypothesis at a conceptual level of where it could work. Use either internal expertise or external expertise to validate. Okay, that's what you should build. But then encourage everyone to be involved in the building and get that building done fast. And I think that's what's really interesting about this, that the value is super compelling versus the traditional large IT project.

[Interviewer]

And this is actually quite interesting to me because we've spoken about the skills gap, and when you see. When you see listings to fill those gaps, it's definitely leaning towards, do you have an engineer mindset? That's what I've seen is people asking, okay, you can do this, but do you have that engineering mindset? And that could be. Be an upskilling within your company, even just figuring out what that means. What is an engineering mindset? And if you get them, if you get the company, it is a bit of a paradigm shift, because I do think that AI has been very, let's call it abstract in the sense that people have been thinking about it. How can we. How can we use AI? Because we need to be using AI. I like the shift towards how can we build it? What are we building? And that may mean that within your company, you don't have that true engineering skills, but you can outsource that. But just the engineering mindset will already get you quite a long way as we wrap up the episode. And this has been extremely practical, which I know the audience really appreciates, because at the end of the day, as we just said, we don't want to keep thinking about it. You want to actually take action and deploy. Are there any last thoughts that you'd like to leave the audience with, especially when it comes to those first steps?

[Lawrence Whittle]

Yeah, I think I sort of maybe restate a couple of things in my experience, both in my own company, actually, with me personally as well, and encourage people to be curious as well. Because there's a. There's a thing that I've seen consistently over the last, you know, certainly over the last five to 10 years, as technology adoption becomes faster and faster, is that the more curious people will typically go faster. So, you know, encourage people to, you know, be curious, encourage people that aren't necessarily the traditional people who are involved in these discussions to actually be part of it. And the more that you can encourage people that aren't necessarily curious to see the people that are curious, you'll actually see, you know, a little bit of a, you know, sort of an effect where, wow, if I'm not, like, actively being curious around this stuff, So I think that's the thing is it really go back to the fundamentals. This is a. I mean, you use the term paradigm shift. I don't whether. I mean, people understand paradigm shifts, but I think this is just a. There are jobs that will be eliminated. That's maybe one thing. But what we're seeing explicitly is that the value that these types of solutions can drive at scale is creating significant increase. Net increases in expertise. Yes, it may be in different areas. And so I also think that's a key message, which is, I think a lot of the discussions I had in 2024 and 2025 with senior executives was, hey, Lawrence, I've got 20 people. Can we do it with 10?

[Interviewer]

And that's not the question.

[Lawrence Whittle]

Now what I say to people, hey, you've got 20 people. Maybe they can do the work of 30 people.

[Interviewer]

Exactly. And that's scale. Right now you're scaling.

[Lawrence Whittle]

Now you're scale, force multiplier. We see this with clients. You know, I've got a client talking to on Friday where we've got a pretty large team deployed things like 70 odd people. And they're seeing massive productivity improvements over the last nine months. I mean, substantial. And they said, we want to do more because the productivity is not about I now, I don't need 70 people, I need 30. No, I see this productivity. I'm making people, I'm making the average person a superhuman. And that's a great productivity driver. And also, I think it will ultimately mean that people that are motivated and curious will be very, very successful. People that are not motivated or less curious will be less successful. And I think that's a good signal for both employees and employers that you're going to build an organization that is ultimately, over time, going to leave behind people that aren't motivated or curious. And those that aren't motivated and curious, even though they may not be skilled in the same way, can really accelerate their productivity. And I think that's what's really exciting

[Interviewer]

and that drives growth because it's cultivating a culture of ideas rather than just sticking to. And I think enterprises do realize the old way is gone. It's not a matter of can we adapt our old way and just layer on the AI? You need to change what you're doing. This has been extremely practical. As I said, I do enjoy it. And it's been very positive in the sense that we've. We've spoken about force multipliers, we've spoken about momentum. These are all very positive thoughts and grounding it in the idea that it's not a job loss situation. Do not consider this because I do think that can stifle enthusiasm for AI within companies. And I really like the idea of thinking as engineers, as builders. It's moving. And I know if I don't like the word pilots anymore, just as you said, it's, it's, it's been overused and it's maybe not. The problem might not be pilots. The problem may be, as you said, the investment. It's investing in ideas rather than investing in something you can build and deploy. So making that shift within the company, making everybody think as builders, not as thinkers, and then ensuring that it's a multi tiered top down, bottom up. And then I'm going to reiterate the encourage, reward and highlight cycle which will encourage curious people to, to come with solutions that are practical enough to deploy and get that cost to value. Thank you so much for your time. No add, please.

[Lawrence Whittle]

Just one final thing. When you talked about thinkers and doers, when you talked about thinkers, I think about thinkers and doers. You can now, you know, historically you'd have a lot of thinkers run a project, project would run and not many people be doing the doers work. Whereas I think now you need people that can think and do currently. And that's again, it's, I think it's a huge opportunity and I think it's a, it's a mind shift, mind shift change for everyone. And I think the future is good but you know, there's still a bit of a change management to be done across the world.

[Interviewer]

Yes, that's the challenge, especially within big enterprises. It's historically taken longer. It takes longer to turn a big ship a ship rather than a tugboat. But I think scaling it down into those little iterations makes it a lot more practical than trying to steer the entire ship. Thank you so much for your time.

[Lawrence Whittle]

Appreciate it. Thank you.

[Podcast Host]

Wrapping up today's episode, I think there are three key takeaways from our conversation with Lawrence. First, enterprises store tool when they treat

[Interviewer]

AI as isolated pilots rather than building

[Podcast Host]

the end to end sequences that create business impact. Second, momentum comes from small real deployments with tight scopes and fast iteration cycles, not long planning horizons or theoretical proofs. And finally, organizations that integrate expertise, shorten cycle times and adopt a builder's mindset are the ones that move from concept to meaningful results. If you have an AI solution, position your brand alongside the Fortune 500 leaders defining the enterprise AI roadmap for the opportunity to showcase your solution to the executives currently funding and scaling global initiatives. Partner with Emerge. Secure your [e-mail] partner. That's go.emerj.com partner for further executive level analysis and to join our network of leaders delivering workflow impact with AI. Visit emerge.com on behalf of the team at Emerge. We'll see you on the next episode.