What can top brands teach us about e-learning?
E-learning designers are still catching up to what brand differentiation experts have known for a long time. Experience matters.
Consider Bubly, a maker of sparkling water, recently purchased by PepsiCo. Bubly doesn’t try to differentiate at the product level: in a blind taste test between Bubly and LaCroix, participants were unable to tell one from the other. Instead, Bubly focuses on the consumer’s experience of the product.
To begin, there’s the enthusiastic welcome: each can features a pull-tab greeting that mimics text messages – “hey u,” “hiii,” or “yo,” – simulating the kind of playful rapport you might have with friends and family. Next, the product’s peach, pineapple, and grapefruit-toned cans and smiling logo work together to convey positivity, creating a look and feel that aligns seamlessly with its slogan: “no calories. no sweeteners. all smiles.” Finally, Bubly gamifies buying. As writer Elizabeth Demolat points out, no store stocks all twelve flavors at any one time, leading to online and in-person buzz about where to find specific flavors. This strategy, along with the release of a variety of limited-edition flavors, has essentially turned “the act of purchasing a product into a treasure hunt.”
Bubly’s brand differentiation leverages enthusiasm, emotion, and excitement—experiential elements that echo the design pillars of best-in-class e-learning. Here’s how to incorporate each.
- Enthusiasm
Find new ways to breath energy into the experience. Take, for example, a short, animated video that uses action film motifs to explore emotional awareness in the workplace.
The sequence begins with an establishing shot of a manager providing constructive feedback to an employee. The action moves quickly into the employee’s brain, which is set up as a command center. A group of intelligence agents, straight out of Mission Impossible, look on with alarm. One more word from the manager on “areas for improvement,” and the emotion-regulating amygdala will be triggered, hijacking the employee’s normal reasoning processes. The intelligence agents strategize, introducing different tools and techniques that can be used to regain perspective, and the learning journey begins to take shape.
Greeted with a fresh, playful take on a critical workplace competency, learners are primed to go deeper.
- Emotion
How do you get beyond the rational regions of your brain – the ones that “control language, but not decision-making” – to tap into feelings and emotions? One particularly creative course on human anatomy leverages powerful visuals to reach learners on that deeper level.
Participants begin by learning that there are more nerve cells in the brain than stars in the Milky Way, observing a close-up of the brain’s circuitry dissolving into tiny specks lighting up the night sky. Because the underlying anatomy remains hidden, medical-aesthetics practitioners learn that they will essentially be working in the dark. The stars fade out slowly, one by one, until there’s nothing left on screen but total darkness—a strange, slightly unnerving experience that drives home the importance of understanding anatomical structures on a visceral level.
- Excitement
Give people something they’ve never experienced before by challenging the norms of typical training.
Data-protection policies, for instance, are critical safeguards wherever they’re in place, but existing e-learning on the subject is almost always designed as a passive, one-way transmission of information. One exceptional data-protection course takes a different approach, using live-action video and a dramatic soundtrack to depict a privacy breach occurring in real time.
While this can get gimmicky, immersing learners in a volatile environment with uncertain outcomes builds tension, a key lever for creating buy-in.
So, how can we help clients build better learning experiences?
Many clients see digital learning as a product, one that looks a lot like what’s already out there: didactic, uninspired, dull. By nudging clients toward digital learning courses that mirror what they already know about branding, we might just be able to help them build experiences that stand out in a crowd.
Related content
In Part 1, I told you about the three decisions we made two years ago and the simulation flywheel that produced our first Applied AI diamond.
Here’s the field-notes version.
Over 80% of our global business have now adopted a new Applied AI approach for doing simulations in the first eight weeks, across 24 countries and every practice.
The flywheel didn’t stop with simulations. It moved into finance, sales enablement, legal, operations, and client delivery. Teams started building agents and bringing them onto their own org charts. We didn’t plan for any of that. We built the conditions for people to find their own breakthroughs.
What it felt like inside the flywheel.
When the simulation team went live with their first clients on the new way of working, the lead person hit a wall. Their words:
“You’re asking too much. You’re making me be a full-stack developer. Up until this point I did a small part, and I sent it to the team, and they built off the back end, and they brought it back. And now I have to end-to-end soup to nuts, basically alone.”
There was graphic UI work nobody had been trained for, the fear of delivering quality below what BTS expects of itself, and the weight of not having a playbook. This was not the joyful adoption story most consultancies tell.
Then something shifted. Six members showed up for product testing, where the usual was two or three. The work created teamwork I hadn’t seen at BTS in years. The breakthrough was not an instantaneous change from skepticism to celebration. It was a breakdown in confidence, then rally, then bonding. If we didn’t make room for the breakdown, we would have lost the rally.
The other breakthrough was global teamwork; not yet a BTS core strength. Our culture is beautiful: high-freedom and entrepreneurial. But people’s first identities are to their countries. Almost every prior attempt we’ve made at a global initiative has failed. The one exception was Covid. So, when I say what happened next surprised me, I mean it.
I asked to join the simulation team’s Slack channel rather than pulling them into status meetings. What I got to watch in the mornings was someone in South Africa waking up, posting “I tried this and got stuck,” then London adding on, then San Francisco weighing in, then a surprise breakthrough overnight from Tokyo. We didn’t engineer that. Curious and determined BTS’ers did. The problem was interesting enough that the org chart didn’t matter. It was amazing to see and a glimpse into the next evolution of the BTS culture.
The pattern: Explore, expand, institutionalize, renew.
What we’ve now seen play out, both inside BTS and with clients, follows the same four-step pattern. Each step asks a specific decision of the leader.
Explore.
Stay stubborn on the aspiration and fluid on the path. Our breakthrough wasn’t the path we originally took. We changed tools and approaches. Nobody could have foreseen that. And if the team had taken the first six months of learnings from AI as their definitive “this is the detailed path we will follow,” we never would have gotten the disruption. Five different tool combinations were tried before we found the one that worked. Companies that lock into a single path or tool too early are betting against compounding capability that doubles roughly every seven months. That is not a bet I’d take.
Expand.
Run the old way and the new way side by side. When the simulation team’s breakthroughs got real, the instinct was to retreat into more internal testing. We did the opposite. They ran old way and new way in parallel on 6 or 8 live client projects across all three geographies. Every single one ended up going live the new way. The backup was always there. They didn’t need it.
Institutionalize.
Burn the boats. The simulation team committed that no new client work would be done the old way after January 1. The other practice leads then committed to dates within Q1, even though most of them had not yet experienced the new way themselves. They had to trust their colleagues. If you can do it for the most complex thing, you could probably do it for the less complex ones. By February 15, we had approaching 90% global adoption across 24 countries, across all practices. I was shocked and proud. We had spent years failing at exactly this kind of global rollout.
Renew.
Treat your agents as contractors. People on our diamond teams are now managing 30+ agents they built themselves. Our teams give agents performance feedback. We terminate their contracts when they don’t deliver. We expand the responsibility of agents when they outperform. The frontier question we’re wrestling with now is token budgeting. Two friends of mine running engineering-heavy companies believe that within 6 - 9 months, their token cost per engineer will exceed the cost of the engineer. Whether that’s the right framing is open. The question is real, and every CEO will be asked some version of it within the year.
What had to be true for this to scale.
Once we achieved this amazing global innovation, the leadership sat down to figure out what made it work. We named five things. None of them were about the technology.
Real pain points as the starting point. We had so many people frustrated from those ways of working, all the back and forth and all the wasted time, that this was gold for them. The old way was already painful. The new way wasn’t a forced disruption; it was relief. Find the workflow where the pain is loudest and start there.
The diamond unlocked creativity, it didn’t constrain it. This was the most differentiated insight, and the one most leaders miss. It wasn't "here's the new tasks and rules." It was, "once you learn how to do this, the sky's the limit. You can be even more creative." If your rollout feels like a new set of rules constraining your people, you’ve built the wrong thing.
Pair deep expertise with fresh eyes. The disproportionate share of our breakthroughs came from a tenured tinkerer with total command of the work, paired with someone new to the role who hadn’t yet built the muscle memory of how it had always been done. Without that pairing, you get incremental improvements to the work you already know how to do, instead of a reinvention.
Refuse the “people are too busy” reflex. When I brought the rollout to the global leadership team, the excuses came fast. “Our people are too busy. They’re burnt out. Q1 is going to be busy. No one’s going to have time.” My response: “This is a chance to eliminate the tasks you dread and expand what you love. I know it is a short push of extra work, and I think after the fact you and your team will feel joy and pride and say it was the best time we ever spent.” This is the moment most AI rollouts die.
Senior leaders must lead by example and do the work themselves. This is not middle manager’s job. This is not something you delegate. Even though you don’t build simulations anymore, you must know what this is. One of our partners proactively put time on senior leaders’ calendars and forced them to do the work. Once they started building, the excitement grew, and they could advocate for the rollout because they understood it. If your executives haven’t put their hands on the keyboard, you don’t have a rollout. You have a memo.
What we’re seeing across clients.
We’re now running this play with client organizations across industries and geographies. The companies whose flywheels are accelerating paired their A-players with their early-career talent, pulled IT and legal into the working sessions, refused the “too busy” reflex, and put their senior leaders’ hands on the keyboard. The companies whose flywheels are stuck almost always have a leadership pattern at the center of the stall. Not a tooling pattern. Not a governance pattern. A leadership pattern.
If this resonates, let’s talk.
If you read Part 1 and asked yourself whether your flywheel was turning, the question I’d add now is sharper: do you have the conditions in place for a diamond to appear? If yes, you’re already moving. If no, the technology will not save you.
Here's where we're starting with clients: a working session, half day to a full day, with a small group that owns one of your highest-friction processes. Together we map where your first diamond is most likely to land, how to set up the side-by-side trial, and what your version of "burn the boats" should look like.
The destination, if we do this right, is a self-reliant culture of applied AI inside your company. 5, 10, 15 diamonds compounding into a fundamentally different way of operating. From what I have experienced this is a once in a career opportunity for dramatic shareholder value creation if you get that muscle going. I say that because I'm watching it happen, in real time, inside our own company and across our client base.
If you want to get your flywheels spinning and map your first diamond, start here. Bring your hardest workflow. We'll bring the playbook.
In the 1990s, Business Process Reengineering (BPR) was the Big Bet. Companies launched tightly controlled pilot programs with hand-picked teams, custom software, and executive backing. The results dazzled on paper.
But when it came time to scale? Reality hit. People weren’t ready. Systems didn’t connect. Budgets dried up. The pilot became a cautionary tale, not a blueprint.
We’ve seen this before with Lean, Agile, even digital transformations. Now it’s happening again with AI, only this time, the stakes are different. Because we’re not just implementing a new solution, we’re building into a future that’s unfolding. Technology is evolving faster than most organizations can learn, govern, or adapt right now. That uncertainty doesn’t make transformation impossible, but it does make it easier to get wrong.
And the dysfunction is already showing up, just in two very different forms.
Two roads to the same cliff
Today, we see organizations falling into two extremes. Most companies are either overdoing the control or letting AI run wild.
Road 1: The free-for-all
Everyone’s experimenting. Product teams are building bots, prompting, using copilots. Finance is trying automated reporting. HR has a feedback chatbot in the works. Some experiments are exciting. Most are disconnected. There's no shared vision, no scaling pathway, and no learning across the enterprise. It’s innovation by coincidence.
Road 2: The forced march
Leadership declares an AI strategy. Use cases are approved centrally. Governance is tight. Risk is managed. But the result? An impressive PowerPoint, a sanctioned use case, and very little broad adoption. Innovation is constrained before it ever reaches the front lines.
Two very different environments. Same outcome: localized wins, system-wide inertia.
The real problem: Building for optics, not for scale
Whether you’re over-governing or under-coordinating, the root issue is the same: designing efforts that look good but aren’t built to scale.
Here’s the common pattern:
- A team builds something clever.
- It works in their context.
- Others try to adopt it.
- It doesn’t stick.
- Momentum dies. Energy scatters. Or worse, compliance says no.
Sound familiar?
It’s not that the ideas are flawed. It’s that they’re built in isolation with no plan for others to adopt, adapt, or scale them. There’s no mechanism for transfer, no feedback loops for iteration, and no connection to how people actually work across the organization.
So, what starts as a promising AI breakthrough (a smart bot, a helpful copilot, a detailed series of prompts, a slick automation) quietly runs out of road. It works for one team or solves one problem, but without a handoff or playbook, there’s no way for others to plug in. The system stays the same, and the promise of momentum fades, lost in the gap between what’s possible and what’s repeatable.
We’ve seen this before
These aren’t new problems. From BPR to Agile, we’ve learned (and re-learned) that:
- Experiments are not strategies. Experiments show potential, not readiness for adoption. Without a plan to scale, they become isolated wins; interesting, but not transformative.
- Culture is the operating system. If the beliefs, behaviors, and incentives underneath aren’t aligned, the system breaks, no matter how advanced the tools.
- Managers matter. Without their ownership and support, change stalls.
- Behavior beats code. Tools don’t transform companies. People do.
Design thinking promised to bridge this gap with user-driven iteration and empathy. But in practice? Most efforts skip the hard parts. We tinker, test, and move on, without ever building the conditions for adoption.
AI and the new architecture of work
Many organizations treat AI like an add-on—as if it’s something to bolt onto existing systems to boost efficiency. But AI isn’t just a project or a tool; it changes the rules of how decisions are made, how value is created, and what roles even exist. It’s an inflection point that forces companies to rethink how work gets done.
Companies making real progress aren’t just chasing use cases. They’re rethinking how their organizations operate, end to end. They’re asking:
- Have we prepared people to reimagine how they work with AI, not just how to use it?
- Are we redesigning workflows, decision rights, and interactions—not just layering new tech onto old routines?
- Do we know what success looks like when it’s scaled and sustained, not just when it dazzles?
If the answer is no, whether you’re too loose or too locked down, you’re not ready.
The mindset shift AI demands
AI isn’t just a tech rollout. It’s a mindset shift that asks leaders to reimagine how value gets created, how teams operate, and how people grow. But that reimagination isn’t about the tools. The tools will change—rapidly. It starts with new assumptions, new stances, and a new internal leader compass.
Here are three essential mindset shifts every leader must make, not just to keep up with AI but to stay relevant in a world being reshaped by it:
1. From automation to amplification
Old mindset: AI automates tasks and cuts costs.
New mindset: AI expands and amplifies human potential, enhancing our ability to think strategically, learn rapidly, and act boldly. The question isn’t what AI can do instead of us, but what it can do through us—helping people make better decisions, move faster, and focus on higher-value work.
2. From efficiency to reimagination
Old mindset: How can we use AI to make current processes more efficient?
New mindset: What would this process look like if we started from zero with AI as our co-creator, not a bolt-on?
3. From implementation to opportunity building
Old mindset: Roll out the tool. Train everybody. Check the box.
New mindset: AI fluency is a core human capability that creates new realms of curiosity, sophistication in judgment, and opportunity thinking. Soon, AI won’t be a one-time training. It will be part of how we define leadership, collaboration, and value creation.
From sparkles to scale
In most organizations, the spark isn’t the problem. Good ideas are everywhere. What’s missing is the ability to translate those isolated wins into something durable, repeatable, and enterprise-wide.
Too many pilots are built to impress, not to endure. They dazzle in one corner of the business but aren’t designed for others to adopt, adapt, or sustain. The result? Innovation that stays stuck in the lab—or dies.
Designing for scale means thinking beyond the “what” to the “how”:
- How will this spread?
- What behaviors and systems need to change?
- Can this live in our whole world, not just my sandbox?
It’s not about chasing the next use case. It’s about setting up the conditions that allow innovation to take root, grow, and multiply, without starting from scratch every time.
Here’s how to make that shift:
1. Test in the wild, not just in the lab
Skip the polished demo. Put your solution in the hands of real users, in real conditions, with all the friction that comes with it. Use messy data. Invite resistance. That’s where the insights live, and where scale begins. If it only works in ideal settings, it doesn’t work.
2. Mobilize managers
Executives sponsor. Front lines experiment. But it’s team leaders who connect and spread. Equip them as translators and expediters, not blockers. Every leader is a change leader.
3. Hardwire behaviors, not just tools
The biggest unlock in AI is not the model—it’s the muscle. Invest in shared language, habits, and peer learning that support new ways of working. Focus on developing behaviors that scale, such as:
- Change readiness: the ability to spot opportunity, turn obstacles into possibilities, and help teams pivot.
- Coaching: getting the best out of your AI “co-workers” just like human ones.
- Critical thinking: applying human judgment where it matters most—context, nuance, and ethics.
4. Align to a future-state vision
To scale beyond one-off wins, people need a shared sense of where they’re headed. A clear future-state vision acts as an enduring focus, allowing everyone to innovate in concert. That alignment doesn’t stifle innovation. It multiplies it, turning a thousand disconnected pilots into a coherent transformation.
5. Track adoption, not just “wins”
Don’t mistake a shiny, clever prompt for progress. A great experiment means nothing if it can’t be repeated by many people. From day one, design with scale in mind: Can this be adopted elsewhere? What would need to change for it to work across teams, roles, or regions? Build for transfer, not just applause.
The real opportunity
AI will not fail because the tech wasn’t good enough. It will fail because we mistook experiments for solutions, or because we governed innovation into paralysis.
You don’t need more control. You don’t need more chaos. You need design for scale, not just scale in hindsight.
Let’s stop chasing sparkles. Let’s build systems that spread.
How AI is accelerating leadership development by enabling more practice
In today’s fast-paced business world, developing leaders who can navigate complexity, inspire teams, and deliver results is more critical than ever. Yet, traditional training methods often fall short in addressing the scale, personalization, and immediacy required to create lasting change. AI-powered practice bots are emerging as a transformative solution, offering leaders unparalleled opportunities to practice, grow, and improve—faster and more effectively than ever before.
Feedback with precision and accessibility
Feedback is the cornerstone of leadership development. However, research from Gallup reveals that only 26% of employees strongly agree that the feedback they receive improves their performance. Feedback all too often misses the mark, because it is too vague, infrequent and not relevant to the job at hand. AI practice bots address this gap by providing instant, objective, and actionable feedback through simulated conversations. Well trained practice bots, armed with leading-edge, business-specific knowledge on the critical skills needed for leaders, offer the most valuable simulated conversations, and the most accurate feedback.
These bots mimic real-world scenarios such as performance reviews, stakeholder negotiations, and high-stakes presentations. Leaders gain immediate insights into their communication style, areas for improvement, and actionable next steps—all without the need for scheduled coaching sessions.
Moreover, AI expands access to high-quality feedback across all levels of leadership. No longer confined by time, geography, or resource constraints, organizations can now equip every leader with the tools they need to grow. This scalability ensures consistent, equitable development opportunities while fostering a culture of continuous improvement.
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Limitless practice for deeper growth
Behavioral change is built through deliberate practice, yet many traditional training programs provide limited opportunities for leaders to apply what they’ve learned. A study from the American Psychological Association (APA) highlights that repetitive, focused practice is essential for mastering new skills.
AI bots remove barriers to practice by offering leaders unlimited chances to rehearse critical conversations, test new approaches, and refine their strategies. Whether delivering constructive feedback, managing conflict, or influencing stakeholders, leaders can practice important conversations without fear of judgment or failure.
Available 24/7, these bots integrate development into daily routines, accelerating skill acquisition and embedding new behaviors. The result is not only faster growth but also greater confidence and readiness to tackle complex challenges.
Amplifying human insight through AI
AI bots enhance leadership development not by replacing human expertise but by amplifying it. They excel at handling repetitive, data-driven tasks such as providing feedback and tracking performance trends. However, the role of human insight—through coaching, mentorship, and relationship building—remains irreplaceable.
According to Deloitte, organizations that combine AI-powered tools with human-led learning experiences see a 33% increase in effectiveness. AI provides the structure and scalability to ensure consistent development, while human experts bring empathy, context, and nuance to guide leaders on their unique journeys.
This synergy between technology and human insight accelerates individual growth while creating a ripple effect across organizations. Leaders not only develop the skills they need to excel but also inspire their teams and drive meaningful cultural change.
Transforming leadership development with AI practice bots
AI practice bots enhance leadership development by:
- Delivering precise, personalized feedback: Instant insights empower leaders to grow faster and with greater clarity.
- Offering unlimited opportunities to practice: Leaders can refine critical skills anytime, embedding growth into their daily routines.
- Providing data-driven insights: Bots analyze performance trends across leaders within an organization to inform targeted training strategies.
- Scaling impactful learning: Accessible to leaders across geographies and roles, AI ensures consistent and equitable development opportunities.
By enabling leaders to practice more, grow faster, and lead with confidence, AI-powered bots are transforming leadership development—one conversation at a time.
Discover how AI practice bots can enhance your leadership strategy and deliver lasting results.
Related content
In Part 1, I told you about the three decisions we made two years ago and the simulation flywheel that produced our first Applied AI diamond.
Here’s the field-notes version.
Over 80% of our global business have now adopted a new Applied AI approach for doing simulations in the first eight weeks, across 24 countries and every practice.
The flywheel didn’t stop with simulations. It moved into finance, sales enablement, legal, operations, and client delivery. Teams started building agents and bringing them onto their own org charts. We didn’t plan for any of that. We built the conditions for people to find their own breakthroughs.
What it felt like inside the flywheel.
When the simulation team went live with their first clients on the new way of working, the lead person hit a wall. Their words:
“You’re asking too much. You’re making me be a full-stack developer. Up until this point I did a small part, and I sent it to the team, and they built off the back end, and they brought it back. And now I have to end-to-end soup to nuts, basically alone.”
There was graphic UI work nobody had been trained for, the fear of delivering quality below what BTS expects of itself, and the weight of not having a playbook. This was not the joyful adoption story most consultancies tell.
Then something shifted. Six members showed up for product testing, where the usual was two or three. The work created teamwork I hadn’t seen at BTS in years. The breakthrough was not an instantaneous change from skepticism to celebration. It was a breakdown in confidence, then rally, then bonding. If we didn’t make room for the breakdown, we would have lost the rally.
The other breakthrough was global teamwork; not yet a BTS core strength. Our culture is beautiful: high-freedom and entrepreneurial. But people’s first identities are to their countries. Almost every prior attempt we’ve made at a global initiative has failed. The one exception was Covid. So, when I say what happened next surprised me, I mean it.
I asked to join the simulation team’s Slack channel rather than pulling them into status meetings. What I got to watch in the mornings was someone in South Africa waking up, posting “I tried this and got stuck,” then London adding on, then San Francisco weighing in, then a surprise breakthrough overnight from Tokyo. We didn’t engineer that. Curious and determined BTS’ers did. The problem was interesting enough that the org chart didn’t matter. It was amazing to see and a glimpse into the next evolution of the BTS culture.
The pattern: Explore, expand, institutionalize, renew.
What we’ve now seen play out, both inside BTS and with clients, follows the same four-step pattern. Each step asks a specific decision of the leader.
Explore.
Stay stubborn on the aspiration and fluid on the path. Our breakthrough wasn’t the path we originally took. We changed tools and approaches. Nobody could have foreseen that. And if the team had taken the first six months of learnings from AI as their definitive “this is the detailed path we will follow,” we never would have gotten the disruption. Five different tool combinations were tried before we found the one that worked. Companies that lock into a single path or tool too early are betting against compounding capability that doubles roughly every seven months. That is not a bet I’d take.
Expand.
Run the old way and the new way side by side. When the simulation team’s breakthroughs got real, the instinct was to retreat into more internal testing. We did the opposite. They ran old way and new way in parallel on 6 or 8 live client projects across all three geographies. Every single one ended up going live the new way. The backup was always there. They didn’t need it.
Institutionalize.
Burn the boats. The simulation team committed that no new client work would be done the old way after January 1. The other practice leads then committed to dates within Q1, even though most of them had not yet experienced the new way themselves. They had to trust their colleagues. If you can do it for the most complex thing, you could probably do it for the less complex ones. By February 15, we had approaching 90% global adoption across 24 countries, across all practices. I was shocked and proud. We had spent years failing at exactly this kind of global rollout.
Renew.
Treat your agents as contractors. People on our diamond teams are now managing 30+ agents they built themselves. Our teams give agents performance feedback. We terminate their contracts when they don’t deliver. We expand the responsibility of agents when they outperform. The frontier question we’re wrestling with now is token budgeting. Two friends of mine running engineering-heavy companies believe that within 6 - 9 months, their token cost per engineer will exceed the cost of the engineer. Whether that’s the right framing is open. The question is real, and every CEO will be asked some version of it within the year.
What had to be true for this to scale.
Once we achieved this amazing global innovation, the leadership sat down to figure out what made it work. We named five things. None of them were about the technology.
Real pain points as the starting point. We had so many people frustrated from those ways of working, all the back and forth and all the wasted time, that this was gold for them. The old way was already painful. The new way wasn’t a forced disruption; it was relief. Find the workflow where the pain is loudest and start there.
The diamond unlocked creativity, it didn’t constrain it. This was the most differentiated insight, and the one most leaders miss. It wasn't "here's the new tasks and rules." It was, "once you learn how to do this, the sky's the limit. You can be even more creative." If your rollout feels like a new set of rules constraining your people, you’ve built the wrong thing.
Pair deep expertise with fresh eyes. The disproportionate share of our breakthroughs came from a tenured tinkerer with total command of the work, paired with someone new to the role who hadn’t yet built the muscle memory of how it had always been done. Without that pairing, you get incremental improvements to the work you already know how to do, instead of a reinvention.
Refuse the “people are too busy” reflex. When I brought the rollout to the global leadership team, the excuses came fast. “Our people are too busy. They’re burnt out. Q1 is going to be busy. No one’s going to have time.” My response: “This is a chance to eliminate the tasks you dread and expand what you love. I know it is a short push of extra work, and I think after the fact you and your team will feel joy and pride and say it was the best time we ever spent.” This is the moment most AI rollouts die.
Senior leaders must lead by example and do the work themselves. This is not middle manager’s job. This is not something you delegate. Even though you don’t build simulations anymore, you must know what this is. One of our partners proactively put time on senior leaders’ calendars and forced them to do the work. Once they started building, the excitement grew, and they could advocate for the rollout because they understood it. If your executives haven’t put their hands on the keyboard, you don’t have a rollout. You have a memo.
What we’re seeing across clients.
We’re now running this play with client organizations across industries and geographies. The companies whose flywheels are accelerating paired their A-players with their early-career talent, pulled IT and legal into the working sessions, refused the “too busy” reflex, and put their senior leaders’ hands on the keyboard. The companies whose flywheels are stuck almost always have a leadership pattern at the center of the stall. Not a tooling pattern. Not a governance pattern. A leadership pattern.
If this resonates, let’s talk.
If you read Part 1 and asked yourself whether your flywheel was turning, the question I’d add now is sharper: do you have the conditions in place for a diamond to appear? If yes, you’re already moving. If no, the technology will not save you.
Here's where we're starting with clients: a working session, half day to a full day, with a small group that owns one of your highest-friction processes. Together we map where your first diamond is most likely to land, how to set up the side-by-side trial, and what your version of "burn the boats" should look like.
The destination, if we do this right, is a self-reliant culture of applied AI inside your company. 5, 10, 15 diamonds compounding into a fundamentally different way of operating. From what I have experienced this is a once in a career opportunity for dramatic shareholder value creation if you get that muscle going. I say that because I'm watching it happen, in real time, inside our own company and across our client base.
If you want to get your flywheels spinning and map your first diamond, start here. Bring your hardest workflow. We'll bring the playbook.
Organizations have long wanted to scale coaching, but have been limited by cost and capacity. With AI, that's beginning to change as new platforms make coaching more accessible, flexible, and available on demand, extending support beyond a select group of leaders to entire populations.
For talent leaders, this shift creates both opportunity and complexity. With greater reach comes a new set of trade-offs: how to balance access with depth, flexibility with accountability, and efficiency with meaningful development.
The limits of unlimited (coaching).
Unlimited coaching sounds like the obvious answer. Remove the barriers, give everyone access, let people engage on their own terms. What's not to like?
In practice, quite a bit.
When coaching has no defined structure or cadence, engagement tends to become episodic - people show up when something feels urgent and step back when it doesn't. The coaching relationship never quite deepens. Conversations cover ground but don't build on it. And the development that was supposed to happen keeps getting pushed to the next session, and the next.
Three patterns emerge:
- Sporadic engagement over sustained development. Without a rhythm to anchor the work, coaching becomes reactive. Clients bring whatever is most pressing that week rather than working toward something larger. Progress happens in bursts, if at all.
- Insights that don't compound. Great coaching reveals patterns over time - things a client can't see in one session but can't unsee after several. Without continuity, and without a consistent coaching relationship to hold the thread, each conversation starts close to zero.
- Outcomes that are hard to measure. No milestones. No defined endpoint. No clear way for the organization, or the client, to know whether it's working. Activity fills the gap where impact should be.
The result is a model that's easy to scale and hard to defend. Which is exactly the problem talent leaders are navigating right now.
The relationship is the lever.
Decades of research into what makes coaching work keeps arriving at the same answer: it's the relationship. Not the platform, not the methodology. The relationship.
When a coach and client build trust over time, developing shared language, and returning to the same themes with increasing depth, something shifts. Conversations get more honest. Insights stick. The client starts doing the work between sessions, not just during them. That's when coaching becomes genuinely transformative, and it can't be rushed or replicated in a one-off session.
The ICF and EMCC are clear on this: continuity is what dives outcomes. The coaching engagements that produce lasting change are the ones where each session builds on the last, not the ones that simply offer more access.
Three principles make that possible: Consistency, Continuity, and Completion.
1. Consistency
The foundation everything else is built on.
The temptation when designing a coaching program is to treat flexibility as a feature - let people book when they want, swap coaches freely, engage on their own schedule. But frequent coach changes reset the clock. Every new coach has to earn trust, learn context, and find their footing with the client. That's time spent getting started, not getting somewhere.
A stable coaching relationship works differently:
- The coach starts to see around corners, uncovering patterns the client can't see on their own
- The client stops performing and starts being honest
- The relationship itself becomes a source of accountability, not just the sessions
Consistency doesn't constrain the work. It's what makes the deeper work possible.
2. Continuity
What turns a series of sessions into genuine development.
Without continuity, coaching tends to be additive at best- each session offers something useful, but nothing compounds. With it, the work builds on itself in ways that can't happen in isolated conversations.
What continuity makes possible:
- A limiting belief surfaced in session three becomes a thread that runs through the rest of the engagement
- A behavioral pattern the client couldn't see at the start becomes impossible to ignore by the end
- Space opens up for the harder work - the kind that requires sitting with discomfort across multiple sessions, not resolving it quickly and moving on
That slower, deeper work is where lasting change actually happens. It doesn't come from more sessions. It comes from the right sessions, in the right order, with the same person.
3. Completion
The most underrated principle of the three.
In a world of unlimited access, there's no finish line, and without one, it's surprisingly hard to know what you're working toward, or whether you've gotten there. A defined endpoint changes the entire shape of an engagement.
A clear endpoint creates urgency and focuses every session on what matters most.
- Shifts the question from "what should we talk about this week?" to "what do we need to accomplish before we're done?"
- Gives both coach and client a body of work to look back on, not just a log of conversations
For talent leaders, this is also what makes coaching legible as an investment. Sessions logged is an activity metric. A cohort of leaders who completed a structured engagement and can articulate what changed, that's a result.
Don't just scale it, design it (here’s how)
The opportunity in front of talent leaders right now is significant. The organizations that will get the most from this moment are the ones that treat coaching design as seriously as coaching delivery.
Practical design decisions:
- Define the arc before you launch: set the number of sessions, the cadence, and the goals upfront, not after people have already started booking
- Protect the coaching relationship: Make coach switching the exception, not the default, and design your program to discourage unnecessary re-matches
- Build in milestones: create structured check-ins at the midpoint and end of each engagement so progress is visible to both the coach and the organization
- Separate on-demand support from developmental coaching: Use AI-enabled tools for in-the-moment guidance, and reserve structured engagements for the deeper work
- Measure completion, not just activation: Track how many people finish an engagement, not just how many start one
Questions to pressure-test your design:
- Does every participant know what they're working toward before their first session?
- Can your coaches see enough context about a client's journey to pick up where they left off?
- Would you be able to show, at the end of a cohort, what changed, and for whom?
Access opened the door. Intention is what makes it worth walking through.
Three decisions that changed everything.
Two years ago, we made three deliberate decisions about how BTS would move with Applied AI.
We would become our own Customer Zero.
While others were building strategies, defining governance, and waiting for clarity, we made a different call: we decided not to wait. Not because the stakes were low, but because they were high. And because in a space evolving this quickly, clarity wouldn’t come from planning. It would come from movement.
So instead of starting with a roadmap, we started with three principles:
- No top-down mandate. The people closest to the work figure it out.
- IT must evolve from gatekeeper to enabler - leading AI trials and fast experimentation.
- Don’t wait for certainty.
We set the organization in motion, and once we did, things started to move quickly.
What if we started this company today?
Waiting for certainty is itself a choice, and it’s costing companies more than they realize.
We started where we knew the work best: our simulations. No perfect plan, just teams moving, trying, and iterating.
Simulations are core to who we are at BTS. Companies that simulate don’t just make better decisions; they execute faster and build more engaged cultures.
The team asked a simple question:
"What if we were to start our company today?”
That question started the flywheel.
They asked IT for a few licenses and started building - vibe-coding, writing agents, and testing tools - moving at a pace that would make any VC-backed start-up smile.
The messy middle.
At first, the team was underwhelmed.
The early reports were blunt:
“Not good with math.”
“Poor graph capabilities.”
The team wasn't discouraged. They kept tinkering - jumping between tools, staying on top of new releases, experimenting constantly.
This was a small team, across 24 countries, building off each other’s ideas. Laughing at crazy creations. Breaking things. Iterating in a sandbox alongside real clientwork.
Each cycle produced something:
- A sharper scenario
- A faster build
- A more powerful simulation
The flywheel was turning, and it was generating something real.
When the diamond appeared.
Then something shifted.
The team moved into client trials across five countries. They figured out ISO compliance and built the architecture to handle the complexity, the “spaghetti.”
And what emerged wasn’t incremental:
- What used to take weeks started happening in days.
- Limited creativity started to feel like unlimited innovation.
- Clients became self-serving.
- Agentic simulations were built directly into client systems for real-time updates and preparation.
This was our first AI diamond - a high-impact outcome created by many cycles of experimentation compounding into real value.
It only appeared because we kept the flywheel turning, each cycle increasing the odds that something would break through.
95% adoption in eight weeks.
Then it was time to take the AI diamond global.
BTS is decentralized and highly entrepreneurial. We operate across 24 countries and 38 offices, where local teams have real autonomy.
And historically? That’s meant a low appetite for adopting something built somewhere else and pushed from the center.
So we expected resistance.
Instead, something surprising happened.
In the first eight weeks, we saw 95% adoption across our global footprint.
It felt completely different from our own digital initiatives, ERP implementations, top-down rollouts of the past.
This moved on its own. Why?
We realized it didn’t start with a framework or a model, it started with a feeling.
The feeling of being at the leading edge of one’s craft and profession.
- Joy
- Excitement
- Pride
As we watched this play out across teams it stopped feeling like isolated wins.
There was a pattern to it. A repeatable, organic, innovation motion.
And the flywheel didn’t stop with simulations.
It spread across finance, sales enablement, legal, operations, and client delivery. Some cycles led to small improvements, and others revealed new diamonds.
Not becausewe planned for them, but because we built the conditions for people to find them.
The question I'd ask any CEO right now: Is your flywheel turning, or are you still waiting for the perfect plan?
In part 2, I’ll share the key success factors behind the breakthrough, and what we’re now seeing across more than 120 global clients.