How to avoid the AI fizzle

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.
Related content

Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.

At BTS, we’re constantly challenging ourselves to innovate at speed. And right now, it feels like we’re standing at the edge of something massive. The energy? Electric. The velocity? Unprecedented. For many of us, the current pace feels a lot like the early days of the pandemic: disorienting, high-stakes, and somehow exhilarating. And honestly—it should feel that way. Our teams have been tinkering with AI, specifically LLMs, for the past 2.5 years and it has really been in the last eight months that I can see the profound impact it is going to have for our clients, for our services and our operating model.
The opportunity isn’t about the technology. The world has it and it’s getting better by the minute. The issue is people and people’s readiness to adopt it and be re-tooled and re-skilled. It’s about leadership. AI is deeply personal, it’s surgical. In fact, that’s its genius. So, getting full scale adoption of AI, re-tooling everyone in the company by workflow, so that they can invent new services, unlock new customer value, unlock new levels of productivity, even use it for a better life, is the current race. The central question I’ve been wrestling with, alongside our clients and our own teams, is this:
What does AI actually mean for leadership and culture?
And the answer is clearer by the day: AI isn’t just a new toolset. It’s a new mindset. It demands that we rethink how we lead, how we learn, and how we build thriving organizations that can compete, adapt, and grow.
The productivity paradox revisited
Let’s start with the elephant in the boardroom. There’s been a lot of buzz around AI and its promises. But many leaders have quietly wondered: Will any of this actually move the needle? A year ago, we were asking the same thing. We had licenses. We had curiosity. We had early experiments. But the results were modest, a 1% productivity gain here or there. But by April, we were seeing:
- 30–80% productivity gains in software engineering
- 9–12% gains in consulting teams
- 5%-20% improvements in client success and operations
Just as importantly, the innovation unlock and creativity across our platforms due to vibe coding along with new simulation layers, is leading to new value streams for our clients. This isn’t theoretical. It’s not hype. It’s real. The difference? Adoption, ownership, and a shift in how we lead in order to energize the AI innovation within our teams. The challenge now isn’t whether AI creates value. It’s how to unlock and scale that value across teams, geographies, and business units—and do it fast.
Two Superpowers of the Agentic AI Era
In working with leaders across industries, I’ve come to believe in two superpowers (there are more as well) that will unlock the potential of this AI era: Jazz Leadership and a Simulation Culture.
1. Jazz Leadership
Forget the orchestra (although personally I am a big fan.) The successful team cultures that are innovating with AI feel more like jazz. In jazz, there’s no conductor. There’s no fixed sheet music. There are core bars and then musicians make up music on the spot based on each other’s creativity, building off of each other’s trials, riffs and mistakes, build something extraordinary together. This is how experimenting with AI today, in the flow of work, feels like.
For each activity across a workflow, how can new AI prompts, agents, and GPTs make it better, codify high performance, drive speed and quality simultaneously? How can we try something totally different and still get the job done? How might we re-invent how we work? That’s how high-performing teams operate in the AI era. The world is moving too fast for command-and-control leadership, a perfect sheet of music with one leader who is interpreting the sheet music and directing. What we need instead is improvisation, trust, shared authorship, courage and a playful spirit because there are just as many fails as breakthroughs.Jazz leadership is about creating the conditions where:
- Ideas can come from anywhere
- People see tinkering and testing as key to survival and AI failures mean your team is at the edge of what’s possible for your services and ways of working
- Leaders say, “I don’t have all the answers, but I’ll go first, with you”
- People feel “I’m behind relative to my peers in the company” and the company sees this as a good sign because the pace of learning with AI means higher chance of success in the new era
At BTS, we recently promoted five new partners who embody this mindset. They weren’t the most traditional leaders. But they were the most generative. They coached others. They experimented and are constantly re-tooling themselves and others. They inspired movement. They are keeping us ahead, keeping our clients ahead and driving our re-invention. Jazz leaders make teams better, not by directing every note—but by setting the stage for breakthroughs. It is similar to the agile movement, similar to how it felt in Covid as companies had to reinvent themselves. It’s entrepreneurial, chaotic and fun.
2. Simulation Culture
The ability to simulate is a super-power in this next agentic, AI era. Simulation has always been part of creating organizational agility, high performance and leadership excellence. But AI and high-performance computing have transformed it into something bigger, faster, and infinitely more powerful. It means that building a simulation culture is within all of our grasp, if we tap its power.Today, companies simulate:
- Strategic alternatives - from market impact all they way to detailed frontline execution
- New business, new markets and operating models
- Major capital deployment e.g. build a digital twin of a factory before breaking ground
- Initiative implementation
- Workflows current and future
- Jobs to assess for talent and critical role readiness
- Customer conversations and sales enablement motions
With a simulation culture, where you regularly engage in scenario planning and expect preparation and practice as a way of working, billions in capital is saved, cross-functional teams are strengthened, high performance gets institutionalized, win rates increase, earnings and cash flow improves.
Where to get started
Below are a few examples of what leading organizations are doing. Consider testing these in your own organization:
- Conversational AI bot platforms used to scale performance expectations and the company’s unique culture.
- Agentic simulations built into tools so people can prepare and practice with 100% perfect context and not a wasted moment.
- Digital twins of the job created so that certifications and hiring decisions are valid.
- Micro-simulations spun up in hours to align 50,000 people to a shift in the market or a new operational practice.
Final Thoughts
- Lead like a jazz musician. Embrace improvisation, courage and shared creativity.
- Build a simulation culture. Because in a world that’s moving this fast, practice isn’t optional—it’s how we win.
This is a brave new world. Not five years from now. Right now.Let’s shape it—together.

Nearly every leader I talk to knows the old planning model doesn’t make sense anymore. Multi-month cycles. Layers upon layers of initiatives. Budgets that quietly replace strategy as the plan. By the time it’s all done, the competitive landscape has already shifted under their feet. And yet, many companies still do it this way. They can feel the mismatch as they strive to move fast. They just don’t know what to do instead. The old game was setting direction. Decide where to go. Communicate it. Cascade it down. It made sense when the future looked enough like the past that you could be certain of your choices. But certainty is gone. In its place: disruption, surprise, and acceleration. Which means the work of leaders has shifted. The new game is adapting direction.
What needs to be new and different
If strategy execution today is about improv, then strategy setting is no longer about choreography. It requires a more flexible approach. Here are four flaws of traditional strategy planning, and what leaders can do differently:
1. Stop pretending there’s only one future.
We know the future won’t unfold exactly as envisioned. Customer needs shift. Competitors surprise you. Economies wobble. So why do we plan for just one version of what’s next? When one “winning” idea emerges too fast, it often gets momentum without being stress-tested. A better approach:evaluate multiple distinct directions at the same time. One executive team we worked with had five competing visions for doubling the business in three years. Instead of forcing consensus, we worked with them to think through the core choices for each, including customer focus, product bets, and geographic expansion. Once leaders saw the real implications, they quickly ruled one option out. The eventual plan blended elements of the others, with contingencies built in. Thinking through alternatives gave them confidence and resilience when the inevitable twists came.
2. Make choices real before you announce them
Too many strategic plans race to the declaration moment at the Town Hall: here’s the big idea, now go execute. The problem? Leaders rarely know what they’ve actually signed up for, or what needs to change in how the work gets done. If you believe that strategy execution requires improv, then even in setting strategy you need to imagine what comes next and rehearse moves, implications, and ripple effects across future time horizons, albeit in a simpler but realistic form. One client we worked with knew that acquisitions were essential to their growth. They had several targets in sight and negotiations underway, but no imminent deal. Instead of waiting, we ran the extended leadership team through a series of acquisition scenarios with different strategic intent that examined variables such as deal size, level of integration, and adjacency of the added business. As they worked through each scenario, they not only got a view into the nature of potential targets but also what changes they, as the leaders of the organization, needed to make now. They were choosing what kind of organization they would become. Based on what they were learning, they were able to make key decisions to position themselves for future success. They agreed on new hiring profiles, streamlined decision processes, leadership shifts, so they’d be ready when the right deal came. Strategy shifted from a conceptual statement to a real, lived preparation for a different future.
3. Work across time horizons.
People can change fast. Infrastructure and capital cannot. Budgets, board approvals, and physical assets move slowly. Leaders need to intentionally plan for what can change now, what will take time, and what’s locked in, while still identifying the opportunities at each stage. Take a pharma company with a pipeline bursting with new drug development. If even half their drugs made it through approval, their manufacturing capacity would be insufficient. Together we built an adaptable manufacturing plan, anchored on essentials, with clear trigger points for future decisions. When 70% of the drugs cleared approval, they were ready. Without that horizon-based thinking, they would have been caught flat-footed.
4. Align at the right level of detail.
Here’s a trap: mistaking varied interpretation of the strategy for purposeful improvisation. They are not the same. Without clarity and alignment at the top, every leader fills in gaps differently. That isn’t agility, it’s chaos. Leaders must turn the conceptual strategy into something tangible and real, in order to be able to align and lead the organization in the same direction. Strategic modeling allows leaders to test choices at the right level of fidelity, so they know what they’re actually agreeing to. Growing “a lot” versus growing 37% are not the same thing. The detail that is uncovered in the modeling exercises provides enough clarity to shape coherent execution, while still leaving room for adaptive moves over time.
From map to compass
Old strategy setting was about certainty. New strategy setting is about clarity of intent and readiness to adapt. It’s less a map and more of a compass. If your strategy and planning process still looks like a marathon toward a finished plan, ask yourself: are you preparing for the world you wish you had, or the one you actually face? The trick is helping leadership teams shift from setting direction to adapting direction—so strategy setting and execution can adapt. The future won’t wait for your plans.
Related content

Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.

This article was originally publish on Rotman Management
IN OUR CONSULTING WORK with teams at all levels—especially senior leadership—my colleagues and I have noticed teams grappling with an insidious challenge: a lack of effective prioritization. When everything is labeled a priority, nothing truly is. Employees feel crushed under the weight of competing demands and the relentless urgency to deliver on multiple fronts. Requests for prioritization stem from both a lack of focused direction and the challenge of efficiently fulfilling an overwhelming volume of work. Over time, this creates a toxic cycle of burnout, inefficiency and dissatisfaction.
The instinctive response to this issue is to streamline, reduce the number of initiatives, and focus. While this is a step in the right direction, it doesn’t fully address the problem. Prioritization isn’t just about whittling down a to-do list or ranking activities by importance and urgency on an Eisenhower Decision Matrix; it also requires reshaping how we approach work more productively.
In our work, we have found that three critical factors lie at the heart of solving prioritization challenges: tasks, tracking and trust. Addressing these dimensions holistically can start to address the root causes of feeling overwhelmed and lay the foundation for sustainable productivity. Let’s take a closer look at each.

You’re buckling in for an overseas flight in a brand-new Boeing 777. The pilot comes on the PA: “Ah, ladies and gentlemen, our flight time today will be six and a half hours at a cruising altitude of 33,000 feet. And I should mention that this is the first time I have ever flown a 777. Wish me luck.”
Before setting foot in the real world, pilots, military personnel and disaster response teams use intense simulations to learn how to respond to high-intensity challenges.Why should we place corporate leaders and their teams in situations without first giving them a chance to try things out? The risks are huge — new strategy investments can run into the hundreds of millions of dollars. BTS offers a better way to turn strategy into action: customized business simulations.
‘Now I Know What it’s Like to be CEO’
A customized business simulation of your enterprise, business unit or process, using real-world competitive dynamics, places leaders in a context where they step out of their normal day-to-day roles and gain exposure to the big picture. Participants make decisions in a risk-free environment, allowing them to experience critical interdependencies, execution best practices and the levers they can use to optimize their company’s key performance indicators. It takes the concept of a strategy and makes it personal, giving each individual the chance to see the direct impacts of their actions and the role they play in strategy execution.
Leading corporations are increasingly turning to business simulations to help build strategic alignment and execution capability when faced with the following business challenges:
- Key performance objective and new strategy implementation.
- Accelerating strategy execution and innovation.
- Improving business acumen and financial decision making.
- Transforming sales programs into business results accelerators.
- Leadership development focused on front-line execution.
- Implementing culture change as tied to strategy alignment.
- Modeling complex value chains for collaborative cost elimination.
- Merger integration.
Within minutes of being placed in a business simulation, users are grappling with issues and decisions that they must make — now. A year gets compressed into a day or less. Competition among teams spurs engagement, invention and discovery.
The Business Simulation Continuum: Customize to Fit Your Needs
Simulations have a broad range of applications, from building deep strategic alignment to developing execution capability. The more customized the simulation, the more experience participants can bring back to the job in execution and results. Think about it: why design a learning experience around generic competency models or broad definitions of success when the point is to improve within your business context? When you instead simulate what “great” looks like for your organization, you exponentially increase the efficacy of your program.
10 Elements of Highly Effective Business Simulations
With 30 years of experience building and implementing highly customized simulations for Fortune 500 companies, BTS has developed the 10 critical elements of an effective business simulation:
- Highly realistic with points of realism targeted to drive experiential learning.
- Dynamically competitive with decisions and results impacted by peers’ decisions in an intense, yet fun, environment.
- Illustrative, not prescriptive or deterministic, with a focus on new ways of thinking.
- Catalyzes discussion of critical issues with learning coming from discussion within teams and among individuals.
- Business-relevant feedback, a mechanism to relate the simulation experience directly back to the company’s business and key strategic priorities.
- Delivered with excellence : High levels of quality and inclusion of such design elements as group discussion, humor, coaching and competition that make the experience highly interactive, intriguing, emotional, fun, and satisfying.
- User driven: Progress through the business simulation experience is controlled by participants and accommodates a variety of learning and work styles.
- Designed for a specific target audience, level and business need.
- Outcome focused , so that changes in mindset lead to concrete actions.
- Enables and builds community: Interpersonal networks are created and extended through chat rooms, threaded discussions and issue-focused e-mail groups; participants support and share with peers.
Better Results, Faster
Well-designed business simulations are proven to significantly accelerate the time to value of corporate initiatives. A new strategy can be delivered to a global workforce and execution capability can be developed quickly, consistently and cost-effectively. It’s made personal, so that back on the job, participants own the new strategy and share their enthusiasm and commitment. This in turn yields tangible results; according to a research report conducted by the Economist Intelligence Unit and sponsored by BTS, titled “Mindsets: Gaining Buy-In to Strategy,” the majority of firms struggle to achieve buy-in to strategy, but those that personalize strategy throughout their organization significantly outperform their peers in terms of profitability, revenue growth and market share.
Business Simulations: Even More Powerful in Combination
Comprehensive deployment of business simulation and experiential learning programs combines live and online experiences. The deepest alignment, mindset shift and capability building takes place over time through a series of well-designed activities. Maximize impact by linking engagement and skill building to organizational objectives and by involving leadership throughout the process.
Putting Business Simulations to Work
Simulations drive strategic alignment, sales force transformation, and business acumen, financial acumen and leadership development, among other areas. A successful experiential learning program cements strategic alignment and builds execution capability across the entire organization, turning strategy into action. Results can be measured in team effectiveness, company alignment, revenue growth and share price.
Learn more about business simulations
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