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
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.
Today’s customers are more informed, more selective, and more time-poor. They need conversations that help them prioritize, decide, and move forward.
And yet, 58% of sales meetings fail to create real value.
Not because sellers lack capability, but because conversations are not designed to move decisions forward.
“Customers don’t act on every need they recognize.
They act when something becomes a priority.”
In this short executive brief, you’ll discover
- Why most conversations inform… but don’t drive action
- What actually makes customers prioritize and move
- How to create urgency without damaging trust
- The shift from presenting solutions to enabling decisions
- What separates conversations that stall from those that accelerate momentum
If your teams are experiencing stalled deals, delayed decisions, or slow pipeline movement, this brief will help you understand why, and what to do differently.
For more than a decade, Agile has played a central role in modernizing organizations. It improved collaboration, accelerated delivery cycles, and reshaped how teams build products and services.
However, Agile was never the final destination.
Scaling ceremonies does not redesign an organization. Implementing frameworks does not guarantee strategic adaptability. And in a world defined by exponential technological acceleration and artificial intelligence, improving execution alone is no longer sufficient.
Today, competitive advantage depends on something deeper: the ability to continuously adapt while still delivering value to customers.
This capability is known as Enterprise Agility.
Enterprise Agility as an organizational capability
Enterprise Agility is often misunderstood as a methodology or framework. In reality, it represents something far more fundamental.
It is the capability of an organization to generate sustainable value in uncertain environments through continuous adaptation of strategy, operating models, and culture.
Organizations with this capability are able to:
- Continuously prioritize the initiatives that generate the greatest impact
- Make faster and better-aligned decisions
- Reconfigure structures without major disruption
- Learn systematically from customers and market signals
- Integrate strategy and execution into a unified operating flow
When this capability becomes embedded within the organization rather than dependent on isolated initiatives, companies evolve into Adaptive Organizations.
But this evolution does not happen automatically. It requires activating structural change mechanisms across the enterprise.
The five levers that enable Enterprise Transformation
Enterprise Transformation is not about implementing Agile practices across teams. It is about building the capabilities required to sustain continuous adaptation.
Five structural levers play a critical role in enabling this transformation.
1. From methods to organizational design
For years, the conversation around agility focused on which framework to implement: Scrum, SAFe, LeSS, or Kanban.
Today, the more relevant question is different:
Is the organization designed to generate a continuous flow of value?
This shift requires moving beyond project-based structures toward operating models built around products, capabilities, and value streams.
Organizations must reduce structural friction and integrate strategy, execution, and learning into a single system.
Without this redesign, agility often remains superficial.
2. From rigid planning to continuous prioritization
Traditional annual planning cycles were designed for stable environments.
In today’s context, adaptive organizations manage dynamic portfolios and adjust priorities continuously based on real-time information.
Planning becomes an ongoing process rather than a yearly event.
Competitive advantage no longer comes from predicting the future better, it comes from the ability to adjust when the future changes.
3. From misaligned autonomy to distributed strategic coherence
Decentralization without alignment creates chaos. Excessive centralization creates slow decision-making.
Adaptive organizations balance these forces by enabling distributed decision-making within clear strategic guardrails.
Transparency, alignment, and shared accountability ensure that teams operate autonomously while remaining connected to the broader strategic direction.
4. From operational efficiency to accelerated learning
In the age of artificial intelligence, the speed of learning becomes more important than the speed of execution.
Organizations must build the ability to:
- Detect market signals early
- Understand strategic implications
- Experiment at low cost
- Learn systematically
- Adjust quickly
Artificial intelligence acts as a cognitive amplifier within this cycle, improving decision quality and enabling faster experimentation.
The objective is not simply to automate work, but to accelerate the organization’s adaptive capacity.
5. From change as a project to permanent adaptation
Many transformation initiatives fail because they are treated as temporary programs.
But adaptation is not a project, it is a strategic capability.
Organizations must integrate change into their daily operations by developing leadership capabilities, building adaptive cultures, and measuring real impact rather than superficial adoption metrics.
Transformation becomes a structural characteristic of the organization rather than an extraordinary initiative.
The real meaning of Enterprise Transformation
Agile was the first step, but it was never the destination.
Enterprise Transformation exists to build Enterprise Agility. And Enterprise Agility enables organizations to become truly adaptive.
In an environment where artificial intelligence accelerates innovation and lowers barriers to entry, stability is no longer a protection. Rigidity becomes a liability.
The critical question for organizations today is not whether they execute their plans efficiently.
It is whether they can change those plans faster than their competitors.
To understand how Enterprise Transformation can help your organization build this capability, discover more at Netmind a BTS company
Related content
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.
La maggior parte delle riunioni di vendita non fallisce.
Semplicemente non porta a una decisione.
Ed è lì che si perde valore.
I clienti di oggi sono più informati, più selettivi e hanno meno tempo.
Non hanno bisogno di altre presentazioni di prodotto.
Hanno bisogno di conversazioni che li aiutino a stabilire le priorità, decidere e andare avanti.
Eppure, il 58% delle riunioni di vendita non riesce a creare valore reale.
Non perché i venditori manchino di capacità, ma perché le conversazioni non sono progettate per far avanzare le decisioni.
“I clienti non agiscono su ogni esigenza che riconoscono.
Agiscono quando qualcosa diventa una priorità.”
In questo breve executive brief scoprirai:
- Perché la maggior parte delle conversazioni informa… ma non porta all’azione
- Cosa spinge davvero i clienti a stabilire priorità e muoversi
- Come creare urgenza senza compromettere la fiducia
- Il passaggio dal presentare soluzioni al facilitare decisioni
- Cosa distingue le conversazioni che si bloccano da quelle che accelerano il progresso
Se i tuoi team stanno affrontando trattative bloccate, decisioni ritardate o un pipeline lento, questo brief ti aiuterà a capire il perché e cosa fare in modo diverso.
Scarica l’executive brief e scopri come progettare conversazioni che portano davvero a decisioni.