A 4-ingredient approach to organizational transformation

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.

Explore the AI Practice Bot Offering Here

You can't predict the future. You can be disciplined about how you face it.

That's where Future Storming comes in. Future Storming is a process for looking at the trends and signals already visible in the market, understanding how those forces connect, and thinking more clearly about where they may lead.

Recently, we've been applying that lens to talent strategy, running Future Storming sessions with talent leaders across industries to understand which forces are already reshaping how organizations find, develop, and retain the people they need. When you look across those conversations, one thing is hard to miss: AI runs through almost all of the most significant trends, and not as a future scenario. It's already reworking the talent systems most organizations have leaned on for years, often quietly, and often faster than leadership teams have had time to respond.

From these sessions, five high-likelihood, high-impact shifts have emerged as the ones every talent leader needs to be watching right now. What follows is what each of them may mean for your organization.

1. The frameworks most organizations use to define great leadership were built for a different era

Skills and competency models describe work that no longer exists in many roles or that AI now performs alongside, or instead of, humans. The gap between what organizations say they're selecting and developing for, and what the work actually requires, is widening quietly.

This creates a real problem. Organizations that don't redefine what great looks like now will be developing the wrong people for the wrong future optimizing for capabilities that are becoming less predictive while under-investing in the ones that matter most.

• Rebuild leadership profiles from a future-back perspective, starting with where the business is heading, not where it has been.
• Focus on the distinctly human capabilities AI cannot replicate judgment in ambiguous conditions, relational intelligence, ethical reasoning, the ability to set direction when there is no precedent.
• Increase the use of behavioral observation in selection and development. It's the only methodology that shows how someone actually thinks and decides under real pressure.

The signal worth chasing isn't on a resume, it's in the room in how someone handles a real situation, under genuine pressure. It's the only place where someone can't prepare their way out of being themselves.

2. Human differentiators are the last mile AI cannot close

Judgment. Empathy. Creativity. The ability to navigate genuine ambiguity. These are increasingly what separates human contribution from AI output and they're precisely the things most talent systems have always found hardest to measure.

For a long time, organizations could afford to treat these as qualities that would emerge naturally with experience. That's no longer an option. The human differentiators are becoming the job. And most organizations still aren't measuring them well.

The methods exist behavioral assessment, simulation, structured observation. And AI is now making them accessible at scale in ways that simply weren't possible before. The question isn't whether to use them. It's how to deploy them thoughtfully, with the governance and transparency that -stakes talent decisions require.

• AI-powered behavioral observation that surfaces how people actually perform in the flow of work, (i.e. judgement, decision-making, adaptability) not self-report
• Assessment that evaluated how people work with AI, not just without it because that's increasingly what the role looks like
• Simulation-based approaches that reveal thinking in action - the kind of evidence no credential or output can provide

3. The talent pipeline is broken

AI is displacing the early-career work that has traditionally served as the on-ramp into organizational life. Those tasks once gave emerging employees something more valuable than work product. They gave them foundational experiences, relationships, and judgment. The kind of judgment that eventually grows into leadership.

The impact won't show up immediately. That's exactly what makes it worth paying attention to now. Within three to six years, benches will thin and succession pipelines will require far more intentional investment. Organizations will find themselves asking why their internal talent isn't developing the way it used to.

The organizations that get ahead of this have a real opportunity to build something more deliberate, more equitable, and better suited to the capabilities the future actually requires.

• Invest in real, simulation-based experiences, putting emerging leaders into the decisions and pressures that build genuine organizational judgment, not just task exposure.
• Redefine what early-career development is, building toward the capabilities the future requires, not the ones the old job description described.
• Build feedback into the flow of work. AI behavioral observation and practice AI role plays make continuous development possible at scale. The experience that used to happen informally has to be designed now.

4. People need to re-skill faster than any development model was built to support

People need to reskill faster than any development model was built to support.  Most organizational development infrastructure was built around a longer, more stable arc of skill acquisition. AI is compressing that arc significantly.

The implication isn't just that training needs to be faster. It's that the whole architecture of how organizations identify, develop, and deploy talent needs to be built for continuous recalibration not periodic refresh.

• Prioritize adaptability and learning agility over static expertise. The ability to acquire new capabilities quickly matters more than the specific capabilities someone holds today.
• Treat reskilling as a continuous organizational process, not an episodic program.

5. AI is absorbing leadership work and culture is losing it's anchor

This is the shift that's easiest to underestimate, and hardest to recover from once it arrives.

Culture is what people see leaders do. The behaviors leaders model how they make decisions, how they show up in hard moments, what they choose to reward and what they let go are how organizational culture gets transmitted. It doesn't travel through stated values. It travels through visible human behavior.

AI is absorbing the work that used to make leaders visible as humans making choices. Performance reviews written by AI. Communications drafted by AI. Coaching conversations mediated by AI. When the distinctly human work disappears, so does the signal. People don't know what to watch anymore. And culture which depends on that watching starts to fray.

The organizations that navigate this well won't be the ones that use less AI, they'll be the ones most intentional about which leadership behaviors remain visibly human, and why.

The behaviors that held culture together need to be rebuilt around what humans uniquely contribute now and that starts with getting the success profile right. That's exactly what the Future Ready Profile is built for.

Strengthen empathy-centered leadership capabilities. The human dimensions of leadership matter more, not less, as AI takes on more of the technical work.

• Strengthen empathy-centered leadership capabilities. The human dimensions of leadership matter more, not less, as AI takes on more of the technical work.
• Reinforce organizational purpose and human-centered culture as anchors.
• Treat culture as something you design, not something you inherit.

What this means

The organizations that navigate this well won't be the ones that adopted AI fastest, they'll be the ones that invested just as deliberately in the human systems around it.

These five shifts aren't warnings. They're design problems, and design problems have answers. The talent systems that come out of this moment can be more intentional, more equitable, and more fit for purpose than anything we've built before.

At BTS, this is the work we're doing every day. If you'd like to think through what any of it means for your organization, we’d love to talk.

The thinking in this article was shapped by Future Storming sessions, including a SIOP 2026 workshop, and by ongoing conversations with talent leaders navigating these shifts in real time.

Hace unos meses terminé una sesión con un equipo de ejecutivos comerciales de una institución financiera mediana. Dos días intensos: cómo prospectar, cómo estructurar conversaciones centradas en el cliente, cómo crear valor en cada interacción. El grupo salió inspirado del taller.

Tres semanas después le pregunté a uno de los mejores participantes sobre cómo le había ido aplicando las nuevas herramientas. Me miró un segundo y me dijo, con total honestidad:

“La verdad... la semana siguiente fue igual que siempre, volví al viejo sistema”

El entrenamiento de capacidades es  necesario. Pero sin una cultura comercial que lo sostenga, es un esfuerzo poco  rentable para las empresas.

1.   Las capacidades sin contexto no sobreviven al día a día

Un ejecutivo de ventas puede salir de un taller sabiendo exactamente qué preguntar, cómo estructurar una conversación de valor, cómo posicionarse como asesor estratégico en lugar de vendedor de productos. La semana siguiente, el peso de las métricas de corto plazo, la presión por resultados y las urgencias del día a día terminan arrastrándolos de vuelta a la rutina de siempre.

McKinsey (2024) encontró que más del 70% de las iniciativas de transformación comercial no logran sus objetivos — y la principal causa no es el diseño del programa, sino la falta de condiciones organizacionales para sostener los nuevos comportamientos.

El problema no es el taller. Es lo que existe o no existe en la realidad de la estructura comercial.

2.   El cambio requiere alinear seis pilares

Lo que diferencia a las empresas que realmente transforman su modelo comercial de las que solo capacitan, está relacionado con seis pilares que operan simultáneamente.

1.    Patrocinio de la alta dirección que empodera en lugar de solo exigir

2.    Disciplina en gestión de cuentas/clientes estratégicos, con metodología y seguimiento

3.    Conversaciones centradas en el cliente, no en el portafolio de productos

4.    Cada interacción con relevancia estratégica, preparadapara crear valor medible

5.    Nuevos comportamientos integrados al ritmo operativodiario y la cadencia del negocio

6.    Líderes comerciales presentes que sostienen la cultura, no solo la expresan

Cuando falta uno, los demás no escalan y terminan provocando un círculo vicioso.

3.   El liderazgo que sostiene vale más que el que exige

El patrocinio de la alta dirección y la presencia de los líderes comerciales sonlos pilares que más frecuentemente fallan. No porque los líderes no crean en el cambio, sino porque el día a día los jala de vuelta a revisar resultados, no a construir comportamientos.

Gartner (2024) señala que los equipos comerciales cuyos líderes hacen coaching activo y visible tienen hasta un 28% mayor probabilidad de adoptar nuevos comportamientos de manera sostenida.

El entrenamiento define el rumbo y entrega el mapa; el liderazgo es lo que realmente ayuda a navegar y sostener el cambio.

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Conclusión

Si tu empresa está invirtiendo en transformar la forma en que sus equipos comerciales se relacionan con los clientes, la pregunta ya no es si el entrenamiento funciona. La verdadera pregunta es: ¿qué tan preparada está la organización para sostener el cambio?

Porque el talento existe. Las habilidades se desarrollan. Pero la cultura no se improvisa; se construye todos los días, con liderazgo, alineación y consistencia.

¿Cuál de estos seis pilares es hoy el más débil en tu organización?

É possível mudar a cultura de uma organização?

Hoje em dia, poucas organizações não estão envolvidas em um (ou vários) processos de transformação cultural. Novas formas de trabalhar em organizações mais horizontais e adaptativas, melhorias na cultura de segurança, orientação ao cliente, transformações nas áreas comerciais e excelência operacional, entre outros.

E é aqui que surge uma das grandes perguntas:

É possível mudar a cultura de uma organização? E, se sim, como fazer isso?

Para ajudar a responder a essas perguntas—frequentes entre nossos clientes e amplamente discutidas—gostaria de compartilhar o que aprendemos na BTS ao longo dos últimos 38 anos sobre o que funciona e o que não funciona (até agora, pois em transformação cultural estamos sempre aprendendo).

A boa notícia é que a resposta é sim.

A dificuldade está na segunda pergunta: como fazer isso?

Um projeto? Uma iniciativa?

Um ponto importante é que a transformação cultural não é um projeto com início e fim, mas sim um processo contínuo e em evolução. Isso muitas vezes gera tensão em organizações acostumadas a uma lógica de projetos.

O que é crítico e frequentemente ignorado?

Existem elementos que, quando considerados e aplicados corretamente, tornam a transformação muito mais eficaz. No entanto, muitas vezes são ignorados.

Esses elementos são:

• Envolver as pessoas. Quanto maior o envolvimento em todos os níveis, maior a probabilidade de implementação das mudanças.
• Tornar a mudança tangível e vivida no dia a dia, conectando teoria e prática. Transparência é fundamental.
• Toda mudança tem impactos positivos e negativos — ambos devem ser comunicados com clareza.
• Mudança cultural exige tempo e transformação de mindsets e estruturas organizacionais.
• A cultura deve estar conectada à estratégia.

Como estruturamos a transformação cultural?

Nosso modelo se baseia em quatro etapas: definir resultados, criar líderes de mudança, incorporar mudanças e sustentar novas formas de trabalho.

1. Definir resultados

O primeiro passo é estabelecer resultados claros e alinhamento executivo. É necessário conectar propósito, visão e objetivos organizacionais.

Ações:

• Coleta de dados (entrevistas, focus groups, visitas)
• Diagnósticos culturais
• Definição de expectativas (Leadership Profiles

2. Criar líderes de mudança

Todos os líderes devem atuar como agentes de mudança. É fundamental engajá-los emocional e racionalmente.

Ações:

• Programas de liderança
• Playbooks
• Feedback contínuo

3. Incorporar mudanças

É essencial transformar mentalidades e sistemas organizacionais.

Ações:

• Coaching
• Sprints culturais
• Cascata organizacional
• Avaliações comportamentais

4. Sustentar o novo modelo

Garantir continuidade através de redes, dados e suporte contínuo.

Ações:

• Integração com processos de talento
• Uso de IA no dia a dia
• Monitoramento da transformação
• Comunidades de prática

A importância de ser paciente e impaciente ao mesmo tempo

Transformações culturais são complexas e não têm fórmula única.

Ser estrategicamente paciente e taticamente ágil é essencial para ajustar e evoluir continuamente.

Esse equilíbrio permite transformar a jornada em algo positivo e sustentável.

Este é apenas um resumo.

Se quiser aprofundar com exemplos e práticas:

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