The Office of the (Near) Future: Remote-First Work

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?

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.

Meetings are a universal ritual in organizational life. While managers on average spend more than half their working hours in meetings, many leaders can’t shake the feeling that meetings are falling short of their potential. Are they advancing the work, or quietly draining energy? At BTS, we study teams not as collections of individuals, but as living systems. This perspective reveals dynamics that traditional methods often overlook. Rather than aggregating individual 360° assessments, we assess the team as a whole to examine how the team functions collectively. Applying that lens to one of the most common team activities (meetings) uncovers patterns worth paying attention to. Drawing on thousands of team assessments in our database, we focused on two meeting behaviors:

• Do teams meet regularly?
• Do team members leave meetings with clear accountabilities and next steps?

Our question: How strongly do these behaviors relate to overall team effectiveness?

What the data revealed

Using data from 1,043 respondents (team members and informed stakeholders) we ran a Bayesian analysis to evaluate the predictive power of each behavior. The results were striking:

• Both behaviors were linked to higher team effectiveness.
• But one mattered far more: leaving meetings with clear accountabilities and next steps was 3.9x more predictive of team effectiveness than simply meeting regularly.
• And teams that often or always wrap up meetings with next steps rated 0.66 points higher on a 5-point scale of team effectiveness than teams who sometimes, rarely, or never close with accountabilities - that's almost a full standard deviation higher (0.96 sd)

Meetings aren’t the problem, muddy outcomes are.

Teams often default to frequency, setting cadences of check-ins or standing meetings. Our data suggest that what differentiates effective teams from the rest is not how many meetings they hold, but what comes out of them. A team that meets less often but ends each session with clear accountabilities will outperform a team that meets frequently but leaves outcomes ambiguous. In other words, meetings aren’t inherently wasted time; they become wasted time when they don’t translate into aligned action.

A simple shift that pays dividends

The good news: improving meetings doesn’t require radical redesign. Small changes reinforce accountability and dramatically increase the value extracted:

• Close with clarity. Reserve the last 5–10 minutes of every meeting to confirm: What decisions have been made? Who owns what? By when? This habit shifts meetings from “discussions” to “decisions.”
• Make commitments visible. Use a shared action log, team board, or project tracker so next steps are transparent, and progress is easy to follow. Visibility builds accountability.
• Assign a “Closer.” Rotating this role signals that closing well is everyone’s responsibility. The Closer ensures the team doesn’t drift into vague agreements, but leaves aligned and ready to act.

When teams adopt these habits, the difference is tangible: less rehashing of the same topics, faster progress on priorities, and a stronger sense of shared ownership. These small shifts compound quickly, making meetings not just more efficient, but more energizing and effective. In a world where teams face relentless demands and limited time, focusing on how meetings end may be one of the fastest ways to improve how teams perform.

Candidates now arrive at interviews pre-coached by AI, with their resumes optimized to pass every checkpoint. Polish has stopped being a signal. The traditional hiring process was built to read exactly the cues that AI is now best at producing, and the signals hiring managers once relied on have weakened as a result. And for roles where the wrong hire carries real business consequences, losing the ability to tell who will actually perform is not a minor inconvenience. It is a material risk, and it exposes the business to unnecessary turnover, reduced performance, and heavier investment for talent growth and development.

So how do you observe the behaviors that matter most, before someone is in the role?

Not by asking better questions, but rather by putting candidates in situations designed to elicit that behavior.

The limits of predicting from paper

Credentials tell you what someone has done. Structured interviews tell you what someone says they would do. Neither lets you observe what they actually do in the moments that count.

This distinction matters most in client-facing, relationship-driven roles, where the performance gap between a strong hire and a weak one plays out in real business outcomes (revenue, retention, client growth). Organizations that hire at scale in these roles carry that gap across hundreds of decisions at a time.

The better approach is to watch candidates do the work before you hire them. Put them in simulated, role-relevant scenarios, and pair the simulation with a second, different kind of measure so no single method carries the whole decision. That combination is what lets you evaluate real performance before anyone is in the role. Organization-specific simulations provide a clear read on who is ready and capable of performing on day one. In a world of AI-supported candidate signals, the use of simulations makes the process harder to prep for. It is harder to fake. And, when designed well, it is substantially more predictive than other hiring methods.  

What counts as evidence

Claims about predictive power are easy to make. Evidence for them is rarer than you would expect.

A predictive validity study, the kind that links pre-hire assessment scores to how someone actually performs once hired, is some of the hardest evidence to produce and the rarest to see. Many assessments are validated against proxies: another test, or a theoretical model of the role, rather than real results on the job. Connecting scores to concrete business outcomes and doing the statistical work to show the link holds, takes years of shared data and a level of commitment from both the assessment provider and the client that most partnerships never reach. That is precisely why it is worth asking for. A provider who can show how assessment scores track to training completion, retention, and first-year output is offering something categorically different from one who can only show a correlation with another test.

Why simulation holds up where other methods do not

When a candidate sits across from a trained assessor (someone playing the client or prospect on the other side of the conversation) and has to work through a real situation, they cannot rely on a rehearsed answer. The scenario is specific. The stakes feel real. What you see is close to what you would get on the job.

That is the value of simulation-based assessment: it does not test what candidates know about the role.

It shows how they use what they know when a real person is on the other side of the conversation, before the stakes are real.

For roles that carry significant business responsibility, this distinction is the whole game. The cost of the wrong hire in a high-stakes client-facing role is not just a missed quota for a quarter - It plays out in relationships that do not develop, clients who leave, and productivity losses that compound over time. Getting those hiring decisions right, at scale, with consistency, requires methods that are built for predictive accuracy, not just candidate experience or hiring speed.

What this means for how organizations think about hiring

Most organizations are still optimizing the wrong things in their hiring process. They invest heavily in employer branding, application flow, and interview structure, all of which matter, but less in the core question: does our hiring process actually predict who will succeed in this role?

AI has sharpened the stakes here. If every candidate can present as polished and prepared, screening based on presentation becomes less useful. What holds up is direct observation of the behaviors that the job requires.

A few principles worth building from:

• Measure what the job requires, not what is easy to measure. Cognitive tests and personality questionnaires have their place, but they do not look much like the job. The closer the assessment is to the actual work, the better it predicts performance in it.

• Ask what your assessment predicts. Training completion? Retention? First-year output? Most organizations cannot answer that question today, largely because providers have rarely been asked to prove it. It is a fair thing to ask for.

• Take the human element seriously. In a simulation, a candidate is having a real conversation, responding in real time, navigating a situation that requires judgment. Even with the help of AI, that is hard to game. And it remains one of the strongest predictors of on-the-job performance available.

The data exists to make hiring decisions more accurate, fairer, and more directly tied to business outcomes. For organizations operating in high-stakes roles at scale, there is too much on the line to rely on methods that cannot hold up to that standard.

You may be interested in BTS’ thought leadership in the five talent shifts AI is forcing now.  

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La IA ya forma parte del día a día de las ventas. Hoy cualquier asesor puede llegar a una reunión con datos, tendencias e insights generados en segundos. Sin embargo, disponer de más información no garantiza conversaciones de mayor valor.

A través de una experiencia real con un consultor comercial, este artículo explica por qué la inteligencia artificial funciona como un GPS: ayuda a interpretar el entorno, pero no conduce la conversación ni entiende las prioridades del cliente.

En este artículo descubrirás:

• Por qué el acceso a la información ya no supone una ventaja competitiva.
• La importancia del business acumen para interpretar los datos con criterio.
• Cómo hablar el lenguaje del cliente genera credibilidad y diferenciación.
• Por qué las relaciones B2B evolucionan hacia relaciones P2P basadas en la confianza.
• Qué capacidades consultivas seguirán siendo exclusivamente humanas incluso en la era de la IA.

La tecnología seguirá evolucionando, pero la ventaja competitiva estará en quienes sean capaces de combinar inteligencia artificial con conversaciones centradas en el cliente, pensamiento estratégico y relaciones de largo plazo.

Leadership is the work of creating shared understanding, and language is the primary tool for doing it. Yet we spend remarkably little time examining our words. Every decision, expectation, priority, and piece of feedback reaches another person through words. If those words aren't doing the job, neither is the leadership.

The lighthouse

Some years ago, a large company hired a strategy consulting firm to rethink its leadership model. The firm came back with a beautifully produced framework built around a central metaphor: the lighthouse.

Leaders, the model declared, should be lighthouses.

The metaphor quickly found its way into playbooks, performance reviews, onboarding decks, and town halls. People repeated it with the confidence of those who had paid a lot of money for it.

There was just one problem: nobody could agree on what a lighthouse was supposed to do.

Was it warning people away from danger? Guiding them toward a destination? Standing firm while everything else changed?

Eventually, the company hired another team to translate the metaphor into specific, observable leadership behaviors.

It was an expensive way to discover that a word everyone confidently repeated wasn't creating nearly as much shared understanding asthey thought.

Why jargon prevails

• Parking lot that
• Double-click
• Close the loop

Business jargon survives because it’s largely designed to manage social risk. Using it signals, I know how this world works. It demonstrates membership, competence, and credibility.

Business is messy, and leaders don't always have complete information. Abstract language lets us project confidence while preserving flexibility.

Altitude without traction

Specific language creates accountability. The more specific you are, the easier it is for people to disagree, question your thinking, orhold you accountable. That's part of the appeal of jargon. It creates distance between the speaker and the detail. The more abstract and elevated your language, the more strategic you sound.

A 2020 study by Harvard Business School professors Laura Huang and Andy Wu, published in the Academy ofManagement Journal, analyzed over 1,000 early-stage startup pitches and found that founders who spoke in abstract, visionary terms were significantly more likely to advance in the funding process.

A separate study, published in Applied Cognitive Psychology in 2025, found the other side: jargon raises how credible a speaker appears and lowers how much the audience retains.

The very language that helps people see you as a leader can make you less effective once you're leading.

The most trusted leaders tend to be the ones who resist impressive-sounding language and say the plainest version of what they mean.

In fact, four words probably do more for a leader's standing than any carefully crafted message: I made a mistake. Not "we encountered some headwinds," not "there were learnings from this experience," but the plain version.

The cost of comfort

When a conversation gets uncomfortable at work, it almost always feels easier to soften your message than to say exactly what you mean. You hedge, add qualifiers, cushion the point with extra reassurance, or leave the hardest part unsaid. Most of the time, you mean well. You don't want to discourage someone, damage the relationship, or create unnecessary conflict. The conversation becomes less uncomfortable for a moment, but the work often becomes harder afterward.

Amy Edmondson, Novartis Professor of Leadership and Management at Harvard Business School and author of The Fearless Organization, found that the fear of making a negative impression pushes people to stay silent exactly when clarity is most needed. According to her research, silence is one of the most consistent predictors of teams that miss problems early and never learn from them. The friction avoided in the meeting resurfaces later, at greater cost.

KimScott, former executive at Google and Apple and author of Radical Candor, calls this ruinous empathy: softening your message to protect someone's short-term feelings comes at the cost of the clarity they need. Her argument is simple and uncomfortable: clear, direct communication, even when it is hard, is an act of care.

The quiet power of saying what you mean

None of this is an argument for bland, colorless language.Vivid, precise writing does the opposite of jargon: it sharpens meaning instead of hiding it. Before reaching for a word, pause on two questions:

• What do I mean by it? 
• What will my audience hear? 

A surprising amount of corporate language wouldn't survive those two questions.

Leaders have more influence over language than they often realize. Whatever tone, vocabulary, and level of directness they model becomes the standard everyone else copies. Word choice is one of the quietest ways leaders shape culture.

This matters even more as we hand our language to AI. These tools can already learn to write in our voice. The question now is whether we've been deliberate enough about that voice in the first place to like what we see.