Think small to accomplish big things in 2023

A while back I heard a few people talking about public speaking. Person A was talking about their anxiety about making presentations. Trying to make him feel better, Person B said, “Public speaking is just like riding a bike!”

That got my attention.  It seemed to be a comforting little sound bite. The only problem was that it was wrong.  Public speaking is not like riding a bike. But it got me thinking about leadership communication and learning in general.

What does it mean if we say that something is like learning to ride a bike?  We’re saying that it’s a skill that initially may seem pretty difficult to learn… but once we figure it out, we can do it successfully without thinking—even if we don’t do it at all for years at a time. It’s the reassuring idea that you’ve acquired a skill that you will never lose.

There’s no question that we all learn many skills that are like riding a bike. Driving is a good example. Most of us were white-knuckle drivers when we first got behind the wheel, but what about now? On long highway drives, I sometimes snap out of a daydream and realize I have no memory of anything that happened on the road in the last 15 minutes. That’s because I don’t have to think about driving when I do it—not unless there is intense traffic or some other unusual circumstance.

Many other skills are the same—reading, typing, doing simple math in your head, and so on.   But quite a few sophisticated skills are quite unlike riding a bike.  In other words, there are skills that are definitely learnable and where your level of mastery can improve substantially. However, you’ll probably never be really great at these skills without vigilant, ongoing practice, preparation, reflection, and reinforcement.

Some examples that come to mind with leadership communication: Selling, managing change, inspiring your teams, and, yes, public speaking. What’s so different about these areas?  A few things:

• They involve an audience. If you were making your first speech in several months or years, would you find that you could do it almost unconsciously? I couldn’t. You can never be on auto-pilot when you’re delivering any sort of message to an audience. Just as the saying goes that you can never step in the same river twice, no two audiences are ever the same—even if you’re speaking to your internal teams each quarter. All sorts of circumstances change regularly, and you have to consciously adjust your message to address the ever-evolving needs of your audience.

• To maintain performance at a high level, sophisticated skills require ongoing practice. Yo-Yo Ma may be the world’s best cellist, but he estimates that he still puts in roughly 2,000 hours of practice each year. That’s an average of 5.5 hours daily.  If he stopped practicing altogether, he obviously could still play the cello.  But he wouldn’t be the best cellist for much longer.

• Skill mastery typically requires continual learning and reinforcement over time. Practice is critical, but it’s not sufficient. When you think about areas such as selling, motivating, and public speaking, there is always more to learn. There is evidence now that 90% of what we learn at a workshop, for example, dissipates within one year. To ensure the needle keeps moving in the right direction, you need to be a perpetual student. That may involve reading about the subject, hearing about it, going to a workshop, and getting expert advice. Whether you’re a tennis pro, a psychiatrist, or a VP of Sales, having a coach to help you with your real-time challenges can have an enormous impact to give you that reinforcement over time.

As a leader, you’ll no doubt hear from companies that want to offer you “quick-fix” solutions for perpetual leadership development challenges—areas such as executive presence, employee engagement, and public speaking.

But lasting, meaningful mastery is not a quick fix.  Sophisticated skills need reinforcement: A better motto for these skills would be “use it or lose it.” Because some things are quite unlike riding a bike,

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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.

AI is reshaping how work gets done—automating tasks, accelerating decisions, and raising expectations for speed and precision. Strategy is shifting faster than structures can adapt, leaving many leaders operating in systems that weren’t built for what’s being asked of them now. Employees are asking more of their managers—while the business is asking more of them, too. And leaders are stuck navigating it all with development priorities, operating norms, and support systems that weren’t designed for this level of speed, ambiguity, or stretch.

As expectations rise, leadership capability is under scrutiny.

But are development efforts evolving fast enough to meet the moment?

Where priorities and expectations diverge

Most leadership development programs today emphasize foundational strengths:

• Executive presence
• Personal purpose
• A growth mindset
• Empowering others
• Stretching others

In contrast, senior executives in the BTS study identified a different set of capabilities as most critical for leaders right now:

• Accountability
• Transparency
• Enterprise thinking
• Divergent thinking

The contrast reveals a disconnect between what development programs are building—and what executives believe their organizations need most from their leaders today.

How did we get here?

The expectations placed on leaders—especially at the middle—have always evolved alongside the business landscape.

In the 1990s, leadership development focused on emotional intelligence and team empowerment. The 2000s brought globalization and lean operating models, with a sharper focus on efficiency and agility. Then came digital transformation, agile ways of working, and flatter, more matrixed structures.

Each wave expanded the leadership mandate—asking leaders to become connectors, coaches, and change agents.

What’s different now is the pace and proximity of change. Strategy no longer shifts annually—it flexes monthly. And mid-level leaders are no longer simply executing someone else’s vision. They’re expected to interpret it, shape it, and deliver results through others—in real time.

At the same time, the psychological contract of work has changed. Employees want more meaning, flexibility, and support—and they often look to their managers to provide it. Add in the rise of AI and the frequency of disruption, and the expectations placed on leaders have outpaced what many development efforts were designed to support.

What’s driving the disconnect?

What we’re seeing isn’t disagreement—it’s a difference in vantage point, shaped by the distinct challenges each group is solving for. This isn’t about misaligned intent—it reflects different priorities and pressures.

Talent and learning teams often prioritize foundational capabilities because they’re proven, scalable, and critical to developing confident, human-centered leaders. These programs are designed to grow potential over time.

Executives, meanwhile, are focused on the immediacy of execution—strategy under strain, shifting priorities, and the need for alignment at speed. Their focus reflects where progress is stalling now.

Both perspectives matter. But when they remain disconnected, development risks falling out of sync with business reality—and the gap is most visible at the middle, where expectations are rising fastest.

What’s the takeaway for talent leaders now?

This moment offers more than a gap to close—it offers insight into how leadership needs are evolving.

What if the differences between these two capability lists aren’t in conflict, but in sequence? Foundational strengths help leaders show up with purpose and empathy. Enterprise capabilities help them lead across systems and ambiguity. The opportunity isn’t to choose between them—it’s to connect them more intentionally.

What’s uniquely now is the acceleration. The stretch. The pressure to reduce friction and support faster alignment. Talent leaders aren’t just being asked to build capability—they’re being asked to build momentum. That means designing development experiences that reflect complexity, enable cross-functional thinking, and help leaders decide and adapt in real time.

It also means listening more closely. The capabilities executives are calling for aren’t just wish lists—they’re signals. Signals of where transformation slows, and where leadership must evolve for strategy to move forward.

This isn’t about shifting away from what works—it’s about expanding it. To connect what leaders already do well with what the business needs next—and to do it in ways that are grounded, human, and built for today’s pace.

Shifting momentum

Leadership development isn’t just a pipeline priority. It’s a strategic lever for how your organization adapts, aligns, and accelerates through change.

This research doesn’t just reveal a skills gap—it surfaces a systems opportunity. The disconnect between talent priorities and executive expectations highlights where momentum gets lost, and how leadership development can close the space between vision and execution.

Talent leaders are uniquely positioned to reconnect the dots—between individual growth and enterprise outcomes, between what leaders learn and how they lead, between what the business says it needs and how that shows up in behavior.

So the next question isn’t just: What should we build?

It’s: How do we enable leaders to build it into the business—faster?

Every organization is navigating this differently. If you’re revisiting your development priorities or rethinking what leadership looks like in your context, let’s connect. We’re happy to share what we’re seeing—and learning—with others facing the same questions.

There’s a specific kind of strategy meeting getting scheduled right now, in nice hotels with bad coffee: the AI reset off-site.

And for good reason. In a 2026 WRITER survey, 48% of leaders described their AI rollout as, in their own words, a "massive disappointment." That's nearly half the room.

What that number really measures is the distance between what these tools can do and what people are doing with them. In our experience, that distance is almost entirely human.

Which is why the off-site is the right instinct. Making the most of that time is the harder part.

What separates an AI reset that actually changes the game from an expensive two-day conversation? In our experience, it comes down to avoiding four common design mistakes.

Mistake 1

Blaming the bots

The gap between AI investment and real adoption is almost always about people, not technology. And when adoption stalls, we usually find it's one of four things.

1. They don't think it will help them
2. Nobody around them is using it
3. They don't feel capable
4. Or they don't have real access to the tools they were promised

Four different problems, and four completely different fixes.

That's why diagnosis comes first. If you don't know which barrier you're dealing with, every intervention becomes an educated guess. And you cannot tell which one you have by staring at a dashboard. A belief gap and a skill gap look identical in a status report and need opposite interventions. Show up guessing, and you'll spend real money teaching people to use a tool they simply don't trust yet. Congratulations - you've just catered the wrong conversation.

Mistake 2

Letting leaders off the hook

One of the biggest predictors of whether change sticks is also one of the most overlooked: leadership.

If your executives show up as observers, nodding along and quietly answering email under the table, your people clock it in about four minutes.

That doesn't mean your CEO has to emcee the thing. It means they use the tools in front of everyone, participate in the conversation, and make it clear this isn't someone else's initiative.

Recently we’ve been working with a Fortune 200 global professional services firm who’s top 120 leaders were at very different points with AI. Some were redesigning entire processes. Others were using it to summarize emails, or not at all. Rather than focus on the technology, the four-hour session focused on what leaders could do with AI, applying it to a live strategic challenge and ending with a personal commitment to lead differently. The response was strong enough that the organization is now cascading the experience globally.

The lesson is simple: when leaders experience AI as a strategic capability, they're better equipped to model the behavior that makes adoption stick. Nothing you build during those two days survives without that entire chain of leadership doing its part.

Mistake 3

Chasing the wrong outcome

Without a behavioral baseline, you have no way to prove anything actually moved. No baseline, no ROI. You're just hoping the energy in the room was good, which is a wonderful feeling and a terrible metric to bring to your CFO.

But the baseline isn't just about proving the off-site worked. It's about understanding where you're starting in the first place. And you'll want that clarity, because the quiet resistance is real. In that same 2026 research, nearly a third of employees admitted to actively working around their company's AI strategy. If you don't win their belief in the room, some of them will keep politely ignoring the whole thing from their desks. You can't measure your way out of that. You have to earn your way out of it.

Which brings us to the biggest reframe of all.

Mistake 4

Leaving follow-through to chance

We've been working with a Fortune 100 medical device company on their AI strategy for three years. It started with their leadership team, a three-hour session built around what those leaders would do differently, and it landed. What became clear afterward was that the same experience needed to happen everywhere else. So, it expanded: 90-minute activations for 15,000 people, and this year intact teams redesigning their own workflows.

Three years in, that first session is the smallest part of the story.

Your event is where momentum gets created. What happens at 30, 60, and 90 days is where results get made.

If you're planning one of these and want to change what happens on Monday, not just how everyone feels on Friday, that the work we do.

‍We'd be glad to help you design it.

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.