When being boring happens to good leaders
Some of the teams I work with report to a boring leader, and it’s a problem. As someone who works with leadership teams, it’s easy to spot when this is happening because the clues are usually obvious. During meetings, the team is multi-tasking, they are checked out, and they’re often quiet, only offering thoughts when called upon by the leader to do so. Not surprisingly, the team retains little, to anything, that was discussed in the meeting, which often results in – you guessed it – more boring meetings and discussions on the very same topic.
Being boring isn’t just a benign idea or an interesting notion. It’s a behavior that presents serious consequences for leaders and teams because the negative impact on productivity and execution is real and here’s why: Boring leaders beget underperforming teams. When a team is led by a boring leader, it’s almost a guarantee that you see a team focused more on compliance and getting the work done, versus real engagement and energy to work together in an above-and-beyond way. That may be because boring leaders often are the last ones to see this behavior in themselves, and consequently, they don’t recognize how their behavior is doing their teams a major disservice – a boring leadership style never brings out the best in others.
What does it mean to be a boring leader? The answer may surprise you.
Most of us can be a little bit boring
It’s easy to hear the word boring and think that it applies to someone else. Personally, I hate the idea of others thinking I’m boring, but here’s the thing. The opposite of boring isn’t being charismatic, energizing, or rah-rah. That’s why it’s easy to think this concept applies to anyone besides us, because it conjures up an image of someone droning on about a dry topic in a monotone voice.
In the context of the workplace, boring leadership often takes on a different look, staring with a lack of awareness about the audience. When a leader is seen as boring, it’s often because they assume that what matters to them matters equally as much to others. We know that in practice, it just doesn’t work that way. This lack of awareness creates a dynamic where you’ll see the boring leader doing more talking than listening, asking few questions, reading slides or reporting out information, and then wondering why the audience is so quiet or unresponsive in the meeting.
The opposite of boring isn’t charisma, it’s curiosity
Here is a very cynical - and effective - place to start if you want to break the habit of being boring. Assume your audiences do not care. Even iif it is the board, your investors, your CEO, even if you’re talking about a matter of great importance. When you start from that cynical point of view, it forces you to ask yourself better questions and get deeply curious about your audiences. For example, rather than assume your board cares about how you’ve evolved your customer value proposition, you might ask yourself: If they don’t care about this, what do they care about? Is there any connection between our evolving customer value proposition and that issue? You might also wonder: Why am I assuming they would automatically see the value in this idea? Is it obvious why this is a good idea to others? You could even ask yourself: If I imagine I am one of my board members, would I remember or understand why we decided to focus on our customer value proposition in the first place? Audiences will only care once they understand the value in your ideas and how that value applies to them. When we’re boring, we simply forget to connect those dots for the audience.
If you want to stop being boring, stop talking about boring things
Several years ago, I was invited to attend a meeting run by a Chief Audit Officer on reporting and I’ll be the first to admit that I was not chomping at the bit to attend what I anticipated would be a major snoozefest. (To all my beloved Chief Audit readers, forgive my ignorance.) Imagine my surprise when this meeting turned out to be one the best I’ve ever attended. What made the discussion so energizing was the leader himself, who had called the meeting together because of legislative changes requiring a major shift in reporting across the company. It would have been easy for the leader to focus on the challenges of making these changes, how tough it would be for the team, the difficulties ahead in managing workload.
To be clear, those challenges were very real, and the leader did address them, knowing that these were valid concerns on the minds of his employees. But he also focused on the future, painting a very powerful picture of what life would feel like once this project was behind the team. He engaged them in the vision of what it could feel like in six months, when the project was completed. As he led the team in discussion, it was interesting to observe the team’s reaction. They went from panicking about the changes to seeing this effort as something that could make them better as an audit team. They began thinking about how the reporting changes would be a short-term hassle, but in the long run, it would help them finally break free from creating hundreds of low-value reports they dreaded generating in the past.
How to shift the balance
It’s easy to talk about what is difficult, tough, or challenging, but the problem is, it’s boring, it motivates absolutely nobody, and it keeps us stuck. After a while, it just gets boring to talk about the workload, the lack of resources, how busy we are, how tough our market is, the challenging quarter we just got through, and so on. What’s crazy is how often we do this inside companies, and it’s incredibly counterproductive, because in most cases, the conversation never really evolves beyond how tough things are. Start to pay attention to whether this a pattern you fall into, and simply shift the balance, so that you’re also talking about solutions, about the future, or about what we can control to influence a different outcome instead.
Being boring isn’t a personality defect; it’s something that can happen to the best of us, and the good news is that with simple behavior changes, we can improve in this area quickly. To start, ask yourself questions like: Do others really engage in my meetings? Do I find myself talking mostly about challenges and how tough things are? Do people describe me as inspiring? Do I do enough listening and asking questions? Become curious about your responses and consider where you can put a few simple changes into place. It’s worth the effort, because when we can do more to inspire and engage others, we inspire and energize ourselves, too.
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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.
- They don't think it will help them
- Nobody around them is using it
- They don't feel capable
- 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.
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.