Resilience: The leadership antidote for our current crisis
On Friday March 20, the DJIA closed at 19,174. That was down more than 35% from its peak in February. For a point of reference, this is about the same decline that we experienced in all of 2008, in just one month. The impact has been equally large and dramatic in just about every aspect of how we live and work. Never has it been more clear that we live in a VUCA world. For those of you who are unfamiliar with that acronym, it is a concept that originated with students at the U.S. Army War College to describe the volatility, uncertainty, complexity, and ambiguity of the world after the Cold War. At this point, the Cold War looks like child’s play compared to our current situation.
Why resilience matters
Resilience is the antidote to our VUCA world. Now, more than ever we need to rely on, and continue to develop, our resilience to help our companies and our teams navigate in this crisis. Resilience is what allows leaders and teams to be calm, steady, and resolute in times of challenge or crisis. It provides for greater agility and flexibility. It enables us to keep our eyes and energy on a better future. It gives us the opportunity to collaborate even more effectively with our colleagues.Resilience can be easy to see and understand when we think about people like Nelson Mandela, Winston Churchill, and Albert Einstein – who was told by a teacher that he “would never be able to do anything that would make sense in this life.” We’ve seen it in many business leaders like Steve Jobs, Akiro Morita and Henry Ford – who went broke 5 times before starting the Ford Motor Company. If you look closely enough, you see it all around – especially today with the tens of thousands of healthcare providers who are working overtime to keep us healthy.If you look even closer, you will see your own resilience. One of the interesting facts we learned in studying resilience in the workplace is that most of us have a great deal of grit, determination and strength.
How resilience can help in times of crisis
In 2012 I co-authored a book on the topic: “Lemonade, The Leader’s Guide to Resilience at Work.” We researched thousands of business leaders and developed a model of resilience that includes 15 leadership behaviors that can help leaders to recover from or adjust easily to misfortune or change.
My favorite of these resilient behaviors is reframing.
Reframing is the ability to find a silver lining no matter how dire the situation. It is the ability to choose how you talk about the facts and create a context for yourself and for others to see them differently. For instance, if you live in Mauritius, you can call it “a small, insignificant island.” Or you can call it “the largest ocean state in the world.”In the context of our current crisis, you could be talking about how “physical distancing” is creating a feeling of isolation. Or you could talk about the opportunities it presents for us all to learn to use collaboration technologies to both get our work done and not feel so isolated. Both are actually “true” in some objective sense. But the ability to reframe a problem or challenge into the more positive perspective makes it more possible for people to take action. In this example, your team will be able to see and embrace the opportunity more readily in learning the new technologies and feel less fear as they sit in their new home offices with no context besides the news.Think of the benefits of applying this to thinking about how to pivot the business to weather the storm. What new business models, markets, partnerships might be out there waiting for you to uncover?
Reframing as a business imperative
The ability to reframe reminds me of an executive I advised a few years ago. Scott was (and still is) a very experienced and successful leader in his organization. He had a reputation for turning around projects and programs that were underperforming. He had a strategic mind, a keen attention to detail and very high standards for performance. Scott was seen as potentially one of the organization’s senior-most leaders in the future. But something was holding him back. His high standards and intense drive translated into zero tolerance for mistakes.When mistakes happened, as they always do, Scott adopted a rigid and unyielding attitude. He simply could not see the learning opportunity that mistakes can present. The people who worked most closely with Scott learned to follow his lead. Some of his people were actively hiding or ignoring mistakes out of fear of Scott’s reactions. This created a dynamic that suppressed any kind of productive problem solving and Scott was operating in the dark about problems cropping up.This all came to a head when the company lost one of its biggest customers. This customer moved its business to another supplier because in their own words, “you kept making the same mistakes and you haven’t kept up with changes in our business.” This was shocking to Scott, who hadn’t realized there were problems with this customer, and that his team didn’t have the capability to solve the problems. This proved to be a much-needed wake-up call for Scott. He was forced to learn to view mistakes differently, to reframe them as learning opportunities. In doing so, he created a different mindset in himself and his team. He went on to become an even more successful and accomplished leader. His mantra became a quote from one of his heroes, General Omar Bradley, who said, “I learned that good judgment comes from experience and that experience grows out of mistakes.”
How to reframe
There is a simple practice you can use to build your own ability to reframe. You can even invite colleagues or your team to join you in this exercise. Try this the next time you encounter a problem.
- Draw a line down the center of a page.
- On one side the headline is “challenges.”
- On the other is “opportunities.”
- Your task is to re-write the problems as possibilities.
Doing so will give you, and those you are tasked with leading, more energy to get through and even to accelerate through this unprecedented crisis.
Related content
Related content
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.