How risk leadership leads to better, more rational decisions
Authors:
- Bhavik Modi, Senior Director, Innovation and Digital Transformation at BTS
- Dr. Annette Hofmann, Director of the Lindner Center for Insurance and Risk Management at the University of Cincinnati, author of The 10 Commandments of Risk Leadership, and Editor-in-Chief of the Risk Management Insurance Review (RMIR).
Dr. Annette Hofmann is a researcher, author, and leading expert in the field of organizational risk. In this interview with Bhavik Modi, Senior Director, BTS, Dr. Hofmann explains how moving beyond a traditional approach to risk and developing risk leadership competence can help companies become more resilient and profitable—and may even save lives.Bhavik Modi: Dr. Hofmann, I’ve been fascinated to explore your research into risk in relation to the healthcare and insurance markets, and to industry in general. Under your leadership as director, the Lindner Center for Insurance and Risk Management is doing fantastic work in researching and collaborating with industry. BTS is excited to be partnering with the center on a new course based on your research. Thank you for agreeing to discuss your insights with me.Modi: I’d like to start with defining terms. Organizations have traditionally focused on risk management. How is this different from risk leadership?Dr. Annette Hofmann: Speaking specifically about the insurance industry, risk professionals are typically trained to think in terms of established concepts of coverage—additional insureds, medical payments, etc. They use risk maps or ratings to classify different risks and then evaluate them to find an appropriate risk-management technique. They focus on the mechanics, the quantitative aspects, of managing risk.Risk leadership addresses not just the quantitative but also the qualitative aspects of dealing with risk. This requires risk literacy. Understanding individual risk perception, risk aversion, risk perception-related behavior, and how emotions respond to risk and the cognitive biases that result is crucial to developing the risk literacy that enables better decision-making. This understanding is especially important for leaders, as their decisions have greater consequences. Risk leadership also requires communicating about risk, providing direction and guiding the company through tough times by coordinating efforts to deal with major risk exposures, anticipating opportunities to avoid risks, and implementing a risk-related strategy to ensure long-term success.Modi: Often, risk leadership is associated with the insurance or actuarial professions. How can this concept be applied to organizations in other industries?Hofmann: Many new titles have emerged in the past decade—Chief Risk Officer (CRO), Director of Risk Management, Vice President Risk Services—demonstrating a broad recognition that risk management deserves to be a top priority. The challenge is to move from competence in risk management to true risk leadership.In The Leader’s Brain, Wharton Neuroscience Professor Michael Platt describes how key areas in our brain work and how insights from this can be used to teach us how to develop better leadership abilities. He writes that while it’s difficult to train people in some areas, such as ethical decision-making, improvements in risk literacy are relatively easy to accomplish. The task consists mainly of acquiring knowledge about the risk-related biases our brains are commonly exposed to and gaining insights into how these biases can be avoided. Understanding the hidden forces that drive our decision-making processes under risk and uncertainty, we can be trained to make more rational decisions.Modi: What is the “uncommon sense” when it comes to risk leadership? What behaviors are well understood but often not put into practice? Or which behaviors are counterintuitive to how we would traditionally think and work?Hofmann: Managers are often seen as having sophisticated information-processing capability, as being able to make rational risk-related decisions based on economic incentives and the fullest available range of information. But managers are human beings, and like all of us, prone to biases and cognitive mistakes when they interpret risk-related information. These mistakes, together with emotions, hinder the objectivity of their decisions, which ultimately may hurt the financial survival of their firms.For example, the lack of objectivity prevents managers from identifying and taking into account secondary risk effects, which is when avoiding or trying to mitigate one risk creates another risk. Risk leadership means not only seeing the potential for secondary risk but also communicating throughout the organization how this is influencing the decision-making process.Consider the case of a U.S. manufacturer that recently experienced a small explosion leading to worker injuries. The company decided, following pressure from employees, to stop producing the part that was responsible for the explosion. Consequently, the part is now being imported from a Chinese supplier. Was this a wise decision? Well, the risk of another explosion is avoided, but now there is a secondary risk: the imported part could be defective; it might be delivered late or not at all. The decision to stop producing the part now creates supply chain risk.Good risk communication, letting employees know about the very small risk of explosion and the firm’s efforts to prevent a recurrence while continuing to produce the part, might have been a superior strategy for the company.Modi: How much of risk leadership is preventive versus reactive? And what are the consequences of poor risk leadership in a reactive or preventive situation?Hofmann: When it comes to reactions, one of the best illustrations of poor risk leadership can be found in the behavior of government officials following the terrorist attacks of September 11, 2001, killing almost 3,000 people.Officials did not provide the public with information about this type of risk that may have headed off a misguided reaction. After 9/11, many people used a car to get to another city or location far from their home rather than taking an airplane. Because driving is so much more dangerous than flying, this switch resulted in an estimated 1,600 more fatalities in the U.S. in the year following 9/11 than would have occurred if people had continued with their previous plans and patterns of travel. Our ability to make decisions based on factual probability data is often influenced by our fears and emotions following big events like the terrorist attacks, and policymakers did not address this effect by communicating potential second-order risk effects to the public.When it comes to preventive risk leadership, people often neglect the potential occurrence of natural disasters by adjusting their subjective probability judgments.People tend to prefer insuring against a high-probability-low-consequence risk such as a bicycle theft, over a low-probability-high-consequence risk, such as a flood. They purchase add-on coverage to the homeowner's insurance policy to cover the risk of bicycle theft rather than covering the risk of loss due to flooding.Studies of insurance demand suggest that individuals tend to ignore or undervalue low probability events.However, after a catastrophe occurs, people suddenly think this will happen again soon and then decide to purchase coverage, which according to probability they are less likely to need the following year. A preventive risk leadership approach would be to inform people about this misguided behavioral pattern and incentivize them to purchase catastrophe insurance before something happens.Modi: It’s clear that developing risk leadership requires a dramatic shift in thinking, including a recognition that, as you say in your book, we are not rational in the face of risk. Change is hard. Why is it worth it for organizations to make this change?Hofmann: Risk leadership enables organizations to identify, and therefore eliminate, the cognitive biases that lead to bad decisions. This needs training and I am happy to help – but of course I am human and therefore my decisions are also far from perfect. Training in risk leadership will lead to better decisions and better communication of those decisions, and ultimately to more profitability and greater long-term success of a company.
Related content
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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.