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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Global spending on AI is forecast to reach $2.52 trillion by 2026, a 44% year-over-year increase, according to Gartner. At the same time, only about 10% of AI pilots scale beyond proof of concept.
What’s the disconnect?
Why aren’t most organizations seeing the ROI they hoped for, despite making such large investments?
It’s not because the technology isn’t ready. And it’s not because the use cases are unclear.
The disconnect exists because many organizations are investing in AI as a technology upgrade and expecting a business transformation in return.
The tools are advancing at breathtaking speed, and most organizations already have AI in motion. But the work itself often stays the same. AI gets layered onto existing tasks instead of being used to rethink workflows end to end. Adoption metrics go up, while decisions, operating models, and value creation remain largely untouched.
When teams first start using AI, they do what makes sense. They try to recreate today, just faster. Can it help me write this? Analyze that? Save a bit of time?
That’s a smart place to begin. But it’s not where ROI, or reinvention, actually shows up.
Getting over the hump
Real returns begin when teams experience what we often call “getting over the hump.”
This is the moment when two things click at once:
- AI can fundamentally change how work gets done.
- People don’t need deep technical expertise to make that change happen.
When teams see weeks of work compress into hours, or watch an end-to-end workflow suddenly run in a new way, something shifts. Confidence replaces hesitation. Curiosity replaces caution. The questions change, from “How do I use this tool?” to “What’s possible now?”
That shift matters, because ROI doesn’t come from using AI more often, it comes from using it to work differently.
Why ROI stalls as AI scales
As AI initiatives expand, many organizations discover that the limiting factor isn’t the technology itself. It’s the environment surrounding the work.
ROI shows up when teams are able to explore and redesign workflows, not just automate steps. That requires clarity on outcomes and guardrails, but also room to experiment, learn, and iterate. When AI is tightly controlled or narrowly deployed, pilots stay pilots. When people are trusted to rethink how work happens, value starts to compound.
Organizations that unlock ROI don’t chase perfect use cases upfront. They focus on learning faster and applying those insights where they matter most.
The early signal that ROI is coming
Long before AI shows up in financial results, there’s an earlier indicator that organizations are on the right path.
People are energized by the work.
You see it when teams start sharing experiments, when ideas move across functions, and when learning becomes visible rather than hidden. Progress feels owned, not imposed.
That energy isn’t accidental. It’s a signal that people feel trusted to rethink how work happens, and that trust is essential to turning investment into impact.
Reinvention happens closer to the work than most expect
AI reinvention rarely starts with a sweeping rollout or a multi-year roadmap. More often, it begins with one meaningful workflow, one team close to the work, and a willingness to ask a different question.
With the right support, that team gets over the hump. What they learn becomes reusable. Patterns emerge. Over time, those insights connect, creating enterprise-wide impact and sustained ROI.
That’s how organizations move from isolated pilots to real returns.
What this means for AI investment
No organization feels fully “caught up” with AI, and that’s true across industries.
The organizations that will realize ROI aren’t waiting for certainty or the next breakthrough tool. They’re reinvesting their AI spend into new ways of working that scale human potential alongside technology.
Handled thoughtfully, AI doesn’t distance people from the work. It brings them closer - to better decisions, stronger collaboration, and better outcomes.
For many organizations, that’s where the real return begins.
Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.
This article was originally publish on Rotman Management
IN OUR CONSULTING WORK with teams at all levels—especially senior leadership—my colleagues and I have noticed teams grappling with an insidious challenge: a lack of effective prioritization. When everything is labeled a priority, nothing truly is. Employees feel crushed under the weight of competing demands and the relentless urgency to deliver on multiple fronts. Requests for prioritization stem from both a lack of focused direction and the challenge of efficiently fulfilling an overwhelming volume of work. Over time, this creates a toxic cycle of burnout, inefficiency and dissatisfaction.
The instinctive response to this issue is to streamline, reduce the number of initiatives, and focus. While this is a step in the right direction, it doesn’t fully address the problem. Prioritization isn’t just about whittling down a to-do list or ranking activities by importance and urgency on an Eisenhower Decision Matrix; it also requires reshaping how we approach work more productively.
In our work, we have found that three critical factors lie at the heart of solving prioritization challenges: tasks, tracking and trust. Addressing these dimensions holistically can start to address the root causes of feeling overwhelmed and lay the foundation for sustainable productivity. Let’s take a closer look at each.