Humility, Judgment and the Actuarial Profession in the Age of AI

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Artificial intelligence is rapidly reshaping actuarial practice, from pricing models to regulatory interpretation. Yet the central question is not what AI can do but how actuaries should think, behave and lead in response.

Humility Before Capability

Artificial intelligence is now woven into the fabric of professional life. It drafts, summarizes, codes, translates, forecasts and explains. For actuaries, this shift appears in reserving workflows, pricing analysis, regulatory interpretation, capital modelling and even routine correspondence. Yet the defining question is not of technological capability but professional posture. How should actuaries think, behave and lead when computational systems grow more capable each year?¹

The answer begins not with dominance, but with humility.

Actuaries are trained within a disciplined boundary. We are qualified to opine on risk, uncertainty and financial consequences because we understand models, assumptions and their limitations. We code, but we code within frameworks of accountability. The presence of AI does not expand or erase those boundaries. It tests whether we understand them deeply enough to maintain them while adapting to new tools.

AI-Readiness Is a Professional Orientation, Not a Technical Skill

When many professionals hear “AI readiness,” they immediately think of coding, machine learning models, or generative AI systems. The panel deliberately reframed this narrative. Technical capability matters, of course, but it is not the defining factor. AI readiness begins with clarity about what makes actuaries indispensable in the first place.

Actuaries have always operated at the intersection of mathematics, uncertainty, and societal impact. Their work influences financial stability, insurance affordability, pension security, and risk management across entire populations. AI may enhance computational speed and pattern recognition, but it does not replace professional responsibility or contextual judgment.

Humility here does not mean hesitation or fear. It means recognizing that neither human nor machine is infallible. AI models may identify patterns beyond the reach of manual analysis. They may detect correlations across vast datasets and generate draft analyses in seconds. But they do not carry context in the way professionals do. They do not bear legal accountability. They do not understand regulatory nuance beyond the patterns on which they were trained.

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Continuous Learning as a Core Discipline Without Chasing Hype

AI technologies evolve rapidly. Techniques that are cutting-edge today may be outdated in a few years. Actuaries cannot afford to remain static. AI development moves rapidly. New tools appear, improve and become obsolete within short cycles. The appropriate response is neither complacency nor constant reinvention. It is structured learning.

However, continuous learning does not mean chasing every new tool. It means developing adaptable thinking. It means understanding foundational AI concepts well enough to evaluate new developments critically. It means being able to collaborate effectively with data scientists, engineers, and risk managers. Actuaries need sufficient literacy in AI concepts to evaluate applications critically. They should understand basic principles of machine learning, model training and validation, even if they are not designing systems from scratch. At the same time, they must resist the temptation to pursue novelty for its own sake. Professional credibility is built on stability and reliability, not on adopting every emerging tool.

For early-career actuaries, this mindset is especially important. As AI automates certain routine tasks, training pathways may shift. Junior professionals may need intentional mentorship to build deep conceptual understanding rather than relying excessively on automated outputs.

For experienced actuaries, the challenge is different. They must remain open to change while preserving professional rigor. Resistance to AI out of fear is unproductive. Blind adoption without scrutiny is equally risky. The balanced mindset lies somewhere in between.

Human Judgment in an Automated World

One of the most important themes was the enduring importance of human judgment. AI models, no matter how sophisticated, operate within defined parameters. They extrapolate from data, optimize for objectives, and detect statistical patterns. What they do not do is understand context in a human, ethical, or societal sense.

Actuaries, by contrast, are trained to interpret results in light of regulatory requirements, stakeholder expectations, fairness considerations, and long-term consequences. They ask questions such as: Does this output make economic sense? Is it consistent with professional standards? Could it introduce unintended bias? What assumptions are embedded in the data?

When an AI tool generates reserving scripts or pricing logic, the actuary remains responsible for verifying methodology, reviewing outputs and ensuring compliance with standards. Delegation of mechanics does not equate to delegation of judgment. In fact, the more automation enters a workflow, the more critical it becomes that someone understands the architecture at a conceptual level.

Consider a reserving team using a generative AI tool to draft a loss development analysis. The model suggests reducing IBNR by 7% because the most recent two accident years show accelerated paid development. However, the acceleration is driven by a temporary claims settlement initiative aimed at clearing small claims backlog before year-end. Without adjusting for this operational shift, reserves would be understated. The AI detected a statistical pattern. The actuary recognized the operational context.

The profession’s strength lies in structured reasoning under uncertainty. Actuaries interpret model outputs in light of business strategy, regulation, market conditions and long-term sustainability. An AI system can optimize a loss ratio or minimize capital volatility; it cannot weigh reputational risk, supervisory expectations or stakeholder trust in the same way.

This distinction clarifies the enduring value of actuarial work. AI may assist in generating results. The actuary determines their appropriateness.

Accountability Does Not Transfer to the Machine

Perhaps the most unequivocal message is that accountability remains with the actuary. AI systems do not bear professional responsibility. They do not sign opinions. They do not answer to regulators. They do not stand behind financial statements.

As AI tools become embedded in actuarial workflows, the profession must ensure that governance and oversight evolve accordingly. Using AI does not dilute professional standards; it intensifies them. Documentation, validation, peer review, and transparency become even more important when AI outputs inform pricing, reserving, capital modelling, or strategic decisions.

The mindset shift here is subtle but significant. Actuaries cannot view AI as a convenient shortcut. They must treat AI models with the same rigor applied to traditional actuarial models. That includes understanding assumptions, stress-testing results, monitoring performance over time, and ensuring explain ability where required. The machine may assist, but the signature remains human.

Actuarial opinions are signed. Financial statements reference professional judgment. Regulatory submissions rest on accountable individuals. AI systems do not carry that burden. If AI contributes to assumption setting, analysis or reporting, the actuary must understand the extent of that contribution. Governance frameworks should clearly define how such tools are used, how outputs are reviewed and how decisions are documented. Treating AI as an opaque black box is inconsistent with actuarial discipline.

A Balanced Perspective on AI’s Promise

We do not need to portray AI as either a binary bipolar threat or a miracle solution. Instead, we need to emphasize balance in this strategic continuum. AI can enhance productivity, uncover new patterns, and enable more dynamic modelling. It can free actuaries from repetitive tasks and allow deeper strategic engagement.

But AI is not infallible. It does not eliminate uncertainty. It does not replace professional judgment. It does not absolve responsibility.

The most effective actuaries in the AI era will likely be those who combine technical fluency with principled skepticism. They will be comfortable using advanced tools while maintaining clarity about their own accountability.

The Profession’s Opportunity: A Discipline That Endures

The actuarial profession has evolved through many technological transitions. From manual calculations to spreadsheets, from deterministic projections to stochastic simulations, tools have changed while principles endured. AI represents another chapter in that evolution.

What differentiates this moment is not that machines can compute faster. They always have. It is that machines can now generate structured language, code and analysis in ways that resemble human output. That resemblance invites both enthusiasm and overconfidence.

Leadership in this context means shaping governance standards, defining best practices, mentoring future professionals, and demonstrating that responsible AI use is not optional but integral to professional excellence.

When actuaries code, they do so within competence. When they opine, they do so with accountability. When they adapt, they do so with humility. In an environment shaped increasingly by intelligent systems, that combination of restraint and openness may prove to be the profession’s most valuable asset. In the age of AI, the machine may generate the output, but the actuary owns the judgment.

¹ Society of Actuaries Research Institute. Actuarial Mindsets for Leading in the AI Era: An Expert Panel Discussion. January 2026. Chicago, IL: Society of Actuaries Research Institute. https://www.soa.org/resources/research-reports/2026/actuarial-ai-mindsets-leadership/ 

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