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Every major technological leap has sparked fears of job losses. Yet history often tells a different story. As AI transforms actuarial work, Jevons Paradox offers a compelling framework for understanding why demand for actuarial expertise may actually grow.

Artificial intelligence (AI) and automation are transforming industries at an unprecedented pace, and the actuarial profession is no exception. Many assume that as machines become capable of performing calculations, analysing data, and generating reports, the demand for actuaries will inevitably decline. However, history suggests a different outcome. Through the lens of Jevons Paradox, the idea that increased efficiency often leads to greater overall consumption it becomes clear that AI may not reduce actuarial work. Instead, it may significantly expand the scope of what actuaries are expected to deliver.

Rather than replacing actuarial expertise, AI is likely to shift the profession toward higher-value activities that rely on judgment, interpretation, and strategic decision-making.

Jevons Paradox originated in the 19th century when economist William Stanley Jevons observed that improvements in the efficiency of coal-powered steam engines did not reduce coal consumption. Instead, because coal became cheaper to use, industries found more applications for it, leading to an overall increase in demand.

The same principle has appeared repeatedly throughout history. Faster computers created more computing needs, affordable cloud storage increased data generation, and high-speed internet expanded digital services. Efficiency did not eliminate demand it fuelled it.

AI may follow the same pattern in actuarial science.

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Many routine actuarial tasks are becoming increasingly automated. Data cleaning, reserve calculations, pricing models, regulatory reporting, and dashboard generation can now be completed faster than ever before.

While automation reduces the time spent on repetitive processes, it also lowers the cost of producing analytical outputs. As a result, management may request more analyses, more frequent reporting, and deeper insights than were previously practical.

Instead of preparing one annual projection, actuaries may be expected to produce monthly or even real-time forecasts. Rather than analyzing a handful of scenarios, they may be asked to evaluate hundreds.

Jevons Paradox holds only when demand is elastic. Cheaper coal expanded consumption because there was an unlimited appetite for mechanical power waiting to be met. Actuarial output is not obviously in that position. A regulator requires 1 solvency return a year and will not require 40 of them because the marginal cost has fallen. A board that reviews reserving quarterly may find no value in reviewing it daily. Where the consumer of the analysis is a committee that meets on a fixed calendar, cheaper analysis does not create more demand for analysis, it creates a smaller team producing the same volume.

The elastic demand, where it exists, sits in pricing, capital allocation, and portfolio steering, activities where a business genuinely can absorb more granular and more frequent answers and convert them into commercial advantage. Actuaries positioned close to those functions will see their work expand. Those whose output terminates in a regulatory filing may find the paradox does not rescue them. The profession's optimism about AI tends to assume the elastic case applies uniformly, and it does not.

Organizations today collect enormous volumes of data from digital platforms, telematics, wearable devices, connected vehicles, and online customer interactions. AI enables businesses to process this information rapidly, but interpreting its significance remains a human responsibility.

Actuaries are uniquely positioned to transform complex data into meaningful business decisions. As the amount of available information grows, so does the demand for professionals who can distinguish genuine risk signals from statistical noise.

In many cases, AI will generate more questions than answers, increasing rather than decreasing the need for actuarial expertise.

Models can calculate probabilities, but they cannot fully understand business context, regulatory expectations, ethical considerations, or organizational priorities.

Actuaries regularly make decisions that involve balancing competing objectives, assessing uncertainty, and communicating risks to executives and boards. These responsibilities require professional judgment that extends beyond mathematical computation.

As AI produces increasingly sophisticated analyses, organizations will rely even more on actuaries to validate assumptions, challenge unexpected results, and explain the implications of complex findings.

It is comfortable to assert that machines cannot exercise judgment, and the claim is currently defensible. What makes it fragile is that judgment in practice is often pattern recognition built from experience, and pattern recognition is precisely what these systems do well. The parts of actuarial judgment that will genuinely hold are the parts that carry accountability: signing an opinion, standing in front of a regulator, accepting professional liability for a number. A model cannot be sanctioned, struck off, or sued.

The defensible position is therefore narrower than the profession likes to state. It rests on the willingness to be answerable for a judgement rather than on any exclusive cognitive capacity, and actuaries who rely on the latter argument are defending ground they may not hold for long.

Modern businesses operate in an environment characterized by economic uncertainty, climate risks, cyber threats, geopolitical instability, and rapidly evolving regulations.

Because AI makes scenario generation significantly faster, decision-makers are likely to request a much wider range of analyses. Questions that once required weeks of work may be answered within hours, encouraging management to explore additional possibilities.

Future actuaries may routinely evaluate hundreds of economic scenarios, stress-test multiple investment strategies, assess emerging risks, and model the financial effects of unexpected events. Greater efficiency expands analytical possibilities rather than limiting them.

The actuarial profession has been steadily evolving from technical calculation toward strategic leadership. AI is likely to accelerate this transition.

Instead of spending most of their time producing numbers, actuaries will increasingly focus on interpreting results, advising senior management, supporting corporate strategy, and communicating uncertainty to stakeholders.

Organizations will value professionals who can connect analytical findings with practical business decisions, making actuarial judgment more influential than ever before.

The future actuary will need skills that complement automation rather than compete with it. Alongside strong technical foundations, professionals will benefit from expertise in data science, machine learning, communication, business strategy, and model governance.

Equally important will be the ability to understand AI's limitations, identify bias, validate automated outputs, and ensure that analytical models remain transparent, ethical, and aligned with regulatory expectations.

These capabilities will distinguish actuaries who can lead organizations through increasingly data-driven environments.

Although AI offers significant opportunities, it also presents new challenges. Organizations may become overly dependent on automated models without fully understanding their assumptions or limitations. Large volumes of analysis can also create information overload, making it difficult for decision-makers to identify what truly matters.

Actuaries will play a critical role in ensuring that increased analytical capacity translates into better decisions rather than greater complexity. Their responsibility will extend beyond producing insights to helping organizations prioritize, interpret, and act on them effectively.

Jevons Paradox offers an important perspective on the future of the actuarial profession. History shows that improvements in efficiency rarely reduce demand; instead, they often create new opportunities and higher expectations. As AI makes actuarial analysis faster and more accessible, organizations are likely to seek more forecasts, more scenarios, more stress testing, and deeper strategic insights.

Rather than making actuaries obsolete, AI is poised to elevate their role. The future actuary will spend less time performing routine calculations and more time exercising professional judgment, validating complex models, communicating uncertainty, and guiding strategic decisions. In an era where data is abundant, but wisdom remains scarce, the actuarial profession's greatest value may lie not in producing numbers, but in helping organizations understand what those numbers truly mean.