Clients, Risk, Decision-Making and the Future of Work

Dr Vina Theodorakopolou shares her insights in this special article coinciding with the AIRMIC Conference.

We often approach risk through models, frameworks and data. Increasingly, I think we should be approaching it through decision-making.

In (re)insurance, uncertainty is not an exception - it is the operating environment. And yet many of the systems we design still assume a relatively narrow range of how people interpret information, identify patterns and make judgements.

This matters more than we acknowledge.

The way individuals process ambiguity, frame risk and respond to incomplete information directly shapes outcomes. When organisations design processes around a “standard” decision-maker, they are not simplifying complexity - they are often masking it.

In my work across insurance, reinsurance and advisory environments, I have seen how different cognitive approaches can lead to materially different interpretations of the same risk. What may appear as inconsistency is often simply variation in how information is processed and prioritised.

When recognised and channelled effectively, this variation can strengthen decision quality.

AI, Systems and the Redistribution of Judgement

AI is often framed as a way to remove subjectivity from decision-making.

In practice, it does something quite different.

• It embeds assumptions into systems

• It redistributes where judgement sits

• It changes how individuals interact with information

  
  

The implication is clear:

The quality of decisions increasingly depends on how human judgement and system design interact.

The Missing Component: People and Cognition

In discussions around transformation, we often focus on:

• technology

• data

• process

What is less frequently examined is: how different people actually think, interpret and decide within those systems. This is not an abstract point.

In (re)insurance, outcomes depend on:

• pattern recognition

• interpretation of uncertainty

• probabilistic judgement

• foresight under ambiguity

These are fundamentally human capabilities and they do not manifest identically across individuals.

Implications for Clients, Risk and the Future of Work

As AI becomes more embedded and as markets become more complex, organisations face a different challenge:

Not simply how to standardise decisions, but how to design environments that support a range of decision-making styles

This has direct implications for:

• Clients: understanding how risks are framed and communicated

• Risk management: improving resilience through diversity of judgement

• Talent strategy: identifying capability beyond traditional profiles

Practical Considerations (Where to Start)

Organisations do not need to redesign everything, but they do need to start asking different questions.

1. Move beyond standard profiles

Talent that strengthens decision-making does not always come from conventional pathways.

2. Design for variation, not uniformity

Build processes that allow for different ways of analysing and interpreting information.

3. Make judgement visible

Understand where human judgement sits in AI-enabled processes and how it is supported.

4. Break functional silos

Strategy, risk, technology and people functions must interact rather than operate in isolation.

5. Test decision environments, not just models

How people engage with systems is as important as the systems themselves.

Looking Ahead

The future of work in (re)insurance will not be defined by AI alone.

It will be shaped by how effectively organisations:

• integrate human capability with technology

• recognise variation in how decisions are made

• create environments where judgement can operate effectively under uncertainty

The opportunity is not simply to improve processes.

It is to strengthen how organisations think, decide and act.