AI demands a rethink of insurance roles and expertise: BCG

As automation removes routine work, insurers risk making human roles more demanding while eliminating the repetition through which expertise is built, BCG’s Nathalia Bellizia warns.

Key points:
Automation concentrates complex work
Expertise becomes harder to build
Human accountability increases

Insurers will have to redesign jobs, training and accountability as they automate routine underwriting and claims work. Without a new talent model, companies risk burning out experienced staff on a relentless stream of complex cases while eliminating the very tasks that build junior expertise.

“We often think, rightly, that AI will remove repetitive, boring work and allow people to do the interesting, higher-value activities. What people sometimes do not realise is that, if you remove all the easy, repetitive work, none of it remains,” Nathalia Bellizia, a managing director and partner at BCG and its global leader for corporate finance and strategy in insurance, told Monte Carlo Today.

Easy claims could increasingly move through straight-through processing, leaving people to handle unusual risks, stressful situations and difficult decisions

“The work will be more engaging, but it will also be more cognitively demanding,” Bellizia said. “There is a real risk of decision fatigue, burnout and, ultimately, attrition.”

In a July BCG report, Bellizia and her co-authors illustrated the shift in work through a possible future underwriting model.

“Today, an underwriter might review hundreds of straightforward submissions. In the future, those will arguably be handled through straight-through processing, and the underwriter’s work will be very different,” she said. “They will work on the 10 or 15 cases that are genuinely unusual, ambiguous or high-stakes.”

“Expertise will be more valuable and, at the same time, scarcer and harder to build.”

Fewer cases, tougher decisions

Concentrating human work on the most difficult cases creates a new challenge for insurers: how to make roles sustainable when employees no longer have routine work between high-stakes decisions.

“As insurers embark on AI transformations, it is critical that they also design the human work,” Bellizia said. “They need to create sustainable roles, supported by decision-support tools and clear escalation paths, and think carefully about workloads.

But removing routine work creates another problem: repetition has traditionally been insurance’s training ground, allowing junior employees to encounter hundreds of cases and gradually develop judgement.

“You might start as an underwriting assistant working with an underwriter, and see hundreds of cases,” Bellizia said. “It takes time to recognise patterns. You may make mistakes and learn from them.”

The expertise gap

If insurers automate that work without replacing the experience it provides, they could find themselves needing more expert judgement while developing fewer employees capable of exercising it.

“Strong human judgement will therefore become increasingly important, while that expertise will no longer be built through repetitive work,” Bellizia said. “Expertise will be more valuable and, at the same time, scarcer and harder to build.”

Insurers will therefore have to become more deliberate about developing judgement. Bellizia pointed to structured mentoring, simulations, AI-enabled training and rotations through different parts of an organisation, comparing the approach with pilots learning to handle emergencies in simulators rather than waiting to encounter them in flight.

“Unless expertise is built by design, it will not happen by accident, as it largely does today,” she said.

Beyond the technology

That workforce redesign could ultimately matter more to the value insurers extract from AI than the technology itself.

BCG estimates that change management, adoption and the redesign of talent and operating models account for roughly 70% of AI’s potential value, with tools and technology accounting for the remaining 30%. She said insurers that simply add AI to existing workflows may gain efficiency without fundamentally changing how decisions are owned or expertise is developed.

“A common failure is that companies focus far too much on the technology and data,” she said. “People and organisational matters, including change management, become an afterthought.”

That is where the model can break down, she said, because roles have not been designed properly, processes have not been transformed and the right governance is not in place.

Accountability at scale

As AI takes over more execution, the people supervising it will also become accountable for decisions made at a far greater scale. Insurers will therefore have to determine who owns automated outcomes and who has the authority to intervene when something goes wrong.

“If an individual underwriter today is working on one file and makes a mistake, that is one thing,” Bellizia said. “If an individual is overseeing hundreds of straight-through processes handled by AI, the scale and importance of their decisions are much greater.

Insurers will need to define decision ownership across business, technology and risk, Bellizia said, alongside small, senior, business-owned teams overseeing automated outcomes as systems operate.

“As technology performs more of an insurer’s work and controls more activity, a small, senior, business-owned team needs to monitor decisions for quality,” she said. “Its members need the authority and expertise to intervene in real time if something does not look right.”

That makes the challenge less about choosing between AI and people than about designing a system in which each adds the most value.

“AI increasingly takes on scale, repetition, synthesis and routine decisions, while humans step in where judgement, ambiguity, empathy and accountability are important.”

Nathalia Bellizia is a managing director and partner at BCG and global leader for corporate finance and strategy in insurance. She can be reached at bellizia.nathalia@bcg.com

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