The Next Insurance Operating Model: Humans, Systems, and AI Agents Working as One Workforce
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Article Summary
Avinash Mukund, CEO of BlitzenX, argues that insurance companies are moving from two workforces to three: people, systems, and AI agents. He positions AI agents not as automation tools but as orchestrators that gather information, coordinate workflows, and route exceptions while humans retain accountability for judgment and compliance. Citing the World Economic Forum (2025) finding that 86% of surveyed businesses expect significant impact from AI and information-processing technologies by 2030, the article contends that most insurers lack governance models for a digital workforce. Readers will understand why operating model innovation, not technology alone, will determine competitive advantage.
By Avinash Mukund, CEO, BlitzenX
For decades, insurance companies have operated with two primary workforces: people and systems.
People made decisions.
Systems stored data, executed transactions, and enforced workflows.
Whether it was underwriting a commercial policy, processing a claim, servicing a customer, or managing compliance requirements, the operating model remained fundamentally unchanged. Humans drove the work. Technology supported it.
Today, that model is beginning to evolve.
Artificial Intelligence is no longer limited to generating content, answering questions, or automating simple tasks. A new class of AI systems—commonly referred to as AI agents—is emerging with the ability to understand context, coordinate workflows, gather information across systems, make recommendations, and execute tasks within defined guardrails.
While much of the industry discussion focuses on AI capabilities, productivity gains, and automation opportunities, a larger transformation is taking place.
Insurance organizations are preparing to manage a third workforce.
Not people.
Not systems.
AI agents.
The carriers that understand this shift early will gain a structural advantage over the next decade.
From Automation to Workforce Transformation
Historically, insurers have viewed technology investments through the lens of efficiency.
Core platform modernization, workflow automation, robotic process automation (RPA), analytics, and cloud migration initiatives were all designed to improve operational performance.
AI agents introduce something fundamentally different.
Unlike traditional software, agents are capable of participating in work.
They can review submissions, gather information, coordinate activities, summarize complex documentation, identify anomalies, recommend actions, and trigger downstream processes.
In practical terms, this means insurers will increasingly operate with three distinct workforces:
Human Workforce
System Workforce
Agentic Workforce
Each plays a different role.
Systems remain the system of record.
Humans remain accountable for judgment, oversight, customer relationships, and regulatory compliance.
AI agents become orchestrators that bridge the gap between the two.
This distinction is critical because the future of insurance is not about replacing people with technology.
It is about creating a coordinated workforce where humans, systems, and AI agents work together.
Why This Matters Now
The timing is not accidental.
According to the World Economic Forum (2025), AI and information-processing technologies are expected to be the most transformative technologies affecting organizations globally, with 86% of surveyed businesses expecting significant impact by 2030.
At the same time, insurance carriers continue to face increasing pressure from:
Aging workforce demographics
Growing claims complexity
Rising customer expectations
Regulatory requirements
Cost management challenges
Talent shortages in specialized insurance functions
Many insurers are already struggling to scale operations through hiring alone.
The traditional model of adding headcount to absorb growth is becoming increasingly difficult and expensive.
AI agents present a new alternative.
Rather than scaling exclusively through people, insurers will increasingly scale through a combination of human expertise and digital workforce capacity.
A Claims Example
Consider a commercial property claim.
In a traditional operating model, a claims professional must gather documents, validate policy information, review claim history, communicate with stakeholders, identify potential fraud indicators, coordinate inspections, and determine next steps.
Much of this work involves information gathering and workflow coordination rather than actual claim judgment.
An AI agent can assist by:
Collecting supporting documentation
Summarizing claim history
Retrieving policy details
Flagging inconsistencies
Coordinating follow-ups
Routing exceptions to specialists
The adjuster remains responsible for the final decision.
However, the adjuster spends less time gathering information and more time applying expertise.
The result is not workforce replacement.
It is workforce augmentation.
The same model can be applied to underwriting, policy servicing, compliance reviews, premium audits, and customer service operations.
The Governance Gap
While many organizations are experimenting with AI pilots, far fewer are addressing the governance implications of managing a digital workforce.
Insurance companies already have mature frameworks for managing employees.
They also have established controls for managing enterprise systems.
Most do not yet have governance models for AI agents.
Questions that insurance leaders will increasingly face include:
Who is responsible for supervising AI agents?
How is agent performance measured?
How are decisions audited?
What escalation mechanisms exist when agents fail?
How are compliance requirements enforced?
Who owns digital workforce productivity?
These are not technology questions.
They are operating model questions.
The insurers that solve them first will be better positioned to scale AI responsibly.
The Emergence of Digital Workforce Leadership
One prediction appears increasingly likely.
Within the next decade, many large insurers will establish formal leadership roles responsible for managing digital workforces.
Just as organizations created Chief Digital Officers during the digital transformation era, future insurers may introduce leaders responsible for governing human and AI collaboration at scale.
These leaders will oversee:
Agent performance
Decision quality
Operational governance
Risk management
Compliance controls
Workforce optimization
Managing blended teams of humans and AI agents will become a core organizational capability rather than a technology initiative.
The Competitive Advantage of the Next Decade
The insurance industry has historically measured scale through workforce growth.
More policies required more underwriters.
More claims required more adjusters.
More customers required more service representatives.
The next generation of scale will look fundamentally different.
Leading insurers will combine human expertise, enterprise systems, and AI agents into a unified operating model capable of delivering greater speed, consistency, and customer experience without proportionally increasing headcount.
Technology alone will not create this advantage.
Operating model innovation will.
The organizations that successfully integrate humans, systems, and AI agents into a coordinated workforce will define the next era of insurance performance.
The strategic question facing insurance leaders is no longer whether AI will impact their business.
The question is whether their operating model is ready for a third workforce.
References
Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2021). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. arXiv. https://arxiv.org/abs/2105.03354
World Economic Forum. (2025). The future of jobs report 2025. World Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
World Economic Forum. (2025). Drivers of labour-market transformation. In The Future of Jobs Report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/1-drivers-of-labour-market-transformation/
Bono, J., Grana, J., & Xu, A. (2024). Generative AI and security operations center productivity: Evidence from live operations. arXiv. https://arxiv.org/abs/2411.03116
Key Takeaways
Insurance operating models are expanding from two workforces — people and systems — to three, with AI agents forming a distinct agentic workforce.
AI agents differ from traditional software because they participate in work: reviewing submissions, gathering information, summarizing documentation, identifying anomalies, recommending actions, and triggering downstream processes.
The World Economic Forum (2025) reports that 86% of surveyed businesses expect significant impact from AI and information-processing technologies by 2030.
Insurance carriers face compounding pressure from aging workforce demographics, growing claims complexity, rising customer expectations, regulatory requirements, cost management challenges, and talent shortages in specialized functions.
In a commercial property claim, AI agents can collect documentation, summarize claim history, retrieve policy details, flag inconsistencies, coordinate follow-ups, and route exceptions, while the adjuster retains responsibility for the final decision.
Most insurers have mature governance frameworks for employees and enterprise systems but no governance model for AI agents, leaving supervision, performance measurement, auditability, escalation, and compliance enforcement unresolved.
Within the next decade, many large insurers are expected to create formal digital workforce leadership roles governing agent performance, decision quality, operational governance, risk management, compliance controls, and workforce optimization.
Competitive advantage in insurance will come from operating model innovation rather than technology alone, enabling scale in speed, consistency, and customer experience without proportional headcount growth.
Frequently Asked Questions
What is the “third workforce” in insurance?
The third workforce refers to AI agents, operating alongside the human workforce and the system workforce that insurers have relied on for decades. Unlike traditional software, AI agents understand context, coordinate workflows, gather information across systems, make recommendations, and execute tasks within defined guardrails. In this model, systems remain the system of record, humans remain accountable for judgment and compliance, and AI agents act as orchestrators bridging the two.
How do AI agents change insurance claims handling?
In a traditional commercial property claim, a claims professional gathers documents, validates policy information, reviews claim history, communicates with stakeholders, identifies fraud indicators, coordinates inspections, and determines next steps — much of which is information gathering rather than claim judgment. An AI agent can collect supporting documentation, summarize claim history, retrieve policy details, flag inconsistencies, coordinate follow-ups, and route exceptions to specialists. The adjuster remains responsible for the final decision, spending less time gathering information and more time applying expertise. The article describes this as workforce augmentation rather than replacement.
Why is AI governance a gap for insurance companies right now?
Insurance companies already have mature frameworks for managing employees and established controls for managing enterprise systems, but most do not yet have governance models for AI agents. Unresolved questions include who supervises AI agents, how agent performance is measured, how decisions are audited, what escalation mechanisms exist when agents fail, how compliance requirements are enforced, and who owns digital workforce productivity. The article frames these as operating model questions rather than technology questions, and argues that insurers who solve them first will be better positioned to scale AI responsibly.
What will competitive advantage in insurance look like over the next decade?
The insurance industry has historically measured scale through workforce growth, where more policies required more underwriters and more claims required more adjusters. The article argues the next generation of scale will combine human expertise, enterprise systems, and AI agents into a unified operating model that delivers greater speed, consistency, and customer experience without proportionally increasing headcount. Technology alone will not create this advantage — operating model innovation will.
The opinions expressed within this article are the personal opinions of the author. The facts and opinions appearing in the article do not reflect the views of IIA, and IIA does not assume any responsibility or liability for the same.
