Written by Sabine VanderLinden 

As AI agents absorb execution work across underwriting, claims and service, the question is no longer which jobs disappear. It is which new responsibilities insurance professionals will own. 

The 30-second version

• The agentic insurance enterprise will create ten new career directions, from AI Agent Orchestrator to Autonomous Insurance Operations Strategist, most of them evolving out of today’s underwriting, claims, operations and leadership roles.
• The World Economic Forum expects AI to create 170 million new jobs while displacing 92 million by 2030, and Lightcast finds AI-related postings already pay 28% more. The talent question is not whether roles change. It is who designs them first.
• Every role runs on the ten human capabilities mapped in the previous edition: the skills decide what humans must own, and these roles decide where that ownership lives in the organization.

If intelligent agents increasingly perform traditional insurance activities, what happens to the professionals who used to do them?

The answer isn't simply fewer jobs. It should be the creation of new responsibilities, career pathways, and sources of human value.

Insurance professionals possess something technology companies cannot easily replicate: deep knowledge of risk, customer behavior, regulatory obligations and the consequences of getting decisions wrong.

The opportunity is to combine that institutional knowledge with new capabilities.

In The Human Advantage in the Agentic Enterprise, I mapped the 10 skills we cannot afford to delegate: judgement, decision-making, systems thinking, problem framing, critical thinking, empathy, ethical reasoning, orchestration, communication, and learning agility, with curiosity underneath them all.

This article is the sequel. Skills are potential. Roles turn potential into accountability, a job description, and a career.

Here are 10 emerging roles I believe will become increasingly important in the agentic insurance enterprise. Some will become dedicated positions; others will evolve from existing underwriting, claims, operations, and leadership roles.

What Is the Agentic Insurance Enterprise?

An agentic insurance enterprise is an insurer that runs core workflows through autonomous AI systems executing multi-step tasks, from claims processing and underwriting to customer service interactions, under explicitly designed human oversight. Its core characteristics are autonomous execution, dynamic integration with enterprise systems, continuous learning, and trust by design.

This is a seismic shift, not an upgrade to the existing operating model.

Earlier generative AI assisted individuals. Gen AI drafted the email; a person sent it. Agentic AI systems are goal-oriented: they plan, use tools, process information (structured and unstructured data alike), and act across complex workflows, typically through four layers of perception, reasoning, action, and memory.

The commercial stakes are material. KPMG, analyzing more than 17 million companies, estimates that fully embracing agentic AI could deliver approximately $3 trillion in global productivity gains. McKinsey reports that some organizations are already on track to automate as much as 70% of customer contact.

The gap between leaders and laggards is widening. OpenAI’s enterprise usage data shows frontier firms, the top decile of adopters, generating 8.3 times more output tokens per user than typical firms, up from 2.6 times in January 2026. At those firms, 21% of active users work with advanced capabilities, against 9% elsewhere.

In other words: the agentic insurance enterprise is not a distant scenario. It is being assembled now, insurer by insurer, workflow by workflow.

The question industry leaders should ask is not how AI changes headcount. It is how AI deployment changes the architecture of human responsibility.

From Ten Skills to Ten Roles

In the previous article, I distilled the ten human capabilities into five responsibilities:

FRAME → JUDGE → DECIDE → ORCHESTRATE → CONNECT.

That ladder answers what humans must keep owning as agents absorb execution.

This article answers the harder organizational question: where does that ownership live? Who holds it, with what authority, measured how?

Skills without roles evaporate. A capability that belongs to everyone is accountable to no one. Roles turn the human advantage into organizational design: named owners, decision rights, escalation paths, and career progression.

So... read each role below as a skills bundle with a mandate attached. The “core human skills” line under each one maps directly back to the taxonomy from the previous edition.

1. AI Agent Orchestrator

From managing workflows to coordinating intelligent systems.

This new role oversees networks of AI agents operating across underwriting, claims, distribution, and customer service, reflecting a new frontier in artificial intelligence beyond earlier generative AI assistance. AI Agent Orchestrators ensure that agentic systems collaborate effectively across complex workflows, use the right tools for complex tasks, respect delegated authority, and escalate exceptions appropriately.

Think of the role as air traffic control for digital labor. The orchestrator does not fly the planes. They decide which agents operate in which corridors, at what altitude of autonomy, and what happens when two agents conflict.

Where it comes from: operations executives and transformation leads who already coordinate people, platforms, and processes. The raw material is the same. The medium changes.

Core human skills: systems thinking, orchestration, and decision-making.

2. Algorithmic Underwriting Strategist

From assessing individual risks to designing intelligent risk decisions.

This role combines underwriting expertise with AI-driven insights to coordinate autonomous AI agents, establish risk appetite, challenge model recommendations, and determine when human judgement should override automated decisions.

Underwriting leaders in this role provide business context to the reasoning layer and decide when to override goal-oriented recommendations. They rely on accurate context to assess the logic behind outputs, especially when systems use tools to complete multi-step tasks across functions. This orchestration can span multiple AI models and other AI agents, with machine learning playing a critical role in how decisions are built and refined.

The strategist’s value is not processing more submissions. It is designing the decision architecture: which risks the machine prices alone, which it refers, and what evidence would trigger a human review.

Where it comes from: experienced underwriters and portfolio managers. Deep class-of-business knowledge becomes the calibration layer for the machine.

Core human skills: judgement, critical thinking, and ethical reasoning.

3. Claims Resolution Architect

From processing claims to designing fair outcomes.

This role designs claims journeys where AI handles routine assessments and data entry while humans intervene in complex, disputed, sensitive, or high-impact cases. In workflow design, the principle is simple: automate repetitive tasks, but keep a human in the loop for exceptions, escalations, and sensitive approvals.

The architect challenges outputs produced by underlying AI models and machine learning systems, and applies human oversight where decisions carry material consequences. A storm-damaged roof is a workflow. A family rebuilding after a fire is a relationship.

The architect designs journeys that know the difference.

Balancing operational efficiency with fairness, empathy, and customer trust is the whole job, not a constraint on it.

Where it comes from: claims leaders and customer experience heads who have lived through the cost of getting a sensitive case wrong.

Core human skills: empathy, judgement and problem framing.

4. AI Decision Rights Architect

From defining approval hierarchies to governing machine authority.

The role establishes which decisions agents can make independently, which require human authorization, and which must remain reversible. This role defines accountability, escalation, and intervention mechanisms.

McKinsey observes that some organizations are on the way to automating as much as 70% of customer contact. Someone must decide where, within that 70%, human oversight or a human-in-the-loop remains mandatory for disputed or sensitive cases. That someone is... this role.

It balances autonomy and control, and helps prevent unauthorized actions when agents handle routine assessments and repetitive tasks, such as data entry, in the claims journey. Authority, accountability, and reversibility: the three questions this architect answers before any agent goes live.

Where it comes from: chief risk officers, delegated-authority specialists and company secretaries. Insurance has governed delegated authority for decades; binding authorities and MGA oversight are the precedent. The grammar is familiar. The counterparty is new.

Core human skills: decision-making, ethical reasoning and systems thinking.

5. Customer Trust and Experience Strategist

From managing customer interactions to protecting relationships.

Ensures increasingly automated insurance experiences remain transparent, understandable, and responsive to individual circumstances. Finds the right balance between agent autonomy and preventing unauthorized actions, especially in real-world customer journeys where speed must not outweigh trust or business impact.

Responsible AI also requires clear accountability and mechanisms for intervention when escalation is needed. Governance protocols must align AI actions with corporate policy and regulatory compliance, and this strategist advocates for the customer within those protocols.

The test they apply is unforgiving: would this automated journey survive being explained, step by step, to the customer it just processed?

Where it comes from: customer experience and conduct leaders, and distribution heads who understand that trust compounds slower than efficiency and collapses faster.

Core human skills: empathy, communication, and sense-making.

6. Agentic Risk and Governance Officer

From managing operational controls to governing autonomous risk.

This role oversees risks autonomous systems create in real-world insurance interactions, including model failures, inappropriate decisions, data misuse, fraud exposure, and agent-to-agent interactions. Establishes monitoring, auditability, and intervention controls across the perception layer, reasoning layer, and connected enterprise systems to address emerging threats.

The threat surface is genuinely new. AI agents can introduce cybersecurity vulnerabilities that traditional control frameworks never contemplated: prompt injection attacks, adversarial data injection, memory tampering that quietly distorts an agent’s decision-making over time, and unauthorized access that escalates privileges inside AI systems.

The same technology also defends. In cybersecurity operations, AI agents already triage phishing alerts autonomously, showing how the discipline works both ways. Synthetic datasets can be used to rigorously test agent behavior before deployment into enterprise systems.

In enterprise settings, this officer also judges where automation improves outcomes and where human connection should remain primary. Responsible AI adoption is their operating standard, not their slogan.

Where it comes from: compliance, audit, and operational risk professionals. The control mindset transfers; the control objects change.

Core human skills: critical thinking, ethical reasoning, and accountability.

7. Human-AI Workforce Transformation Lead

From managing headcount to redesigning human contribution.

Redesigns jobs, skills, career pathways, and performance measures as employees increasingly collaborate with agentic AI systems in enterprise settings, giving business leaders a clearer way to augment the human workforce and elevate human talent.

This lead ensures productivity gains translate into workforce capability rather than organizational dependency. The failure mode is subtle: an insurer that automates its entry-level work without redesigning development pathways is quietly dismantling the apprenticeship system that produces its future experts.

Measurement changes too. In our work at Alchemy Crew, we frame this through the Human-Agent Ratio: how much human attention each workflow genuinely requires, and where that attention creates the most value.

Where it comes from: HR and people leaders with operating credibility, and transformation executives who treat workforce design as business strategy rather than a staffing exercise.

Core human skills: empathy, learning agility, and communication.

8. Agentic Insurance Product Innovator

From developing insurance products to designing adaptive protection.

Creates insurance propositions built on continuous risk intelligence, embedded distribution, and agent-enabled services. Reframes the insurance business around prevention, resilience and customer outcomes rather than policies alone.

Agentic systems allow insurers to shift from detecting issues to preventing them. A policy becomes a protection service: sensing risk, nudging behavior, arranging mitigation and settling losses when prevention falls short.

Business leaders and enterprise leaders should treat this as a business strategy, digital transformation and AI adoption challenge, not just a product refresh. The innovator’s craft is problem framing: asking what protection the customer actually needs before asking what the technology can automate.

Where it comes from: product managers, pricing actuaries and proposition designers with the curiosity to work beyond the policy wording.

Core human skills: problem framing, curiosity and systems thinking.

9. AI Ecosystem and Partnership Orchestrator

From managing vendors to building intelligent business ecosystems.

Coordinates relationships between insurers, technology ventures, brokers, data providers and AI platforms in the next phase of AI innovation beyond traditional digital transformation. Establishes commercial arrangements, interoperability expectations and accountability across organizational boundaries.

Data readiness shapes how partners share access and how systems process information, so this orchestrator negotiates data rights as carefully as commercial terms. They also help partners decide where large language models fit in the stack and how human oversight governs shared workflows.

Some of those workflows will become genuinely autonomous. In embedded or service-led insurance experiences, AI agents may autonomously negotiate pricing based on historical data, which makes cross-boundary accountability a commercial question, not a technical afterthought.

Where it comes from: corporate development, venture clienting and strategic partnership leaders. The venture client muscle, sourcing and validating external innovation against real business problems, becomes the backbone of ecosystem design.

Core human skills: negotiation, orchestration and strategic judgement.

10. Autonomous Insurance Operations Strategist

From optimizing processes to reinventing the operating model.

Redesigns end-to-end insurance operations around human-agent collaboration. Determines where autonomy creates value, where human expertise remains essential, and how performance should be measured as operations increasingly depend on large language models and connected AI capabilities, not just vendors and platforms.

The sequencing discipline matters: redesign the process first, then automate it. Many organizations automate the existing workflow and wonder why the economics barely move. Scaling AI across an operation is an operating-model decision before it is a technology decision.

Like software development and software engineering, where agents already handle delegated work such as drafting code, testing routines and debugging, early adoption works best where outputs are testable and linked to clear business impact. Data readiness shapes how well agents process information across organizational boundaries in enterprise settings.

Where it comes from: COOs, operational excellence leaders, and the operations strategists who have already lived through one or two waves of transformation.

Core human skills: systems thinking, decision-making and learning agility.

The Market Signals Behind These Roles

These roles are not speculation dressed as foresight. The labor market is already moving.

The World Economic Forum’s Future of Jobs research expects AI and related technologies to create 170 million new jobs this decade while displacing 92 million by 2030: a net gain, but a brutal reshuffle for anyone standing still.

Pay signals point the same way. Lightcast’s analysis of 1.3 billion job postings found that roles requiring AI skills offer 28% higher salaries, and the fastest demand growth now sits outside the technology sector.

New titles are surfacing across the industry’s job boards and talent taxonomies: AI Agent Architect, Insurance AI Decision Scientist, AI Governance Lead, even Digital Anthropologists studying how humans and agents actually work together, and AI Artists shaping the creative layer of customer communication. Titles will vary. The underlying mandates look remarkably like the ten above.

A caution on the numbers, because rigour is part of the human advantage: some widely circulated figures, such as claims that agentic AI improves productivity in sales by 45% or significantly reduces procurement cycle time, come from vendor-side studies rather than audited results. Treat them as directional signals of where automation pressure is building, not as business cases.

The verified signals are enough. The $3 trillion productivity estimate, the 8.3 times frontier-firm output gap and the 28% salary premium all say the same thing: the agentic shift is repricing human work, and insurance is squarely in scope.

The Real Workforce Transformation

These are not necessarily ten entirely new job titles appearing overnight; they reflect ten directions in which insurance careers can evolve as enterprises redesign how they operate over the next decade.

Business leaders in insurance will increasingly depend on these roles to identify where agentic approaches create measurable impact, even as many organizations still struggle to move beyond pilots.

The career mapping is concrete:

• An experienced underwriter could become an algorithmic underwriting strategist.
• A claims leader could become a claims resolution architect.
• A compliance professional could transition into agentic risk governance.
• An operations executive could become an orchestrator of intelligent workflows.

In software engineering, agents already handle delegated work such as drafting code and testing routines, and that same lesson now applies to insurance operations, where the human workforce shifts toward oversight, exception handling and judgment.

The opportunity is to elevate insurance expertise rather than discard it.

But this transition will not happen automatically.

Insurers must invest in reskilling, redesign career pathways and create meaningful opportunities for professionals to exercise higher-order judgement.

Otherwise, they risk automating entry-level activities while simultaneously weakening the talent pipeline that produces tomorrow’s experienced decision-makers.

That would be a dangerous trade-off for an industry built on judgement and trust.

The insurance professional of the future will be valued less for the number of transactions they process and more for the quality of the decisions, systems and customer outcomes they influence.

The Playbook: Three Moves for the Next Quarter

Insurance leaders do not need a five-year workforce masterplan to start. They need three moves this quarter.

1. Map your ten. Take the roles above and ask which mandates already exist in your organization, which are scattered across committees, and which belong to no one. The gaps are your exposure. An agentic system with no decision rights owner is an incident report waiting for a date.
2. Name your first two owners. Appoint the AI Decision Rights Architect mandate before your next agent goes live, and the Workforce Transformation Lead mandate before your next restructure. One governs machine authority; the other protects the talent pipeline. Both are cheaper as design choices than as remediation.
3. Rebuild one career pathway, end to end. Pick underwriting or claims. Define what the junior role learns when agents handle the data entry, what the mid-career professional governs, and what the strategist owns. Publish it internally. Nothing signals that the agentic shift creates careers, rather than ending them, like a pathway people can actually see.

Design the roles, develop the skills, and measure what changes. That sequence is the transformation.

Key Takeaways

• The agentic insurance enterprise creates roles, not just redundancies: ten career directions, each anchored in capabilities machines cannot own.
• Every role is a skills bundle with a mandate: the ten human skills from the previous edition supply the capabilities; these roles supply the authority, accountability and career path.
• The market is already repricing human work: 170 million new jobs projected by 2030 (World Economic Forum), a 28% salary premium on AI-skilled postings (Lightcast), and an 8.3 times output gap between frontier firms and the rest (OpenAI enterprise data).
• Sequencing beats speed: redesign the process, define decision rights and governance, then deploy the agents.
• The talent pipeline is the hidden risk: automate entry-level work without redesigning development pathways and you starve tomorrow’s judgement.

Frequently Asked Questions

Are these ten roles entirely new jobs?

Mostly no. They are evolutions of existing insurance careers. An underwriter becomes an algorithmic underwriting strategist; a claims leader becomes a claims resolution architect. A few, such as the AI Decision Rights Architect and the Agentic Risk and Governance Officer, are likely to become dedicated positions because regulators will expect named accountability for autonomous decisions.

Which role should an insurer create first?

Start where autonomy meets authority. The AI Decision Rights Architect (or an equivalent mandate inside risk) should exist before agents make material decisions, because decision rights, escalation and reversibility must be designed before deployment, not retrofitted after an incident.

What skills do these roles require?

All ten roles draw on the same taxonomy of human capabilities: judgement, decision-making, systems thinking, problem framing, critical thinking, empathy, ethical reasoning, orchestration, communication and learning agility, with curiosity underneath. The mix varies by role; the taxonomy does not.

How quickly will the agentic insurance enterprise arrive?

Faster than comfortable, unevenly distributed. McKinsey reports organizations on the way to automating as much as 70 percent of customer contact, and OpenAI’s enterprise data shows the frontier-firm output gap widening from 2.6 times to 8.3 times in roughly six months. Adoption will be workflow by workflow, with claims triage, service interactions and submission preparation moving first.

Will AI reduce insurance jobs overall?

The World Economic Forum projects 170 million jobs created against 92 million displaced by 2030 across the economy. For insurance specifically, the realistic picture is recomposition: fewer transaction-processing roles, more design, governance and orchestration roles, and a widening premium for professionals who combine domain depth with AI fluency.

The Question That Decides Who Leads

The defining question for insurance leaders is therefore not simply:

Which jobs will AI replace?

It is:

Which new responsibilities will we empower our people to own?

Because the greatest opportunity in the agentic enterprise is not to remove humans from insurance.

It is to make human expertise more consequential than ever.

In the previous edition, I argued that the human advantage lives in ten capabilities we cannot afford to delegate. This edition names where those capabilities go to work. The organizations that win will do both: build the skills and design the roles, deliberately, in that order.

If your organization deployed its first autonomous agents tomorrow, could you name the person who owns their decision rights?

If the answer is a job title that does not exist yet, that is your starting point.

45 minutes. One conversation. A clear view of which of these ten roles your operating model needs first.