Insurers are facing three simultaneous pressures:
Do more with less: Improve operational efficiency and reduce costs.
Grow faster: Hit growth targets without proportional headcount increases.
Compete for talent: Attract employees who want to work in AI-forward environments.
GenAI is uniquely suited to these challenges because it can:
Automate routine knowledge work (summaries, emails, reporting).
Help teams analyze complex documents and data faster.
Support agents, underwriters, and service teams with AI “copilots” and workflow assistants.
But simply handing people AI tools is not enough.
Many insurers have licensed leading GenAI productivity suites and copilots. A typical rollout:
Tools are provisioned to hundreds or thousands of employees.
Teams receive basic onboarding, newsletters, and office hours.
Adoption centers on low-value tasks like email rewrites or meeting notes.
This is the tool access trap: You have AI tools, but you don’t have AI capability.
Employees often:
Don’t know which use cases are allowed or high-value.
Worry about making mistakes or violating compliance policies.
Lack a clear sense of how AI will be evaluated in performance reviews.
Result: AI remains a side experiment, not a core part of how work gets done.
Two large life insurers chose a different strategy: treat GenAI as a workforce capability problem, not just a technology problem.
They focused on:
In one pilot cohort of just over 50 people, the results included:
100% program completion
High live-session participation
Measurable jumps in AI knowledge and confidence
A portfolio of working GenAI prototypes that addressed real business problems
Here are a few examples of solutions created by non-technical employees:
Problem:
New sales hires needed more personalized practice and feedback. Role-plays were time-consuming for managers.
Solution: An AI agent that:
Simulates realistic customer conversations.
Provides in-the-moment coaching and suggestions.
Tracks performance over time.
Impact:
Faster ramp-up for new hires and thousands of hours saved in manual role-plays.
Problem:
IT teams were losing more than a thousand hours each month searching across systems to find configuration details and ownership information.
Solution: A GenAI assistant that:
Connects to internal knowledge repositories and tickets.
Answers natural language questions like, “Who owns this system?” or “Where is this config documented?”
Surfaces relevant information instantly.
Impact:
Problem:
Teams were manually extracting key fields from policy and product PDFs and reformatting them before analysis.
Solution: A GenAI workflow that:
Reads complex documents and pulls required fields.
Structures the data for spreadsheets or BI tools automatically.
Reduces human error in extraction and formatting.
Impact:
Most organizations struggle with four human barriers:
The blank-page problem
“Where should I even start with AI?”
Fear and uncertainty
“Will this replace my job, or will I get in trouble if I use it wrong?”
Siloed experimentation
“I built something, but no one else sees or uses it.”
Unclear success metrics
“How will we know if our AI usage is ‘good’ or worth scaling?”
The successful insurers tackled these by:
Providing guided use cases tied to everyday workflows.
Making it clear from leadership that AI is there to augment, not replace, people.
Creating branded academies and shared project repositories so wins were visible and reusable.
Framing projects as innovation challenges tied directly to strategic goals (e.g., efficiency targets, customer experience metrics).
Across sectors, AI value in knowledge work tends to emerge at three levels:
Drafting emails, summarizing documents, generating first drafts of presentations.
Automating team processes like reporting, escalations, quality checks, and information retrieval.
Rethinking end-to-end journeys (e.g., underwriting, claims, policy servicing) around AI from the ground up.
Many insurers assume Levels 2 and 3 require large IT builds. The pilots showed that:
Business users can build Level 1 and Level 2 solutions with little or no code.
IT can then focus on high-impact Level 3 transformations where deeper integration and governance are needed.
This division of labor accelerates time-to-value and reduces bottlenecks.
The real advantage comes from how quickly and broadly an insurer can turn AI tools into AI capability.
Leaders tend to:
Invest in people first
Make GenAI literacy a standard skill, not an optional extra.
Empower the front line
Encourage employees closest to the work to identify and prototype use cases.
Move with urgency
Recognize that capability gaps compound over time. The difference between early adopters and late movers widens every quarter.
This mirrors the early internet era: nearly every retailer put up a website, but only a few reimagined their business models. Today, nearly every insurer has GenAI licenses. Only a minority are redesigning work around them.
Treat GenAI as a strategic priority, not just a tech project.
Tie AI initiatives to clear business goals: growth, expense ratio, loss ratio, NPS.
Ask: Where are we still paying people to do tasks that AI can augment or automate?
Pair tech rollout with systematic workforce enablement.
Build architectures that allow safe experimentation by business users, not just central teams.
Measure success by the number and impact of AI-powered workflows created across the business.
Position GenAI literacy as the new digital literacy.
Use AI training as an asset in your talent brand—especially for early-career hires.
Partner with business leaders to redesign roles so reclaimed time turns into higher-value work, not fear.
Yes—if they act now.
Insurers have powerful advantages:
Deep domain expertise.
Rich data and complex workflows that benefit massively from AI.
Large knowledge-worker populations, where small efficiency gains scale quickly.
By investing in GenAI enablement for their people, insurers can:
Unlock tens of thousands of hours in staff capacity.
Launch new AI-enabled services without waiting on large IT programs.
Shift culture from cautious observers to confident innovators.
The core question is no longer whether insurers should build GenAI capability.
It’s how quickly they can do it—before their competitors do.
A: No. Most insurers can achieve major gains by applying existing, secure GenAI tools to their workflows and enabling their people to use them effectively.
A: Start with a structured enablement program focused on real use cases in sales, servicing, operations, and IT—then scale successful pilots.
A: Define clear guardrails, approved use cases, and data policies up front. Train people on what is and isn’t allowed and provide safe environments for experimentation.
A: Track saved hours, reduced cycle times, error reductions, and revenue impacts from AI-enabled workflows—not just “number of users” or logins.
Correlation One designs and delivers GenAI enablement programs for large enterprises across insurance, financial services, consumer goods, and other sectors.
Our academies have helped tens of thousands of professionals in more than 20 countries build practical AI capabilities, develop real-world solutions, and drive measurable business impact.