Insurance CEOs are bullish on growth-and on AI that actually ships
New data from a global KPMG insurance CEO survey signals a busy 2026: strong growth expectations, bigger tech bets, and plenty of deal activity. The catch is clear-AI ambitions are outpacing workforce readiness.
If you lead an insurance team, this isn't theoretical. It's hiring plans, budgets, org design, and how fast you can put AI into production without breaking trust or process.
Quick takeaways
- Confidence is high: most CEOs expect industry and company growth in 2026.
- Digitization is the top investment priority for nearly a quarter of carriers.
- Workforce readiness is the main barrier to AI (77%), yet 86% plan to embed AI agents into teams.
What the KPMG survey says
The survey captured responses from 110 insurance CEOs across life, auto, home, P&C, health, reinsurance and brokerage-each with $500M+ in annual revenue across 11 markets. More than 70% reported confidence in industry and company growth.
Digitization, including customer service modernization, led investment priorities for 24% of respondents. Appetite for M&A is also elevated, with 90% signaling moderate to high interest in deals. Source: KPMG Insurance.
The constraint isn't tools-it's people
Despite strong intent, 77% of executives flagged workforce readiness and upskilling as the top blocker to AI. Leaders want the benefits, but struggle to find or grow the skills to deliver them at scale.
This is the practical bottleneck: model access is easy, operational change is hard. You'll need role clarity, training paths, and governance baked into daily work-before pilots sprawl.
AI agents move from experiment to headcount
Here's the shift that matters: 86% plan to embed AI agents inside teams. That pushes insurers away from a top-down staffing model to an "hourglass" pattern-AI handling volume work from the bottom up, with experts focusing on judgment calls and exceptions.
Expect three new role types to show up on org charts:
- Agent bosses: Build, deploy, and govern AI agents (policies, prompts, guardrails, metrics).
- Agent evaluators: Operate, test, and QA agents in production (accuracy, bias, drift, audit trails).
- "Superhumans": Frontline pros who work with agents as teammates to improve speed and quality.
What to do in the next 90 days
- Map the work: Inventory processes by volume, risk, and decision type. Flag high-volume, low-risk tasks for agent pilots (intake, triage, document classification, first-notice summaries, routing).
- Define job architecture: Write role profiles for agent boss, evaluator, and superhuman. Attach competencies, KPIs, and escalation rules.
- Skill up fast: Stand up short, role-based training for prompts, evaluation, and human-in-the-loop review. If you need a head start, see AI courses by job.
- Launch two pilots: One in underwriting ops and one in claims ops. Measure cycle time, accuracy, leakage, and customer effort. Baseline now, then re-measure at 30/60/90 days.
- Establish guardrails: Access controls, data classification, prompt and output logging, bias and fairness checks, and documented human oversight. Publish a one-page governance standard.
- Communicate the plan: Share the "why," the metrics, and what changes for each role. Make outcomes and accountability visible.
M&A: underwrite the AI upside
With deal appetite high, diligence needs an AI lens. Evaluate target data quality, process standardization, and model governance maturity-these drive post-close synergies.
Plan early for agent harmonization: identity, access, audit, and a shared evaluation framework. Without this, integration delays will erase expected productivity gains.
Where the talent comes from
You won't hire your way out of this alone. The fastest path is upskilling your best operators and analysts into superhuman roles, and re-skilling select engineers into agent boss/evaluator tracks.
Build a lightweight internal academy with clear badges and real use cases. For plug-and-play options, explore an AI automation certification to accelerate readiness.
What this means for insurance leaders
- Budget for skills before software. Training without role clarity wastes money.
- Treat AI agents as teammates, not tools. Give them owners, metrics, and review cycles.
- Make compliance a feature, not an afterthought. Audit trails and human oversight should be default.
- Focus on measurable ops outcomes: cycle time, FNOL-to-decision, straight-through rate, indemnity accuracy, and customer effort.
Bottom line
Growth is there for the taking. The winners will be the carriers that turn AI from a slide into a staffed, governed part of the team-and prove it with cleaner workflows, faster decisions, and tighter risk control.
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