Insurance Part 1: AI Adoption and Business Strategy

02.09.2026

The “AI Adoption” Series: An Overview

This six-part series is designed for C-suite leaders in the insurance, reinsurance, and specialty markets. We are moving past the theoretical “art of the possible” to discuss the practical “art of the profitable.”

Over the next six articles, we will trace the adoption of AI from a boardroom concept to a feedback loop that refines your entire operation:

The Strategic Roadmap (This Article): Defining the business outcomes that AI must serve.
Subordinate Strategies: Aligning IT, HR, and Operations to support the mission.
The Data Foundation: Moving from “Big Data” to usable, hygienic, compliant data.
Analytics & Machine Learning: Predictive modeling to refine risk selection before automating it.
Automation & Efficiency: Practical application of AI to reduce friction and cost.
The Feedback Loop: Using AI to answer difficult questions and refine the original strategy.

The Industry Landscape: Size, Composition, and Pressure

Before discussing strategy, we must ground ourselves in the current reality of the market. The insurance industry is seeing robust top-line growth, but the pressure on margins is intensifying.

Market Size & Growth: The global insurance industry grew by approximately 8.6% in 2024, bringing the total premium pool to EUR 7.0 trillion, according to Allianz’s Global Insurance Report.
Reinsurance Capital: The sector is well-capitalized, with dedicated global reinsurance capital reaching a new high of $769 billion in 2024, up 5.4% from the previous year (Gallagher Re).
Specialty Market: The specialty insurance sector (covering unique and complex risks) was valued at approximately $126 billion in 2024 and is projected to more than double over the next decade (Precedence Research).

Why This Matters Now:

While capital is abundant, profitability is under threat. The combined ratio for US P&C insurers, for example, is forecasted to deteriorate to 98.5% in 2025 due to rising claim costs and “social inflation” (liability payouts growing faster than economic inflation) (Swiss Re).

In this environment, AI is not a luxury for innovation labs; it is a necessary lever to compress the combined ratio and protect underwriting margins.

The Business Strategy: Outcomes First, AI Second

The most common failure mode in AI adoption is treating “AI” as the strategy. AI is not a strategy; it is a capability. The strategy must be defined by the business outcome you are trying to achieve.

For insurance leaders, the strategy generally falls into three practical buckets:

Risk Selection Improvement: reducing the loss ratio by identifying bad risks earlier.
Operational Efficiency: reducing the expense ratio by automating intake and claims processing.
Market Expansion: Entering new specialty niches with better data confidence than competitors.

The Strategic Underpinnings

To execute this effectively, your strategy must rely on five specific pillars.

1. Strategy Includes Governance

Governance cannot be an afterthought or a “compliance tax” added at the end. It must be woven into the strategy. If you are building an algorithmic underwriting model, the governance framework for explaining that model to regulators must be part of the initial build, not a patch applied later.

2. Governance is Finalized Last

While governance is started early, it is finalized late. You cannot fully govern a capability until you know exactly what it is. The strategy must allow for an evolving governance framework that tightens as the specific tools (e.g., a claims triage bot vs. a catastrophic modeling engine) are defined.

3. The Goal is Better Outcomes

Every AI initiative must track back to the P&L. If a machine learning model has 99% accuracy but does not reduce claim processing time or improve loss ratios, it is a strategic failure. The strategy must explicitly state: “We are using AI to reduce claims leakage by X%,” not “We are adopting AI to modernize claims.”

4. KPIs & OKRs Are Essential

You cannot manage what you do not measure. A strategy without metrics is just a wish.

Bad Metric: “Number of AI models deployed.”
Good Metric: “Percentage of straightforward claims settled without human intervention” or “Reduction in submission-to-quote time for specialty lines.”

5. Execution is Essential

The strategy must be doable. It is better to have a simple, executed plan (e.g., automating intake forms) than a complex, visionary plan (e.g., fully autonomous AI underwriting) that never leaves the pilot phase.

The Direction: Where We Are Going

The insurance industry is moving away from “detecting” risk and toward “preventing” it.

Current State: Most insurers use historical data (actuarial tables) to price risk that has already happened.
Future State: Leading insurers are using real-time data (IoT, satellite imagery, telematic feeds) to predict and mitigate risk before a claim occurs.

The Strategic Shift:

The strategy is shifting from financial indemnification to risk partnership. Reinsurers, for example, are no longer just capital providers; they are becoming data providers, helping cedents avoid losses entirely.

Next Step: Defining Your Subordinate Strategies

You now have a business strategy template focused on outcomes (e.g., “Reduce Combined Ratio to 92%”). The next step is to determine how your IT, HR, and Operations teams must change to support that goal.

In Insurance Part 2, we will break down these Subordinate Strategies—specifically, how to align your workforce and technology stack to execute the business mandate.

Salvatore Magnone is a father, veteran, and a co-founder, a repeat offender in the best way in fact, and a long-time collaborator at DOOR3. Sal builds successful, multinational, technology companies and runs obstacle courses. He teaches business and military strategy at the university level and directly to entrepreneurs and military leaders.

https://www.linkedin.com/in/salmagnone/