A new category of risk is quietly emerging as AI systems move from assisting decisions to influencing—and in some cases making them autonomously—across industries. Trades are executed, diagnoses are informed, contracts are shaped, and operational outcomes are determined by systems operating at machine speed.

Each of these actions carries potential liability. Yet most insurance frameworks were designed for a world in which humans—not autonomous systems—were the primary decision-makers.

As AI deployment accelerates, many organizations are beginning to recognize that existing coverage structures may not fully align with this evolving risk landscape. In 2024, we saw real-world examples that highlight the complexity of AI-related liability:

• Air Canada faced legal exposure after its AI chatbot provided inaccurate information to customers, resulting in a court ruling against the airline. (CBC)
• McDonald's paused its AI drive-through pilot after performance challenges during its multi-year test with IBM. (WSJ)
• The FTC took action against DoNotPay, penalizing the company for marketing its "AI lawyer" beyond its capabilities. (FTC)

These situations raise similar questions: when autonomous systems influence outcomes, where does responsibility ultimately sit—and how does insurance respond? Many carriers are already introducing AI-related exclusions while frameworks for underwriting this exposure continue to develop.

This suggests we may be entering a period in which risk is evolving faster than traditional coverage models.

The Mathematics of Exponential Disruption

Market projections illustrate the speed at which autonomous and agentic AI markets are expanding. Research from The Business Research Company estimates that the global autonomous AI and autonomous agents market could grow from approximately $6.97 billion in 2024 to $41.37 billion by 2029. (TBRC)

These systems do not operate with human pacing. Their decision cycles are continuous, parallel, and high-volume, introducing different dynamics into risk exposure. Errors or mispredictions can cascade across thousands of transactions before detection, amplifying potential impacts.

Traditional insurance models were calibrated for:

• Human judgment
• Discrete events
• Slower feedback loops
• Clear causation

Autonomous systems challenge each of these assumptions.

The Architecture of Institutional Blindness

Risk models in insurance historically lean on extensive historical loss experience. Autonomous systems introduce exposures with limited precedent, evolving behavior, and outcomes sensitive to data quality and model feedback loops. This does not reflect lack of awareness from carriers—it reflects the difficulty of underwriting a risk class still taking shape.

Many existing products weren't designed for this environment:

• Professional liability assumes human professionals
• Tech E&O focuses on software defects
• Cyber insurance addresses data compromise, not incorrect but technically successful AI decisions

Traditional policy cycles also assume periodic evaluation, while autonomous systems operate continuously.

This mismatch raises an important industry question:
How should insurance adapt when risk becomes dynamic rather than periodic?

The Embedded Revolution

Parallel to the rise of autonomous systems, insurance distribution itself is shifting. Embedded insurance—coverage delivered within a product, platform, or transaction—has become one of the fastest-growing models in the industry.

According to Fortune Business Insights, the global embedded insurance market was valued at approximately $119.16 billion in 2024 and is projected to reach $802.57 billion by 2032. (FBI)

What matters here is not only market size, but the structural shift:

• Coverage delivered contextually
• Real-time data informing pricing and eligibility
• Reduced friction between risk recognition and protection
• Protection integrated directly into digital experiences

This architecture represents a more adaptive approach to risk delivery—one that may be more compatible with increasingly autonomous systems.

Convergence: Where Autonomous Risk Meets Embedded Protection

If autonomous systems continue to expand and embedded insurance models continue to mature, a convergence point may lie ahead.

Instead of annual, static policies, we could see movement toward:

• Coverage aligned to AI-driven decision units
• Pricing adjusted dynamically to performance history
• Policy triggers embedded within workflows
• Ongoing monitoring rather than retrospective assessment

These are not predictions, but plausible structural evolutions when viewed through the lens of how technology and insurance architecture are both trending.

The underlying components—APIs, real-time analytics, automated underwriting, dynamic pricing—already exist. What remains uncertain is how quickly carriers, platforms, and regulators will converge around such models.

Industry Implications Worth Exploring

Different sectors may experience this evolution in unique ways:

Across industries, the theme remains consistent: as AI systems influence outcomes, risk models will need to evolve alongside them.

Strategic Implications for Insurance Models

If insurance evolves toward more dynamic, usage-based, or transaction-level protection, the economics of risk transfer could change meaningfully.

Potential implications include:

• More granular pricing models
• Increased reliance on real-time performance data
• Revenue structures aligned to activity rather than annual premium
• Greater integration between technology platforms and insurance infrastructure

Whether the industry moves collectively in this direction—or adapts incrementally—remains an open question.

The Strategic Moment

Two trajectories are clearly underway:

• The accelerating deployment of autonomous systems
• The gradual modernization of insurance delivery models

How and when these pathways intersect remains uncertain. But organizations that begin thinking about these questions now are more likely to engage risk proactively than those who wait for loss events to force the conversation.

The surging blind spot is not merely a technical challenge—it is a strategic one.

Leaders may not need perfect answers today. But they will benefit from asking better questions, such as:

• Where does autonomous decision-making already exist in our organization?
• How do we think about accountability when systems act independently?
• How are our carriers and risk partners preparing for emerging exposures?

Companies that engage these questions early may be better positioned to shape the future of risk management, rather than be shaped by it.