Category: Tech

As insurers move from digital transformation into the AI era, many are discovering technology alone cannot transform organizations designed for a different era.

The insurance industry is asking increasingly urgent questions about artificial intelligence and the next era of transformation. Years of observing large-scale operational and transformation efforts in insurance reveal a clear pattern: the conversation often begins in the wrong place.

Across boardrooms, the discussion frequently starts with technology. Platforms, data architecture, automation tools, and AI capabilities dominate the agenda.

In some organizations, the dialogue expands to include digital strategy, focusing on customer interfaces, distribution channels, and the digitization of workflows.

Both conversations matter. But they are not the same thing.

What often receives far less attention is something deeper — the structural design of the enterprise itself. The organization’s system of decisions, authority, and workflows ultimately determines whether or not transformation produces real enterprise value.

AI changes the system, not just the tools

In many insurance companies today, the real constraint is not technological capability. Nor is it a lack of experimentation with artificial intelligence, investment in modern platforms, or even awareness of digital opportunity. The deeper challenge begins elsewhere. It begins with operating model clarity and the executive discipline required to redesign it.

Across industries, fewer than 10% of senior executives report their organizations have successfully scaled artificial intelligence across the enterprise. This ambition is widespread, but the organizational systems required to support it are still emerging. To understand why, we need to step back and look at how transformation in insurance has evolved.

Over the past two decades, insurers have moved through successive waves of technological change.

Early automation improved efficiency in transaction-heavy, back-office processes.

The subsequent digital transformation focused on modernizing customer interactions, digitizing operational workflows, and connecting distribution and service channels.

Artificial intelligence represents a fundamentally more structural shift.

Whereas automation improved tasks, and digital transformation modernized interactions, AI begins to influence how decisions themselves are produced across the enterprise.

AI introduces new possibilities for how human expertise and intelligent systems interact across growth functions, underwriting, claims, service, risk management, and financial oversight.

This progression pushes transformation beyond technology adoption toward something deeper; that is, the redesign of enterprise architecture, so that human expertise and intelligent systems work together across the insurance value chain.

In practice, this is where many transformation efforts begin to stall. Not because of technology, but because the underlying system has not been redesigned to support it.

AI’s value: interaction, not isolation

Many insurers are now attempting to enter the AI era while still operating within structures designed for earlier technological cycles.

Historically, insurance organizations were built around functional expertise. Underwriting, claims, service, actuarial, and finance developed as distinct domains, each with deep subject matter knowledge and clearly defined responsibilities.

In the automation and early digital eras, those structures remained largely effective. Technology improved the efficiency of individual functions without fundamentally altering how the enterprise as a whole operated.

However, AI places far greater pressure on the connections between those functions.

AI systems do not simply automate tasks. They interact with decision architectures that span underwriting, claims, service, risk management, and financial oversight. For AI to generate meaningful enterprise value, those decision architectures must be understood in their full enterprise context.

Introducing AI into a workflow is not simply a technical change. Leaders must first clarify the value proposition of the change, the outcomes AI is expected to produce, and how those outcomes affect the broader insurance value chain.

Leaders must understand how AI’s new capabilities interact with upstream and downstream decisions, what data structures are required to support them, and how responsibilities shift across functions.

Equally important are the human implications. Operational leaders, subject matter experts, technology teams, and executive leadership must align on who defines the problem, who validates the solution, and who ultimately owns the results.

Without that alignment, technology can move forward while the organization itself remains uncertain about how the system is meant to evolve.

This raises a more fundamental question for the insurance industry.

It is not merely a question of how insurers deploy artificial intelligence, but who has the mandate to redesign how the enterprise actually works.

The design of that mandate is rarely simple. It reflects the strategy of the organization, the markets it serves, its capital structure, and its appetite for structural change. In some companies, transformation authority sits within business units. In others, it is coordinated centrally through enterprise leadership.

The model will vary. The discipline required to define it does not.

Transformation becomes an executive discipline

Transformation rarely falters because of technology alone. More often, it stalls when organizations underestimate the discipline required to redesign how decisions, incentives, and authority flow through the system.

Decision-making increasingly depends on the integration of data, workflows, and judgment across multiple parts of the value chain. Structures that once optimized specialization can struggle to support the level of coordination now required by AI.

Across the industry, insurers are experimenting with different structural approaches. Some organizations are moving toward vertically oriented business units with greater end-to-end accountability. Others maintain matrix structures that connect product, function, and market leadership. Still others retain enterprise operating models anchored in centralized capabilities.

Each of these models can succeed when designed thoughtfully. What matters most is whether the organization understands how its chosen structure connects expertise, authority, and decision-making across the system.

AI transformation requires leadership capabilities that many organizations have not yet fully developed — including the ability to translate between technology capability, operational reality, and enterprise value creation.

Technology leaders understand what AI systems can do. Operational leaders understand how underwriting, claims, service, and risk management actually function. Enterprise leadership focuses on capital allocation, growth, and competitive positioning.

But true transformation occurs only when those perspectives converge.

That convergence rarely happens automatically. It requires leaders who can see the architecture of the enterprise system and understand how technology, workflows, authority structures, and human judgment interact to produce outcomes.

Successful organizations will treat transformation as an executive discipline. This means clarifying authority, redesigning operating models, and aligning technology, operations, and strategy around measurable value creation.

Ultimately, artificial intelligence alone will not define the next era of insurance transformation.

It will be defined by leaders who have the clarity, accountability, and discipline to redesign how insurance actually works.

Canadian insurtech MyChoice Financial and brokerage Billyard Insurance Group (BIG) have joined forces on an underwriting and pre-risk assessment platform designed to stop soft (or unintentional) fraud and rate evasion from both broker and consumer perspectives.

The PrecisionX platform embeds underwriting intelligence directly into the broker workflow, identifying underwriting deficiencies and mispriced risks earlier in the process, BIG explains in a press release. This helps manage errors before they become costly exceptions for carrier underwriting teams or downstream claims activity.

Currently, most broker management systems allow brokers to submit applications with errors, missing documents, or eligibility violations, relying on the carrier to catch them downstream, Aren Mirzaian, MyChoice CEO, tells Canadian Underwriter. “PrecisionX effectively places a senior underwriter over the shoulder of every single broker, 24/7.”

For example, the system instantly identifies and flags the following areas of premium leakage that brokers are expected to manually check (along with 70+ other validations):

• Missing claims and convictions: It cross-references the quote against MVRs (motor vehicle records) and DASH (driver and auto search history) to find undisclosed history
• Date first licensed mismatches: It specifically catches out-of-country experience that doesn’t align with domestic equivalents
• Garaging location: PrecisionX identifies address discrepancies between the driver’s licence and the policy address
• Unlisted drivers and vehicles: Flags household members or vehicles from prior policies that were omitted. This is critical for preventing rate evasion, where high-risk drivers (like a teen or spouse with convictions) are intentionally hidden to artificially suppress premiums, Mirzaian says.

“There is currently nothing in the market just quite like PrecisionX,” he says. “To our knowledge, no other brokerage in Canada is pre-underwriting at this level of technical enforcement.”

Core underwriting objectives

The artificial intelligence-based platform is built around three core underwriting objectives:

• Reducing loss-ratio volatility through improved risk assessment prior to bind
• Improving underwriting accuracy by standardizing and validating data inputs in real time during underwriting and binding
• Increasing operational efficiency by minimizing manual reviews, rework and exception handling.

“The goal was to improve how risk is assessed before it ever reaches binding,” BIG CEO Stephen Billyard says in the release. “If you can standardize, validate, and enrich submissions upstream, you reduce friction across the entire value chain.

“What we’ve seen so far is a measurable improvement in speed, efficiency and accuracy, with early indicators pointing to stronger risk assessment and reduced downstream underwriting friction.”

MyChoice and BIG have been working in partnership on the project over the past two years. The technology has resulted in material loss-ratio improvements for carrier partners by identifying risks earlier in the process, Mirzaian reports. The platform has been in the market for more than a year now.

MyChoice takes over the printing of the insurance application, as well as the billing and signing process. “If the risk doesn’t fit the carrier’s appetite or the data doesn’t validate, the application simply cannot be generated,” Mirzaian says.

If there’s an issue, the system “forces compliance,” he adds. It triggers a ‘hard stop’ when specific proof is required, such as a winter tire invoice, letter of experience, or driver’s licence history report.

PrecisionX reads and validates the proof upon upload and references it against the discrepancy. While a human might miss a small date discrepancy or hidden claim, the system catches it instantly.

“Industry observers note that this type of pre-emptive underwriting support is becoming increasingly critical as insurers tighten guidelines and place greater emphasis on submission quality, consistency, and predictability from distribution partners,” BIG says in the release. “That shift has increasingly drawn carrier attention upstream, toward how risk is prepared and validated before it enters core underwriting systems.”

Mirzaian says the hope is this type of technology will lead to better results not just for insurers, but consumers as well through more transparency and less bait and switch, for example.

MyChoice also recently partnered with Guidewire through its Insurtech Vanguards program and will be demonstrating PrecisionX capabilities at the Guidewire Marketplace Summit in Toronto on Mar. 5.