The gap between tech spectacle and operational reality is closing — but only for insurers willing to treat AI as infrastructure rather than experimentation.

Every January, practically every industry from around the globe descends on Las Vegas to showcase the future. And then the future is quickly forgotten as attendees and the attention they command return to the present.

It may seem a little late to share an assessment of CES 2026 and the implications for the insurance industry. However, stepping back from the spectacle allows for a clearer view of what truly matters: how these innovations influence AI adoption in insurance and whether insurers are prepared to move from experimentation to scalable operational impact.

And at CES 2026, there was so much to take in, with over 4,100 exhibitors presenting everything from AI-powered autonomous vehicles and industrial robots to solid-state batteries addressing lithium-ion fire risks. Despite the backdrop of trade tensions and economic uncertainty, the show demonstrated the usual optimism that propels tech innovation ahead at remarkable speed and scale.

For insurance leaders trying to separate genuine operational opportunity from tech spectacle, the challenge is knowing what to pay attention to. It’s easy to be captivated by headlines about butler robots — LG’s chore robot struggling to close an oven door or reach into a laundry basket serves as a humorous reminder that even cutting-edge tech has off-days. But alongside the flashy fails, there were some new technologies with genuinely profound implications for insurers seeking to improve crucial day-to-day processes, like risk assessment, pricing policies, and operations management.

The question I’m hoping to answer for insurers is “how can we translate these latest prototypes into practical, scalable solutions rather than letting them languish in our memories as mere experimental pilots?”

The Filtering Process: Spectacle Versus Substance

How do we know what’s worthy of our attention? Not every innovation at CES warrants immediate recognition from insurance executives. The key is to begin with a target-state operating model in our mind’s eye, and then to view technological advancements through that lens. In every instance, consider what will have a direct impact on your business in the near term and address those changes first.

To filter out technology as spectacle, I’d suggest asking a few questions: Can this technology significantly change loss costs in the next one to two years? Is there a realistic path to data access? Can it help carriers improve the customer experience? Can it help reach new markets? Can it enhance operations?

Technologies that pass this filter deserve serious consideration. AI-powered 4G dashcams, for instance, have the potential to revolutionize vehicle insurance by providing real-time data and insights that improve risk assessment and streamline claims processing. CP6’s ACAT data-collection technologies, which certify autonomous vehicle status during accidents, not only prioritize safety but also help drivers negotiate lower premiums while aiding insurers in claims triaging.

Privacy-conscious innovations like Aqara’s FP400 smart spatial thermal sensor — which detects room occupancy and tracks movement patterns like fall detection without recording video — represent the kind of adoption-friendly technology that could actually gain traction with policyholders hesitant about 24/7 surveillance.

These aren’t moonshots. They’re technologies addressing real barriers to adoption while creating clear pathways to better risk data.

The State of AI Adoption in Insurance: From Pilot to Infrastructure

One critical insight I took away from CES 2026 concerned the deployment gap between what’s technically possible and what organizations actually implement at scale.

A ReSource Pro survey of 59 insurance professionals shows that 60% of insurers today are implementing or have deployed AI tools. A clear signal that AI adoption in insurance is accelerating, even if many organizations remain stuck in pilot mode.

What does treating “AI as infrastructure” actually look like? It means embedding these capabilities into existing core insurance systems rather than bolting them on as standalone experiments. This requires insurers to first map operational guidelines and establish robust data and technology management before implementing tools that require advanced computing and discipline.

Infrastructure thinking manifests in two ways. First, there’s a wide recognition that everything from a technology standpoint must consider AI’s current and potential use cases and where the pitfalls may crop up. The second way infrastructure issues present themselves is through the formalization of organizations’ approach to AI education. This includes formal AI governance frameworks, comprehensive inventories and documentation, rigorous testing and review processes, third-party risk management protocols, and continuous monitoring systems.

The insurers keeping infrastructure front of mind share common characteristics: they secure commitment and clear vision from leadership, establish explicit accountability for AI projects, implement strong governance frameworks to guide decision-making, and ensure cross-functional collaboration. They also build robust and scalable technology infrastructure from the start rather than trying to retrofit pilots into production systems.

Perhaps most importantly, they demonstrate a willingness to fast-fail unsuccessful projects while extracting learnings to inform the next iteration. They establish clear evaluation criteria and checkpoints rather than letting pilots drift indefinitely without resolution.

The Data Quality Imperative

A necessary first step is ensuring data is accurate, current, complete, and consistent. Insurance activities like underwriting, pricing, reserving, claims, and compliance all depend on these values. Failure to invest in governance and data quality causes mispriced portfolios, failed audits, and expensive remediation.

The problem intensifies as real-time data becomes more prevalent. Managing continuous data streams from IoT devices, connected vehicles, and smart home systems is inherently complex and requires robust systems and processes. As real-time data grows increasingly important for competitive advantage, insurers who haven’t prioritized data governance will find themselves unable to capitalize on the opportunity.

The theme of real-time data enabling flexible pricing and proactive risk management has been discussed for years, yet adoption remains uneven. The constraints are technical, organizational, and regulatory.

But the biggest obstacle is that legacy architectures make it difficult to ingest and act on continuous IoT data streams at scale. Many insurers lack operating models that reward dynamic pricing, so device data remains underused even when available. Regulatory frameworks assume stable rating variables and annual cycles, complicating granular, dynamic approaches.

Customer adoption is another barrier. The value proposition remains unclear to many policyholders, particularly when bundled with existing agent relationships. Until insurers can articulate why sharing real-time data benefits customers in tangible ways, adoption will lag regardless of technical capability.

AI Governance Gaps at Scale

As insurers expand AI deployments, operational control gaps are becoming apparent. Our conversations with executives and industry professionals have revealed that a large number of organizations have no clear or formal structure for AI planning and management.

When controls are weak, insurers are faced with the risk of inconsistent decisions across regions and products, regulatory sanctions for discriminatory outcomes, financial mispricing, and reputational damage. Organizations often lack visibility into how employees use AI tools, creating data privacy violations and unauthorized deployments. The fast pace of AI evolution makes oversight difficult, with many insurers unable to track the AI models their own vendors are utilizing.

This explains the growing interest in AI administration and governance tooling showcased at CES. Platforms like Avon AI for automated testing, monitoring, and governing of AI systems, and Core Trust Link for multi-agent orchestration with internal control authorization management, document security, and anomaly detection, represent the kind of operational controls insurers need as AI scales across their organizations.

Though not glamorous technologies, these platforms, at first glance, look like essential infrastructure for responsible AI deployment.

Underestimated Risk Categories

Beyond the highly recognized EV-related exposures from lithium-ion battery risks, several risk categories deserve more attention as consumer electronics and robotics become embedded in daily life.

Smart home devices and home assistants create new vulnerabilities around data privacy, system failures, and integration risks. As homes become more connected, the potential for cascading failures increases.

Workplace safety faces new challenges as robotics and drones become more prevalent in commercial settings. The more ubiquitous robots become, the more accidents could occur related to their operation in workplaces and homes.

Health-monitoring wearables generate sensitive personal data while potentially influencing medical decisions. The intersection of consumer electronics and healthcare creates novel liability questions around data accuracy, privacy, and the consequences of automated health recommendations.

These are all risks that insurers should be modeling now.

What Actually Needs to Change

The implications of CES 2026 go beyond chasing newness. Savvy industry leaders will see through the bling and past the robo-butlers, noting instead the many adaptable foundation technologies that can be introduced to their systems. Insurance leaders should focus on several key priorities:

• Treat AI and automation as core infrastructure, not one-off initiatives. This means enterprise-wide thinking about AI capabilities rather than departmental experiments. It requires executive commitment, clear accountability, and governance frameworks that can scale.
  Invest in data quality and governance before expanding data volume. The temptation to collect more data is strong, but without the infrastructure to manage it properly, more data creates more problems rather than more insights.
• Reevaluate underwriting and claims models as risk becomes more dynamic. Traditional insurance models built around annual policy cycles increasingly conflict with the continuous data streams from connected devices. Organizations need to begin exploring how to reconcile this tension.
• Design workflows that balance automation with human judgment. The goal isn’t to eliminate human decision-making but to augment it with better data and tools. This requires thoughtful workflow design that clarifies where automation adds value and where human expertise remains essential.
• Align technology strategy with regulatory, customer, and workforce realities. Technology capabilities alone don’t drive success. Insurers must consider adoption barriers, regulatory requirements, and how technology changes jobs rather than assuming technical feasibility equals business viability.

Moving From Potential to Practice

CES demonstrates year after year that tech capabilities far outpace adoption. The potential is enormous, but there’s a significant gap between initial announcements and actual usage at scale.

The insurers that succeed will be those that translate emerging technology into practical, scalable capabilities rather than accumulating a portfolio of experimental pilots. They’ll be the ones who recognize that while the future is bright, it occasionally stumbles—and build their strategies accordingly.

The difference between organizations that make that leap and those stuck in perpetual pilot mode comes down to commitment, governance, and willingness to make hard decisions about what to pursue and what to abandon. It requires viewing technology through the lens of operational impact rather than innovation theater.

As trade tensions and economic uncertainty create headwinds across industries, insurance leaders face a choice: build the infrastructure to capitalize on technological innovation, or watch as more nimble competitors do so instead.

The technologies showcased at CES 2026 are fast becoming operational requirements for insurers who want to remain competitive. The question is whether organizations have the discipline to focus on substance over spectacle and the commitment to do the hard work of turning innovation into infrastructure. Ultimately, the future of AI adoption in insurance will depend less on technological possibility and more on disciplined execution, governance, and operational readiness.