AI Insights: February 13, 2026
Your weekly analysis of AI developments in insurance.
ChatGPT Apps Just Changed Insurance Distribution Forever
This week, OpenAI approved the first insurance applications for ChatGPT, marking what may be the most significant shift in insurance distribution since the internet. Spanish digital insurer Tuio and U.S. aggregator Insurify can now offer quotes directly inside ChatGPT conversations, where consumers are already doing their research.
The timing matters. According to Express Legal Funding, 33% of U.S. adults already use ChatGPT for financial advice. Now they can actually buy insurance there, transforming passive research into active transactions.
Why This Matters:
Insurance broker stocks responded immediately. On Monday, the S&P 500 Insurance index dropped 3.9% in its biggest single-day decline since October. Willis Towers Watson fell 12%, its worst session since 2008. Arthur J Gallagher dropped 9.9%, and Aon declined 9.3%.
Wall Street’s message was clear: AI-powered distribution represents a genuine threat to traditional broker economics. Bloomberg Intelligence analyst Matthew Palazola noted the applications “may be a threat to some consulting businesses of insurance brokers,” though he views them more as force multiplier than existential threat.
For carriers and agents, the implications extend beyond stock prices. Consumers uploading commercial offers into ChatGPT can now get independent analysis and advice. Businesses can use AI procurement agents with access to internal data to find coverage. Buyers can perform deep comparisons of policy conditions, exclusions, and customer satisfaction beyond simple price comparison.
According to WaniWani, which powers the infrastructure for these apps, insurers saw a 30% increase in AI-originated traffic in 2025, with this traffic converting at significantly higher rates than traditional search leads. With native apps now live, insurers move from being passively referenced by AI to actively selling through it.
The shift affects every segment. Any insurance product can potentially be quoted, compared, and soon purchased inside an AI conversation. WaniWani announced a dozen additional insurance AI apps are in OpenAI’s approval pipeline and expected live within weeks. The technology isn’t limited to ChatGPT either. Anthropic’s Claude has already adopted similar standards, with Google’s Gemini expected to follow.
Strategic Implications:
The traditional insurance distribution funnel just collapsed. Research, comparison, and purchase now happen in a single conversational interface. Carriers must decide quickly whether to build their own AI apps, partner with aggregators, or risk being excluded from where consumers increasingly make buying decisions.
This represents more than channel disruption. It’s the emergence of agent-to-agent distribution, where AI assistants negotiate coverage on behalf of buyers, comparing offers with sophisticated analysis that exceeds human capability at scale. The question for insurance executives isn’t whether to participate, but how fast they can move before competitors establish dominant positions in this new channel.
Remember: According to our previous analysis in “Wall Street Just Told You What It Thinks Your Agency Is Worth,” markets don’t wait for laggards. Monday’s sell-off suggests Wall Street already sees the future taking shape.
Travelers Scales AI to 20,000+ Users, Cuts Claims Call Centers
During its fourth-quarter 2025 earnings call, Travelers CEO Alan Schnitzer revealed that more than 20,000 employees now use AI tools regularly, positioning the carrier at the forefront of enterprise-scale AI deployment in insurance.
Days earlier, Travelers announced a deal to equip 10,000 engineers and data scientists with AI assistants. The carrier spends over $1.5 billion annually on technology, with a significant portion dedicated to artificial intelligence, leveraging 65 billion clean data points accumulated over decades.
Why This Matters:
Schnitzer described AI-powered automation providing efficiency boosts in claims call centers, making them leaner. But he emphasized benefits extend beyond claims: “Other use cases enhance underwriting decision quality and efficiency and improve the experience for customers, agents, brokers and employees.”
Business Insurance President Greg Toczydlowski detailed gen AI agents recently rolled out to mine internal and external data sources, ensuring appropriate business classifications and better understanding risk characteristics. This accelerates underwriting and improves segmented pricing.
Personal Insurance President Michael Klein said AI makes renewal underwriting “more effective and efficient.” The company leverages AI to analyze aerial imagery for property damage, immediately identifying total losses and advancing payments on wildfire claims before in-person inspection.
Since 2016, Travelers has invested $13 billion in technology while returning $20 billion to shareholders and growing its investment portfolio nearly 50%. The company cut its expense ratio by 300 basis points during this period.
Strategic Implications:
Travelers demonstrates that successful AI implementation isn’t about pilot projects. It’s about systematic deployment across 20,000 users with enterprise-scale infrastructure. Schnitzer’s assertion that Travelers’ size gives it an edge “in an environment where technology and AI will continue to segment the marketplace” sends a clear message: AI advantage compounds with scale.
For carriers, Travelers proves the business case. Massive technology investment ($13 billion) while simultaneously returning capital to shareholders ($20 billion) and improving expense ratios shows AI isn’t just cost, it’s competitive weapon. Smaller carriers face a critical decision about whether they can compete at this scale or need different strategies.
Accenture and Acrisure Show AI’s Human Cost
While carriers celebrate AI efficiency gains, two major industry players revealed the technology’s impact on employment. Accenture laid off more than 11,000 employees as part of an $865 million restructuring tied to AI initiatives. Despite the cuts, Accenture expects overall headcount to increase in AI and digital services areas, requiring remaining employees to complete AI training or face potential termination.
Closer to insurance, broker Acrisure announced 400 accounting job cuts beginning early 2026, with 200 positions in Grand Rapids, Michigan. The company cited “advancements in technology and automation” as the reason, specifically deploying AI technology it acquired through its 2020 purchase of Tulco’s AI insurance business for $400 million.
Why This Matters:
Acrisure’s situation crystallizes AI’s employment paradox. The company acquired AI technology, deployed it to automate accounting functions, and now offers that same platform to clients, 90% of which are small businesses employing 100 or fewer people. CEO Greg Williams told Crain’s Grand Rapids: “If we don’t bring them a full suite of product and service solutions, including technology, I’m not sure how they remain relevant and competitive on a long-term basis.”
An economics professor at Grand Valley State University estimated the layoffs could cost Grand Rapids $20-30 million in economic impact. Yet Williams characterized the changes as a “healthy” reality in an economy that grows with technology, noting “When we reach the end of this tunnel, we’ll be better off as a society.”
The timeline matters. Acrisure notified employees months in advance, providing severance packages and outplacement support. The company maintains about 2,000 Michigan employees and has 100 open positions in West Michigan, suggesting transformation rather than simple reduction.
Strategic Implications:
For insurance executives, these cases force uncomfortable questions about AI’s true cost-benefit analysis. Travelers demonstrates AI creating efficiency while maintaining workforce. Acrisure and Accenture show AI replacing entire job categories, particularly entry-level and mid-skill positions.
The strategic choice isn’t whether to adopt AI, it’s how to manage the transition. Companies implementing AI successfully provide training, create new roles requiring AI-augmented skills, and manage workforce transitions transparently. Those failing to address the human dimension risk talent flight, morale collapse, and public relations crises.
The question for your organization: Are you deploying AI to augment human capability or replace it? The answer will define your culture, retention, and ultimately, competitive position in talent markets already strained by AI transformation across industries.
AI Policy Exclusions Spread Across Commercial Lines
Major insurers including AIG, WR Berkley, and Great American Insurance Group have formally requested state regulatory approval to exclude AI-related liabilities from commercial policies. Verisk, whose ISO forms underpin approximately 82% of global P&C policy templates, introduced new optional endorsements excluding bodily injury, property damage, and personal or advertising injury arising from generative AI usage, effective January 2026.
WR Berkley proposed what it calls an “absolute AI exclusion” across D&O, E&O, and fiduciary liability products. The exclusion aims to remove coverage for AI-generated content, failure to detect AI-created materials, poor oversight of AI systems, AI-driven operational errors, and regulatory investigations involving AI technologies.
Why This Matters:
Insurers describe the problem bluntly: they cannot explain why an AI system generates a specific output, making it extremely difficult to determine responsibility when technology causes harm. According to Jenner & Block partner David Kroeger, writing in Law360, risk managers must now determine how AI exclusions are interpreted and applied, and how they define AI.
The exclusion language is remarkably broad. Berkley’s AI definition states: “any machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” As one analyst noted, this definition is “subject to a myriad of interpretations and perhaps incapable of comprehension for all but the most sophisticated AI engineers.”
An executive from global brokerage Aon described the systemic risk: “We can handle a $400 million loss to one company. What we can’t handle is an agentic AI mishap that triggers 10,000 losses at once.” The Arup deepfake fraud case, where criminals used AI-generated video to steal $25 million during a live video conference, demonstrated how sophisticated and scalable AI-enabled fraud has become.
Interestingly, cyber insurers have taken the opposite approach. Alexandra Bretschneider, cyber practice leader at Johnson Kendall Johnson, told Insurance Business that “some insurance carriers come outright to add AI endorsements to clarify they’re still intending to cover losses that are initiated by an AI threat actor.”
Strategic Implications:
For risk managers, these exclusions create immediate action items. First, negotiate exclusion language during renewal before a loss occurs. Where removal isn’t possible, push for clearer definitions, narrower lead-in language, and targeted carve-backs aligned with your risk profile.
Second, map your AI footprint comprehensively. Insurers are asking increasingly granular questions about AI usage. You need to know for all business aspects who is using AI, how, and for what purpose. Build and maintain an inventory to respond accurately and avoid rescission claims based on incomplete answers.
Third, don’t assume broad exclusions are enforceable. Exclusionary language must be construed narrowly. Courts won’t enforce broad language defeating the policy’s purpose. Policies with “absolute AI” exclusions will generate coverage disputes centered on whether remote connections to AI defeat reasonable coverage expectations.
The gap between AI exclusions and AI adoption creates opportunity for specialized coverage. Carriers like Armilla AI (partnering with Lloyd’s) and Munich Re now offer dedicated AI insurance products. These remain scarce, with fewer than five meaningful AI-specific products available, according to industry experts.
For carriers, the exclusion trend represents strategic choice: retreat from AI risk entirely, develop specialized AI underwriting capability, or risk being caught in the middle with inadequate pricing for risks you can’t properly assess. Wall Street’s Monday sell-off suggests the market believes those choosing to engage will win.
Underwriting at a Critical Inflection Point
The commercial insurance market shows clear rate fatigue and overall softening after years of significant increases. According to Alera Group and CIAB research cited in Insurance Journal, average premium increases fell from 4.2% in Q1 2025 to 3.7% in Q2 2025. Commercial property rates declined 0.94% in Q2 2024, their first decline since 2017.
An Accenture survey of 430 senior insurance underwriting executives across 11 countries shows AI and gen AI adoption expected to jump from 14% today to 70% in the next three years. The survey reveals growing optimism despite patchy and incremental adoption to date.
Why This Matters:
Michael Reilly, writing in Insurance Journal, argues that historical approaches focused on process reengineering, offshoring, and training won’t address this market dynamic. Winners will employ AI tools to drive underwriting discipline and efficiency.
The traditional underwriting function remains largely unchanged, relying heavily on manual data collection, administrative tasks, and historical actuarial models. This legacy approach hinders innovation and limits data-driven decision-making potential. To remain competitive, insurers must embrace AI to enhance data ingestion, generate better insights, and enable more consistent, accurate pricing.
AI’s key benefit lies in automating non-core tasks, allowing underwriters to focus on high-value activities. AI can handle data collection and synthesis, providing advice for underwriting decisions. Breaking down underwriting into key decision points and using AI to support those decisions consistently can deliver significant improvements.
Strategic Implications:
The timing of this market softening creates urgency around AI adoption. When rates decline and competition intensifies, efficiency and segmentation become critical competitive advantages. Carriers deploying AI effectively can maintain underwriting discipline while competitors chase volume with deteriorating terms.
According to Patra’s 2026 AI and Insurtech Trends report, insurance organizations successfully scaling AI capabilities will separate from those stuck in pilot mode. The report emphasizes that weakness at any layer of the AI technology stack compromises all layers above it, requiring systematic capability building.
For underwriters, the shift means evolving from generalists processing submissions manually to specialists using AI to handle volume while focusing expertise on complex risks requiring judgment. The 56-point jump in expected AI adoption over three years suggests executives recognize this imperative. The question is whether their organizations can execute the transformation before market conditions demand it.
The Bottom Line
This week showed AI moving from experimentation to operational reality across insurance. ChatGPT apps change distribution fundamentally. Travelers demonstrates enterprise-scale deployment. Acrisure and Accenture reveal workforce implications. Policy exclusions reflect insurers’ struggle to price AI risk. And market softening makes AI efficiency critical for competitive survival.
The pattern is clear: AI advantage compounds with speed and scale. Organizations moving decisively gain competitive separation. Those waiting for certainty fall further behind.
The question isn’t whether AI will transform insurance. This week proved it already has. The question is whether your organization is positioned to lead the transformation or be disrupted by it.
AI Insights appears every Friday, analyzing AI developments through an insurance lens. For deeper analysis of strategic implications, visit InsuranceIndustry.ai.
