AI and Insurance: Insights From The IBM-HFS Roundtable

Takeaway

Human-Centered AI Starts With User Understanding AI delivers value in insurance only when it is designed around real customer and employee needs, not just technical capability.

Empathy Matters At Critical Moments Insurance interactions often happen during stress or loss; AI must support users with clarity, sensitivity, and timely guidance.

AI Should Augment Human Judgment—Not Replace It The strongest outcomes occur when AI supports underwriters, claims handlers, and agents rather than removing human decision-making.

Transparency Builds Trust Explaining how and why AI-driven decisions are made increases confidence among customers, regulators, and employees.

Personalization Improves Experience, Not Just Efficiency AI enables more relevant products, communication, and service when user context is responsibly applied.

Less Friction Creates More Meaningful Engagement By removing complexity and repetitive tasks, AI frees people to focus on problem-solving and relationship-building.

Human Oversight Remains Essential Clear accountability, review processes, and escalation paths ensure AI aligns with business values and ethics.

Not Every Interaction Should Be Automated High-emotion or high-impact moments still require a human touch to maintain trust and fairness.

Employee Enablement Is as Important as Customer Experience AI adoption succeeds when employees feel supported, not displaced, by intelligent systems.

This Is a Cultural Shift—Not Just a Technology Upgrade Long-term success depends on embedding empathy, trust, and user understanding into AI strategy.

Introduction: Why AI Is Reshaping The Insurance Industry

AI and insurance have always been about managing uncertainty. For decades, that meant actuarial tables, historical data, and cautious assumptions. But the pace of change has outgrown those tools. Customer expectations shift faster. Risks evolve unpredictably. And data arrives in volumes no human team can fully digest.

That reality framed the conversation at the IBM–HFS roundtable. The message was clear: AI isn’t a futuristic upgrade for insurers anymore—it’s becoming core to how the industry survives and competes.

The Role Of Industry Roundtables In AI Adoption

Roundtables like this matter because they strip away marketing noise. Instead of “what could AI do,” the discussion focused on “what’s actually working” and “what keeps failing.” Leaders shared real progress, stalled pilots, internal resistance, and lessons learned the hard way. That honesty is where insight lives.

Key Themes From The IBM–HFS Roundtable

➡️ Why Insurers Are Moving Beyond AI Experiments

Many insurers have already tested AI. Chatbots. Claims automation. Fraud detection pilots. But experimentation alone isn’t enough anymore. The roundtable highlighted a growing shift—from isolated use cases to enterprise-level thinking.

AI is being evaluated not as a tool, but as infrastructure.

➡️ Aligning Technology Strategy with Business Outcomes

A recurring theme was misalignment. AI projects often succeed technically but fail commercially. The most mature insurers start with a business question—reducing claims leakage, improving underwriting accuracy, retaining customers—then work backward to the technology.

How AI Is Being Applied Across Insurance Functions

➡️ Underwriting and Risk Assessment

AI is reshaping underwriting by combining traditional risk factors with real-time data: behavioral signals, external data sources, and emerging risk indicators. This allows underwriters to move faster while making more nuanced decisions.

➡️ Claims Processing and Fraud Detection

Claims remain one of the clearest AI success stories. Automation reduces processing time, flags suspicious behavior earlier, and frees adjusters to focus on complex cases. The roundtable stressed that accuracy improves most when AI supports—not replaces—human judgment.

➡️ Customer Experience and Personalization

From proactive policy reminders to personalized coverage suggestions, AI is helping insurers shift from transactional interactions to ongoing relationships. Customers notice when communication feels timely instead of scripted.

Data As The Foundation Of Insurance AI

➡️ Breaking Down Data Silos

Data fragmentation remains one of the biggest obstacles. Policy data, claims data, customer interactions, and third-party sources often live in separate systems. AI struggles when fed partial truths.

Roundtable participants emphasized integration before innovation.

➡️ Improving Data Quality and Accessibility

AI doesn’t fix bad data. In fact, it amplifies flaws. Insurers making progress invested heavily in cleaning, labeling, and governing data before scaling models. That groundwork determines everything that follows.

Trust, Transparency, and Responsible AI

➡️ Managing Bias and Explainability In Insurance Models

Bias in insurance isn’t just a technical issue—it’s a reputational and regulatory one. Models must explain decisions clearly, especially when outcomes affect pricing, coverage, or claims approvals.

Explainability isn’t optional. It’s table stakes.

➡️ Regulatory Expectations and Compliance Challenges

Regulators are paying close attention. The roundtable underscored the importance of designing AI systems that can be audited, explained, and defended—not retrofitted later under pressure.

The Human Element In AI-Driven Insurance

➡️ Augmenting Insurance Professionals, Not Replacing Them

One fear surfaced repeatedly: job displacement. The reality is more nuanced. AI handles volume and pattern detection. Humans handle empathy, judgment, and edge cases. The strongest implementations treat AI as a colleague, not a competitor.

➡️ Building Skills and Confidence Across Teams

Adoption stalls when teams don’t trust the system. Training, transparency, and gradual exposure build confidence. When people understand how AI supports them, resistance fades.

Operationalizing AI At Scale

➡️ Moving From Pilot Projects To Enterprise Deployment

Many insurers get stuck at the pilot stage. Scaling requires standardized processes, shared platforms, and leadership commitment. Without that, AI remains fragmented and fragile.

➡️ Measuring ROI and Business Impact

Success isn’t measured by model accuracy alone. It’s measured by faster claims resolution, improved loss ratios, customer retention, and employee productivity. The roundtable emphasized defining these metrics early.

Challenges Slowing AI Adoption In Insurance

➡️ Legacy Systems and Integration Barriers

Mainframes and legacy platforms still dominate core insurance operations. Integrating AI into these environments is possible—but requires patience, planning, and modernization strategies.

➡️ Change Management and Cultural Resistance

Technology rarely fails alone. Culture often does the damage. Insurers that invested in communication and change management saw smoother adoption than those who treated AI as a purely technical rollout.

What Insurers Can Learn From The IBM–HFS Discussion

➡️ Practical Takeaways For Insurance Leaders

Start with business outcomes, not tools. Fix data foundations early. Keep humans in the loop. And plan for governance from day one—not after deployment.

➡️ Common Mistakes To Avoid When Scaling AI

Chasing shiny tools. Over-automating sensitive decisions. Ignoring employee concerns. And assuming AI success in one area automatically translates elsewhere.

The Future Of AI In Insurance

➡️ From Automation To Intelligent Decision-Making

The next phase isn’t faster processes—it’s smarter ones. AI will increasingly support strategic decisions, scenario modeling, and proactive risk management.

➡️ Why Collaboration Will Shape The Next Phase

No insurer builds this alone. Partnerships between technology providers, insurers, regulators, and academia will define how responsibly AI evolves in the industry.

Conclusion: Turning Insight Into Action

➡️ Why AI Success In Insurance Depends On Strategy, Data, and Trust

The IBM–HFS roundtable revealed a simple truth: AI works best when it’s grounded in strategy, fueled by quality data, and trusted by people. Insurers that treat AI as a long-term capability—not a quick fix—will be the ones shaping the industry’s future.

FAQs