What's Changing in Insurance: 5 Trends to Watch in 2026

The insurance market is changing faster than expected. Legacy systems that worked for decades are becoming liabilities. Competitors who embraced digital transformation are pulling ahead while others struggle with manual processes and siloed systems.

Here’s what’s happening…

Insurers are wrestling with operational inefficiencies that eat into margins. Claims processing stretches into months. Underwriting decisions that could be instant require multiple touchpoints. Customer service teams fail to cope with the huge volume of inquiries. The market is quickly changing amidst the growing influence of AI. Insurers are now chasing the benefits that AI had promised. But are they able to maximize their investments?

The global insurance market is projected to reach $13.9 trillion by 2032. And yet, many insurers are still struggling with legacy systems that can’t keep pace with customer expectations and manage data silos.

Source: Verified Market Research

The technologies that were experimental three years ago are now being perceived as “proven”. Early adopters have moved past pilots to enterprise-scale deployment. Enterprises are preparing to leverage more AI use cases as their customer data grows.

So, where should you focus your investment and attention? Based on our internal client communication and latest industry reports, we’ve compiled a list of five insurance market trends that may help you prepare better heading into 2026.

Trend #1: Digitizing Core Functions to Be a Top Priority for Insurers

Insurance operations were initially designed for a paper-based world. This infrastructure worked perfectly when customer expectations were different, and competition was limited. But not anymore. The cost of maintaining legacy systems is increasing. The inability to launch new products quickly is costing market share. And the customer experience gaps are driving policyholders to more agile competitors.

Research shows that 40% of insurers will digitize their operations by 2027.

Source: KPMG

Digital transformation in insurance means end-to-end process automation. Claims submitted through mobile apps are automatically triaged, evaluated, and processed without any human intervention (at least for straightforward cases). Underwriting decisions that previously required days of manual review are completed in minutes using data from multiple sources. Policy changes that once demanded phone calls and paperwork are handled through self-service portals with instant confirmation.

Insurers looking to adopt this change will enjoy faster time-to-market, reduced operational costs, improved customer satisfaction, and the ability to compete with insurtech startups that were born digital.

How to Prepare for a Completely Digitized Ecosystem

The temptation is to tackle everything at once. Resist the urge. Start by mapping your current operations honestly. Try answering:

• Where are the biggest bottlenecks?
• Which processes generate the most customer complaints?
• What tasks consume the most staff time without adding value?

Focus on processes that meet these criteria: high volume, rule-based decision-making, and clear success metrics. Claims intake and initial assessment. Policy renewals. Document processing. These workflows deliver quick wins while your team builds the muscle for larger transformations.

Build your technology foundation with intent. Your data needs to be clean, accessible, and structured. Integrated policy admin, claims management, and CRM systems form the backbone of your operations. Companies that rush digital transformation without fixing data and integration issues end up with expensive pilot projects that never scale.

Think about your workforce early. Start training programs now. Help your staff understand how their roles will change. Insurers succeeding with digitization are the ones who brought their people along rather than surprising them with change.

And be realistic about timelines. Core system modernization isn’t something that can be rushed. But you can show measurable progress within quarters by targeting specific workflows. Measure everything from processing times to error rates, customer satisfaction, and cost per transaction. Use these metrics to justify your investments and guide your roadmap.

Trend #2: Insurers to Focus More on Modern and Cloud-Based Ecosystems in 2026

Insurance IT infrastructure was built for a time when businesses moved slowly, and change was gradual. But market dynamics have changed in the last few years. Customer expectations, time-to-market, regulatory guidelines, and what not. On top of that, add the cost of maintaining an aging infrastructure. The cost increases while flexibility reduces.

A Gartner report predicts that spending on public clouds will increase to 72% by 2029. This highlights the necessity of migrating to modern, cloud-based ecosystems to sustain in the long run.

Cloud-based ecosystems provide what legacy infrastructure cannot. The ability to:

• Scale resources based on demand
• Launch new capabilities without months of infrastructure planning
• Integrate with modern tools and platforms seamlessly
• Access latest capabilities from cloud providers without building everything in-house

Considerations for Cloud Migration

The lift-and-shift approach won’t work. It will fail to deliver real value and might present you with operational headaches. Instead, rethink how technology supports your business.

Start with a clear assessment of what you have. Try answering…

• Which systems are business-critical?
• What data needs to be moved? What integrations exist?
• What regulatory requirements apply to your data?

This assessment prevents surprises midway through migration and helps you build realistic timelines.

Prioritize based on business value, and not technical convenience. Apps that are easy to migrate might not be the ones delivering the most business impact. Focus on systems that will enable new capabilities.

Address security and compliance from day one. Financial services regulations around data protection, privacy, and system availability still apply in the cloud. Work with your compliance and security teams early to define requirements. Build security into your architecture rather than treating it as something to add later.

And don’t underestimate the organizational change required. Cloud operations require different skills than traditional IT infrastructure management. As a BCG research shows, scaling AI effectively demands moving from operations-heavy structures to centralized, technology-driven models.

Source: BCG

Your team needs training on cloud platforms, modern development practices, and new operational models. Start building these capabilities early.

Trend #3: Customer Interactions Set to Be Fully Automated

Policyholders have questions about coverage, need help filing claims, want to update their policies, or require assistance understanding complex insurance terms. And the volume is unpredictable. It spikes after storms, during renewal periods, or when regulatory changes affect policies. Staffing for peak demand means excess capacity during normal periods. Staffing for average demand means poor service during peaks.

And given the current expectations of customers, it’s natural that they’ll expect instant responses across every channel, 24/7.

In 2026, insurers will look to automate the entire customer engagement process. 48% of insurers have already adopted AI for customer interactions, with 29% looking to adopt in the next two years.

We’re talking about modern, AI-powered, autonomous customer service agents here. Agents that understand context, handle complex multi-step inquiries independently, and escalate cases when human judgment is needed.

How to Prepare for Automated Customer Service

The biggest mistake insurers make is setting up customer service AI agents before fixing these issues:

• Fragmented customer data
• Outdated knowledge base
• Lack of process documentation

Automation will only amplify these problems.

So, start by fixing your data and content. Consolidate customer information so AI systems can access complete context. Update your knowledge base with clear, accurate information. Document your processes. This groundwork determines whether your AI implementation succeeds or fails.

Begin with use cases that deliver real value. Policy inquiries about coverage details. Claims status updates. Billing questions. These interactions are high-volume, relatively straightforward, and perfect for building confidence in automated systems.

Ensure that your customers are able to reach a human agent when they need. Agents should be able to access the complete interaction history when they take over. Make the process as smooth and transparent as possible.

Measure what matters. Response time and containment rates are important, but they’re not the full story. Track customer satisfaction scores for automated interactions. Monitor resolution rates. Measure how often customers abandon automated channels to call instead. These metrics tell you whether your automation is actually helping customers.

And prepare your team for their new role. As routine inquiries get automated, your agents will handle more complex situations requiring empathy, judgment, and problem-solving. This requires different training and support. Invest in developing these capabilities.

Trend #4: Claims Will Be Processed Faster with AI, at a Lesser Cost

Claims processing sits at the heart of insurance operations. Yet, for most insurers, it remains painfully manual and slow. Adjusters spend hours reviewing documentation, cross-referencing policies, assessing damages from photos, detecting potential fraud, and coordinating with multiple parties. Simple claims that should be processed in days, take weeks. Complex claims can drag on for months. The backlog builds during catastrophic events when customers need help most urgently.

AI is going to change this equation. A McKinsey report shows that AI-powered claims accelerate assessment time by up to 23 days and improve accuracy by 30%. AI-powered tools can:

• Quickly scan through photos and videos to assess damage severity
• Review policy documents to determine coverage automatically
• Cross-reference multiple data sources to detect inconsistencies
• Identify potential fraud patterns
• Auto-recommend settlement amounts based on historical data

Furthermore, it comes in handy during catastrophic events. And it frees adjusters to focus on complex cases that actually require human expertise.

How to Prepare for AI-Powered Claims

Go for a phased approach. Start with first notice of loss (FNOL) and initial triage. AI can instantly categorize claims by severity, identify required documentation, flag potential fraud indicators, and route claims to appropriate adjusters or auto-adjudication workflows.

Measure the impact carefully with parameters like time from first notice to initial assessment, accuracy of triage decisions, fraud detection rates, and adjuster satisfaction with AI recommendations.

Then layer in damage assessment capabilities. Deploy AI that can analyze photos and videos to estimate repair costs, identify damage patterns consistent with fraud, assess whether damages align with the claimed incident, and recommend next steps based on findings.

The key is positioning AI as augmentation rather than replacement. Adjusters should always have the ability to override AI recommendations. They need clear explanations for why AI made specific assessments. And they should maintain full control over final decisions, particularly for complex or high-value claims.

Build the technical foundation for success. Your AI needs access to comprehensive policy data, historical claims records, current pricing information for repairs and replacements, fraud indicators and patterns, and industry benchmarks for similar claims. Fragmented data undermines AI effectiveness.

Trend #5: Modern Underwriting Will Be Faster and Completely Driven by AI

Underwriting determines whether an insurer profits or fails. Price too high and you lose business. Price too low and you lose money. Traditional underwriting relies on historical data, actuarial tables, and underwriter judgment. It works, but it’s slow and often misses important risk signals.

Applications sit in queues waiting for underwriter review. Complex cases require multiple touchpoints and additional documentation. Turnaround times measured in days or weeks create poor customer experiences and lost opportunities.

A McKinsey report indicates that insurers using AI in core processes will be able to work faster, personalize better, and assess risks smarter.

AI-driven underwriting analyzes hundreds of variables instantly, identifies risk patterns humans would miss, personalizes pricing based on individual risk profiles, and processes straightforward applications without human review. More importantly, it learns continuously from outcomes to refine its outcomes.

Considerations for Implementing AI-Powered Underwriting

Moving to AI-driven underwriting requires addressing technical, regulatory, and organizational challenges simultaneously. Start by identifying which lines of business and risk profiles are best suited for automated underwriting.

Keep high-risk, complex, or unusual applications in human underwriter queues. Play to the strengths of both human and AI.

Build a solid data infrastructure first. AI-led underwriting workflows demand access to multiple data including credit reports, claims history, and application information. The more comprehensive your data foundation is, the better your AI will perform.

Mitigate regulatory and fairness concerns. Insurance regulators scrutinize AI underwriting models for discrimination and bias. Here’s how you can overcome this challenge. Ground your model with explainability. Properly document how decisions are made. Ensure transparency. Work with your compliance team to ensure your approach meets regulatory standards.

What’s Next

Insurers who moved past pilot projects and committed to enterprise-wide transformation are pulling ahead. You’ll need to time your tech investments with realistic expectations, build proper data and cloud foundations, design automation that extends human expertise, and measure metrics that actually matter.

Companies still evaluating their options will need to move quickly. The gap between digitally transformed insurers and those running legacy systems widens every quarter. Customer expectations won’t wait. Competitive pressures won’t ease. And the advantages of having a modern system compound over time.

Identify where your organization can realize the most value from digital transformation, AI automation, and legacy modernization. Consult an experienced insurance technology partner to assess your current capabilities, identify use cases, and craft enterprise-grade solutions that work for you.