What was experimental in 2024 and promising in 2025 has become operational reality in 2026. AI-driven claims processing, algorithmic underwriting, and predictive analytics are no longer competitive advantages for insurance companies - they're table stakes. The global insurtech market is projected to reach $23.5 billion in 2026, and 65% of insurers are planning scaled AI agents for claims processing this year.
The question for insurance leaders in 2026 isn't whether to adopt advanced technology - it's which trends to prioritise first, and how to deploy them responsibly. Here are the 7 that matter most.
Agentic AI is the defining insurance technology story of 2026. Unlike task-specific AI that automates individual steps, agentic systems coordinate intelligence across entire workflows - from first notice of loss through settlement - with human oversight only for exceptions.
By the end of 2025, one in three insurers reported at least one AI agent running in production (ScienceSoft). Insurance executives now view 2026 as the tipping point where agentic AI becomes a default requirement for core platforms, not a pilot programme. The evidence from early adopters is compelling: UK insurer Aviva deployed more than 80 AI models across its claims domain, cutting liability assessment time for complex cases by 23 days, improving routing accuracy by 30%, reducing customer complaints by 65%, and saving more than £60 million in 2024 alone (McKinsey).
For insurers still running isolated automation across narrow use cases, the strategic imperative is clear: coordinated, end-to-end AI systems outperform fragmented point solutions on every metric that matters.
The speed of claims resolution has always been the most visible measure of insurer performance. In 2026, AI is transforming it structurally. Insurers using AI-powered claims automation are resolving claims 75% faster than traditional methods - what once took 30 days now averages 7.5 days, with simple claims moving through straight-through processing in 24-48 hours.
The cost impact is equally significant. Cost per standard claim has fallen from $40-60 to $25-36 - a 30-40% reduction. Straight-through processing rates have jumped from 10-15% to 70-90% for eligible claim types. Manual document handling, which previously consumed 80% of adjuster time, now accounts for just 20%.
The communications layer is critical here. Claims handlers need unified platforms that surface customer history, policy documents, and AI-generated summaries in a single view - not separate systems that require manual consolidation. Insurers that have invested in integrated contact centre infrastructure alongside AI are seeing the fastest resolution improvements.
Insurance customers in 2026 expect Amazon-speed service. Over 60% of homeowners are now comfortable sharing digital property data to accelerate claims and underwriting - a dramatic shift in consumer willingness to exchange data for speed and convenience.
Meeting this expectation requires omnichannel contact centres that unify voice, email, chat, and digital self-service into a single customer view. When a policyholder starts a claim via app and follows up by phone, the agent needs the full context immediately - no repetition, no transfers, no delays. McKinsey found that AI-enabled insurers see a 10-20% improvement in sales conversion rates and a 10-15% increase in premium growth through better customer engagement - gains that compound directly from omnichannel capability.
Investing in robust omnichannel solutions and AI-driven routing ensures customers are connected to the right resource through their preferred channel, with context preserved throughout the journey.
Insurance fraud costs the industry an estimated $80 billion annually in the US alone. AI is proving to be the most effective countermeasure available. Modern fraud detection systems analyse patterns across text, imagery, metadata, and behavioural signals to identify anomalies that human adjusters would miss - including real-time deepfake detection to counter increasingly sophisticated fraud attempts.
AI fraud detection delivers 30%+ improvement in detection rates and a 40% reduction in false positives, meaning legitimate claims move faster while fraudulent ones are caught earlier. Deloitte estimates that AI-driven, real-time fraud analytics could save P&C insurers up to $160 billion by 2032. Zurich has already deployed machine learning to detect anomalies in filed claims, with measurable results.
The shift from point-in-time fraud checks to continuous fraud intelligence across the entire claims lifecycle is where the biggest gains are found - and where integrated AI platforms outperform standalone fraud tools.
One of the most significant structural shifts in 2026 is the move from static, annual underwriting to continuous underwriting - where risk is assessed in real time based on streaming data from telematics devices, connected vehicles, IoT sensors, and geospatial analytics.
In auto insurance, telematics provides real-time driving behaviour data that enables dynamic, risk-adjusted pricing. Insurers using these models have seen 30-50% reductions in claims frequency because policyholders are incentivised to drive more safely when premiums reflect actual behaviour. Underwriting timelines are collapsing - from three days to three minutes in some implementations - as AI systems analyse submissions, assess risk against underwriting guidelines, and generate explainable quotes automatically.
Deloitte's 2026 insurance outlook highlights drones for roof inspections, satellite imagery for catastrophe triage, and IoT sensors for real-time monitoring as the geospatial analytics layer that makes continuous underwriting viable across property lines. For insurers still running annual policy reviews, this represents a structural competitive disadvantage that compounds over time.
AI doesn't just process claims faster - it makes the entire customer journey more personal. McKinsey found that domain-level AI rewiring delivers a 20-40% reduction in customer onboarding costs and a 3-5% accuracy improvement in claims, alongside measurable improvements in customer satisfaction.
Hyper-personalisation in insurance means tailored policy recommendations based on individual risk profiles, proactive communication through AI-driven channels before customers need to ask, and automated claims communications that are clearer and more empathetic than those written by humans. One carrier now generates approximately 50,000 claims-related communications daily using AI - and customers rate them more positively than the human-written equivalents.
The communications infrastructure that powers this personalisation matters. Insurers need platforms that connect customer data, policy history, and AI-generated insights into a unified agent interface - so every interaction, regardless of channel, feels informed and relevant rather than generic.
The productivity gains from AI don't only flow to customers. Insurance employees - claims handlers, underwriters, contact centre agents - benefit directly when technology removes the low-value work that consumes their time.
Consolidating engagement tools into a single unified platform reduces the need for agents to switch between systems, eliminates manual data entry, and surfaces the right information at the right moment. AI-generated call summaries, automated post-interaction notes, and real-time coaching tools mean agents spend more time on complex, high-value interactions and less time on administration. AIG launched a gen AI-powered underwriting assistant that ingests and prioritises every new submission, allowing review of additional policies without adding new staff (Deloitte, 2026) — a direct productivity multiplier without headcount increases.
For insurance leaders, the strategic question is whether your current communications stack enables this kind of augmentation - or whether fragmented tools are limiting the productivity gains that AI should be delivering.
Fortay Connect helps insurance organisations select, deploy, and optimise the unified communications and contact centre platforms that make these seven trends operationally viable. Vendor-neutral, outcome-focused, and with deep expertise across all major platforms. Get in touch to find out where the fastest gains are hiding in your current setup.
1. What is agentic AI in insurance and why does it matter in 2026?
Agentic AI refers to AI systems that autonomously coordinate multi-step workflows - from claim intake through settlement - rather than automating individual tasks in isolation. In 2026, one in three insurers has at least one AI agent in production. The shift matters because end-to-end AI coordination delivers compounding efficiency gains that task-specific automation cannot replicate.
2. How is AI changing claims processing for insurers?
AI-powered claims automation resolves claims 75% faster than traditional methods, cutting average resolution from 30 days to 7.5 days. Straight-through processing rates for simple claims have jumped from 10-15% to 70-90%, and cost per claim has fallen 30-40%. Aviva's deployment of 80+ AI models saved £60 million in 2024 alone.
3. What is telematics-based insurance and how does it benefit policyholders?
Telematics uses real-time data from connected vehicles or IoT devices to assess risk dynamically rather than through annual reviews. Policyholders who drive safely or maintain low-risk properties pay premiums that reflect their actual behaviour. Insurers using these models have seen 30-50% reductions in claims frequency - a win for both sides.
4. How can insurance companies improve customer experience with AI?
AI improves insurance CX through hyper-personalised policy recommendations, proactive claims communications, and omnichannel contact centre platforms that preserve customer context across every touchpoint. McKinsey found AI-enabled insurers see 10-20% improvements in sales conversion and 10-15% premium growth through better customer engagement.