Majesco, a cloud and AI-native software provider for insurance, has published a new research report mapping out the strategic technology priorities insurers are setting for 2026. The report, based on the company's primary research, identifies a widening performance gap between insurers classified as Leaders and those falling into Follower or Laggard categories, driven largely by how deeply they integrate artificial intelligence into their operations.
The emerging divide: AI as a competitive boundary
The research describes a market where advanced insurers are moving beyond isolated AI pilots. These Leaders are building business models on automation, intelligence-driven processes, and cloud-native technology foundations. Majesco links this approach directly to lower expense ratios, stronger operational performance, and better customer experiences. The company also associates it with improved talent attraction and retention, faster innovation cycles, and greater long-term competitiveness.
"AI is rapidly becoming the new divider between Leaders versus Followers and Laggards in insurance," said Denise Garth, Chief Strategy Officer at Majesco. "Insurers leading are redesigning their operational model leveraging a cloud and AI-native core technology foundation that deeply integrates AI into the business and redefines them as a Frontier Insurer that achieves unprecedented agility and value creation faster than traditional companies."
Garth added that the stakes are particularly high for an industry that is data-intensive, regulated, highly competitive, and often under financial pressure. The potential business value, she said, outweighs the challenges.
Where AI investment is landing across the value chain
Insurers face a familiar set of pressures: rising operating costs, growing risk exposure, talent shortages, ageing infrastructure, and shrinking profitability. Majesco's report argues that AI and generative AI can touch virtually every link in the insurance value chain. Underwriting, claims management, policy servicing, and fraud detection are all areas where the technology is being applied to streamline work and increase capacity.
Generative AI is seeing some of its strongest uptake in customer servicing, claims, and underwriting functions, according to the findings. Meanwhile, interest in agentic AI is growing as carriers evaluate its potential to automate workflows, coordinate multi-step activities, and support decision-making across the enterprise.
Majesco points out that efficiencies gained at the individual task level through automation and intelligence-led decisions can compound. Over time, they contribute to greater organisational agility, lower operating costs, and stronger financial performance. For insurers heading into 2026, cost management, operational efficiency, customer experience, and AI capability development are becoming tightly aligned strategic goals.
For deeper context on how AI is reshaping core insurance functions, AI for Insurance training resources cover underwriting, claims, and operational transformation in detail.
Data maturity as the foundation for scaling AI
The report emphasises that long-term success with AI depends on the strength of underlying data capabilities. Data maturity, governance, integration, and risk management are identified as the key factors that will determine whether insurers can scale AI initiatives and extract sustainable value from their investments.
Insurers with more mature AI programmes are already extending their lead through greater adoption of analytics, generative AI, agentic AI, and practical business applications. This creates a competitive distinction that Majesco describes as increasingly significant within the market. The company concludes that carriers investing in cloud-native technology foundations, modernising core systems, and redesigning operating models to support collaboration between employees and AI tools are likely to be better positioned to improve efficiency and respond to changing demands.
Why this matters for insurance professionals
The research signals that the window for treating AI as an experimental side project is closing. Insurers that remain in pilot mode risk falling into a structural disadvantage as competitors hardwire intelligence into their operating models. For leaders across underwriting, claims, operations, and strategy, the 2026 planning cycle requires decisions about data readiness, core system modernisation, and how to move from point solutions to enterprise-wide AI deployment.
Understanding how AI-native architecture changes the economics of the business is no longer a technology question alone. It is a strategic one that will shape cost structures, talent strategies, and competitive positioning. Professionals looking to connect these dots can explore how leadership teams are approaching the shift through AI for Executives & Strategy resources.
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