The insurance sector is at a turning point. Once defined by legacy systems, complex actuarial models and decades-old policy structures, the industry now sits on the cusp of transformation powered by artificial intelligence (AI), including its subsets, generative AI (GenAI) and agentic AI.
According to EY, nearly 99% of insurers are either already investing in GenAI or exploring it due to its expected productivity, cost and revenue benefits, while KPMG highlights that 81% of insurance CEOs now list GenAI as a top investment priority despite economic uncertainty.
In this reality, we're seeing insurers move beyond proofs of concept into enterprise-scale adoption, unlocking outcomes across cost optimization, customer engagement and productivity. However, to truly embrace AI and its benefits, insurers need to rethink their approach to the operating model.
In this article, we'll explore why a product-aligned operating model is essential for scaling AI, where AI delivers tangible outcomes and the reinvention of the software development lifecycle (SDLC) with the ultimate goal of building long-term agility and growth.
From projects to products: How operating models are changing
Historically, change in insurance was delivered through projects. Teams formed temporarily around a scope and budget, handed off work across functions and disbanded at "go-live." Ownership was fragmented: Business wrote requirements, IT built, operations supported and data sat apart. That model optimized for completion, not continuous outcomes, and every new initiative restarted the learning curve.
Today, leading insurers organize around enduring products, including claims intake, quoting, billing, fraud detection and agent experience, which are each owned by a cross-functional team spanning business, data, engineering, design and risk. These product teams run on backlogs and objectives and key results (OKRs), ship frequently and treat AI, data and controls as integral. The shift concentrates accountability, shortens decision time and turns change into a repeatable capability.
The benefits are material. Product-aligned models reduce handoffs, embed governance where work happens and scale AI consistently across lines of business. They improve cycle time and quality, make investment transparent and help talent focus on customer and agent outcomes instead of internal coordination. For AI specifically, this model unites infrastructure, data and process expertise under clear ownership, giving organizations the trust, agility and repeatability required to move beyond pilots to production at scale.
The lesson here is that technology transformation must be matched by operating model transformation. Traditional structures, designed for incremental change, can't fully harness the potential of AI. That is why HCLTech's research found that 88% of surveyed businesses are moving toward product-aligned operating models.
Culture plays a decisive role. Those who embrace AI along with an operating model and cultural transformation will emerge as winners.
Where AI is delivering tangible outcomes
Insurance is inherently data-driven. From decades-long life policies to property and casualty (P&C) lines dependent on climate, location and risk data, the industry generates vast amounts of structured and unstructured information. Historically underused, this data is now being unlocked by GenAI, which can connect directly to disparate sources and derive insights without extensive re-engineering. What was once too expensive to modernize has suddenly become viable, enabling insurers to transform legacy systems, streamline claims and fraud detection and create new growth opportunities.
In this environment, there are three areas that stand out where insurers are realizing measurable value today:
1. Driving productivity and reducing costs
AI-powered platforms are streamlining IT operations, the software development lifecycle, QA and testing. Productivity improvements range from 12–15% up to 40–45%. For example, AI-assisted testing and code generation have cut cycle times significantly.
2. Enhancing customer and agent experiences
Whether in contact centers, claims processing or agent interactions, AI is reimagining engagement. Automation is not just about efficiency; it's about building more intuitive, personalized journeys.
3. Empowering the workforce with AI assistants
Digital assistants for underwriters, claims analysts and agents are emerging as powerful tools. Rather than replacing human expertise, these AI co-pilots augment decision-making with real-time insights and recommendations.
These outcomes are why 65% of insurers expect AI to deliver revenue lifts of over 10%, while 52% anticipate cost savings.
Moving from experimentation to scale
For several years, insurers explored AI through proofs of concept. That period of over-experimentation is now giving way to a new phase: implementing AI at scale to deliver enterprise-wide impact.
Scaling AI, however, is not just a technical challenge; it is an organizational one. Insurers must start by establishing a clear value realization framework. Without a baseline, it is impossible to track benefits such as cost savings, productivity gains or customer experience improvements.
Equally important is organizational change management. AI alters workflows, including how underwriters assess risk, how claims are processed and how customer service agents interact with policyholders.
In underwriting, for instance, AI is already enabling faster, more accurate risk assessment and reducing time-to-quote. Similarly, in group insurance, AI-driven automation is streamlining the quoting process, cutting cycle times and improving pricing accuracy. Unless employees are engaged and supported through such changes, adoption falters.
Responsible AI must also be embedded from the outset. Governance frameworks, regulatory monitoring, bias mitigation and continuing risk assessment are critical in a sector where trust is paramount.
Success will hinge on culture. Organizations that treat AI as an isolated initiative risk marginalizing its potential. By contrast, those that democratize AI by placing tools in the hands of underwriters, claims handlers and IT engineers foster adoption at scale.
Redefining the software development and IT operations lifecycle
One of the less visible but highly important areas where AI is transforming insurance is the end-to-end software development lifecycle (SDLC). While many organizations deploy point solutions for specific stages, the real opportunity lies in orchestrating AI across the entire lifecycle.
Consider the chain reaction: Inaccurate requirements gathering leads to flawed code; flawed code creates more defects in testing; weak testing allows problems into production. From demand capture and code generation through QA and release, embedding AI throughout the lifecycle enables insurers to improve quality, reduce cycle times and lower costs.
Similar benefits extend into IT operations, where insurers are moving away from traditional machine learning models toward agent-based automation. These adaptive systems empower administrators to build agents that can "skill themselves on the fly," creating resilience in run environments.
Building long-term agility and growth
AI is no longer a futuristic ambition. Instead, it is a present-day competitive differentiator. It enables insurers to cut costs, accelerate modernization, elevate customer and agent experiences and empower employees with intelligent tools.
But success will depend on more than technology. It requires clear value frameworks, responsible governance, cultural adoption and new operating models. With KPMG finding that 62% of insurance CEOs citing talent gaps as a barrier to growth, investing in people is also crucial. Here, AI should be seen as a partner to human expertise, not a replacement.
The winners in insurance will be those who seize this turning point to not only re-engineer processes but also reimagine possibilities. AI is not just reshaping the industry; it is redefining its future.
