According to Deloitte, 79% of business and technology leaders believe generative AI will drive substantial transformation within their organization over the next three years. A survey from Celent shows 84% of senior insurance executives expect to gain a sustainable competitive edge from investments in generative AI.
In truth, all the enthusiasm for generative artificial intelligence (or “GenAI”) is as well-founded as it is palpable. But it’s also predicated on identifying the most compelling use cases for this technology—and effective implementation. Given the level of hyperbole that continues to swirl around this technology, that second part may be easier said than done.
As it stands now, nearly 60% of large insurers are actively running up to 15 different proofs of concept or early-stage prototypes of generative AI-enabled applications. But as the transition from conceptual to operational picks up speed, insurers risk being drawn into larger issues surrounding the technology. There’s the increased scrutiny from governments here and abroad over privacy concerns and fraud, for instance. There is also a growing list of lawsuits stemming from AI “hallucinations” and other risks. But for the majority of carriers, the most significant challenges come from the practical realities of supporting and leveraging generative AI to its fullest potential.
For some, these obstacles could derail their ability to achieve the business outcomes they desire. For others, the frissons inspired by this technology must give way to a more pragmatic approach that enables them to make full use of what is arguably the most consequential technology of the next decade.
The Generative AI Craze Comes to P&C Insurance
To understand the importance of this pivot, let’s start with the basics. Generative AI is a subset of deep learning that leverages sophisticated algorithms to digest vast amounts of structured and unstructured data, learn patterns, and then generate original content—including text, audio, images, video, code, and more.
Given its ability to generate virtually any form of content based on the inputs it’s trained on, the potential use cases in P&C insurance span distribution, underwriting, claims, and beyond. According to McKinsey, GenAI could help unlock up to $1 trillion in annual value for the insurance industry worldwide.
To be clear, generative AI isn’t the same as predictive AI. While both use advanced algorithms to solve complex business challenges, their functions are quite different. Predictive AI recognizes patterns and is leveraged to boost the performance of large-scale processes and predict outcomes or behaviors such as who will “buy, lie, or die,” or which property is more likely to suffer damage that results in claims. Generative AI turns machine learning inputs into content.
GenAI also isn’t new. Its foundations and the large language models (LLMs) it utilizes trace back to at least the 1960s. It was the public release of OpenAI’s ChatGPT in November 2022 that catapulted generative AI into the popular imagination. By December 2023, a survey found that 76% of personal lines and 79% of commercial lines carriers were either studying or piloting GenAI applications in their operations. What they’re learning is eye-opening.
GenAI’s Best Use Cases Are Human-Centric
It may seem counterintuitive, but human enablement is a, if not the, key use case for generative AI. This technology isn’t about replacing human roles. It’s about empowering them. Coaching or “co-piloting” agents through the sales process for complex products like cyber and summarizing submission data to streamline underwriting workflows, for instance, have emerged as some of the most promising applications. So has synthetic claims analysis to help teams identify opportunities for new lines or potential enhancements to existing ones.
According to a recent study published by Harvard Business School, teams using AI completed 12.2% more tasks on average, completed them 25.1% faster, and produced 40% higher-quality results than those not using AI. Better still, workers with the lowest scores before the study saw the most significant jump (43%) in performance when they could use AI—suggesting the technology works as a skills leveler.
Bain & Company points out that GenAI’s use in hyper-personalization, self-service assistance, and guiding insureds through the underwriting or claims processes could also be game-changing. But as many carriers are learning, identifying advantageous use cases is one thing. Implementing the requisite infrastructure and data ecosystems to support them is another. That’s where the reality check comes in.
The Criticality of a Cloud-based Architecture
Infrastructure considerations are paramount to seizing AI’s full potential, generative or otherwise. Yet even with more traditional forms of AI, P&C insurers struggle with technological implementation. According to an April 2024 study from Capgemini, for instance, 83% of carriers believe predictive models are critical to underwriting. Yet only 27% say they have the advanced capabilities and data ecosystem in place to operationalize them effectively.
Generative AI is an order of magnitude more complicated. For one thing, diverse and continuously refreshed datasets are required to train GenAI for use cases throughout the insurance life cycle. As Forbes reports, a single model must often span multiple servers and accelerators to execute a trained LLM that may have billions of parameters.
This level of complexity requires an average 4X improvement in hardware compute performance and a 50X increase in processing workloads. Most organizations won’t have the appetite for the investments and overhead needed to support that kind of performance on their own. Nor is it likely they’d be successful if they did.
According to Deloitte, a modern, cloud-based infrastructure is required to support and scale the computational demands of GenAI applications. Insurers will need one that combines core, data, and digital to integrate with and interpret data from an expanding ecosystem of data partners and make it actionable.
Governance Takes Center Stage
As the industry navigates the complexities of GenAI adoption, governance will become the linchpin in ensuring effective and responsible deployment. Establishing frameworks and guardrails for data privacy, security, transparency of training data, and compliance with evolving regulatory mandates will be business critical.
In view of these needs, look for more insurers to establish Centers of Excellence (CoE) to oversee GenAI implementations. For each use case, the CoE must define factors such as: Who is the user? What is the user experience? What data and functionality are they permitted to access—and why? Making GenAI truly effective and empowering means giving the people using it—employees, customers, partners—an understanding of how it’s being used and what data serves as its inputs.
Workforce Readiness Is Key
According to McKinsey, leaders must take a broad view of GenAI’s capabilities and deeply consider its implications for workforce needs. Focused training, upskilling, and reskilling will be required. So might new roles such as AI trainers, interpreters, ethicists, and more. Humans with domain expertise will be needed to identify use cases and develop, test, deploy, and manage GenAI-enabled applications. A human-in-the-middle must also vet AI-produced output for factual errors and bias.
That means assessing technological skills gaps is only part of the equation. As Deloitte points out, soft skills such as leadership, emotional intelligence, and problem-solving are innately human, business critical, and irreplaceable.
Organizations that thrive in the age of generative AI will be those that prepare their workforce to take on more complex, strategic roles that can be optimized, enhanced, and scaled by using generative AI.
A Five-Point Checklist for Joining the GenAI Revolution
As the industry moves forward, a checklist approach can guide insurers in refining their strategies for generative AI:
- Get informed: Learn about the latest advances in generative AI, and start incorporating new insights and technologies into your strategic road map.
- Modernize Your Infrastructure: If you haven’t deployed a modern, cloud-based insurance platform, now is the time to do it—implementing and scaling generative AI use cases just isn’t possible with legacy systems.
- Initiate Experimentation: Identify impactful use cases with a clear and low-stakes path to success.
- Create a Center of Excellence: Develop the governance frameworks needed to ensure data privacy, security, and regulatory compliance while developing the agility to navigate evolving market dynamics.
- Foster GenAI Capabilities: Implement training initiatives to develop the human and technological capabilities needed to deploy, manage, refine—and make the most of—GenAI applications.
The transition from irrational exuberance to reality check will not be easy for some carriers. But it will be necessary. With more than $1 trillion in annual value at stake, the P&C insurers that refine approaches, focus on governance, and foster human-generative AI collaboration won’t just navigate the near term safely—they’ll shape the future of our industry.