The annual Accenture Tech Vision report is in its 25th year and continues to be a huge source of insight for our technological future. This year, AI: A Declaration of autonomy features four key trends that are set to upend the tech playing field: The Binary Big Bang, Your Face in the Future, When LLMs Get Their Bodies, and The New Learning Loop. “The New Learning Loop” is a particularly compelling trend to me for the insurance industry. This trend explores how the integration of AI can create a virtuous cycle of learning, leading, and co-creating, ultimately driving trust, adoption, and innovation.
The virtuous cycle of trust between AI and employees
Trust is obviously important in any industry but since the insurance industry relies on the trust-based relationship between the customer and the insurer, especially when it comes to claims payouts, in essence, insurers effectively sell trust. Customer inertia when it comes to switching insurance providers comes down to the fact that they are happy with a repeatable insurer who makes good on this trust promise at the emotional moment of truth and pays in a timely fashion. This trust ethos needs to carry through to an insurers’ relationship with its employees. For any responsible AI program to be successful, it must be underpinned by trust. No matter how advanced the technology, it is worthless if people are afraid to use it. Trust is the foundation that enables adoption, which in turn fuels innovation and drives results and value. In fact, 74% of insurance executives believe that only by building trust with employees will organizations be able to fully capture the benefits of automation enabled by gen AI. As this cycle continues, trust builds, and the technology improves, creating a self-reinforcing loop. The more people use AI, the more it will improve, and the more people will want to use it. This cycle is the engine that powers the diffusion of AI and helps enterprises achieve their AI-driven aspirations.
From ‘Human in the loop’ to ‘Human on the loop’
In fostering this dynamic interplay between workers and AI, initially, a “human in the loop” approach is essential, where humans are heavily involved in training and refining AI systems. As AI agents become more capable, the loop can transition to a more automated “human on the loop” model, where employees take on coordinating roles. This approach not only enhances skills and engagement but also drives unprecedented innovation by freeing up employees’ thinking time, exemplified by the fact that 99% of insurance executives expect the tasks their employees perform will moderately to significantly shift to innovation over the next 3 years.
Capitalize on employee eagerness to experiment with AI
Insurers need to take a bottom-up rather than a top-down approach to employee AI adoption. Stop telling your employees the benefits of AI- they already know them. Everybody wants to learn and there is already huge excitement amongst the general public about the endless possibilities of AI. We see this in our daily lives. We use it to help our children do their homework. The AI action figures trend is just one that shows how people are eager to demonstrate their willingness to try it out and have fun with the technology. The key is to actively encourage employees to experiment with AI. Build on the conviction that we think it will be useful and enhance our and their careers if we all become proficient users of AI. We are already building this generalization of AI at many of our clients. Our recent Making reinvention real with gen AI survey revealed that insurers expect a 12% increase in employee satisfaction by deploying and scaling AI in the next 18 months. This increase is expected to lead to higher productivity, retention, and enhanced customer trust and loyalty, all of which drive efficiency, growth, and long-term profitability.
Insurers need to turn any perceived negative threat into a positive by emphasizing the fact that AI will lead to the reduction of mundane, repetitive tasks and free up employees to work on innovation projects like product reinvention. With 29% of working hours in the insurance industry poised to be automated by generative AI and 36% augmented by it, the necessity of this constant feedback loop between employees and AI is reinforced. This loop will help workers adapt to the integration of technology in their daily lives, ensuring widespread adoption and integration.
Cut out the mundane and the noise for your employees
Underwriters, in particular, can benefit from AI by using LLMs to aggregate and analyze multiple sources of data, especially in complex commercial underwriting. This can significantly reduce the time spent on tedious tasks and improve the accuracy of risk assessments. The international best-selling book “Noise: A Flaw in Human Judgment” by Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein, one of my personal favorites, focuses on how decisions and judgment are made, what influences them, and how better decisions can be made. In it, they highlight their finding at an insurance company that the median premiums set by underwriters independently for the same five fictive customers varied by 55%, five times as much as expected by most underwriters and their executives. AI can address the noise and bias in insurance decision-making, even among experienced underwriters. AI can provide acceptable ranges and objective criteria for premium calculations, ensuring more consistent and fair outcomes.
Addressing the readiness gap through accessibility
Despite 92% of workers wanting generative AI skills, only 4% of insurers are reskilling at the required scale. This readiness gap indicates that insurers are being too cautious. To bridge this gap, insurers can take a more proactive approach by making AI tools easily accessible and encouraging their use. For example, within our own organization, all employees are using AI tools like Copilot and Writer on a regular basis. We don’t have to tell them to use these tools; we just make them easily accessible.
To foster this proactivity, insurers should recognize and advertise successful use cases, showcasing both the people and the learnings. The key is to find the spearheads—those who are already using AI effectively—and highlight their achievements. The insurance industry is still in the early stages of AI adoption, and no one knows the full extent of the killer use cases yet. Therefore, it is crucial to allow employees to experiment with the technology and not be overly prescriptive.
Reshaping talent strategies through agentic AI
This integration of AI is also disrupting traditional apprenticeship-based career paths. As insurers develop AI agents, new capabilities and roles will emerge. For instance, the product owner of the future will engage with generated requirements and user stories, while architects will be able to rapidly generate solution architectures and predict the implications of different scenarios and outcomes. With AI embedded in the workforce, insurers will need to focus on sourcing skills needed to scale AI across market-facing and corporate functions. This may involve looking beyond their own walls for expertise and capacity, covering a wide spectrum of low to high domain expertise roles.
How to capture waning silver knowledge
With a retirement crisis looming in the very near future in the industry, in an era of fewer employees, how can AI agents drive a superior work environment, providing choice and better balance? The new generation of insurance personnel can leverage the knowledge and experience of retiring experts by extracting decisions and risk assessments from historical data, free from bias. For example, Ping An’s “Avatar Coach” transforms training with immersive scenes and customizable avatars powered by an LLM, reducing training expenses by 25% and achieving a stellar 4.8 NPS for high engagement. An AI use case that we increasingly encounter is documenting the functionality of legacy systems where control has been lost or is very scarce. We have come across instances where tens of millions of lines of code are not documented due to the age and size of the systems. LLMs are extremely useful here as they can effectively read the code and tell us what the modules do. This will help insurers regain control before the mass employee exodus.
A cultural shift to embed AI in the workforce is the key to success
The New Learning Loop is not just a technological shift but a cultural one. By fostering a dynamic interplay between employees and AI, insurers can create a virtuous cycle of learning, leading, and co-creating. This cycle will not only enhance employee satisfaction and productivity but also drive innovation and long-term profitability. The key is to build trust, encourage experimentation, and recognize and celebrate successful use cases. As the insurance industry continues to evolve, the integration of AI will be a cornerstone of its future success.
