The annual Accenture Tech Vision Report is in its 25th editionTh year and continues to be a great source of insights for our technological future. This year, AI: A Declaration of Autonomy presents four key trends that will upend the technology landscape: “The Binary Big Bang,” “Your Face in the Future,” “When LLMs Get Their Bodies,” and “The New Learning Loop.” For me, “The New Learning Loop” is a particularly compelling trend for the insurance industry. This trend explores how integrating AI can create a virtuous cycle of learning, leading and co-creating that ultimately promotes trust, acceptance and innovation.
The virtuous circle of trust between AI and employees
Trust is of course important in any industry, but because the insurance industry relies on the trust-based relationship between the customer and the insurer, especially when it comes to paying out claims, insurers are essentially selling trust. Customers’ inertia in switching insurance providers stems from the fact that they are satisfied with a repeatable insurer that delivers on this promise of trust in the emotional moment of truth and pays on time. This ethos of trust must also impact an insurer’s relationship with its employees. For a responsible AI program to be successful, it must be based on trust. No matter how advanced the technology is, it is worthless if people are afraid to use it. Trust is the foundation that enables adoption, which in turn drives innovation and increases results and value. Actually, 74% of insurance managers believe that only by building trust among employees will companies be able to fully realize the benefits of automation enabled by genetic AI. As this cycle continues, trust builds and technology improves, creating a self-reinforcing cycle. The more people use AI, the better it will get and the more people will want to use it. This cycle is the engine that drives the adoption of AI and helps companies achieve their AI-driven goals.
From “Human in the Loop” to “Human on the Loop”
To foster this dynamic interplay between workers and AI, a “human in the loop” approach is essential first, in which humans are heavily involved in training and refining AI systems. As AI agents become more powerful, the cycle may shift to a more automated “human-on-the-loop” model, with employees taking on coordinating roles. This approach not only increases skills and engagement, but also drives unprecedented innovation by giving employees time to think, as evidenced by 99% of insurance managers assume that their employees’ tasks will shift moderately to significantly towards innovation over the next three years.
Benefit from your employees’ willingness to experiment with AI
Insurers need to take a bottom-up approach rather than a top-down approach to adopting AI among their workforce. Stop telling your employees the benefits of AI – they already know it. Everyone wants to learn, and there is already great excitement among the general public about the endless possibilities of AI. We see this in our daily lives. We use it to help our children with their homework. The AI action figures The trend shows that people are willing to try out the technology and have fun with it. The key is to actively encourage employees to experiment with AI. Build on the belief that if we all become competent users of AI, it will be useful and improve our careers and theirs. We are already building this generalization of AI with many of our customers. Our current one Making reinventions a reality with genetic AI The survey found that insurers expect a 12% increase in employee satisfaction over the next 18 months through the use and scaling of AI. This increase is expected to result in higher productivity, retention, and greater customer trust and loyalty, all of which lead to efficiency, growth, and long-term profitability.
Insurers must turn any perceived negative threat into a positive one by emphasizing the fact that AI leads to a reduction in mundane, repetitive tasks and frees up employees to work on innovation projects such as product reinvention. With 29% of working time In the insurance industry, which is set to become automated and 36% augmented by generative AI, the need for this constant feedback loop between employees and AI is only amplified. This cycle will help workers adapt to integrating technology into their daily lives, ensuring widespread adoption and inclusion.
Avoid the mundane and noise for your employees
Underwriters in particular can benefit from AI by using LLMs to aggregate and analyze multiple data sources, particularly in complex commercial underwriting. This can significantly reduce the time spent on tedious tasks and improve the accuracy of risk assessments. The best-selling book internationallyNoise: An Error in Human Judgment“by Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein, one of my personal favorites, focuses on how decisions and judgments are made, what influences them, and how better decisions can be made. In it, they highlight their discovery at an insurance company that average premiums set independently by insurers for the same five fictional customers fluctuated by 55%, five times what most insurers and their executives expected. AI can combat the noise and bias in insurance decision-making can provide acceptable ranges and objective criteria for premium calculations even among experienced insurers, ensuring more consistent and fair results.
Addressing the readiness gap with accessibility
Although 92% of workers want generative AI capabilities, Only 4% of insurers carry out retraining to the extent required. This readiness gap suggests insurers are being too cautious. To close this gap, insurers can take a more proactive approach by making AI tools easily accessible and encouraging their use. For example, in our own organization, all employees regularly use AI tools such as Copilot and Writer. We don’t have to tell them to use these tools; We simply make them easily accessible.
To encourage this proactivity, insurers should recognize and promote successful use cases, showcasing both the people and the insights. The key is to find the leaders – those who are already using AI effectively – and highlight their successes. The insurance industry is still in the early stages of AI adoption, and no one yet knows the full extent of the killer use cases. Therefore, it is crucial to give employees the opportunity to experiment with the technology and not to impose too many requirements.
Redesigning talent strategies with agent AI
This integration of AI is also changing traditional career paths that rely on education. As insurers develop AI agents, new skills and roles will emerge. For example, the product owner of the future will deal with generated requirements and user stories, while architects will be able to quickly generate solution architectures and predict the impact of different scenarios and outcomes. As AI becomes embedded in the workforce, insurers must focus on acquiring the skills needed to expand AI into market-facing and enterprise functions. This may mean looking beyond your own borders for expertise and capacity, covering a wide range of roles from low to high expertise.
How to capture dwindling silver knowledge
At a time when the industry is facing a retirement crisis in the near future with fewer employees, how can AI agents provide a better work environment and provide choice and balance? The new generation of insurance professionals can leverage the knowledge and experience of retiring experts by extracting decisions and risk assessments from historical data without bias. For example, Ping An’s “Avatar Coach” transforms training with immersive scenes and customizable avatars based on an LLM, reducing training costs by 25% and achieving an excellent NPS of 4.8 for high engagement. An AI use case we are seeing more and more often is documenting the functionality of legacy systems where control has been lost or is very rare. We have encountered cases where tens of millions of lines of code remain undocumented due to the age and size of systems. LLMs are extremely useful here as they can effectively read the code and tell us what the modules are doing. This will help insurers regain control before the mass exodus of employees.
A cultural shift to embed AI into the workforce is key to success
The New Learning Loop is not only a technological change, but also a cultural one. By fostering dynamic interaction between employees and AI, insurers can create a virtuous cycle of learning, leading and co-creating. This cycle will not only increase 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, integrating AI will be a cornerstone of its future success.
