Insurers Boosting Their Use Of AI

The recent launch of ChatGPT has underscored artificial intelligence’s newfound scale, speed and mass accessibility, as well as its long-term potential to complete complex tasks currently performed by humans. Suddenly, AI is in the headlines everywhere, drawing the attention of commentators, business executives and even policymakers.

For the insurance industry, AI and machine learning (ML) technologies are by no means new. Across the sector, organizations have begun launching AI and machine learning programs over the past several years and are using the technologies to drive efficiencies in core parts of their business, including underwriting, risk management and claims. More recently, these efforts have intensified, driven in no small part by concerns about an uncertain economy and companies looking to ‘right size’ their workforces.

According to a survey of IT leaders we conducted in 2023, the insurance industry has been accelerating the use of AI over the past five years. But recently the rate of adoption has recently seen a remarkable surge, with a 30% increase in new AI/ML projects from 2021 to 2022. 62% of insurers say implementation of AI/ML has resulted in an overall reduction of their overall headcount, while other companies have focused on retraining employees whose jobs have been affected by AI. Most remarkably, 81% of insurers cited AI as their leading strategic IT priority, outpacing the use of cybersecurity at 63% and cloud at 58%.

Over half of insurers say they are using AI/ML for product lifecycle management, as well as to drive innovation and data analysis. Intelligent search, document processing and customer engagement are other fast-growing areas. A sizable majority (65%) said they are leveraging AI and ML to improve speed and efficiency, while half say it is helping them predict business performance, and 46% are turning to these technologies to better manage risk.

Challenges and Pushback

AI adoption across the industry hasn’t been without its challenges. First and foremost, insurers continue to face internal resistance to implementing projects. Over half (56%) of insurance IT decision makers said they have encountered pushback or scrutiny regarding the use of AI/ML in their organization. This could be the result of differing perspectives between business leaders and IT departments.

Building trust in the results of AI/ML projects is another common issue. When asked if they feel the data that AI generates is reliable, more respondents (42%) said they only ‘slightly trust’ the data than those (38%) who ‘completely trust’ it. Moreover, less than half say there is sufficient governance in place to safeguard against any misuse of the technologies.

Finally, more than two-thirds of respondents (67%) identified a shortage of skilled talent as the greatest challenge to greater AI/ML adoption. Other roadblocks include a lack of new use cases, algorithm/model failure and a lack of infrastructure necessary to support AI/ML. Despite these hurdles, 90% of insurers say they have grown their AI and machine learning workforces over the past 12 months.