The adoption of artificial intelligence (AI) across claims organizations and defense firms is rapidly evolving from abstract ideas and experimentation to real and measurable impact. Successful AI adoption, however, is challenging without the right mindset and strategy. Salina Chan, associate partner, and Jacqueline Montgomery, partner, McKinsey & Company, will lead a discussion at CLM’s CCO Summit on Sept. 9 in Baltimore covering the critical building blocks required to enable effective AI transformation in claims and beyond.
The session, according to the speakers, will discuss the components necessary to “rewire claims organizations across strategic roadmap, talent, operating model, technology, data, adoption, and scaling.” The speakers will then share observations on both why this is difficult to get right and what effective AI transformation looks like.
The Challenges of Effective AI Adoption
“Capturing lasting value requires developing a long-term, bold, business-backed aspiration and defining a clear roadmap that balances making meaningful investments in key AI capabilities (e.g., technology, data, key skills) while ensuring financial impact is delivered along the way,” explain Montgomery and Chan. “In many cases, AI adoption requires…[a] shift from manual, human-led processes to re-imagining work that fundamentally optimizes AI [use] and automation.”
The two experts note that although many organizations have launched isolated AI pilots, they have yet to see financial impact or scale capabilities. They emphasize that, in addition to the technology and data capabilities required to adopt AI, comprehensive change leadership, an AI talent strategy, and clear risk protocols are all likely to enable effective AI adoption. Without these important pieces to a well-thought-out AI adoption plan, transformation with measurable results may not be as effective.
Building Blocks for Effective AI Transformation
Some of the elements the presenters will explore during the presentation to enable effective AI transformation include the following:
- A clearly defined AI strategy and roadmap aligned across business and technology leaders with clear, measurable business outcomes.
- Defined roles, skills, and responsibilities to implement and sustain new ways of working with AI.
- Shifts in operating model, including the governance cadence, collaboration model, and build vs. buy posture to enable AI transformation.
- Technology and data, with key considerations to establish a target state tech and data architecture to enable AI implementation at scale.
- Targeted change management, including strategies for all stakeholders (senior leaders, managers, frontline employees, customers, and brokers) to manage the risks of the rollout.
Transforming Claims With AI
“Claims is the launching ground for many insurance AI transformations—both because accurate claims handling is so consequential and because there is a real opportunity to automate and use AI for many data-driven tasks in claims,” according to the two experts. As a result, they continue, adjusters’ roles are “shifting from executing a defined process to spending more time applying human judgment on key claims decisions….adjusters could also spend more time with customers to improve the customer experience throughout the claims handling process.”
The presentation will offer attendees an opportunity for questions, plus a live demo showcasing the key capabilities leading carriers are deploying.