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Traditionally, the world of insurance is manual and process-heavy, forcing insurance executives to seek the best solutions for improving tedious workflows, with a priority on better decision making. The challenges are significant: even today’s best-in-class software lacks the power to optimize the huge amount of data and variables originating from all the different clients, coverage plans and claims. To read the details for every case would be a near-endless task. Enter AI for the insurance industry: AI is allowing insurance firms to meet the ever-growing volume of client submissions and claims with quick response times, precision pricing and quoting, and streamlined workflows.
Due to the urgency to deploy an efficient AI framework, and respecting the specific challenges of evaluating where a company is at in its AI readiness journey, insurance companies should follow the following four pillars of intelligent AI adoption:
- Building a strategy
- Assessing data and technology
- Training people
- Building a governance model
These four pillars have already helped some of the world’s leading insurance companies transform their businesses and make them AI-ready.
1) Building a Strategy
AI is reshaping the competitive landscape and there are many potential areas where AI can be applied in the insurance business. Building a coherent AI strategy is the only way to achieve short and long-term goals. This is done through:
Studying possible scenarios. Pause and assess trends that impact the future of your business, the insurance industry and AI itself. For example, most insurers regularly lose out on policies that they could have written, given more time to process. This is not a sustainable position for insurers, nor is it good for the economy overall. AI could help in processing these submissions.
Aligning on a shared vision. Insurers who move quickly to lock in the benefits of AI will pull ahead of competitors who do not. This vision of digital transformation must be shared across the organization for an AI implementation to move forward.
Building a strategic roadmap to achieve that future. The right partner can bring outside expertise to help you deploy an AI roadmap through a strategic lens. For instance, should you begin your journey in underwriting, pricing, claims or distribution? In which order and how will each of those areas be improved over the course of your journey? What will your customer’s insurance journey look like when supported by AI? The right partner should be brought in early to help advise your business on what your customized AI roadmap should look like.
2) Assessing Data and Technology
Insurance companies have a massive inventory of unstructured data, and most estimate they use only 20 percent. At the outset of an AI deployment, not only is most of this data not being used, but the data is not labeled properly for training an AI insurance model. If data is too sensitive or requires subject matter expertise to interpret, you’ll need to decide whether to dedicate expert talent to labeling data all at once, or incrementally by leveraging a specialized tool kit to facilitate necessary labeling steps in existing work processes.
By assessing data fields, and labelling them correctly, in part through leveraging AI based techniques, insurance companies are witnessing entire businesses transformation, allowing them to strengthen products, policies, claims and billing systems across all parts of their business.
3) Training People
To fully leverage the narrow yet powerful abilities of AI, insurance companies should plan to train and organize people for and with AI systems.
Organizing for AI means helping individuals across your organization develop the AI literacy to recognize AI opportunities, and the AI skills to make the most of them. This holds true whether building or buying AI systems—and most insurers will want a mix of both.
Organizing with AI means using it to complement people’s jobs at all levels of the organization. A key opportunity today is helping new employees entering the commercial underwriting workforce. Expertise is difficult to transfer because it’s difficult to write down, and transferring it from experienced underwriters to new hires is a growing challenge.
AI can help because it learns, too. AI systems use past decisions to infer rules and learn the unwritten rules and tacit knowledge that are so hard to capture manually. An AI model that learns from underwriters’ previous decision-making can help underwriters build and transfer their expertise more efficiently. For new underwriters, this means becoming an expert faster—if the system was designed with their needs in mind.
4) Building a Governance Model
Whether moving fast or slow, you need to be proactive about designing and implementing governance for AI, including new policies, procedures and principles to ensure safe, ethical and trustworthy AI.
The challenge businesses face is that current approaches to governing IT systems are not comprehensive enough for AI applications. Insurance companies need to plan to work collaboratively across business, technical, risk and compliance teams to define mitigation plans for each use case of their roadmaps. Over time, these strategies can be synthesized into more top-down guidance that generalizes to more use cases.
Insurance companies have the luxury of being data rich and with a plethora of opportunity to strategically position themselves to become leaders and innovators in AI. With data and processing power readily available, insurance companies need to focus on the four pillars of AI adoption with trusted advisory that will guide them systematically through intelligent AI adoption.