Which AI tech will deliver results for your organization?

Measuring the ROI of new tech

Insurance technology is constantly evolving. Rapid advancements in generative AI and other digital tools are giving insurers new ways to connect with clients and partners and streamline their operations. There are new ways of measuring and pricing risk, new ways of selling and servicing products, and new ways of running an efficient business, and insurance leaders and their teams need to measure their worth quickly to gain an edge.

GenAI: Benefits now or down the road?

Many insurance leaders have already started implementing genAI at their firms, but anyone with a tech budget must parse whether the many new applications live up to the hype. In addition to streamlining business processes like risk assessment, claims processing, operations, and customer service, genAI can also unlock faster time-to-market for products and services. 

As they assess these technologies, industry decision-makers need to measure them on multiple dimensions:

• What are the specific business objectives this technology will help my firm reach?
• How does this technology fit with my existing tech stack? Are they compatible?
• How long will it take for this investment to pay off?

In its new report, The Top Emerging Technologies in Insurance, 2024, Forrester assesses technologies on their ability to deliver results now, or in the short-, medium-, or long-term, placing particular emphasis on generative AI. 

Is your data house in order?

“For genAI investments to pay off, insurance data and analytics teams need to get their data houses in order,” Forrester says. GenAI only generates useful insights when it’s fed the right data. Not only collecting enough data, but correctly structuring data is crucial for any insurance organization, no matter how far along it is in its exploration of AI technology. 

Gaps in data collection only become more problematic as time goes on, so identifying and filling them early is key. When paired with well-structured data, AI can help optimize sales and other consumer interactions. Important data considerations for insurance organizations include:

• Setting up the best data structure by standardizing website experiences and sales processes. This will allow analysts to more easily understand and parse all the signals that come into an organization, and make decisions based on those signals.
• Using data in product development to maximize the reach of a product once launched.
• Prioritizing data security. The life insurance & annuity industry in particular collects sensitive information from customers, so from day one, it’s crucial to work with compliance and privacy experts to ensure any data collection meets the relevant regulations.

AI in the insurance industry – today & in the future

To some extent, AI already has applications in the insurance industry. Insurers use AI to aid in pattern recognition and predictive modeling in risk assessment and underwriting. But in many areas, the industry is still reliant on manual processes, and is ripe for digital transformation. For example, Zinnia’s call center management product uses AI to analyze data from consumer conversations and provide insights for automation and cost reduction. 

Given the scale of the opportunity from genAI-driven transformation, analyses like Forrester’s can help leaders identify whether some of the most hyped technologies are actually a good match for their organizations today, or whether they’re still something to keep in mind for the future.