The rise of artificial intelligence has sparked a sense of urgency in nearly every industry, and insurance is no exception. As insurance companies race to integrate AI into their operations, driven by the pressure to adopt this emerging technology quickly, it’s important to remember that AI isn’t a silver bullet. Rushing in without careful consideration can result in significant costs with little return—and in some cases, it may even hurt your business.
Avoiding common AI missteps in insurance
At Cake & Arrow, we’ve seen insurers chase the latest tech trends—whether chatbots or blockchain—without fully understanding how they align with their business goals. Now, many are approaching AI in a similar way, eager to adopt it just to keep pace with competitors. But this approach can easily lead to wasted resources and solutions that fail to deliver real value. While AI has the potential to transform processes, reduce costs, and improve customer experiences, it’s important to take a step back before diving in headfirst.
One of the most common missteps we see is adopting technology for the sake of technology—as an end, not the means, without a clear purpose or plan. Beyond that, companies often severely underestimate the complexity of implementing and maintaining these solutions. Many assume that the initial setup is the hardest part, when, in fact, the ongoing costs, updates, and operational adjustments can be just as demanding, if not more so.
And then there’s the data. AI is only as smart as the data feeding it, so if the data is messy or incomplete, personalization efforts will miss the mark. Poor data can lead to confusing recommendations, bad experiences, and customers wondering, “Do they even know me at all?”
Additionally, there’s often a failure to ask whether the technology solution can truly address a real need for the business or its users. Just because a process can be automated or optimized doesn’t necessarily mean it should be.
So, how can you avoid these missteps?
Before jumping into AI adoption, it’s critical to assess how the technology fits into your broader strategy and supports your specific business objectives.
Five essential questions to answer before pursuing AI in insurance
To ensure AI will actually deliver real value for your business, you need to ask the right questions upfront. Here are five essential questions to consider before investing in any AI solution:
- What are our core business goals over the next 1–3 years?
AI can be powerful, but it must be aligned with your specific business objectives. Consider whether it can meaningfully contribute to those goals in the near term. - What problems are we trying to solve?
Be clear on the challenges your business or customers face, and assess if AI is truly the right approach to address them. AI may not be the most effective solution for every problem. - What data do we have, and is it sufficient for AI to be useful?
AI relies on useful, usable data to function effectively. Without the right data infrastructure in place, even the best AI models won’t deliver the desired results. - Is this something our users (customers or employees) truly need or want?
Don’t adopt AI because it’s cool. Ensure that what you’re implementing solves a real need for your users. Not everything needs to be optimized or automated. - What is the potential return on investment?
AI solutions come with significant costs—not just in implementation but also in ongoing maintenance. Will the expected benefits justify the resources needed?
Don’t underestimate the power of an iterative product development process
If you can confidently answer those questions and believe AI has the potential to benefit your business, the next step is to follow a structured, iterative product development process. This is the same approach you would take when launching any new product, and it’s especially important when dealing with AI due to its complexity and cost.
Start by identifying small-scale opportunities to test AI solutions, like piloting tools internally or addressing a specific pain point. Use these early stages to gather feedback, assess performance, and refine your approach. With each iteration, you’ll either find that AI delivers on its promise—like how Netflix’s recommendation algorithm improved customer retention—or that a different solution may be more appropriate.
The key is to avoid treating AI as a one-size-fits-all solution. By maintaining an iterative mindset, you’ll be able to pivot or expand based on real-world results.
Practical tips for implementing AI in insurance
If your company is ready to move forward with AI, here are a few tips from this product strategist’s perspective to ensure smooth implementation:
- Start small: Begin with a pilot or prototype. Test a single AI solution in a controlled environment to gauge effectiveness before scaling up.
- Don’t underestimate the ongoing costs: AI is more than an initial investment. Maintenance, data structure and integrity, updates, and continuous learning are required to keep it functioning effectively over time.
- Ensure alignment with user needs: Whether the AI tool is for internal use or customer-facing, make sure it adds value to their experience. If users don’t see the benefit, the tool will likely go underutilized.
- Choose the right tools: To avoid disruption, AI tools must integrate smoothly into your existing workflows and tech stack. Be prepared for additional costs if your infrastructure isn’t compatible with the chosen AI solution.
- Stay flexible: AI adoption should be dynamic. If a solution isn’t working, don’t be afraid to pivot and explore other technologies or approaches.
Human-centered design: solving people problems to solve business problems
At the heart of every successful AI adoption is the understanding that AI is a tool meant to solve real human problems. Starting with a human-centered design approach, insurance companies can ensure they’re solving the right problems—the ones that matter most to their customers and employees. When you solve for people, the business benefits naturally follow.
By focusing on the pain points that your users experience, rather than on the technology itself, you’re more likely to develop solutions that improve workflows, enhance customer satisfaction, and ultimately drive business growth. This alignment between user needs and business goals is what will separate successful AI initiatives from those that fail.
Thoughtful adoption that drives meaningful outcomes
AI has the potential to revolutionize the way insurers operate—transforming processes, elevating customer experiences, and empowering employees. But unlocking this power isn’t just a matter of jumping on the AI bandwagon. When approached thoughtfully and strategically, AI can be a game-changer, driving real value across your business.
Instead of adopting AI simply to keep pace with industry trends, focus on how it can make a tangible impact—streamlining operations, reducing costs, and creating meaningful experiences for customers and employees alike. By asking the right questions, following an iterative development process, and keeping user needs at the forefront, you can harness AI’s potential to move the needle in ways that truly matter for your business.
The difference between success and failure lies in how deliberately you integrate AI into your strategy. Done right, it can elevate everything from customer satisfaction to employee efficiency, helping your company not only keep pace with the industry but lead it.
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by Emily Smith Cardineau
