Every AI conversation in insurance right now seems to start in the same place. Models, platforms, copilots, automation. So it might sound strange to say the industry's AI problem isn't a technology problem. It's a design problem.
I'm the founder and CEO of a design agency that has worked with insurance companies for more than 15 years, and I keep seeing the same pattern. We invest in technology before we understand the people who are supposed to use it. Then, when adoption is low, we blame it on people.
AI is just the latest, highest-stakes version of that same old mistake.
Whether investments in AI pay off won't come down to what model you pick. It'll come down to whether the people you built it for actually use it.
I am a designer by trade: art school, design school, maker through and through. Cake & Arrow did not start in insurance. We began in retail and e-commerce, designing digital experiences for consumer-centric brands. Then, about 15 years ago, a CIO at an insurance company asked us to help reimagine a sales platform for agents.
We came in as outsiders, and that outside view helped us see something people deep inside the business often can't: insurance experiences are too often built around the business, the policy, and the transactional moments—not around the customers, employees, agents, and brokers trying to navigate them.
Investing in Technology Is Not the Same as Progress
The instinct in insurance is often to start with the technology. A new tool shows up, a new capability emerges, and every executive team wants to show progress. I get the pressure. Boards are asking about AI. Everyone wants to move fast.
But buying technology is not the same as making progress.
A powerful AI tool is still worthless if it isn't solving an actual problem for an actual person. The most advanced chatbot, copilot, or automation platform will fail if it gets bolted onto a broken process. That's the actual risk with AI right now. Making the same old mistake, only faster and at greater expense.
When talking to insurers, I often make a distinction between "design" and "Design with a capital D." When I talk about "Design," I'm not talking about colors, fonts, or pretty screens. Design is the research, the strategy, and the deliberate decision-making underneath every product and experience. It's understanding who a tool is for, what its purpose is, where the work breaks down, and how a solution earns its place in someone's day.
And here's the thing: Design is already happening, whether companies acknowledge it or not. Every agent portal, claims experience, policyholder app, and AI workflow is the result of a decision someone made. The real question is: where and with whom did the decision originate? With the person doing the work, or with an executive mandate, business requirement, vendor pitch, or short-term goal? Too often, the decisions made in insurance have little to do with the human beings they impact.
Agents Are Not the Barrier
In our recent report, The Connective Thread: From Agent and Broker Research to a New Design Vision for AI-Enabled Insurance Work, we spoke directly with agents and brokers about how they are using AI today, where they are finding value, and what is still getting in the way. What stood out was not the agents' resistance, but their resourcefulness.
Agents are already experimenting. They're drafting emails, summarizing policies, comparing quotes, prepping for meetings, and translating complex insurance language into something clients can actually understand. Some are quietly building workarounds because the official systems around them do not support how they actually work.
So the problem is not that agents do not want to use AI. It's that the tools too often do not map to the real friction in their work. The industry keeps talking about AI as an automation story. But when you talk to agents, what they want is integration.
They are not asking for another tab, login, or disconnected assistant. They're already moving between agency management systems, CRMs, email, spreadsheets, carrier portals, and rating tools. They're entering the same information over and over, hunting across systems for context and trying to track what changed, what a client needs, and what follow-up might fall through the cracks. That is not a single-task productivity problem. It is a workflow problem.
AI that helps write an email is useful. AI that understands the context behind the email, pulls from the right systems, shows where the information came from, flags what needs review, and keeps the human in control… that's something else entirely. That is where AI becomes connective tissue, instead of one more tool to add to the pile.
Design Around People, Not Around Replacing Them
For decades, the insurance industry has strived for ways to disintermediate agents. AI has only added fuel to that fire. There's a real temptation to see AI as a way to replace human labor, cut costs, and eliminate the messiness of human relationships.
But that framing misses where the value actually lives.
Sure, AI can create efficiencies. It can reduce administrative burden, help agents manage bigger books, and spend less time on repetitive work. But if your starting point is replacement, you'll miss the bigger opportunity to design tools that unlock capacity, judgment, and relationship-building.
The best agents are valuable because they know what matters. They understand their clients, and they understand risk. They can feel when something is off, and they know what to ask next. That's how they turn complexity into confidence. AI should be making more room for that work, not pushing it to the side.
This is where human-centered design stops being a nice-to-have and becomes a business necessity.
If you want agents to adopt AI, you have to understand how they actually work, not how leadership assumes they work. And that requires more than a survey. It means observing real workflows, listening for friction, and noticing the invisible work that quietly holds the system together. Research embedded in the design process points toward solutions.
Adoption Is the Whole Game
One of the biggest misconceptions about AI is that adoption is what happens after the rollout. It's not. Adoption is the whole game.
A tool is only successful if the people it is built for actually want to use it. People want tools that fit into their world, solve problems they recognize, and make their work meaningfully better in a way they can feel.
Insurance has a long history of underestimating this. The industry spends significant money on technology that never lands because it has never fully accounted for the human experience surrounding it. Then, when usage is low, the conclusion is often that people are "resistant to change."
Most of the time, that's the wrong diagnosis.
People are not resistant to change that helps them. They're resistant to tools that make their day harder, add complexity, create risk, ask them to trust outputs they cannot verify, or worse, are designed to replace them.
AI cannot simply generate confident answers. It has to earn trust. Agents need to see where information came from, verify recommendations, correct outputs, and approve what goes to a client. "Trust but verify" is not just a user preference here. It's a design requirement.
What Leaders Should Do Differently
If a carrier, brokerage, or insurtech CEO asked me where to start right now, I'd say this: Catch yourself before you jump to the solution.
The pressure to move fast is real, and speed does matter. But moving fast doesn't mean skipping the work that makes speed useful. Before you decide what AI feature to build or what vendor to buy, sit with these three questions:
- Who is this for?
- What problem are we solving?
- And what outcome are we actually after?
Then go talk to the people who'll use it. Watch how they work, find where the friction really lives, and let that learning shape the AI strategy before the roadmap hardens. A few focused weeks of research and design up front can save you months, or years, of expensive misalignment down the line.
The Opportunity Is Still Enormous
Despite the industry's habit of chasing tech before thinking about people, I remain optimistic. The opportunity to differentiate in insurance is astounding. The bar for better experiences is still too low.
AI can help agents spend less time searching and re-entering the same information. It can help newer employees get up to speed faster. It can preserve institutional knowledge, make complex decisions more transparent, and free up time for the things that actually build loyalty. Advice, empathy, and relationship-building.
But only if it is designed around people.
The companies that get this right understand that technology alone does not create transformation. People do. It comes down to whether they trust the tool enough—and find it valuable enough—to actually use it.
Insurance doesn't need more AI for the sake of AI. It needs AI that solves actual problems for the people doing the work, in the real flow of their day. That's the design challenge. And if the industry takes it seriously, it's also the clearest path to transformation.
