How To Build an AI-Powered Tech Platform, Avoiding Common Failure Points
More companies and engineering teams are building AI-powered tech platforms to capitalize on expected growth. But building a successful model demands more than technical prowess or a great idea. It requires adaptability, product-market fit, and a focus on creating genuine customer value.
At Qlik, I led the platform’s transition to an Enterprise SaaS offering. Now, as the CTO of Accelerant, a multibillion-dollar platform company, I’ve helped build the first Risk Exchange in the insurance industry. In other words, I’ve seen how the right strategies can pave the way for success — and how missteps can lead to avoidable failures.
Here’s what I’ve learned about building differentiated software platforms that will stand the test of time — even as AI changes the game.
1. Build the Right Product.
Sounds simple — but you have to start with the right product. The thing that solves real problems that the market is ready for. Every time I have built something and failed, it has been because I’ve built the wrong thing.
Take, for example, the move to the cloud. Early in my career, as cloud computing was starting to take off, there was skepticism about whether the world would genuinely shift to the cloud. So many incumbent companies — especially those that missed the early cloud shift — hedged, betting on “hybrid cloud” as the future. They invested heavily in software products designed around that model. I fell into this trap, spending months building a product leveraging hybrid cloud architecture. The problem was that by the time we were ready to launch, it had become clear that no one wanted the product as the world shifted to public clouds.
Our product-market mismatch was evident in hindsight. But a hard lesson made me passionate about using a “product-led” approach, constantly adjusting to market trends. This is especially important today as new technologies entrench themselves across sectors. This means adapting to market trends and letting real-world insights guide the product roadmap. The right product isn’t just innovative—it’s necessary.
2. Iterate Using Customer Feedback.
A product-led approach only works if you prioritize solving real problems for your users rather than simply adding features. At Accelerant, we’ve seen firsthand how this principle transforms the development process.
In industries like insurance software, many products have bells and whistles that add little value. Instead of solving pain points, these largely unhelpful and cumbersome tools complicate workflows and frustrate users.
That’s why we focus on launching lightweight pilots and gathering real-time feedback directly from your users. Each iteration is guided by what users truly need, not what we think they might want.
By continuously adjusting to customer feedback, we’ve created an in-demand product that evolves with the market’s needs. This fosters deeper trust and engagement with our users. Prioritizing learning over perfection keeps our customers coming back.
3. Design for Flexibility Early on.
Software architecture, like life, is a series of one-way and two-way decisions. One-way decisions are hard to reverse, so they require careful consideration. At Accelerant, we strive to make these choices in ways that maximize flexibility down the line.
For example, our API-first design has been the cornerstone of quick movement and scalability. It enables our internal product teams to add new capabilities to our platform and allows external ecosystems to integrate seamlessly. Without this capability, we couldn’t have delivered the diverse and innovative offerings that define our platform today.
Two-way decisions should allow for adaptability. Consider how we handle observability in our stack: By embedding observability from the start, we’ve gained the agility to fine-tune our platform based on evolving production costs and market demands. This increases our productivity and frees up resources for innovation — ultimately resulting in a better customer experience.
4. Combine Domain Expertise With Technical Expertise.
One common pitfall in building technology platforms—especially in highly regulated industries like financial services, healthcare, or insurance—is neglecting to bring domain experts into the fold. At Accelerant, we’ve prioritized pairing deep domain expertise with cutting-edge technical know-how. This dual expertise approach has helped us solve the most challenging problems in insurance tech platforms—problems no one else has been able to solve.
Take specialty insurance as an example. Capturing and modeling all the data associated with a risk has long been a challenge. The hurdles are technical (thanks to rigid legacy systems) and business model-oriented. By going back to first principles on both fronts, we’ve tackled this problem head-on while building one of the industry’s most comprehensive data sets. This allows us to train AI models that drive meaningful business outcomes.
Every time prospective partners see our platform, their question is the same: “How did you solve this?”
The answer is simple but not easy: We combined experts who understand the insurance value chain gaps with experts who have built industry-defining enterprise software. More companies should embrace this winning combination.
5. Seek To Redefine by Delivering Continuously.
As an engineer at heart, I’ve always wanted to build software that redefines industries. Why re-create an existing solution or a tool that doesn’t move the needle?
At Accelerant, empowering cross-functional teams to iterate quickly and deliver continuously has been key to achieving our ambitious business goals. Our quest for true innovation means we often ship, take calculated risks, and fail fast until we have a game-changing product.
It shouldn’t be surprising that we constantly look to accelerate every development aspect at Accelerant. This mindset is precisely what you need to build and consistently deliver high-value, world-class technology.
Building an industry-defining tech platform that avoids common failure points isn’t about avoiding mistakes altogether — it’s about learning from them and adapting quickly. By iterating quickly and continuously, designing for flexibility, and integrating diverse expertise, you can create a top-selling product that thrives even as the market changes. These lessons are just as essential in the era of AI, where technological shifts demand a strong foundation of product-market alignment and iterative development.
The key is simple: Stay flexible, be open to learning, and always keep the customer at the center of everything you do.
