The Percipience co-founder and managing partner has spent four decades watching many insurers make the same mistake: installing the next big thing while ignoring legacy issues.
Throughout his four-decade career in insurance technology, Percipience co-founder and Managing Partner Bruce Broussard has seen a dismal pattern relentlessly repeating itself. To curtail the cycle, Broussard and the company’s other managing co-founder, Ajay Kelshiker, started Percipience five years ago.
Fifteen years at IBM designing enterprise data solutions for many of the largest U.S. carriers, followed by six and a half years launching Insurity’s first data software product led Broussard to question why insurers invest millions in multiple policy and claims administration systems; then millions more in time, staffing and training, trying to consolidate them. Often those consolidation projects failed because extending any one system to replicate the functionality and customization buried in all of them is far more difficult and costly than anticipated. The alternative — consolidating data rather than systems — is faster, cheaper, and far less disruptive. But it requires building something most insurers have never prioritized: a unified data foundation.
Now, with artificial intelligence having passed the emergence stage at insurance organizations large and small, Broussard is watching a familiar instinct take hold.
Many insurance organizations are investing heavily in AI, but without that strong insurance data infrastructure, these initiatives often fail. Instead of building a unified data foundation, insurers attempt to layer AI on top of fragmented systems—policy, billing, and claims platforms that were never designed to work together.
This approach leads to inconsistent insights, unreliable models, and ultimately failed implementations. AI does not fix operational or data fragmentation; in fact, without proper preparation, it amplifies the problem. When data is siloed, incomplete, or misaligned, AI simply scales those inefficiencies across the organization.
The reality is that insurance data infrastructure is not just a technical prerequisite—it is a crucial strategic foundation. Without a unified, governed data layer that creates a single source of truth, insurers cannot fully unlock the value of AI, analytics, or digital transformation initiatives.
“A lot of insurance organizations are skipping the essential enterprise data layer and are going straight to launching their AI systems,” says Broussard. “They try to deploy AI as a use case before they’ve established an enterprise data foundation, building point solutions on top of siloed data buried in policy, billing, and claim systems.”
The result is a list of failures, Broussard says. These include insights that don’t reconcile across the organization, models working from inconsistent data, and ultimately, failed implementations that join the graveyard of ambitious technology projects.
The necessary grunt work nobody wants
Broussard doesn’t mince words about the role Percipience, a New Orleans, Louisiana-based insurtech data and analytics provider, is meant to play.
“AI gets all the headlines, and rightly so — that’s where competitive differentiation will be won,” he says. “But we’re focused on making sure it actually works. That starts with building a data infrastructure that supports today’s requirements and tomorrow’s opportunities.”
That foundation takes form in Percipience’s Data Magnifier platform. It’s intended to act as “the Rosetta Stone for insurance,” Broussard says. In essence, Data Magnifier translates data from disparate transaction systems into a unified enterprise foundation. That the work is so foundational may explain why there’s so little competition doing it well, Broussard says.
“The core vendors are primarily dealing with data coming out of their core system, as opposed to all of the data coming out of all of the systems an insurance organization has to address,” Broussard explains.
Beyond policy, billing, and claims systems, insurers operate accounting platforms, investment systems, underwriting tools, actuarial modeling constructs, and often receive bordereaux files in Excel or PDF format from Managing General Agents (MGAs).
“The ability to take all of the data from every source and bring it in quickly and cost-effectively — that’s not something those packages have typically done well,” Broussard contends.
The distinction matters because transaction systems and data systems are fundamentally different. The core information captured on a personal auto application has remained relatively consistent for decades — but the data insurers need to evaluate risk, design products, and manage claims is a different matter entirely. That data evolves constantly, driven by new external sources, analytical capabilities, and market dynamics that can shift within months. Just because a system handles the former well doesn’t mean it’s equipped to handle the latter.
“Dealing in data systems is a very different beast than dealing with policy, billing, or claim systems,” he says. “The skills, architecture, and mindset required are fundamentally different — and the industry has the failed implementations to prove it.”
Where policy systems were 3 decades ago
The insurance data platform market today occupies roughly the same stage of maturity that policy administration systems did in the late 1980s and early 1990s. In Broussard’s view, it’s nascent, fragmented, and dominated by custom builds.
Before 2013, none of the major core vendors offered a packaged enterprise data solution, he says. Broussard helped bring a solution to market. Other vendors quickly followed. So did consolidation.
The big problem as Broussard tells it is that these combinations weren’t engineered from the ground up as enterprise data platforms.
“They were really trying to provide reporting layers on top of their transaction processing capabilities,” Broussard says.
Many insurance companies still attempt to build their own data solutions, either from scratch or through consulting engagements with firms that construct custom systems. It’s an expensive, time-consuming, and risky approach. According to Gartner, more than half of all data warehouse projects fail — victims of poor design, fragmented data, and solutions that can’t keep pace with evolving business needs, much less effectively positioned to support AI capabilities.
Broussard explains:
“A custom build reflects the requirements of the day it was designed. Business requirements and technology don’t stand still — and without sustained investment, neither does the gap between what the solution does and what the business actually needs. In most organizations, that sustained investment to close that gap never materializes. History bears this out: just as insurers inevitably transitioned from homegrown systems to vendor policy and claims packages decades ago, the transition to vendor data solutions is following the same path.”
For insurers buying vendor solutions, the current challenges are different but equally frustrating.
Many proprietary systems lock customers into specific platforms and tools. Source code remains hidden. Changes require going back to the vendor or certified system integrators, which is generally an expensive proposition. Also, these projects rarely guarantee the vendor’s priorities align with the customer’s needs.
It’s a challenge Broussard says Percipience was specifically designed to address — giving insurers the flexibility and control of a custom build but without the unsustainable investment burden, and the ongoing innovation of a vendor platform but without the lock-in.
Beyond implementation: Total cost of ownership
Because Broussard and his co-founder Kelshiker are both former insurance carrier chief information officers, they developed Percipience’s platform for insurance organization CIOs, designing its Data Magnifier software with long-term viability in mind.
“We’re not looking for something we can sell to somebody,” Broussard says. “We tried to build something that would work for people. We wanted this to be a solution that would last 20-, 25-, 30-years-plus.”
That philosophy reveals itself in several ways. Unlike proprietary vendor platforms, Percipience provides full source code, complete data model designs, and comprehensive documentation with the software subscription. It’s got everything customers need to control their environment without incurring the usual “tech debt” that demands more and more software add-ons, Broussard says.
Percipience’s Client Extension Framework takes that a step further, allowing clients to extend any component of the platform to meet their specific business requirements — without sacrificing their ability to benefit from Percipience’s ongoing product investment and innovation. It’s an approach designed to keep total cost of ownership predictable and sustainable over the long term — not just at initial deployment.
The approach has earned industry recognition. Mitsui Sumitomo Insurance Group (MSIG) received a 2025 Celent Model Insurer Award following its Data Magnifier implementation — a project that integrated 17 systems across 16 lines of business, brought in more than 30 years of historical data balanced to the penny, and was delivered in just 10 months.
Platform independence is another area of focus for Percipience. Data Magnifier runs on Google Cloud, AWS, and Azure with various databases and integration tools.
The AI foundation thesis
Can insurers derive some AI benefit without fixing all their data problems? “Absolutely,” Broussard acknowledges. But significant enterprise transformation requires a solid, broad enterprise-level data foundation.
“If you can’t get insights quickly, it doesn’t mean you’re not using AI. You probably don’t have the right foundation.”
The foundation metaphor isn’t accidental. Just as buildings require structural integrity before adding floors, insurance organizations need data infrastructure before layering on advanced analytics.
“Foundation is everything,” Broussard says. “For buildings as well as for software systems.”
Owning the competitive advantage
When Broussard and Kelshiker founded Percipience, they set out to solve three core problems: integrating data from multiple vendors and homegrown systems into a coherent foundation; enabling insurers to own their data constructs without requiring specialized expertise; and protecting investments in analytics, machine learning, and AI from becoming obsolete when underlying platforms change.
The urgency has only intensified.
“Margins continue to get tighter and competition is fierce,” he says. “Those who can turn data into insight will have a decisive competitive advantage.”
That advantage requires speed. Five-year project planning cycles don’t work in data and analytics, where requirements evolve within months.
“Things change much more quickly than they do in the transaction processing world of policy and claims,” Broussard says.
For example, the core information a personal lines auto application collects today isn’t dramatically different from what carriers were collecting 30 years ago — but how insurers evaluate, enrich, and act on that data has changed completely.
With the right platform in place, insurers can respond to new MGA partnerships, regulatory requirements, third-party data sources, or business needs in tight timeframes.
“You can answer the business needs as they happen, especially if they’re reporting, analytics, and GenAI and AI-based,” Broussard says. “A lot of times the data is there and you can get it instantly — if you’ve got the right foundation in place.”
Without that foundation, insurers face a harder truth: no matter how sophisticated their AI ambitions, they’re building on sand.
“The question isn’t whether insurance will keep changing — it will, faster than anyone expects,” Broussard says. “But if you’ve built a foundation where all your data is aggregated, auditable, and trusted — a true single source of truth — you won’t need to predict it. You’ll be ready for it. At Percipience, that’s not a vision statement — it’s what we deliver.”
