How Tech-Stack Fragmentation is Breaking MedTech’s Promise

Every innovation in MedTech ultimately serves the same core purpose: to deliver with reliability, quality, and speed. Whether manufacturing surgical instruments, diagnostic equipment, or connected care systems, providers and patients rely on MedTech organizations to meet that promise daily.

But behind the scenes (and increasingly, in full view of customers), that promise is getting harder to keep.

While pressure builds to keep costs sustainable and maximize profitability, MedTech operations also face a perfect storm of external pressures: shifting global trade policies, localized manufacturing demands, complex production environments, and relentless M&A activity.

As soon as they attempt to adapt to these external pressures, MedTech organizations quickly run into the same, pervasive internal barrier: tech-stack fragmentation. I sat down with Allan Jaenicke, SVP of Transformation at Waters Corporation, to discuss how organizations can overcome this fragmentation, and get back to fulfilling MedTech’s promise.

The root cause: system silos and tribal knowledge

Life sciences organizations have traditionally prioritized scientific discovery and strict regulatory compliance over modernization initiatives, leaving their underlying IT infrastructures a step behind. Following decades of organic expansion and acquisitions, most now rely on a patchwork of isolated systems to keep up with the pace of change.

"The technology and process landscape of MedTech companies is fragmented and often outdated," says Allan. "Our industry is very much playing catch-up at a time when the world is changing faster and faster."

I see the same patterns among the customers I work with today. Their business-critical processes run across siloed systems —ERP, QMS, MES, CRM — that were designed for distinct functional tasks instead of shared objectives. As a result, visibility and accountability are fragmented. Take manufacturing for example: production completion in MES doesn’t automatically mean a product is available for shipment. You still need to coordinate things like quality review, batch or device release requirements, and distribution readiness. In supply chain and field service operations, the same need for cross-system coordination applies.

Jaenicke himself points to a common example of how these functional boundaries can lead to operational blind spots in regulated environments: "If a customer files a quality complaint, that information lives in a system run strictly by the quality team. That data isn't automatically accessible to commercial teams in CRM, so when sales reps visit that account, they lack critical context about ongoing service issues."

Compounding this system fragmentation is a reliance on tribal knowledge. In tenured MedTech organizations, deep operational expertise often lives inside employees' heads rather than in documented digital models. As experienced engineers reach retirement age and organizations expand global footprints, working without the “ground truth” of operational reality that they once harboured makes it extremely difficult to standardize manufacturing lines across regions, or adjust workflows on a continuous basis.

Why context is key

To move fast and bypass the need for sweeping infrastructure overhauls or reskilling, many organizations are turning to Enterprise AI. However, inserting AI models directly on top of a fragmented foundation is a risky endeavour. Without a unified data foundation, AI tools waste time (and a lot of money) wrangling mismatched records or generating unreliable outputs.

To reason correctly, decide sensibly, and act reliably in a MedTech environment, AI needs full operational context: from the underlying business logic to the regulatory guardrails that govern how your business actually operates.

That’s what the Celonis Platform is for. It extracts and unifies data across your ERPs, CRMs, and specialized legacy tools, to deliver a living representation of your business in motion. Unlike a static snapshot, you unlock real-time visibility into how your end-to-end processes actually operate in real time, including their dependencies, business rules, and constraints. This operational context is exactly the input AI needs to make informed decisions, and support effective execution in regulated MedTech environments.

For Waters Corporation, the impetus to build this living representation of the business became critical as the company prepared to double in size following a major acquisition and transition to a new ERP platform. With legacy processes historically living as undocumented tribal knowledge, scaling consistently across business units demanded a verifiable, single source of operational truth.

"As we doubled the size of the business through acquisitions, while going through an ERP transformation, we realized we couldn’t scale unless we had that foundation in place and a true understanding of our processes," explained Jaenicke. "What Celonis has enabled us to do is create a system-agnostic data model across our applications to build a foundational understanding of the company. This allows us to bring process data together and establish a single home for information that directly fuels analytics, process mining, automation, and AI."

Explore Celonis for MedTech in more detail

Overcoming fragmentation is now an imperative for any MedTech organization looking to safeguard margins, maintain regulatory compliance, and build the foundation for Enterprise AI. Most importantly, it’s critical to staying true to MedTech’s promise: ensuring that devices are manufactured and released on time, that equipment is repaired promptly, and that customers receive consistent, reliable service.

To explore how the Celonis Platform can transform your core MedTech operations—from reliable supply chains to more effective field services and inventory optimization—read our newly published guide at celonis.com/med-tech/brochure

With special thanks to Waters Corporation for sharing their insights.