Why Conglomerate Operating Systems Are Stifling Commercial Innovation in Life Sciences

In the life sciences sector, conglomerates like Danaher, Thermo Fisher Scientific, GE HealthCare, and others have excelled at driving internal product innovation and enabling client-side breakthroughs. Their operating models—systems such as the Danaher Business System (DBS) or Thermo Fisher’s Practical Process Improvement (PPI)—have become gold standards for operational excellence. However, these same systems that catalyze product and process innovation are increasingly hampering commercial innovation. Specifically, they are slowing the adoption of AI-driven solutions, modern go-to-market strategies, dynamic pricing models, and customer orchestration capabilities critical for the future of healthcare and life sciences.

The Strength of the Machine: Conglomerate operating systems bring undeniable strengths: relentless focus on continuous improvement, deep-rooted Lean and Six Sigma principles, and operational discipline. These frameworks have been instrumental in:

• Scaling manufacturing precision.
• Improving R&D cycle times.
• Standardizing quality across complex portfolios.

Their impact on product excellence and customer enablement has been transformative, particularly in complex verticals such as bio-manufacturing, precision diagnostics, and MedTech innovation.

The Hidden Cost

Yet, this rigid adherence to predefined processes comes at a significant cost when organizations attempt to innovate “front of house.” Commercial teams—sales, marketing, pricing, and customer success—operate in a radically different environment today:

• Buying cycles are increasingly digital, dynamic, and consultative.
• Customers demand value-based outcomes, not just product features.
• AI and data analytics can (and should) rewire account management, sales forecasting, and pricing strategies.

Traditional conglomerate systems prioritize efficiency, control, and standardization. Commercial innovation, by contrast, requires adaptability, experimentation, and speed. Embedding AI into these legacy operating systems becomes difficult because the systems were never designed to support fluid, feedback-driven commercial dynamics.