Operational excellence in life sciences is changing. It is no longer enough to run Lean projects, reduce deviations, improve yield, or drive local cost savings. Those things still matter, but the environment has become more demanding. Pharma and MedTech companies are under pressure to improve productivity, accelerate decision-making, strengthen quality, build resilience, and use digital and AI tools more effectively across increasingly complex operations. McKinsey describes 2025 as a turning point in which gen AI and agentic AI began moving from experimentation toward enterprise impact in operations, while its broader life-sciences work highlights the need for greater productivity and operational agility.
That shift is what makes this the next generation of operational excellence. It is not a rejection of traditional excellence. It is an expansion of it.
From efficiency programmes to operating-system redesign
For years, operational excellence in life sciences was often treated as a structured improvement programme. It focused on waste reduction, variation control, performance management, and compliance support. Those foundations still matter. But today’s operating environment demands more than better local execution. It requires organisations to rethink how work flows across functions, how decisions are made, how digital tools are embedded, and how quality and resilience are designed into the system. McKinsey’s life-sciences compendium says companies face pressure to boost growth, improve productivity, and increase operational agility, not merely report performance more effectively.
In other words, the next generation of operational excellence is less about running improvement activity and more about redesigning the operating model.
Digital makes operational excellence more important, not less
A common mistake is to assume that digital transformation replaces operational excellence. In reality, digital makes it more important.
Life sciences companies are investing in connected systems, analytics, digital quality, automation, and AI. But these technologies do not automatically create better operations. If processes are fragmented, decision rights are unclear, or data foundations are weak, digital tools can simply make those weaknesses more visible. McKinsey’s operations work argues that the real story in 2025 was not just technology adoption, but how organisations rewired operations to improve productivity, innovation, and resilience.
That matters especially in regulated sectors. A dashboard can show a recurring deviation, but it does not prevent recurrence. An AI tool can generate insight, but it does not replace good judgement. A workflow system can route a CAPA, but it does not guarantee a strong investigation. The next generation of operational excellence therefore sits at the intersection of process, quality, data, technology, and human capability.
Life sciences excellence now has to be cross-functional
One of the biggest changes is that operational excellence can no longer sit inside one function. In life sciences, performance depends on how well technical development, manufacturing, quality, supply chain, engineering, regulatory, and commercial interfaces work together.
Many operational problems do not belong to a single department. They live in handoffs, slow escalations, competing priorities, and disconnected data. That is why the next generation of excellence has to be cross-functional by design. McKinsey’s recent life-sciences perspective points to increasing operational interdependence and the need for greater agility across the enterprise.
This changes the role of the operational-excellence leader. It is no longer enough to be a method expert. The role increasingly requires integration, influence, and the ability to translate between quality, operations, digital, and leadership teams.
Quality must be embedded, not inspected afterward
In life sciences, operational excellence has always had a close relationship with quality. In the next generation, that relationship becomes even tighter.
Quality cannot be treated as something checked after the process. It has to be built into how the process is designed, monitored, and improved. McKinsey’s “smart quality” work argues that pharmaceutical and medtech companies can dramatically improve quality-assurance processes by combining modern ways of working with technology, rather than relying on traditional quality models alone.
This is a major shift. The best life-sciences organisations will not separate operational excellence from quality excellence. They will use operational thinking to improve investigations, CAPA, process capability, release performance, and the reliability of decision-making. In that model, quality is not just a control function. It becomes part of the operating system.
The next generation is human-centred
Another important shift is the move toward a more human-centred model of excellence. The European Commission’s Industry 5.0 framework explicitly defines the future of industry as human-centric, sustainable, and resilient, with worker wellbeing placed at the centre of the production process.
That matters in life sciences because many digital and improvement programmes still focus too heavily on process mechanics and not enough on the experience of work. A system may be compliant and technically powerful, but still be awkward to use. A process may be standardised, but still overload teams with complexity. A reporting routine may be comprehensive, but still fail to help people make better decisions.
The next generation of operational excellence therefore has to ask a different question: not only “Is the process efficient?” but also “Does the process help people perform well?” That is not a soft issue. It is an operational one. Human-centred design affects adoption, judgement, escalation quality, and ultimately performance.
Skills are now a core part of the excellence agenda
Operational excellence used to be associated mainly with tools and methods. Today it is also a skills agenda.
The World Economic Forum’s Future of Jobs Report 2025 says analytical thinking remains the top core skill among employers, with resilience, flexibility, agility, leadership, and social influence also ranked highly. It also notes major expected shifts in workforce skills through 2030.
For life sciences, that means the next generation of excellence depends on more than Lean knowledge. It depends on people who can interpret data, think critically, work across functions, operate in digital environments, and make sound decisions under complexity. The old model of the excellence practitioner as a tool expert is becoming too narrow. The newer model is someone who can improve systems, build capability, and connect operational discipline with digital fluency.
Resilience is now part of performance
Another major change is the role of resilience. Life sciences operations are exposed to supply volatility, regulatory pressure, product complexity, network constraints, and increasing digital dependency. A process that is efficient under perfect conditions is no longer enough.
Industry 5.0 explicitly treats resilience as a core pillar alongside sustainability and human-centricity. McKinsey’s recent operations work also emphasizes resilience as a central outcome of operational rewiring.
This has practical implications. The next generation of operational excellence should not only ask how to improve throughput or reduce cost. It should also ask how well the operation senses disruption, responds to instability, recovers from failure, and continues to perform under pressure. In life sciences, that is increasingly part of what excellence means.
AI will raise the bar, not lower it
AI is likely to intensify all of these trends. McKinsey’s recent life-sciences and operations material points to AI and agentic AI as technologies that can help reshape operations and enterprise performance.
But AI will not reduce the need for operational excellence. It will raise the bar for it.
If data is inconsistent, processes are unclear, or governance is weak, AI can amplify confusion rather than remove it. If an organisation lacks critical thinking and operational discipline, faster tools can accelerate the wrong actions. The next generation of operational excellence therefore needs to provide the structure around AI: better process logic, better data stewardship, clearer ownership, and stronger human judgement.
What the next generation looks like in practice
In practice, next-generation operational excellence in life sciences looks broader and more integrated than before.
It connects operational improvement with digital transformation.
It treats quality as part of performance, not separate from it.
It focuses on cross-functional flow, not just local optimisation.
It develops people capabilities alongside systems and tools.
It measures resilience and decision quality, not just output.
It uses technology to support better work, not just more reporting.
That direction is consistent with McKinsey’s operations and life-sciences research, the Industry 5.0 framework, and the WEF skills outlook.
Conclusion
Operational excellence in life sciences is entering a new phase. The next generation is not about abandoning Lean, Six Sigma, or classical improvement disciplines. It is about extending them into a world shaped by digital transformation, AI, workforce change, resilience pressures, and Industry 5.0 expectations.
The organisations that will lead are unlikely to be the ones with the most dashboards or the most improvement activity. They will be the ones that build a more integrated operating system: one that combines quality, productivity, human capability, digital enablement, and resilient execution into a coherent whole. In life sciences, that is what the next generation of operational excellence should look like.