A few months ago, I came across the latest report from PEX Network on operational excellence and business transformation. While it comes as no surprise that, in a context of increased constraints and a constant search for agility, operational excellence initiatives have become more strategic than ever, they are no longer confined to industrial operations. They now affect the entire organization, and all business lines are involved, whether customer service, finance, HR, or marketing.
Nor will it come as a surprise to learn that, in this context, artificial intelligence is emerging as the main investment driver over the next 12 months, with change management as the skill required to ensure its success.
But after reading the report, one question that is close to my heart remains unanswered: in this large-scale transformation, what will become of knowledge work (Knowledge workers, the excluded from operational excellence?)? What about the experience of those who produce value not through manual labor, but through analysis, coordination, creativity, problem solving, and collaboration?
In short:
- Operational excellence initiatives now extend to all business functions and have become a strategic lever for transformation.
- Artificial intelligence is seen as a major investment lever for simplifying, accelerating, and increasing human capabilities, without replacing them.
- The success of transformations depends on change management, with a focus on clear objectives, team involvement and collective momentum.
- Continuous improvement is increasingly being applied to cognitive functions, with the aim of reducing mental workload, structuring workflows and restoring meaning to work.
- The development of a data culture must be accompanied by a strengthening of judgment, critical thinking, and understanding of practical applications.
A few figures to start with
And if you don’t like figures and want to get straight to the heart of the matter, you can scroll down to the next section.
Key points
- AI will be the main area of investment in 2024-2025 to support transformation.
- Nearly half of businesses (48%) are rolling out company-wide transformation strategies.
- Operational excellence (OPEX) has become critical to supporting growth, turnarounds, and strategic objectives.
- Change management is the most widely used approach to support these transformations.
- Process improvement remains an investment priority for businesses.
Overall status of OPEX and transformation
- The leading departments are: operations, lean/OPEX, experience/digital.
Businesses are at different stages of maturity:
- 33% are building their OPEX framework
- 20% are undergoing continuous improvement
- 10% have not yet started
- The main sponsors are CEOs (27%), the board (17%) and COOs (11%).
AI: a growing strategic imperative
- 58% of businesses have started thinking about AI.
- 14% have an initial pilot project in development, and only 5% have industrialized several.
The main use cases are:
- Operations (35%)
- Customer service (29%)
- Data processing (24%)
- 47% of businesses plan to invest in AI in the next 12 months.
- Generative AI coupled with process mining is seen as a lever for gaining operational insights.
Motivations and KPIs
Main drivers of transformation:
- Strategic objectives (47%)
- Efficiency gains (43%)
- Customer satisfaction (32%)
Preferred success indicators:
- Productivity gains (26%)
- Cost reduction (17%)
- Revenue growth (14%)
Major obstacles:
- Budget constraints (28%)
- Disconnect between strategy and processes (11%)
- Resistance to change (11%)
- Cultural alignment and leadership issues
The importance of change management
- 56% will invest in employee culture and engagement.
- A human, empathetic and structured approach is vital to limiting resistance.
- Proactive communication, co-construction and recognition are key to successful change management.
The return of (common) sense and purpose in work
The report emphasizes the importance of steering by clear strategic objectives, which may seem obvious but is not always the case.
Too many tools, too much reporting, and too many layers of management have contributed to a loss of clarity about what really matters and even about the purpose of work. This is where well-executed operational excellence initiatives prove their worth: by reconnecting teams to what matters, to the famous “north star”, by enabling them to understand what they produce, for whom, and why. Not to justify their existence, but to restore consistency between effort and usefulness.
AI as a lever for rehumanization (not replacement)
Artificial intelligence is omnipresent in transformation plans, and in the PEX report, it is not presented as a brutal replacement for human jobs, but as a means of accelerating execution, simplifying decision-making, and processing volumes of information that would otherwise be inaccessible, in line with the approach recently adopted by Moderna (HR and IT merger: Moderna redesigns its organization for and with AI).
This is a real opportunity, provided it is used wisely. For knowledge workers, AI should not become a cognitive crutch that infantilizes or a driver of disempowerment, but can and must free up time, relieve repetitive tasks, and reduce unnecessary reporting. It can therefore give more space to analysis, creativity, and reflection, but this requires designing use cases with a view to augmentation, not short-circuiting.
The rediscovery of continuous improvement applied to teams
One of the most interesting points in the report is the strong comeback of continuous improvement. Long confined to production lines and logistics flows, it is now finding its place in so-called intangible functions: support, management, marketing, finance, and I’m not complaining.
We can finally consider applying lean principles to cognitive professions (Just because work is invisible, it doesn’t mean that it can’t be improved), not to monitor or rush them, but to reduce noise, information overload, and cognitive overload, improve workflows, and structure interactions.
This is where, in my opinion, we find a pitfall shared by lean and AI. Lean has often been misused to reduce headcount, which is one of the reasons for the failure of many projects and mistrust on the part of employees, even though this is not the nature of the approach at all (Lean Isn’t About Cutting Heads — It’s About Growing People). And there is much to suggest that AI is heading down the same path.
As for the concept of flow applied to knowledge workers, although I find it appealing, it must be admitted that it raises questions to which few businesses have the answers (Thinking of work as a flow: appealing, but is it realistic?).
Whatever the case, the idea is to allow teams to suggest improvements themselves (Improving a team’ s work: a story of continuous improvement). It is not up to cross-functional management or an algorithm to say how work should be done, but rather the teams themselves, if given the means to do so, as they know what is not working and what needs to be adjusted.
Let’s keep in mind the concept of the “iceberg of ignorance” coined by researcher Sidney Yoshita, who told us that while only 4% of problems are visible to managers, 100% are visible to the people on the field!
Collective intelligence before technology
Another implicit lesson from the report is that transformations rarely fail because of the tools, but rather because of a lack of human alignment, listening, and attention to collective dynamics.
Even the most powerful technologies only have an impact if roles are clear, expectations are explicit, and implementation is carried out in collaboration with users. Knowledge workers, in particular, need room to experiment, quick feedback, and open dialogue about the objectives being pursued. Without this, they develop avoidance or circumvention strategies and may even end up disengaging.
Change management is not a component of the project but is at the heart of transformational work. It cannot be reduced to training and two webinars. It begins at the design stage, in the way the problem is framed, the right people are involved, and the path is collectively built (Change and transformation need a new approach). Operational excellence then becomes a catalyst for collective intelligence, not just a management system. In a way, it is found at the end in the end and the means.
The challenge of data literacy and judgment
Finally, the report highlights a paradox that many people experience on a daily basis: the abundance of data does not necessarily create clarity, and can even create an illusion of understanding. More and more employees have access to indicators, dashboards, and metrics without always knowing how to interpret them, put them into context, or cross-reference them with reality.
This poses a twofold risk: on the one hand, the risk of acting on the basis of poorly understood data, and on the other, the risk of no longer daring to exercise one’s judgment. However, judgment remains irreplaceable in knowledge-based professions. We must therefore strengthen the data culture, not by training everyone in Python or SQL, but by developing critical thinking, the ability to ask the right questions, and the ability to articulate data, field experience, and professional intuition.
Bottom line
This report points to a promising approach : a reimagined operational excellence that is less focused on costs and speed and more attentive to the quality of work (Let’s talk about the quality of work and Productivity: what if quality was the new quantity?). Augemented OPEX, in the positive sense of the term, i.e., OPEX that clarifies its purpose, reduces irritants, automates what needs to be automated, but without extinguishing human intelligence.
This means moving away from a techno-centric vision, which piles on tools in the hope that the machine will solve the problem, and returning to the essentials: work done well, the ability to collaborate, to decide, to understand what we are doing and why we are doing it. What we say about corporate governance also applies to production activities (Augmented governance: AI as a lever for collective lucidity).
Image credit: Image generated by artificial intelligence via ChatGPT (OpenAI)
Related articles:
Thinking of work as a flow: appealing, but is it realistic?
Just because work is invisible, it doesn’t mean that it can’t be improved
People Centric Operations 2.0: how AI is reinventing knowledge work at scale
People Centric Operations: adapting work and operations to knowledge workers
The importance of soft skills in operational excellence
Show me a completely smooth process and I’ll show you someone who’s covering mistakes, real boats rock. (Frank Herbert)
