Persona
The following points capture my arguments from a discussion that I held during the conference about whether AI fundamentally changes everything in business operations. While AI undoubtedly creates new technological possibilities, my view is that the underlying struggle for efficiency, quality and better operating models is much older. The key argument is that AI should not be treated as a complete reset of enterprise transformation. It should be understood as a new layer that builds on existing foundations such as process maturity, governance, continuous improvement and operational discipline.
The organisations have been trying to improve how work is structured, managed and optimised for more than a century. Scientific management, quality control, Toyota Production System, Lean, Six Sigma, Theory of Constraints and DevOps all represent different stages of the same journey of making work more reliable, measurable, efficient and scalable.
The main fact is therefore not that AI replaces all previous thinking, but rather that AI should be seen as the next layer in a much longer evolution of enterprise improvement. It gives organisations new tools, but it does not remove the need for clear processes, ownership, governance, feedback loops and disciplined execution.
A recurring problem in transformation is the tendency to start with technology before understanding the operating model. AI can accelerate processes, automate decisions and support knowledge work, but if the underlying process is unclear, fragmented or poorly governed, AI may simply amplify existing inefficiencies.
For that reason, business transformation should build on the foundations that already exist. Lean teaches us to reduce waste and improve flow. Six Sigma teaches us to measure and reduce variation. Theory of Constraints teaches us to identify bottlenecks. DevOps teaches us to shorten feedback loops and integrate delivery with operations. These principles remain highly relevant in the AI era.
The practical conclusion is that AI changes the available means, but not the fundamental management challenge. Businesses still need to understand how value is created, where work gets blocked, what should be standardised, what should be automated and how outcomes should be measured.
AI should therefore be positioned as an accelerator of mature operating models, not as a substitute for them. The organisations that benefit most from AI will likely be those that combine new technology with proven foundations of process maturity, quality management, continuous improvement and clear accountability.
In short, AI may change the speed and scale of transformation, but the need to improve efficiency, quality and flow is an old challenge. Successful transformation should not ignore that history but build on it.