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As companies integrate AI into traditional process excellence frameworks, the fusion of technology and disciplined methodologies is reshaping operational efficiency.
In recent years, frameworks like Lean Six Sigma and Business Process Management (BPM) have been the go-to strategies for bringing order to complex operations. Lean Six Sigma emphasized statistical rigor and quality control, while BPM provided end-to-end maps of workflow processes across departments. Both approaches embedded habits of measurement, analysis, and accountability into company culture.
However, these time-tested methodologies are now evolving as businesses seek to leverage AI to enhance operational excellence. According to recent estimates, the market for AI-powered process optimization is projected to exceed $113 billion within the next decade. A study found that 88% of business leaders anticipate increasing investments in AI-infused process intelligence over the next 12 to 18 months.
The integration of AI into established process excellence methodologies is not just a trend; it's a strategic imperative. Companies with mature process disciplines are better positioned to translate AI ambitions into tangible outcomes. This is because they already have a strong foundation in data-driven decision-making and process discipline, which are essential for AI systems to deliver value.

For example, a company with a robust Lean Six Sigma program can use AI to identify inefficiencies in real-time, optimize workflows, and predict maintenance needs. This synergy between process excellence and AI can lead to significant improvements in operational efficiency and cost savings.
In essence, while AI can accelerate process excellence, existing process excellence is what makes AI truly impactful. Technology and process are no longer separate levers; they must be aligned for organizations to achieve operational excellence in the AI-driven era.
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Achieving operational excellence with AI
↗ https://www.technologyreview.com/2026/07/02/1140045/achieving-operational-excellence-with-ai
About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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