Embracing Operational Excellence in the Age of AI

Mark Fitzsimmons
Oct 18
3 min read

Updated: Nov 5

A decade ago, operational excellence revolved around value-stream maps, gemba walks, Kaizen events, and Green Belts tracking cycle times and defects. Today, the best process-improvement tools don’t reside in dusty binders; they live in your AI sidebar.

As we navigate faster markets, greater complexity, and a squeeze on talent and time, organizations are adopting a hybrid model. This model combines proven methods with generative AI and real-time analytics. The outcome? Shorter improvement cycles, deeper insights, and greater agility.

From Gemba Walks to Generative Loops

Traditional improvement programs still hold value, but they tend to be slower, more resource-intensive, and heavily manual. In contrast, AI-assisted models can summarize Voice of the Customer (VOC) comments in minutes, detect patterns in supply-chain data, and generate draft control plans or Standard Operating Procedures (SOPs) in hours.

Example: A manufacturer utilized AI to identify a pattern in warranty claims that engineers had previously overlooked. What once took a week of analysis was reduced to just 18 hours after deploying a targeted AI model.

Supporting Data:

62% of organizations now use AI for operational efficiency.
92% of executives expect to increase AI investment in the next three years.
In Canada, AI adoption doubled from 6% to 12% between 2024 and 2025.

The trend is clear: AI is transitioning from pilot projects to practical applications, and process-improvement leaders should take the lead.

Three Forces Driving the Shift

Data Density – Every process now generates more data than we can analyze manually.
Decision Velocity – Markets evolve faster than traditional DMAIC cycles can keep up.
Digital Confidence – Boards demand data-driven transparency and measurable speed.

This shift mirrors what we see in GAO schedule best practices: traceable logic and credible reasoning form the backbone of trust. In AI-driven improvement, traceable reasoning is the new non-negotiable.

D·AI·MAIC — The Hybrid Framework

Introducing a refreshed take on DMAIC that embeds AI across every phase:

| Step | AI Enhancement | Example |

|---------|----------------------------------------------|----------------------------------------------|

| Define | Natural-language summaries of VOC data | ChatGPT creates SIPOC in minutes |

| Measure | Power BI Copilot dashboards | Real-time performance baselines |

| Analyze | AI-assisted root-cause mapping | Automated Fishbone & clustering |

| Improve | AI brainstorming & simulations | Suggests high-impact interventions |

| Control | Automated monitoring & alerts | Generates control charts & SOPs |

PwC 2025: Companies using AI-augmented Lean saw 2.3× faster cycle-time reduction.

Case: In logistics redesign, AI produced 12 routing scenarios in just 10 minutes, while analysts took a week pre-AI.

Risks and Governance

With great speed comes greater responsibility. AI doesn’t eliminate the need for process discipline; it amplifies it.

According to BCG (2025), only 5% of companies report real ROI from AI, while 60% see little to none. Why is this the case? Because tools alone don’t drive excellence; structured governance does.

Smart organizations embed:

Human-in-the-loop reviews
Data lineage tracking
Ethical and bias controls
AI audit trails

Governed AI equals credible AI.

The Human Factor

AI can generate solutions, but it cannot generate conviction. Process excellence remains fundamentally human; it thrives on empathy, curiosity, and accountability.

“During one AI workshop, a frontline supervisor asked, ‘Does this mean we’ll stop having huddles?’ The facilitator replied: ‘No, we’ll just walk into them with smarter data.’”

Getting Started

Audit your processes for data richness.
Select one pilot project with measurable outcomes.
Layer AI onto a strong Lean/DMAIC foundation.
Track ROI in terms of time saved or defects reduced.
Scale gradually, ensuring governance is baked in.

Closing Thought

“The future of operational excellence isn’t just faster; it’s smarter, safer, and more scalable.” Organizations that thrive won’t replace human ingenuity with algorithms. Instead, they will teach algorithms to serve disciplined human judgment.

In this evolving landscape, it’s essential to recognize that the integration of AI into operational processes is not merely a trend but a transformative journey. By embracing this change, we can unlock new levels of efficiency and effectiveness, ensuring sustainable growth and improved financial performance.