Gen AI in Operations: A Lean Approach to Sustained Productivity

Why Traditional Operations Fall Short

Many businesses still rely on manual data entry, spreadsheet juggling and siloed decision-making. Each hand-off—from procurement to production to quality control—introduces opportunities for error, delay and rework. These inefficiencies translate into longer cycle times, frustrated customers and ballooning costs. From a Lean vantage, these are textbook examples of waste: waiting, defects and unnecessary motion.

Gen AI as the Next Lean Tool

1. Value Stream Mapping with AI

   Begin by mapping the current process: identify every step an order or a maintenance request takes, and note where tasks pile up or errors occur. Then ask: where can AI eliminate non-value-added work? For example, an AI copilot can sift through thousands of maintenance logs in seconds, flagging only those that deviate from normal patterns—replacing manual review.

2. Error Proofing (Poka Yoke)

   In traditional settings, a single mis-typed digit or an overlooked approval can set off a chain reaction: a machine failure, a shipment delay, even a recall. Gen AI can embed validation checks at each touchpoint—whether it’s verifying supplier data in procurement or cross-checking invoice amounts in finance—preventing mistakes from cascading through the value stream.

3. Pull System & One-Piece Flow

   AI-enabled workflows can shift organisations away from ‘batch and queue’ towards one-piece flow. Instead of waiting for a manual bundle of orders to be processed overnight, AI can route each request instantly to the right team. Teams only work on what’s demanded in real time—reducing lead time, inventory and the cost of holding excess work-in-progress.

4. Respect for People

   Lean is not about replacing people with machines; it’s about freeing human creativity. With AI handling repetitive analyses—whether forecasting demand, reviewing contracts or scanning for quality anomalies—teams can focus on root-cause problem solving. This fosters a culture of continuous improvement, where frontline teams propose small experiments (kaizens) that cumulatively yield significant gains.

Real-World Examples

• Heavy-Industry Maintenance: A global manufacturer used gen AI to build a “technician maintenance copilot.” Previously, technicians spent hours diagnosing equipment failures. Now, AI aggregates sensor data, historical logs and expert annotations to suggest probable causes—cutting diagnosis time by 40 percent. This not only reduces downtime but channels human expertise into resolving complex issues rather than fishing for data.

• Procurement Optimisation: A multinational resources company teamed up with legal and finance to deploy AI-driven contract review. Scanning thousands of clauses across multi-billion-dollar deals, AI pinpointed $15 million in quick-win savings by ensuring payment terms aligned with best-practice benchmarks. Rather than poring over PDFs for weeks, teams get instant insights—freeing them to negotiate from a position of strength.

Best Practices for Lean AI Implementation

1. Start with a Pilot, but Aim for Scale:

   Identify a high-impact use case (e.g., quality-control checks in production). Prove ROI quickly, then expand AI to adjacent processes. This builds momentum and secures executive buy-in.

2. Engage Cross-Functional Teams Early:

   AI tends to work best at process hand-offs. Involve procurement, IT, operations and finance in mapping the as-is state and designing the to-be state. Their input ensures the AI solution fits real-world workflows.

3. Invest in Data Governance:

   If data is inaccurate or fragmented, AI outputs will be flawed. Establish clear protocols for data ownership, quality checks and access rights. Treat data hygiene as an ongoing Lean activity, not a one-time project.

4. Foster a Culture of Experimentation:

   Encourage frontline teams to suggest tweaks (for example, adjusting AI thresholds or refining alerts). Use Plan-Do-Check-Act cycles to test changes, measure impact and standardise successful practices.

Conclusion

Generative AI is not a silver bullet—it’s a Lean tool in a broader toolkit of continuous improvement. By embedding AI in targeted processes, organisations eliminate waste, reduce errors and free people to drive genuine innovation. The result? A resilient, adaptive operation that consistently delivers customer value. Embrace gen AI not as a flashy gadget, but as a catalyst for Lean transformation—and watch your operations evolve from reactive to relentlessly proactive.