In our PDCA series we have looked at models ranging from Lean and Agile to PACE, BABOK and PMBOK. Now we turn to SPAR, an approach described by Pascal Bornet in his work on artificial intelligence and automation. SPAR stands for Sense – Plan – Act – Reflect, and it provides a way of thinking about intelligent systems and decision-making cycles.

The Four Steps of SPAR

• Sense: Collect data from the environment—sensors, systems, feedback, or human input. This is how an AI or a team understands the current state of the world.
• Plan: Analyze the data, define objectives, and design possible courses of action. This step mirrors the planning in PDCA but is enriched by continuous data flows.
• Act: Carry out the chosen plan—whether that’s a decision made by humans, an AI agent taking action, or an automated process.
• Reflect: Review the outcomes, learn from the results, and feed that learning back into the system. Reflection ensures that future sensing and planning are smarter and more accurate.

SPAR Compared to PDCA

SPAR is often described as an evolution of PDCA. Both cycles emphasize planning, doing, and learning. But SPAR adds a stronger emphasis on sensing upfront—recognizing that in today’s world, organizations and AI systems must continuously process incoming data to make informed decisions. Similarly, the reflect step highlights the importance of deliberate learning, which in AI can mean retraining models or refining algorithms.

Why SPAR Matters

SPAR is particularly relevant in the age of artificial intelligence, where systems must sense vast amounts of data, plan using analytics, act in real-time, and reflect to improve. But the model applies equally well to humans and organizations: better sensing, smarter planning, decisive action, and thoughtful reflection create a virtuous cycle of improvement.

An Example of SPAR in Action – Energy Use

Imagine an AI system that manages energy use in a smart building. It senses temperature, occupancy, and energy prices. It then plans by calculating the best heating/cooling schedule. It acts by adjusting thermostats and lights. Later, it reflects by comparing predicted savings with actual results, learning to improve future performance.

Closing Thoughts

SPAR takes the spirit of PDCA and reimagines it for a world of intelligent systems. By highlighting sensing and reflecting, it emphasizes the importance of data and learning in continuous improvement. This makes SPAR a valuable addition to our series, showing how improvement cycles continue to evolve in the age of AI.