There is tremendous enthusiasm today for AI-enabled solutions. Leaders across industries are encouraging their teams to experiment, identify use cases, and accelerate adoption. The intent is admirable, and AI has enormous potential to transform how work gets done.
Yet much of this enthusiasm reminds me of sending thousands of treasure hunters into uncharted territory with shovels, compasses, and boundless energy, but without a treasure map.
Everyone is digging while few are finding treasure.
The result is often a significant amount of well-intentioned effort focused on the wrong places. Teams chase productivity opportunities wherever they appear, hoping that enough experimentation will eventually uncover value.
What's frequently missing is a structured method for identifying where the real opportunities exist.
This is where Lean, Continuous Improvement (CI), process mapping, and tools such as the A3 become invaluable. They provide the treasure map.
The Problem with AI Business Cases
Many AI business cases appear compelling at first glance but struggle under deeper scrutiny.
They are often built on vendor benchmarks claiming productivity improvements of 30 to 50 per cent.
These benchmarks are usually developed by sincere and knowledgeable people who are genuinely enthusiastic about the potential of their products.
The challenge is not necessarily the benchmark itself. It’s how those benchmarks are applied.
Most commonly, the benchmark reflects improvement in a specific task.
For example, AI may help an employee draft an email 50 per cent faster, reducing the effort from twenty minutes to ten.
That task-level improvement is then extrapolated into a much larger business case:
150 employees × 20 emails per day × 10 minutes saved per email = approximately 63 FTEs of capacity.
The conclusion is that AI will create a 40 per cent productivity improvement across the department.
On paper, the treasure has been found. In reality, the team may simply be digging in the wrong place.
