Across industries, organizations are reporting a disconnect between AI hype and tangible results. While adoption rates are high—70% of firms now use AI according to recent surveys—measurable productivity gains remain elusive for most.
The issue isn’t necessarily with the technology itself but rather how it’s being applied. Many companies are using AI to accelerate existing processes without first questioning whether those processes should exist at all.
As one industry observer noted, ‘There is zero value in doing a task faster if that task shouldn’t exist.’ Automating inefficiencies only creates faster inefficiencies—a problem that spans generations of technology adoption from mainframes to cloud computing.
The solution? Prioritize process simplification before automation. Redesign workflows, eliminate unnecessary steps, and then apply AI to the streamlined foundation. The few firms seeing real productivity gains aren’t using better models; they’re doing the foundational process work first.
This approach requires a shift in mindset—from asking ‘How can we use AI?’ to ‘What should we stop doing?’ When implemented correctly, AI becomes not just an accelerator but a catalyst for fundamental improvement.