Applying AI to an inefficient workflow with unnecessary approvals or handoffs won't solve the core problem. Teams must first optimize their manual processes to be efficient before looking to AI for automation. This ensures AI adds value rather than just automating existing flaws.

A critical error in AI integration is automating existing, often clunky, processes. Instead, companies should use AI as an opportunity to fundamentally rethink and redesign workflows from the ground up to achieve the desired outcome in a more efficient and customer-centric way.

Companies can either augment existing processes with AI for incremental efficiency (e.g., co-pilots) or completely redesign workflows. While augmentation is common, the most transformative value and disruptive business models will emerge from a clean-sheet redesign of how work is done.

Drawing a parallel to the slow adoption of PCs in the 90s, Boris Churney argues companies won't see AI productivity boosts by simply layering it onto old workflows. The biggest benefits come from placing AI at the core of the business and redesigning processes around its capabilities, eliminating old bottlenecks entirely.

The historical adoption of electricity in factories shows that true productivity gains came from redesigning the factory floor, not simply replacing steam engines. Similarly, companies must fundamentally re-engineer processes around AI to unlock its transformative potential.

The greatest value of AI isn't just automating tasks within your current process. Leaders should use AI to fundamentally question the workflow itself, asking it to suggest entirely new, more efficient, and innovative ways to achieve business goals.

Before any AI is built, deep workflow discovery is critical. This involves partnering with subject matter experts to map cross-functional processes, data flows, and user needs. AI currently cannot uncover these essential nuances on its own, making this human-centric step non-negotiable for success.

Don't put AI on a broken process. Before applying AI, first map and optimize your current workflows. AI can't fix fundamental flaws like too many approvals or unnecessary handoffs; it can only accelerate an already efficient process.

The primary barrier to corporate AI adoption is not the technology but the 'capability overhang'—the gap between AI's potential and a company's ability to use it. Many organizations lack documented processes for how work actually gets done, making it impossible to apply AI effectively.