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Automation cannot succeed when procedures remain undocumented or confined to a single employee's memory. Leaders must first map out clear, replicable steps from start to finish, establishing a solid process before introducing AI tools to execute work.
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.
Many companies rush to automate messy processes, which only locks in inefficiency. Instead, learn and refine the process by doing it manually first, as early Amazon and DoorDash did. Only automate once the system is optimized, using technology to speed up good systems, not paper over bad ones.
Automating a flawed process is like "pouring cement" on it. Before implementing AI or automation, firms must rigorously question every requirement, delete unnecessary steps, simplify what remains, and then accelerate cycle time. Automation should always be the final step to avoid locking in complexity and wasting energy.
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.
The biggest gains from AI come not from automating steps in an existing process, but from starting with the desired outcome and co-creating a new workflow with AI. This "first principles" approach leverages AI's capabilities far more effectively than piecemeal automation.
Successful AI implementation isn't about applying it superficially over existing operations. It demands deep process mapping and rebuilding workflows from the ground up. Simply automating flawed processes only makes the business execute its mistakes faster.
Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.
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.
To identify prime automation opportunities, analyze your company's existing SOPs. These documents explicitly list the sequential steps, data sources, and transformations in a predictable process. If a process is documented for frequent human use, it's a strong candidate for a high-value automation workflow.
The first step to building an AI workflow is mapping the existing manual process. This exercise alone reveals significant waste and inefficiencies that have nothing to do with AI. The push to adopt AI acts as a forcing function for companies to finally audit and streamline their core operational workflows for the first time.