Here's a pattern we see regularly: a business has a workflow that's been frustrating everyone for years. Someone says "let's use AI to fix it." They bolt AI onto the existing process. And now they have the same frustrating workflow, except it runs faster and produces errors at scale.
AI amplifies, it doesn't fix. If your client onboarding process has eight unnecessary steps, AI will help you do those eight unnecessary steps more efficiently. It won't tell you that five of them shouldn't exist.
Fix first, then automate. Before introducing AI to any workflow, ask: if we were designing this process from scratch today, what would it look like? Strip out the steps that exist because "that's how we've always done it." Then look at where AI accelerates the streamlined version.
The diagnostic question. When someone says "this takes forever and AI could help," ask: why does it take forever? If the answer is "because the task itself is complex and data-intensive," AI is probably a great fit. If the answer is "because we're waiting on three approvals and reformatting the same document four times," you have a process problem, not a technology problem.
The silver lining. AI adoption is actually an excellent forcing function for process improvement. The exercise of defining a workflow clearly enough for AI to follow often reveals the inefficiencies that humans have been working around for years. Use the AI implementation as the excuse to finally fix what's been broken.