AI adoption often starts with automating a workflow, which requires defining the workflow, assigning ownership, setting measurement, and specifying what “good” looks like. Many organizations find workflows are not clearly defined, KPIs are missing, reporting is manual and inconsistent, or the process exists only as informal knowledge. In some cases, the assumed work is not happening at all. AI does not create these issues; it exposes them because ambiguity cannot be automated and vagueness cannot be scaled. Remote work is not the problem; unmeasured work is. AI forces evaluation of value, output, quantification, and human responsibility, removing hiding places and reducing “process theater.”
"AI adoption usually begins with a simple question: Can we automate this? To answer that, companies have to define what the workflow actually is, who owns it, how it's measured and what "good" looks like. That's where things get interesting. In more than a few cases, leaders discover the workflow wasn't clearly defined, the KPI didn't exist, the reporting was manual and inconsistent or the "process" was really just institutional knowledge floating around the organization."
"AI doesn't create those problems; it reveals them. You can't automate ambiguity. You can't scale vagueness. And you definitely can't plug AI into something that never truly worked. Sometimes it's worse. The thing everyone assumed was happening simply wasn't happening at all."
"Remote work is not the problem. Unmeasured work is. For high-output professionals, AI and remote flexibility are a dream combination. These employees deliver results, communicate clearly and create measurable value. No one questions their autonomy. But for those operating in the gray area-long lunches, two-hour walks, knocking out their latest DIY project while their Slack status stays green-AI adoption is not their friend."
"Because AI forces measurement. When companies start evaluating which workflows can be automated, they inevitably begin asking harder questions. What value does this role actually create? What does a top performer in this position produce? Can we quantify it? If AI can complete a large portion of this work instantly, what should the human actually be responsible for? And that's when the spotlight turns on. Not out of cruelty, but out of necessity."
Read at Forbes
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