3 articles
Anthropomorphic “digital worker” language leads teams toward brittle automation. Reliable applied AI starts by redesigning the flow of work around bounded tasks, observable state, and explicit authority.
As model capabilities become easier to buy, the scarce advantage is shifting to the unglamorous work of redesigning queues, approvals, data contracts, and exception paths. Companies budgeting only for licenses are budgeting for a demo.
Enterprises keep blaming weak results on prompts, model choice, or employee training. More often the failure is structural: the AI is layered onto a workflow that was never designed to let automation carry real responsibility.