"AI can't make informed decisions when workflows are improvised, institutional knowledge is undocumented, and escalation paths live in someone's head. Approvals that happen ad hoc in Slack and inconsistent team processes leave no single source of truth for AI to follow. And when data is scattered across siloed platforms-the leading cause of lost institutional knowledge in the past year-even the most dynamic, context-aware models struggle to generate accurate insights or identify risks."
"Your company rolls out an AI agent to assign tasks, draft updates, and nudge overdue approvals. But within days, it's flagging completed work, tagging the wrong people, and creating confusion instead of clarity. It's a familiar outcome for companies that adopt agentic AI without the workflows, data, or systems to support it. New research from Wrike reinforces that disconnect: 74% of employees say their company treats data like gold, yet most don't manage it well enough for AI to use it effectively."
Agentic AI often magnifies broken operations when organizations deploy it without stable workflows, documented institutional knowledge, and unified data. Scattered approvals, ad hoc processes in tools like Slack, and siloed platforms prevent AI from accessing a single source of truth, causing misassignments and confusion. Automation without structured ownership, execution order, and visibility amplifies dysfunction at scale. A phased approach is required: tighten processes, define roles and escalation paths, consolidate and manage data, and clarify what to automate. Proper foundations let AI act with accurate context, enforce accountability, and actually move work forward.
Read at Fast Company
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