"Here's a story I've heard 10 times in as many weeks: XYZ company has a new AI-enabled internal workflow initiative. Rollout happens as a decisive move toward "AI-first operations." The rollout fails. What happened? Not long after launch, usage data shows us the story. A fraction of frontline teams use the new system consistently. Others participate occasionally but revert to legacy ways of working when there's pressure or a problem. The remainder bypass the change altogether, relying on spreadsheets, email, or familiar workarounds."
"A fraction of frontline teams use the new system consistently. Others participate occasionally but revert to legacy ways of working when there's pressure or a problem. The remainder bypass the change altogether, relying on spreadsheets, email, or familiar workarounds."
XYZ company launched an AI-enabled internal workflow initiative positioned as a decisive move toward "AI-first operations." The rollout failed to gain broad adoption. Usage data reveals three adoption patterns: a fraction of frontline teams use the new system consistently; others use it occasionally but revert to legacy ways under pressure or when problems arise; and a remainder bypass the new tools entirely. Many teams rely on spreadsheets, email, or familiar workarounds instead of the new system. Incomplete adoption undermined the intended operational transformation and left legacy processes intact.
Read at Harvard Business Review
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