The AI Efficiency Gap: Why Deployment Doesn't Equal Readiness
Half of organizations piloted general-purpose AI tools last year, according to MIT research. But buying the software and making it work are two different problems.
Executives are moving fast. Employees are catching up-sometimes with mixed results. A ServiceNow executive recently watched the company's own AI tool make a basic math error during a live session. The gap between what these tools promise and what they actually deliver has become its own job.
Who Bears the Cost When AI Doesn't Work
The burden falls on workers who had no say in the decision to adopt the technology in the first place. Rumman Chowdhury, former U.S. Science Envoy for AI and CEO of Humane Intelligence, describes the dynamic plainly: executives face pressure to deploy AI and incentives to claim it works well. When it doesn't, responsibility shifts to employees.
"If and when it doesn't work, the responsibility is on the employee who had no say in whether or not this technology was adopted and used, or even often what it was used for," Chowdhury said.
The Hidden Labor in AI Output
For employees without technical backgrounds, the promise of efficiency comes with a catch. Getting useful output requires time and effort that often goes untracked. Employees spend hours crafting prompts, checking results for errors, and refining requests-work that wasn't in their job description.
The question now facing organizations is whether the fix is better training or more realistic expectations about what AI can deliver. For now, companies are absorbing the cost of this additional labor.
Managers implementing AI should consider prompt engineering training to close the gap between deployment and actual productivity. Understanding how to structure requests and validate outputs reduces the hidden labor burden on teams.
Beyond training, managers need clearer frameworks for managing AI adoption that account for the real time required to make these tools useful.
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