A company-wide AI initiative has produced no successful projects and questionable use cases, raising questions about the financial priorities behind the adoption push.

What happened: The author's employer, despite budget constraints elsewhere, has funded ChatGPT and Copilot licenses, external AI workshops, and consultant-led AI adoption strategies. Dozens of department teams have presented LLM projects in company-wide meetings, but none have worked out—all concluded the technology either wasn't workable, didn't save time, or overcomplicated tasks. Presented use cases for general employee adoption included asking a bot how it feels, using ChatGPT to summarize a one-page cafeteria menu, and uploading suspicious emails to ChatGPT for security assessment.

Why it matters: The employer has cut employee bonuses permanently, left open positions unfilled, and dropped necessary licenses and databases to save money—yet found immediate funding for expensive, unreliable AI tools. The author observes this reveals how quickly leadership can mobilize resources when it chooses to, while suggesting the AI spending may reflect hype and institutional pressure rather than genuine workplace need. Employees are spending significant time testing tools that produce no measurable benefit while core departmental work suffers.

What to watch: The author notes that in their non-coding field of work, hundreds of people across teams have tested AI projects without a single success. Hallucinations, inability to properly fill or edit documents, and the time required to verify and correct outputs remain core obstacles. The question raised is whether this pattern—zero successful deployments despite sustained investment—will eventually prompt a reassessment of the spending priorities.