I've spent time in enough enterprise software deployments to know the pattern: the demo is flawless, the rollout is announced with fanfare, and six months later you're looking at adoption dashboards that make everyone in the room uncomfortable.
A detailed first-hand account posted to Reddit by someone working on AI deployment inside a large organization has gone viral in tech circles this week - not because it's surprising, but because it articulates so precisely why most corporate AI rollouts quietly fail. The problem isn't the technology. It's everything around the technology.
The account identifies five failure modes that will be familiar to anyone who has worked in enterprise software:
Tool access without use cases. Companies roll out Microsoft 365 Copilot licenses across the organization and call it "AI adoption." Nobody explains what people should actually use it for. It's like handing everyone a Swiss Army knife and wondering why they only use the blade. Licenses get provisioned. Usage doesn't follow.
The trust gap among experienced specialists. Senior engineers and specialists with 20-plus years of experience have built careers on judgment and precision. They're not going to blindly trust AI output - and for safety-critical or compliance-heavy work, they absolutely shouldn't. But for drafting, summarizing, structuring first passes? The resistance is costing them hours every week without actually improving outcomes.
The measurement problem. "We deployed AI" is a meaningless metric. The relevant questions are: which exact workflows got faster? Which tasks became more accurate? Which processes became cheaper? Most organizations never measure at that level, so they can't demonstrate value, and momentum fades before it compounds.
Governance gaps that generate panics. Legal, compliance, cybersecurity, and HSE functions need clear boundaries. Where can AI be used? What data is permitted? What decisions require human review? Companies that skip governance because it's slow eventually get the governance conversation forced on them by an incident - someone uses ChatGPT to draft a contract with a client's confidential information, and suddenly the entire AI program is frozen.
Siloed wins that never spread. One team discovers an AI workflow that saves hours every week. It stays within that team. There's no structured mechanism to identify, validate, and propagate what works. Instead of compounding gains across the organization, you get isolated experiments that die when the champion moves to a different role.
This maps precisely onto the CEO productivity paradox data published this week - thousands of executives reporting zero impact from AI - but adds the practitioner's eye for why the impact isn't showing up.
Enterprise AI adoption is a behavior-change problem dressed up as a technology problem. And behavior change is genuinely hard. Harder than software deployment. Harder than training. It requires changing incentives, workflows, mental models, and organizational culture - none of which come included in an enterprise software license.
The account's list of what actually works is pragmatic and unsexy:
Prompt libraries tailored to specific roles, not generic guides. Clear governance on when AI is and isn't appropriate. Department-level champions who actively share workflows. Measurement of time saved on specific tasks rather than vague productivity claims.
None of this is magic. All of it requires sustained organizational attention. That's exactly why most companies aren't doing it - and exactly why the productivity statistics look the way they do.
