In early 2024, IBM made headlines when it cut nearly 8,000 jobs. The reason? Artificial intelligence. According to CNBC, the company believed it could replace a significant portion of its workforce with AI systems—especially in departments like HR, finance, and customer support. The plan was to lean into automation and cut long-term costs.
But a year later, IBM began posting job openings again. Many of them looked a lot like the roles they had eliminated. The same departments that had been downsized were now hiring.
So what happened?
IBM’s Bet on AI
IBM has always been bullish on technology. From early mainframes to Watson and now enterprise-grade AI tools, it’s built its reputation on innovation. So when AI became the buzzword of the decade, it made sense that IBM would lead the charge.
They weren’t alone. Across the tech world, executives were talking about automating tasks and improving efficiency. But IBM acted fast. Too fast, maybe.
The roles cut weren’t just assembly-line positions. Many were administrative or customer-facing—jobs that required some level of judgment or interaction.
The Short-Term Wins
At first, the AI transition looked like a success. IBM’s internal systems became faster at routing requests. Some tasks were completed in minutes instead of hours. Fewer people were needed to handle repetitive questions or forms.
Cost savings showed up quickly. Investors liked what they saw.
But behind the scenes, cracks were forming.
What AI Couldn’t Handle
IBM’s AI tools were good at rules—but not at judgment. They could sort through data, send emails, flag errors. But they didn’t understand nuance. They couldn’t read the tone of a frustrated client. They didn’t know when an exception was needed or when to escalate something quietly before it became a public issue.
In departments like HR, employee concerns were misinterpreted or ignored. In customer support, conversations became slower, not faster. People had to re-explain themselves. Complex issues bounced between systems with no resolution.
That’s when managers started to realize: AI wasn’t replacing people. It was removing the human glue that held things together.
The Silent Rehiring
Instead of issuing a press release, IBM began quietly opening positions again. Recruiters reached out to former employees. In many cases, they were offered similar roles to the ones they had lost months before.
The shift was gradual, not public. IBM didn’t say they were walking back their AI strategy. But the message was clear: the experiment hadn’t worked as planned.
📎 See: Why AI-Powered Hyper-Personalization Still Needs Humans
What This Says About AI in the Workplace
This story isn’t about IBM alone. It reflects a bigger lesson in the rush toward automation. AI is fast, efficient, and scalable—but it’s not always reliable where real judgment is needed.
Some things still need a human. When someone’s upset, or when a decision has more than one “right” answer, or when there’s no clear rule—humans are still better.
And in many companies, that’s most of the job.
The Real Cost of Cutting Too Deep
IBM saved money at first. But it came at a cost:
- Internal confusion
- Delayed responses
- Frustrated clients
- Declining morale
And then there’s the long-term damage. Once you let people go, it’s hard to get them back. Many employees had moved on. Others didn’t want to return. Some just didn’t trust the company anymore.
That’s not something AI can fix.
Could This Have Been Avoided?
Probably. IBM could have started smaller—tested AI on specific tasks, kept people on in hybrid roles, and gradually shifted responsibilities based on what worked. Instead, they made a big move and assumed it would work out.
In the world of AI, confidence is easy. Caution is harder.
Where AI Fits In
This doesn’t mean AI has no role. It does. It’s great for scanning documents, sorting data, and automating clearly defined tasks. But it works best when paired with people—not instead of them.
The real future of work will be hybrid: humans doing what they do best, machines handling the rest.
Final Thought
IBM’s story is a reminder that technology moves fast, but people still matter. When companies chase automation without a clear plan, they risk more than efficiency. They risk trust, quality, and relationships.
AI is powerful—but only when used in the right way. And that, for now, still takes human judgment.
