IBM confirms Q4 2025 workforce cuts as it integrates AI: What HR needs to know
IBM has confirmed it will reduce a low single-digit percentage of its global workforce in Q4 2025 as part of an AI-driven workforce strategy. In a statement, an IBM spokesperson said: "IBM's workforce strategy is driven by having the right people with the right skills to do the work our clients need. We routinely review our workforce through this lens and at times rebalance accordingly. In the fourth quarter we are executing an action that will impact a low single-digit percentage of our global workforce."
Per IBM's 2024 Annual Report, the company employs just over 270,000 people across 175 countries. A low single-digit adjustment would translate to an estimated few thousand to roughly ten thousand roles, depending on the final percentage. The exact number and functions were not detailed.
This move comes alongside strong performance. In its third quarter update, IBM reported results above expectations, with Chairman, President and CEO Arvind Krishna stating: "Given the strength of our business, we are raising our full-year outlook for revenue growth and free cash flow."
According to Reuters, while some US employees may be affected, IBM expects US employment to be roughly flat year over year. Other major employers, including Amazon and Nestlé, have also announced workforce adjustments recently, signaling similar pressure to re-skill and reorganize around AI and automation.
What HR leaders should do now
- Clarify the objectives: define which work will be automated, augmented, or sunset, and the roles affected by each.
- Run a skills inventory: map current skills to AI-augmented workflows and identify gaps by function and location.
- Prioritize redeployment first: build pathways into growth roles (data, automation ops, AI-enabled customer success, delivery).
- Codify selection criteria: document role-based, business-led criteria to reduce bias and strengthen compliance.
- Localize for law and works councils: align timelines, notices, severance, and consultation requirements market by market.
- Strengthen manager enablement: provide talking points, FAQs, and training for sensitive, consistent conversations.
- Offer meaningful upskilling: short, role-relevant AI literacy and tooling sessions with clear proficiency goals.
- Protect wellbeing: provide access to EAP resources and stagger milestones to reduce burnout.
- Set AI governance: define approved tools, data handling, accuracy checks, and accountability.
- Track outcomes: monitor redeploy rate vs. reductions, time-to-proficiency, and post-change productivity.
Reskilling is the lever
The fastest win is targeted upskilling tied to specific use cases-coding assistants for developers, workflow automation for operations, and prompt skills for client-facing teams. Keep it practical: tool access, job aids, and hands-on labs beat theory.
If you're building a learning plan by function, see curated options by role at Complete AI Training.
Metrics to watch
- Internal mobility rate into AI-augmented roles
- Reskilling participation and completion by function
- Time-to-proficiency on new tools and workflows
- Severance vs. redeployment ratio
- Post-change productivity per FTE and quality metrics
- Voluntary attrition in critical skill groups
Bottom line for HR: AI isn't a side project-it's a workforce redesign. Make the moves visible, measurable, and tied to client work. That's how you keep trust high while shifting skills at scale.
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