TCS Confirms 20,000 Job Cuts: What HR Leaders Need to Do Now
Tata Consultancy Services has reduced its workforce by 19,755 in the quarter ending September 30, 2025, dropping headcount to 593,314 from 613,069. This is the company's steepest decline on record and takes the workforce below the six-lakh mark for the first time since 2022.
The cuts are driven by automation, shifting business models, and global uncertainty. The company says "voluntary and involuntary attrition" led to the decline, with about 6,000 involuntary exits and a broader plan to trim 2% of global headcount by March 2026.
What's Driving the Restructure
TCS is realigning around AI-led and digital services, citing a "skill mismatch" especially among mid-level and senior roles. The company has set aside Rs 1,135 crore for severance and restructuring costs this quarter. Earlier guidance suggested 12,000 reductions; the actual number came in 66% higher.
External pressure is real: client caution, slowing demand for traditional services, and policy uncertainty in the U.S. (including proposed changes to visa fees and new tariffs) are pushing large IT firms to reset their cost and skills base.
Transparency Questions and Labor Concerns
Employee group NITES has alleged that the true scale of exits is higher than disclosed, claiming "nearly 8,000 employees, more than what TCS admitted, have disappeared from the rolls," and criticizing the company for reporting continued revenue growth alongside large job cuts. These claims add scrutiny to how reductions are reported and managed.
Talent Metrics to Watch
- Headcount change: -19,755 in Q2 FY26
- Restructuring cost: Rs 1,135 crore (roughly ~Rs 5.7 lakh per separation if averaged across all restructuring costs)
- Involuntary exits: ~6,000
- Voluntary attrition: down to 13.3% from 13.8%
- Women in workforce: 35.2% vs 35.1% in the prior quarter
- Campus hiring: no new guidance; market expects minimal intake
The HR Playbook: 30-60-90 Days
Days 0-30: Stabilize and Protect Value
- Codify selection criteria: Document role-criticality, skills adjacency, and performance signals used for decisions. Be ready to evidence fairness if challenged.
- Compliance sweep: Validate notice, severance, and documentation requirements by jurisdiction. For U.S. exposure, review WARN triggers and thresholds (U.S. DOL WARN).
- Manager enablement: Provide scripts, FAQ, and escalation paths. One unprepared conversation can fuel reputational damage.
- Knowledge transfer: For each exit, assign a clear handover owner, artifact list, and deadline. Track completion.
- Alumni and outplacement: Offer referral help, verified skills profiles, and access to job boards. Keep the door open for boomerang hires.
Days 31-60: Redeploy and Reskill
- Skills inventory: Map current workforce to an AI/cloud/data/security skills graph. Identify adjacency paths for mid-career talent.
- Internal marketplace: Post project-based gigs and build two-track paths-delivery and product/AI operations-so talent can move, not stall.
- Reskilling sprints: 8-12 week programs tied to live projects. Measure by billable deployment rate, not course completion.
- Role redesign: Update job architectures to "AI-augmented" workflows (e.g., developer + code generation, analyst + copilots, tester + automation).
- Diversity guardrails: Monitor impact on women and underrepresented groups; protect leadership pipelines during cuts and redeployments.
Days 61-90: Institutionalize and Forecast
- Scenario planning: Build capacity plans under three demand cases (base, pessimistic, optimistic) and two policy cases (status quo, tightened). Tie hiring and subcontracting thresholds to these triggers.
- Financial model: Track restructuring cost per separation, salary savings, time-to-revenue for redeployed talent, and gross margin lift from automation.
- Automation ROI: For each function, quantify tasks redeployed vs. eliminated. Set policy for AI tool usage, data security, and quality review.
- Governance: Quarterly talent review across business, HR, finance, and delivery to adjust hiring, reskilling, and vendor mix.
Practical Moves You Can Execute This Week
- Publish a one-page "Skills We Need Next" memo to all managers. Remove guesswork.
- Launch a redeployment queue with SLA: 14 days to match, 30 days to place, then escalate.
- Stand up an AI enablement guild for mid-career roles most exposed to automation. Pair experts with delivery managers.
- Create a risk map of accounts and projects affected by exits. Add backup owners and cross-train plans.
- Set a transparent severance and support framework; keep it consistent to limit grievances and noise.
Industry Context HR Should Track
Across India's IT sector, "silent layoffs" are rising: non-renewals, benching without redeployment, and opaque performance exits. Mid-career roles face the most pressure as automation eats repetitive work and clients demand smaller, more senior squads.
Analysts estimate up to 50,000 sector-wide job losses by end-2025. Expect slower campus intake, more contract-to-hire, and tighter skill expectations. Plan for smaller, cross-functional teams with stronger AI fluency.
Metrics Dashboard for Your ELT
- Headcount, involuntary vs. voluntary exits, and regrettable loss
- Redeployment rate and time-to-place for at-risk talent
- Training participation to billable conversion within 90 days
- Automation coverage by function and quality outcomes
- Diversity impact across levels post-restructure
- Employer brand signals: offer acceptance rate, time-to-fill, alumni rehires
Resources
- Future of Jobs (WEF) for demand and skills direction.
- Complete AI Training: Courses by Job to stand up targeted upskilling paths for HR, delivery, and engineering.
Bottom Line for HR
This isn't a blip. It's a shift in how work gets done and who is equipped to do it. Reduce noise with clear criteria, keep your best people by moving them to the work that matters, and prove value with a tight loop between training, deployment, and revenue.
If you get the operating system right-skills graph, internal marketplace, and automation policy-you cut with precision now and hire with conviction later.
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