About Us
The second wave — TCS and the Moral Math of AI Efficiency
CULTURE & CODE
By Joey Briones
It’s happening again.
Barely a week after Accenture’s announcement that over 11,000 employees would be “exited” as part of its AI-first reinvention, Tata Consultancy Services (TCS) — one of India’s largest tech employers — confirmed it is cutting about two percent of its global workforce, or roughly 10,000 people.
The reason? The same one reshaping every boardroom conversation today: artificial intelligence.
TCS executives described it not as a layoff, but as “workforce optimization.” Automation, they said, has made certain delivery and support roles redundant. The company insists it is simultaneously hiring in AI, cloud, and data analytics
But beneath the calm corporate language of “optimization” and “redeployment,” a deeper shift is unfolding — one that goes beyond numbers and roles, and speaks to the soul of work itself.
The Domino Effect
Accenture called it “responsible reinvention.”
TCS calls it “optimization.”
Different words. Same reality.
We are watching the early waves of a much larger tide — the AI rationalization of work.
For decades, digital transformation meant doing the same things faster.
Now, AI is making some of those “things” — and some of those jobs — unnecessary.
Across industries, this quiet restructuring is becoming the new normal: a steady recalibration where companies invest billions to automate functions while “redeploying” or “exiting” those who can’t transition.
This isn’t just business strategy. It’s social surgery — performed at global scale, often without anesthesia.
The human gap
If AI is replacing repetitive, rule-based work, what happens to the people who built their careers mastering those very rules?
Many of these workers are not failing — they are simply outpaced by the velocity of change. AI fluency, prompt engineering, data interpretation — these are not just new skills; they are new literacies.
Yet, reskilling programs — however ambitious — rarely address the emotional, psychological, and identity dimensions of such transitions.
Because when someone’s role is automated, it’s not just a job that ends. It’s also a chapter of self-worth that closes.
What organizations must do
If this is the next wave of transformation, then the measure of leadership will not be how efficiently companies adopt AI — but how humanely they manage the changeover.
Here’s what that means in practice:
1. Make reskilling proactive, not reactive.
Don’t wait for roles to disappear before teaching new skills. Build continuous learning into the flow of work.
Don’t wait for roles to disappear before teaching new skills. Build continuous learning into the flow of work.
2. Design human-centered transitions.
Offer not just technical training but also career coaching, mentoring, and emotional support. Reinvention is as psychological as it is digital.
Offer not just technical training but also career coaching, mentoring, and emotional support. Reinvention is as psychological as it is digital.
3. Create visible internal pathways.
Show employees where their new value lies in an AI-driven world — what they can become,
Show employees where their new value lies in an AI-driven world — what they can become,
not just what they can no longer be.
4. Communicate with honesty and empathy.
Words like “optimization” may soothe shareholders, but they distance people. Be transparent about what’s changing and why.
Words like “optimization” may soothe shareholders, but they distance people. Be transparent about what’s changing and why.
5. Balance AI investment with social innovation.
For every dollar spent on automation, spend another one on upskilling, inclusion, and human development. Progress is not just efficiency — it’s equity.
For every dollar spent on automation, spend another one on upskilling, inclusion, and human development. Progress is not just efficiency — it’s equity.
What Employees Must Do
For employees, this era demands a new kind of mindset — one that blends humility, adaptability, and lifelong learning.
1. Stay curious. Treat every new tool not as a threat, but as a teacher.
2. Invest in learning. The skill you’ll need next year likely doesn’t exist today.
3. Build AI fluency. You don’t have to be a coder — but you must understand how AI changes your craft.
4. Cultivate human skills. Empathy, storytelling, creativity, collaboration — these are the enduring currencies of work.
The future will belong not to those who resist technology, but to those who partner with it consciously.
The New Moral Frontier
AI has forced a reckoning.
Efficiency can no longer be the sole metric of progress.
Human dignity must be part of the algorithm.
If companies like Accenture and TCS are to be the architects of the future of work, then they must also be its moral engineers — ensuring that innovation does not outpace empathy, and that reinvention includes those who made reinvention possible in the first place.
Because technology doesn’t make transformation humane.
Leaders do.
Technology will also keep getting smarter. The real question is — WILL WE?
RELATED STORY: