Saturday, 26 September 2026

Kya Main Bhi Krishna?



The battlefield has changed. The questions haven't.

What if Kurukshetra is not a place?  What if it is a moment?

A moment when you have to make a decision you do not want to make. A moment when your head says one thing and your heart says another. A moment when every option has a price, when nobody can tell you with certainty what the future holds, and when, despite having more information, more technology and more choices than any generation before us, you still don't know what to do.

Perhaps that is our Kurukshetra.

We may never stand on a battlefield with a bow in our hands, surrounded by armies waiting for our command. But we stand on battlefields of our own every day. A leader stands before an impossible business decision. A parent struggles to choose between protection and freedom. A professional wonders whether to continue on a successful path that no longer feels meaningful. An entrepreneur has to decide whether persistence is courage or simply an inability to let go. A person sits alone at night, surrounded by everything that was supposed to make life successful, and quietly asks a question that has no spreadsheet, algorithm or expert answer: “Is this really the life I want?”

More than five thousand years ago, Arjuna stood at the beginning of a war and discovered something that we continue to discover in our own lives: knowing how to fight is not the same as knowing why to fight.

He had the weapon. He had the training. He had the ability. What he did not have, at that moment, was clarity.

And beside him sat Krishna.

That is what makes the story so extraordinary. Krishna did not take the bow from Arjuna's hands. He did not fight the battle for him. He did not remove the battlefield, eliminate the uncertainty or promise him an easy victory. He did something far more consequential. He changed the way Arjuna saw the battlefield.

Perhaps that is why the conversation between Krishna and Arjuna still matters today.

Because our problem has never really been a shortage of information. It has been a shortage of clarity.

Today, we have artificial intelligence that can analyse millions of pieces of information in seconds. We have algorithms that can predict, recommend and optimise. We can ask a machine to write for us, analyse for us, code for us and increasingly even decide for us. Yet when the decision becomes deeply personal—when it involves responsibility, relationships, values, fear, ambition or purpose—we discover that intelligence alone is not enough.

We can have all the answers and still not know which question matters.

And perhaps that is where the question “Kya Main Bhi Krishna?” begins.

Not as a question of becoming Krishna. That would be neither possible nor the point. It is a question about whether, in the middle of our own confusion, we can cultivate the qualities Krishna represents: clarity when emotions are clouding judgment, courage when fear is asking us to retreat, wisdom when information is overwhelming us, detachment when the outcome is beyond our control, and compassion without surrendering our responsibility.

Perhaps the question is not, “Can I become Krishna?” . Perhaps it is much more uncomfortable.

“When my Kurukshetra arrives, will I have enough clarity to listen?”

We often imagine Krishna as the one who knows all the answers. But there is another way of looking at him that may be more relevant to our lives today. Krishna does not remove Arjuna from the battlefield. He does not tell him that the situation is too complicated and therefore he should walk away. He does not fight the battle on Arjuna's behalf. Instead, he changes Arjuna's understanding of the situation. The circumstances remain largely the same, but Arjuna begins to see them differently. That distinction is enormously important. There are moments in life when we keep asking for a change in circumstances when what we actually need is a change in perspective.

We do this constantly. We tell ourselves that if the organisation changes, we will be happy; if the market improves, we will be confident; if the other person changes, the relationship will become easier; if we get the promotion, we will finally feel successful; if we have more money, more recognition or more control, the uncertainty will disappear. Sometimes circumstances do need to change. But there are also moments when no external change can solve an internal conflict. The problem is not always the battlefield. Sometimes it is the way we are looking at it.

That may be one of the first lessons we can take from Krishna. Before asking, “What should I do?”, perhaps we should ask, “What is actually happening here?” The distinction appears small, but it can change the quality of a decision. When we are emotionally involved in a problem, our perception becomes selective. We see the insult but not the context. We see the threat but not the opportunity. We see the person who is opposing us but not the larger system in which both of us are operating. We see the immediate consequence but not the longer-term implication. Clarity begins when we stop reacting long enough to see the whole field.

This is perhaps why the metaphor of Krishna as Saarathi is so powerful. A charioteer does not replace the warrior. He does not take the bow from his hands. His role is to guide the chariot, understand the terrain and help the warrior navigate the battlefield. The warrior must still make the decision and ultimately carry the responsibility for the action. In that sense, the Saarathi is not someone who lives your life for you. He is someone who helps you see your life more clearly.

Modern leadership could learn something from this.

We often measure leaders by how many problems they solve themselves. We celebrate the leader who has every answer, makes every decision and becomes the centre around which the entire organisation revolves. But perhaps leadership at its highest level is different. A great leader does not create dependence; a great leader creates capability. A good mentor does not tell you what to think; he teaches you how to think. A good manager does not fight every battle for the team; he helps the team become capable of fighting its own battles. The best leaders are often Saarathis. They bring perspective when emotions are high, clarity when priorities are confused and courage when people begin to doubt themselves.

There is an interesting parallel here with the world we are entering through artificial intelligence.

For the first time, humanity is building tools that can process information, analyse patterns, generate ideas, challenge assumptions and make recommendations at a scale that no individual human being can match. AI can become a remarkably capable Saarathi. It can help us see more of the battlefield. It can bring information together, identify patterns, simulate possibilities and expose us to perspectives that we might otherwise miss. In many situations, it can help a person move from confusion to clarity much faster.

But there is an important boundary.


AI can be the Saarathi. It cannot be the conscience.

AI can tell us what is possible. It can help us understand what has happened before. It can suggest what might happen next. It can compare options and identify trade-offs. But the responsibility for choosing remains human. Technology can improve our intelligence, but it does not automatically give us wisdom. It can help answer the question, “How can I do this?” but the more difficult questions -- “Why should I do this?” and “Should I do this at all?”-- remain questions of judgment, values and responsibility.

This distinction will become increasingly important as AI becomes more powerful. The danger is not simply that machines may become capable of making decisions. The deeper danger is that humans may become comfortable allowing machines to make decisions that they themselves should remain responsible for. There is a difference between delegating a task and delegating accountability. The first can make us more capable. The second can make us less human.

Perhaps the future will therefore require a new kind of relationship with technology. We should not ask AI to become our Krishna. We should ask it to become a better Saarathi.

We remain Arjuna. We hold the responsibility. We make the final choice. And we live with the consequences.

This brings us to another idea that has become increasingly difficult in our modern world: Karma. We live in a culture obsessed with outcomes. Everything is measured. Performance is quantified. Careers are compared. Businesses are ranked. People count followers, revenue, market value, promotions, achievements and recognition. Even our personal lives are increasingly presented as a collection of measurable outcomes.

There is nothing inherently wrong with ambition. There is nothing wrong with wanting to succeed. The problem begins when the outcome becomes the only measure of the value of our action.

Imagine working on something important and constantly asking whether it will definitely succeed before giving it your best. Imagine refusing to begin because the result is uncertain. Imagine allowing someone else's reaction to determine whether your work was worthwhile. In such a state, our actions become hostage to outcomes we cannot completely control.

The idea of detachment is often misunderstood as indifference. It is not. Detachment does not mean that we stop caring. It means that we stop allowing the uncertainty of the outcome to control the quality of our action.

A surgeon cannot control every variable in an operation, but that does not mean the surgeon stops preparing. A leader cannot control every market condition, but that does not mean the leader stops making decisions. An entrepreneur cannot guarantee success, but that does not make disciplined execution irrelevant. A parent cannot control what a child ultimately becomes, but that does not reduce the responsibility to provide guidance, values and love.

We control our effort far more than we control the final result.

That is not a philosophy of passivity. It is a philosophy of disciplined action.

And perhaps this is where Karma becomes especially relevant to modern professional life. We should not work because success is guaranteed. We should work because the work deserves our best effort. We should not act only when recognition is certain. We should act because the responsibility is ours. We should not make integrity conditional upon whether anyone is watching.

The quality of a person's character is often revealed most clearly when there is no immediate reward for doing the right thing.

There is another dimension of Krishna that modern life frequently misunderstands: adaptability. Krishna cannot easily be placed inside one fixed role. He is a strategist, diplomat, friend, philosopher, guide and negotiator. He understands that different situations require different responses. Yet adaptability does not mean abandoning principles. It means changing the method without losing the purpose.

That distinction is particularly important today because we live in a world where change is no longer an occasional disruption. Change has become the environment itself. Technologies evolve rapidly. Business models disappear. New competitors emerge from unexpected places. Skills that were valuable yesterday may become less valuable tomorrow. Artificial intelligence is already changing how knowledge work is performed, and the pace of transformation is unlikely to slow.

In such a world, rigid thinking becomes a liability. But constant change without a stable foundation can be equally dangerous. We need something that remains constant while everything around it changes. Perhaps those constants are our values, our principles and our sense of purpose. The strategy can change. The technology can change. The organisation can change. The career can change. But if every external change also changes who we are, then adaptability has become another form of instability.

The wiser approach is to know what can change and what must not.

There is a similar lesson in the idea of Maya. The modern world has created an extraordinary number of things that compete for our attention and tell us what we should desire. Social media shows us carefully edited versions of other people's lives. Advertising constantly reminds us of what we supposedly lack. Professional networks display achievements without showing the years of uncertainty behind them. Technology introduces something new every day, creating the impression that whatever we have today is already insufficient.

We begin comparing our ordinary life with someone else's carefully selected moments.

We begin confusing visibility with value.

We begin chasing recognition because recognition is easier to measure than meaning.

Perhaps modern Maya is not simply an illusion. Perhaps it is the mistake of assuming that whatever is visible must therefore be valuable.

The answer is not to reject the world. Krishna does not teach Arjuna to run away from life. He teaches him how to participate in the world without becoming completely imprisoned by it. That distinction is subtle but profound. We need ambition, but ambition should not own us. We need money, but money should not become the measure of our worth. We need technology, but technology should not determine our values. We need recognition, but recognition cannot become the foundation of our identity.

We can participate fully without becoming possessed by what we participate in.

Perhaps that is what maturity ultimately means.

  • To be deeply involved without becoming completely attached.
  • To care without becoming consumed.
  • To act without becoming obsessed with control.
  • To succeed without believing that success defines us.

And to fail without believing that failure defines us either.

So, after all this, we return to the original question.

Kya main bhi Krishna? Perhaps not. And perhaps that is exactly the point.

We do not need to become Krishna. We do not need to imitate the external form of a figure who belongs to another age. What we can do is recognise the qualities represented in the story and ask whether they have a place in our own lives.

  • Can I remain clear when everyone around me is confused?
  • Can I have courage when fear is telling me to retreat?
  • Can I act without becoming completely dependent on the result?
  • Can I tell someone the truth even when the truth is uncomfortable?
  • Can I change my strategy without abandoning my principles?
  • Can I use intelligence without losing wisdom?
  • Can I help another person find their direction without taking away their responsibility?

And when I find myself standing in the middle of my own Kurukshetra, can I pause long enough to understand what the moment is asking of me?

Perhaps every generation receives its own Kurukshetra.

For Arjuna, it was a physical battlefield. For us, it may be a world of extraordinary opportunity and extraordinary uncertainty. We have more knowledge than ever before, but knowledge has not eliminated confusion. We have more technology than ever before, but technology has not eliminated difficult choices. We have more ways to communicate, yet meaningful communication remains difficult. We have more ways to measure success, yet we still struggle to define what success actually means. Maybe that is why the conversation between Krishna and Arjuna continues to matter.

  • It does not promise a life without conflict.
  • It does not promise certainty.
  • It does not promise that every decision will produce the outcome we desire.

Instead, it asks something more demanding of us: to see clearly, to understand our responsibility, to act with integrity, to remain courageous in uncertainty, and after doing everything within our control, to accept that the outcome may still belong to something larger than us.

Perhaps Krishna is therefore not someone we need to search for outside ourselves. Perhaps Krishna is the voice that appears when we become quiet enough to distinguish wisdom from fear, responsibility from ego and purpose from desire.

Perhaps Krishna is the part of us that says, “Look again,” when we are ready to react. The part that asks us to think beyond the immediate moment. The part that reminds us that difficult does not always mean wrong, and comfortable does not always mean right.

And perhaps, after years of searching for answers, we eventually realise that the real journey was never about becoming Krishna.

It was about becoming a little less confused Arjuna.

A little more aware. A little more courageous. A little less attached. A little more responsible. A little more capable of seeing the whole battlefield before deciding where to place our next step.

The world will continue to change. Our Kurukshetras will continue to appear in different forms. Technology will become more powerful, organisations will become more complex, and the decisions we face will become increasingly difficult. But perhaps the essential question will remain unchanged.

When the moment comes, when the noise becomes too loud, when the choices become difficult and when we find ourselves standing in the middle of our own battlefield—will we have the wisdom to listen?

Maybe that is the real meaning behind the question:

Kya Main Bhi Krishna?

Not, Can I become Krishna?

But -- Can I become wise enough to recognise Krishna when I need him?

And perhaps, one day, when someone else is standing confused in the middle of their own Kurukshetra, can I be the Saarathi who helps them see clearly?

Because the battlefield has changed. The weapons have changed. The world has changed.

But the human being standing in the middle of the battlefield -- is still searching for Krishna.

Tuesday, 8 September 2026

India's AI Landscape (2024–2026)

India's artificial intelligence ecosystem has shifted decisively from exploratory proof-of-concepts (PoCs) to core enterprise and industrial deployment. Driven by a strategic convergence of national digital infrastructure, enterprise investment, and deep supply-chain digitization, AI adoption is reshaping major industries,with manufacturing serving as a primary testing ground.


1. Macro Indicators: India's AI Landscape (2024–2026)






2. Latest Trends in How India Uses AI Today

  • Sovereign Foundation Models & Public Compute: Rather than relying entirely on foreign APIs, MeitY’s IndiaAI Mission sanctioned 12 indigenous foundation model initiatives in 2024–2025, including BharatGen (₹1,058.52 Cr project led by IIT Bombay) and Sarvam AI (₹246.72 Cr compute allocation) to train multimodal models on Indian languages and industrial datasets.

  • Shift to Edge AI and TinyML: In edge-heavy industries such as automotive and energy, inference is moving directly onto devices. Global embedded AI growth (15.1% CAGR through 2030) has translated locally into localized setups, such as Lenovo building AI servers at Puducherry and opening specialized labs in Bengaluru.

  • High-ROI Operations over Chatbots: The primary corporate budget allocation has migrated away from general conversational tools toward operational optimization: dynamic supply-chain rerouting, computer vision for plant safety, and automated asset monitoring.

3. How AI Is Changing the Future of Manufacturing

Indian manufacturing is transitioning toward "Industry 4.0" to offset operational bottlenecks, logistics friction, and material scrap. By FY2024, approximately 48% of leading industrial enterprises had integrated AI into operational workflows, up from 28% in 2022.

  • Predictive Maintenance (PdM): Using acoustic, thermal, and vibration IoT sensor data processed by machine learning models to detect component degradation before mechanical failure occurs.

  • Automated Visual Quality Assurance: Deep learning models scanning line units at full production speed, detecting micro-fractures, paint unevenness, and component alignment errors that escape human spot checks.

  • Dynamic Process Parameter Control: Algorithmic regulation of complex thermal, chemical, or mechanical settings (e.g., blast furnaces, chemical mixers) to balance fuel consumption with material strength.

4. Real-World Case Studies in Indian Manufacturing (2023–2026)

Tata Steel (Jamshedpur & Kalinganagar)

  • Application: Recognized by the World Economic Forum (WEF) as a Global Lighthouse facility, Tata Steel deployed over 260 production AI/ML models running concurrently.

  • Scope: Models control blast-furnace parameters, continuous casting operations, liquid steel temperature management, and plant-wide energy distribution.

  • Impact: Delivered a ~90% first-pass yield, over $1.4 billion in cumulative bottom-line savings, and achieved an estimated 10× return on AI infrastructure investments.

Mahindra & Mahindra (Automotive Plants)

  • Application: Machine-learning-based predictive maintenance deployed directly across automated robotic welding cells and engine assembly lines.

  • Impact: Significant reduction in unplanned assembly-line stoppages; real-time failure prediction on pneumatic systems and robot arms lowered line-clearance delays and improved shift productivity.

Tata Motors (Passenger & Commercial Vehicle Lines)

  • Application: Scaled high-speed computer-vision systems on sheet metal and chassis lines to detect surface flaws and micro-cracks at line velocity.

  • Impact: Measurable reduction in post-assembly rework cycles, combined with an automated inventory planning engine that aligns factory throughput with dealer demand cycles.

Godrej & Boyce

  • Application: Rolled out its proprietary Factory360 AI/IoT platform across shop floors in 2024 to centralize machine telemetry and optimize schedule balancing.

  • Impact: Deloitte India case study projections outline ~$25 million in total savings over a 3-year period driven by scrap reduction, lowered power consumption, and fewer line breakdowns.

Tata Metaliks

  • Application: Deployed IoT vibration sensors and ML classification algorithms on critical sinter-plant gearboxes.

  • Impact: Slashed scheduled inspection downtime by 28% while recording zero catastrophic equipment breakdowns after full deployment.

5. Adoption Comparison Across Indian Industrial Sectors




6. Structural Hurdles to Wider Deployment

Despite the measurable gains among large conglomerates, widespread deployment faces three structural bottlenecks:

  1. The MSME Adoption Chasm: While Micro, Small, and Medium Enterprises account for 35.4% of India's manufacturing output, their AI penetration remains below 30% due to initial capital equipment costs exceeding $1M for high-precision robotic/vision systems. Government interventions like the World Bank-backed RAMP project are attempting to subsidize edge sensors and cloud compute to bridge this gap.

  2. OT/IT Integration Debt: Most tier-2 and tier-3 factories operate legacy machinery without standardized SCADA/PLC interfaces, requiring expensive IoT retrofitting before any machine-learning model can ingest clean operational data.

  3. Data Readiness vs. Talent: While India leads globally in algorithmic software skill penetration (Stanford AI Index 2024), there remains a scarcity of cross-disciplinary industrial engineers who understand both machine floor mechanics (metallurgy, mechanics) and data science

The Reality Check: Legacy Debt & The MSME Chasm

Despite the scale at major conglomerates, two massive bottlenecks remain:

  1. OT/IT Integration Debt: Legacy tier-2 and tier-3 factories cannot run modern inference without retrofitting SCADA and PLC sensors to extract clean telemetry.

  2. The MSME Gap: Small and medium enterprises generate ~35% of India’s manufacturing output, yet AI penetration among MSMEs sits under 30% due to capital expenditure constraints.

The Bottom Line: The enterprise winners aren't chasing generalized AI models. They are instrumenting unglamorous shop-floor telemetry, putting edge models on assembly lines, and treating machine downtime as a solvable math problem.

Is your leadership treating AI as an office productivity tool, or deploying it where physical margins are made?

Kya Main Bhi Krishna?

The battlefield has changed. The questions haven't. What if Kurukshetra is not a place?   What if it is a moment? A moment when you have...