Recently, I came across some interesting data points:
- India moves at the very top of the global chart on both trust in AI (roughly 80%) and active use of AI at work (roughly 90%)
- 76% of Indians say they are willing to trust AI and 90% accept or approve of it, yet 78% are simultaneously concerned about negative outcomes from it
- 67% intentionally use AI at work; 77% report increased revenue-generating activity, and 82%+ report increased efficiency, quality of work, and innovation
However,
- 78% are concerned about negative outcomes from AI, 60% report experiencing a loss of human interaction and connection because of AI
- 70%+ (depending on the specific measure) also report increased workload, stress, and pressure — productivity gains and strain are rising together, not one instead of the other
Yet,
- 81% relied on AI output at work without evaluating its accuracy
- 79% presented AI-generated content as their own
- 73% made mistakes in their work because of AI
- 72% used AI in ways that contravene company policies or guidelines
- 70% used AI at work in inappropriate ways
Surprising, isn’t it?
These numbers are India-specific, as reported in Stanford’s 2026 AI Index Report along with Melbourne/KPMG 2025 Global Trust Study. Stanford also notes that India recorded the sharpest rise in concern about AI of any country between 2024 and 2025 – a 14-point jump – against only a marginal 2-point rise in excitement over the same period.
These facts are not contradictory – they tell the same story. Our people are willing to work with AI. What they haven’t yet been told – clearly, consistently, by us – is what that means for their role, their manager, and their future. That gap is not a technology problem. It is a leadership problem, and it belongs on the same agenda as resource allocation, capacity planning, managing talent and mitigating risks.
Adoption has outrun explanation
The numbers on adoption are no longer in question. EY’s 2025 Work Reimagined Survey found 88% of Indian employees now use generative AI at work, 37% of them daily, giving India the highest “AI Advantage” score of any market surveyed – 53 points against a global average of 34 – with 86% of employees reporting a genuine productivity gain. NASSCOM’s AI Adoption Index puts enterprise usage at 87% as of December 2025, and McKinsey’s 2025 survey of nearly 2,000 firms found 88% of organisations now use AI in at least one business function.
What hasn’t kept pace is guidance. Genius HRTech survey of over 1,700 Indian professionals, reported in January 2026, found that while 67% already use AI at work and 71% expect their roles to change in the coming years, 61% say they have received no formal guidance from their employer on how to use it. Trust in what the tools deliver remains divided. We have handed people a powerful new instrument and largely left them to work out the etiquette themselves.
Manufacturing leaders will recognise this pattern immediately, because we have seen it before on the shop floor. A new machine installed without operator training doesn’t just underperform – it gets worked around, mistrusted, and eventually blamed for problems it didn’t cause. AI deployed without explanation follows the same trend; except the “workaround” this time is disengagement, quiet non-adoption, or employees quietly trusting their own judgment over a system they were never taught to question or rely on.
Within manufacturing itself, adoption is far from uniform. NASSCOM names Industrials & Automotive as one of four sectors expected to generate 60% of India’s projected $500 billion in AI-driven value by FY26, but automotive and electronics are pulling ahead while heavier process industries lag – a pattern echoed globally, where industrial-AI benchmarks find transportation & logistics and automotive far ahead of engineered products and process industries. Predictive maintenance remains the single most common shop-floor use case, with Indian steel and process-industry deployments reporting unplanned-downtime reductions from low single digits up to roughly 50% in the best cases. For a leadership team overseeing more than one plant type, an automotive-adjacent line and a heavy-process line are simply not on the same AI journey – and neither are their people.
There is a quieter version of this same divide sitting inside a single company, and it shows up in how people expect their own jobs to change. McKinsey’s HR Monitor 2026 asked employees across functions how they expect AI to change their role, and manufacturing and production workers were the least likely of any function to say much would change at all – 44% said their job wouldn’t change, against just 14% in finance and 18% in IT. That isn’t necessarily reassuring. It can just as easily mean plant employees are the least prepared for change that’s already headed their way, while their finance and IT colleagues at the same company are already bracing for it. Left unaddressed, that becomes a relationship gap between plant and headquarters, not only a skills gap – two parts of the same organisation experiencing “AI at work” as two different realities.
Trust is relational, not just technical
The part of this that most boardroom AI conversations miss is that trust in AI is rarely just trust in software. It is trust in the person who deployed it. When a quality-inspection or scheduling algorithm starts influencing decisions a supervisor used to make alone, employees don’t relate to the algorithm in the abstract – they relate to their supervisor differently, because the supervisor is now visibly acting on recommendations the employee can’t see the logic behind. Microsoft’s 2026 Work Trend Index makes this point directly: employees expect clear guidance on how AI tools affect their roles and how they’re evaluated, and ambiguity fuels suspicion faster than any productivity gain can offset it.
The India-specific numbers behind the topline trust score make this concrete. With majority of responded alluding to be using AI in-appropriately or without validating its results, the point is proven. These are not people who distrust AI – they are people who trust it enough to stop checking it. That is the failure mode a purely technical view of trust misses entirely: the risk on our floors isn’t employees refusing to adopt AI, it’s employees relying on it past the point their own judgment should re-enter the loop, with no governance structure telling them where that point is.
This isn’t an India-only pattern, and it isn’t new – AI has simply landed on top of a manager relationship that was already under strain. McKinsey’s HR Monitor 2026, surveying roughly 1,300 HR leaders and 5,500 employees across Europe, the US, and China, found that HR consistently underestimates how much an employee’s relationship with their direct manager and colleagues drives their decision to stay, while overestimating the pull of formal training programmes. Feedback itself remains stubbornly hierarchical: 68% of employees say their formal feedback comes from a line manager and just 15% get any structured peer feedback, while one in five employees report no career or feedback conversation at all in the past year – a figure HR believes is closer to 3%. If the manager relationship is already under-credited and under-resourced before AI enters the picture, layering an opaque algorithm onto that relationship without deliberate redesign only widens the gap between what leadership believes is happening on the floor and what employees actually experience.
This becomes scary, in organisations already carrying retention pressure. A workforce watching AI deployed around them with no explanation of what it will and won’t decide is collecting one more reason to leave, layered on whatever else is already driving attrition on the floor.
There’s a second, quieter failure pattern worth naming: who gets trained. Research on India’s reskilling gap has found that frontline employees typically receive structured AI training and senior leaders get strategic briefings – but middle managers, the people actually responsible for embedding new behaviours on the floor, are routinely skipped, despite being the people frontline staff trust most when something changes.
What’s in it for the Top Table?
Indian CEOs already treat this as strategic, at least on paper. KPMG’s 2025 India CEO Outlook found 74% of CEOs believe AI workforce readiness will materially affect their organisation’s growth prospects over the next three years, and 65% now name AI a top investment priority despite economic uncertainty. Yet separate industry analysis suggests only around a quarter of Indian companies have reached genuine “AI maturity at scale” – most deployments remain pilots. That gap between strategic intent and operational maturity is exactly where the human side of AI either gets built deliberately or left to chance.
What leadership can do now
Four simple steps every manufacturing leadership can ponder upon:
- Be explicit – in writing, on the floor – about what AI will and will not decide, especially anywhere it touches evaluation, scheduling, or safety.
- Train middle managers before, not after, the frontline; they are the trust conduit, not an afterthought.
- Treat trust as something you measure, through pulse surveys and floor conversations, not something you assume because usage numbers look healthy.
- Calibrate communication to each site’s actual stage of AI adoption rather than one corporate-wide script – adoption still concentrates in urban centres and larger firms, leaving Tier-2/3 sites and smaller plants genuinely further behind, so an automotive-adjacent line and a heavy-process plant are having two different experiences of “AI at work” and need two different conversations.
India’s workforce has given manufacturing leadership a genuine head start: a workforce more willing to trust and use AI than almost any peer country’s. That advantage is not permanent, and it is not self-sustaining. The organisations that keep it will be the ones that treat the human side of AI – relationships, transparency, and trust – as seriously as they treat the technology itself.
Sources
EY Work Reimagined Survey 2025 (EY India); Stanford HAI 2026 AI Index Report – Public Opinion chapter (Stanford University); University of Melbourne & KPMG International, “Trust, attitudes and use of artificial intelligence: A global study 2025” (India insights); Genius HRTech AI @ Work survey, reported by Press Trust of India, January 2026; NASSCOM AI Adoption Index, December 2025 and Index 2.0, 2024; McKinsey Global Survey on AI, 2025; McKinsey HR Monitor 2026 (People & Organizational Performance Practice, surveying ~1,300 HR professionals and ~5,500 employees across Europe, the US, and China); KPMG India CEO Outlook 2025; Microsoft Work Trend Index 2026; India’s AI Landscape 2025 Index Report (sectoral AI maturity data); BCG “AI in the Factory of the Future” industrial-AI adoption benchmark; NASSCOM community analysis on predictive maintenance in Indian steel and process industries; EY-CII “AIdea of India Outlook 2026.”