According to Kyndryl’s Readiness Report, which surveyed 3,700 business and technology leaders across 21 countries in 2025, a quarter of mission-critical servers, storage and networking systems are already at or nearing end-of-service. More than half of those surveyed, 57 per cent, say innovation is being delayed by foundational technology issues. And among organisations that have not seen positive returns on their AI investments, 35 per cent point to integration difficulties as the reason.

A separate Kyndryl report published recently surveyed 1,100 business and technology leaders across eight markets, including India, and found a parallel problem on the human side. Even as AI adoption surged, with 57 per cent of leaders saying AI is now deployed broadly or embedded in core business processes, the share who believe their workforce is actually ready to work with it fell to just 23 per cent, down six points from the year before. Seventy-nine per cent of leaders agree that the speed of AI adoption will outpace their organisation’s ability to adapt its workforce, governance and operating model.

Old systems, new demands

The report treats end-of-service infrastructure, meaning hardware and software that vendors have stopped supporting or updating, as an urgent opportunity for modernisation rather than a routine maintenance backlog. Old systems make it harder to adopt cloud-based and AI-ready technologies, and companies that delay upgrades build up what the report calls technical debt: essentially, the growing cost of keeping ageing systems running instead of investing that money in something new. In this year’s survey, 22 per cent said technical debt is actively holding their organisation back.

The scale of the problem varies sharply by industry. Kyndryl’s data puts retail highest, with a median of 32 per cent of systems at or near end-of-service, followed by manufacturing and telecommunications at 29 per cent each. Banking and financial services sit close to the overall average at 26 per cent. What this means in practice is that the sectors under the most pressure to adopt AI quickly, banking, telecom and manufacturing among them, are also the ones carrying the heaviest legacy load. For an Indian bank rolling out an AI-based fraud detection system, this is not an abstract statistic: it is the reason a pilot project that works in a lab can behave unpredictably once it touches core systems still running on ageing infrastructure.

Integration, not imagination, is the constraint

Among those surveyed, 31 per cent cite the sheer complexity of their IT environment as a top barrier to scaling technology investments. The report’s argument is straightforward: even the smartest AI tool is only as useful as the systems it can actually connect to. Among the organisations doing this best, 100 per cent of SSL certificates, the digital credentials that keep data encrypted, are kept valid and up to date. Among the bottom quarter on this specific measure, only 85 per cent are, and an expired certificate can quietly block a company from connecting new AI tools to its existing systems. Patching tells a similar story: the top performers keep 100 per cent of their systems patched, but the median company manages only 89 per cent.

Infrastructure first, AI second

Asked about their top priorities for reducing risk, 42 per cent named upgrading IT infrastructure and 39 per cent named strengthening cybersecurity, both ahead of any AI-specific initiative. At the same time, three in four organisations are investing in AI for cybersecurity purposes, making that the single most common use of AI. Organisations reporting a positive return on their AI investments are more likely than others to also be upgrading infrastructure, the report finds, suggesting companies with their basics in order are also the ones getting more out of AI.

The workforce hasn’t caught up either

The 2026 People Readiness Report suggests the same pattern is playing out with people. Fifty-two per cent of leaders say it has become harder in the past 12 months to find employees with the right skills for their AI strategy, and skills or talent gaps rank as the second-biggest challenge in executing an AI strategy, just behind cybersecurity concerns. Yet most organisations have not built the basic systems needed to manage this. Only 34 per cent have an accurate inventory of employee skills. Only 28 per cent have implemented enterprise-wide workforce resourcing plans. Just 25 per cent have built career transition pathways for employees whose roles are affected by AI.

The gap is starkest between leaders and the people actually doing the work. Non-C-Suite respondents are more likely than their C-Suite leaders to describe skills gaps and role redesign as major implementation challenges, 55 per cent versus 43 per cent, suggesting executives may be underestimating how disruptive this shift feels lower down the organisation. Senior executives also believe enthusiasm for AI thins out the further it travels from the top: they estimate 50 per cent of executive leadership is enthusiastically embracing AI, compared with just 31 per cent of individual contributors and 30 per cent of entry-level employees.

Trust and governance are lagging behind autonomy

Perhaps the sharpest contradiction in the report concerns trust. Only 25 per cent of leaders say they completely trust AI systems that make decisions or act without human oversight. Yet 66 per cent have already given AI permission to read from and write to core systems of record fully autonomously, without approval from a person. Companies, in other words, are extending far more autonomy to AI than they say they’re comfortable with.

Governance has not kept pace either. Just 23 per cent of leaders say governance and compliance are currently ready to support AI adoption, and only 33 per cent have fully implemented what the report calls the most basic governance measure: clear policies on which decisions AI is prohibited from making on its own. Just 41 per cent say they are confident in the guardrails their organisation has put in place.

A small group is pulling ahead, on both fronts

Both reports independently arrive at a similar structural finding: a small group of organisations gets meaningfully more out of AI, not because of better technology, but because of what they built underneath it. In the 2025 report, these “pacesetters,” 13 per cent of those surveyed, are 32 percentage points less likely than laggards to have innovation delayed by basic technology problems. In the 2026 report, a differently defined group of pacesetters, 9 per cent of organisations, stands out for redesigning job roles around AI, fully implementing change management, and reaching a workforce genuinely ready to work with the technology. Notably, this group also reports the strongest concern about what more is still required, which the report frames as a sign of clear-eyed awareness rather than complacency.

The India question

Schroeter’s February remarks are where this becomes an India story specifically. He said nearly three in four Indian organisations see their AI initiatives stall after the proof-of-concept stage, for the same reasons the global infrastructure report describes: fragmented data, legacy systems, and business processes not built for AI. India was also one of the eight markets covered in the People Readiness Report, alongside the US, Japan and five European countries, underscoring that the workforce readiness gap is not a peripheral concern here either.

He also pointed to India as a proving ground for AI at the national scale, citing government-backed initiatives such as Digital India and the IndiaAI Mission, and platforms like the Unified Lending Interface, which is already using AI to expand access to credit and cut loan processing times. The contrast is worth sitting with: India has shown it can build ambitious, functioning AI systems at a population scale when the state drives them. What both pieces of Kyndryl’s research suggest is a harder problem underneath that, one playing out inside individual banks, telecom firms and manufacturers, where legacy infrastructure, technical debt and unprepared workforces are limiting progress on the ground.