For the past few years, enterprise conversations around artificial intelligence have centred on models, infrastructure, copilots and more recently agentic AI. The latest Kyndryl 2026 People Readiness Report suggests that while AI adoption has accelerated across enterprises, workforce readiness has moved in the opposite direction.
The findings indicate that AI has already moved beyond experimentation. Nearly 16 oragnisations report broad or embedded AI deployment, while over three-quarters have scaled generative AI across multiple business functions. More importantly, 59% of leaders now view AI as either central to competitive differentiation or a key driver of competitive advantage.
Yet despite this progress, many organisations are struggling to convert AI investments into business outcomes. Only 32% of respondents reported achieving one of their two most desired AI outcomes, while just 11% achieved both.
The readiness gap
One of the significant findings of the report is the widening disconnect between technology readiness and workforce readiness. While 35% of leaders believe their IT infrastructure is ready to support AI, only 23% say the same about their workforce. Organisational culture fares little better at 25%. More concerning is the fact that workforce readiness has declined by six percentage points compared with the previous year.
This concern is reflected in executive sentiment. Nearly 80% of respondents believe AI adoption is advancing faster than their organisations can adapt their workforce, governance structures and operating models. Moreover, leaders expect people-related issues such as workforce skills, role redesign and organisational change to take longer to solve than infrastructure or technology challenges.
Skills as the primary bottleneck
Skills gaps rank among the top obstacles to AI execution, ahead of many technical concerns. More than half of organisations report increasing difficulty in finding external talent with AI-related skills, while 94% believe upskilling existing employees will ultimately be more effective than relying solely on new hires.
Despite recognising the importance of skills development, few organisations have established the mechanisms required to manage workforce transformation at scale. Only a third maintain an accurate inventory of employee skills. Fewer than one-third have proactive upskilling strategies, and only a quarter have created formal career transition pathways for employees whose roles may be affected by AI.
Human-AI collaboration
Contrary to widespread concerns around job displacement, the report suggests organisations are focusing more on redesigning work than eliminating it.
More than half of surveyed organisations have already begun redesigning roles to accommodate AI. Most leaders view AI as a collaborative partner rather than a replacement for human labour. An overwhelming majority believe future roles will require human-AI collaboration, while many organisations are creating entirely new positions dedicated to managing AI workflows, outputs and governance.
Perhaps, executives still expect humans to remain the primary source of value in areas requiring judgment, ambiguity management, accountability and relationship-building. AI is expected to dominate data analysis and process execution, but decision-making authority remains firmly linked to human oversight
The pacesetter
Among the organisations surveyed, only 9% qualified as “Pacesetters”—companies that have simultaneously redesigned roles, implemented formal change management and achieved workforce readiness.
What distinguishes these organisations is not superior technology. Instead, they have invested heavily in workforce planning, skills visibility, role redesign, change management and governance.
The results are tangible. Pacesetters report significantly stronger outcomes in innovation, revenue growth, risk management and infrastructure modernisation. They also demonstrate substantially higher trust in autonomous AI systems and customer-facing AI deployments.