AI adoption has reached a difficult stage. Most organisations are no longer asking whether the technology matters, but why progress still feels uneven, slow and hard to scale.
The issue isn’t only technical. McKinsey’s State of Organizations 2026 found that 86% of leaders feel their organisations aren’t prepared to adopt AI in day-to-day operations, and that fewer than 20% of companies trying to adopt AI see significant, tangible impact on their bottom lines.
For boards, this sharpens the conversation. AI value will not come from isolated pilots, scattered tools or one-off training, but a double transformation. In addition to the technology, organisations also need to rethink work, roles, skills and decision-making around it.
Upskilling Has to Move Closer to Work
AI training often starts too broadly. You give your employees a general introduction, a few examples and access to tools, then they return to their roles with little guidance on what should change. That approach creates curiosity, but not adoption.
The World Economic Forum’s Future of Jobs Report 2025 found that employers expect 39% of workers’ core skills to change by 2030, and 85% of them plan to prioritise workforce upskilling between 2025 and 2030.
You cannot treat upskilling as a side activity. People need to learn how AI applies to their actual work. For example, a finance team needs different use cases from a sales team, HR needs different guardrails from operations, and senior leaders need different judgement from early career employees.
Useful upskilling is practical, role-based and repeated. It allows people to test, but it also provides standards.
Data and Employee Insight Should Shape Adoption
Leaders can overestimate how ready employees are for AI. Some people will adopt quickly, while others will worry about accuracy, job security, risk or relevance. You need employee insight before you scale adoption.
This means asking where AI already helps, where people feel blocked, which teams need more support and which risks managers are seeing. It also means using workforce data to spot patterns rather than relying on the loudest voices. Organisations must go beyond piecemeal adoption and rethink how work is performed, how decisions are made, and how operating models are designed.
That is where data matters. It helps leaders move from enthusiasm to evidence and shows where AI is improving productivity, where trust is low and where extra training is needed. Without that insight, AI adoption can become a campaign rather than a business change.
Employee Confidence Needs Time, Permission and Peers
AI confidence grows when people can experiment without fear of getting everything right on day one, and managers play a significant role in this. If you give teams time to test tools, share examples and learn from mistakes, adoption becomes more natural. If people feel they must use AI perfectly straight away, they may avoid it or use it quietly.
Peer learning can be more powerful than formal training. Employees often trust a colleague who shows how AI improved a real task. That makes the use case feel possible rather than theoretical.
Senior leaders also need to give clear guidance. People want to know where AI should be used, where it should not be used and how outputs should be checked.
Confidence grows when the organisation is honest about both value and risk.
HR and Technology Teams Need One Plan
AI adoption cannot sit with IT or HR alone. IT understands platforms, security and systems, while HR understands skills, culture, roles and employee experience. The strongest adoption plans bring both together from the start.
This partnership should answer practical questions. Which tools are approved? Which skills matter most? How will managers support teams? How will productivity gains be measured? How will risk be controlled?
AI success requires humans and AI agents to collaborate, with roles redefined and employee capability built around the technology. That means AI adoption isn’t a launch event, but an operating model shift.
Novo Perspective
AI adoption will not succeed through access to tools alone. It needs leaders who can connect technology to work, skills and performance. For boards, the priority is to move beyond piecemeal pilots. You need a clear view of where AI can create value, what skills your people need and how managers will lead the change.
At Novo Executive, we believe the organisations that gain the most will be those that build both confidence and capability. They will use data, listen to employees and redesign work with care. AI may change how work gets done, but people will decide whether it delivers.
