The Readiness Gap Is a Trust Gap
77% of organisations have scaled generative AI, but only 23% of their people are ready to use it effectively, and that number has dropped six points since the last reading.
The Kyndryl People Readiness Report calls this a readiness gap. I’d call it something more uncomfortable: a trust gap that leadership created and now has to close.
When I talk to senior leaders across Hong Kong and Singapore, the conversation usually starts with training plans and upskilling roadmaps. Those matter, but they assume the problem is capability when the deeper issue is belief. People won’t lean into AI tools unless they’re convinced those tools are a multiplier for their work, not an adverse disruptor of their job, their security, and their career prospects. And right now, most organisations have deployed systems without making that case.
The Gap Between Those Who Decide and Those Who Use
The data backs this up. Executives report 50% confidence in their workforce’s AI readiness, while individual contributors sit at 31% and entry-level employees at 30%. That gap between the people who decide and the people who use is where readiness goes to die.
I see this pattern repeatedly. A CEO or CHRO commissions an AI strategy, signs off on a platform rollout, and reports to the board that the organisation is “AI-enabled.” Meanwhile, three levels down, a mid-career manager in Singapore is quietly avoiding the new tool because nobody has explained what it means for her team’s headcount, her performance review, or her relevance in two years. She is not resistant. She is unaddressed.
The executives who report 50% confidence are reading from a dashboard. The people reporting 30% are reading from their lives. Those two readings will never converge on their own.
Why Training Programmes Miss the Point
Most organisations respond to the readiness gap by commissioning training. Modules, workshops, certifications, lunch-and-learns. I’m not against training, but it treats the symptom while the disease spreads underneath.
The disease is that employees are asking a question their leaders haven’t answered: does this make my future bigger or smaller? Training programmes teach people how to use a tool. They don’t answer whether the tool is safe to bet your career on. And in the absence of a clear, honest answer from leadership, people fill the silence with the most defensive interpretation available. That is rational behaviour, not resistance.
In my experience, the organisations that move the needle on readiness are the ones that send a different signal. They don’t start with “here’s how to prompt.” They start with “here’s what this means for your role, here’s what we’re committing to protect, and here’s where the upside sits for you personally.” That conversation is harder to have than a training rollout, and it takes longer. But it addresses the actual question.
What the Pacesetters Understand
Kyndryl identifies a small group, roughly 9% of organisations, they call “Pacesetters.” These are the companies where AI adoption and human readiness are advancing together. The interesting part is not their technology stack or their budget. It’s their sequencing.
Pacesetters treated human readiness as the operating system that makes AI safe to scale. They didn’t bolt people onto technology. They built the human layer first, or at least concurrently, so that when the tools landed, there was a workforce that could evaluate them, push back on bad outputs, and integrate them into real work without supervision theatre.
To me, the Pacesetter distinction is less about maturity and more about honesty. These are the organisations where leaders were willing to say, “This will change how we work, some roles will shift, and here is how we will handle that with you, not to you.” That level of candour is what builds the trust that training programmes cannot manufacture.
The APAC Context Makes This More Urgent
In Hong Kong and Singapore, the pressure is compounded. These are markets where talent is expensive, retention is fragile, and the cost of getting AI deployment wrong is not just inefficiency but attrition of the people you most need to keep. A senior analyst who doesn’t trust the AI strategy is not going to raise a formal objection. She is going to update her LinkedIn profile and take a call from a recruiter who promises clarity.
I spoke recently with a head of strategy at a financial services firm in Central who told me his team had been given access to three different AI tools in six months, with no guidance on which to use for what, no articulation of how their roles would evolve, and no acknowledgement that the ground was shifting. His words: “We’re not resisting AI. We’re just not being told what it means for us.” That firm is scaling AI on paper and losing trust in practice.
The Question Every Employee Is Silently Asking
Every AI deployment runs into a silent question from every employee: does this make my future bigger or smaller? Leaders who answer that question first, with honesty and specificity, will close the readiness gap faster than any training programme. Leaders who don’t will keep scaling AI on top of a workforce that doesn’t trust it, can’t evaluate it, and in some cases doesn’t want to.
The 23% readiness figure is not a training problem. It is a leadership communication problem. It is the measurable cost of deploying technology faster than you have been willing to talk about what it means for the people who will use it.
If your people can’t evaluate the AI they’re already using, what exactly are you scaling?