New research from Kyndryl has put a number on something many business owners have been feeling without being able to name it. 57% of organisations now have AI embedded in core business processes. But the gap between organisations seeing a real return on that investment and those that are not has almost nothing to do with which tools they chose. It comes down to whether they prepared their people first.
What the Kyndryl Research Actually Shows
The Kyndryl report, published in mid 2026, found that workforce readiness is the single biggest differentiator for AI return on investment. Not the technology stack. Not the vendor. Not the size of the budget. Whether the people were ready before the technology went live.
Companies that invested in preparing their workforce before deploying AI are measurably outperforming those that went the other way around. The deploy first, figure out the people later approach is showing up in the data now, and the gap in outcomes is not marginal.
This matters because the deploy first approach has been the default for most organisations. The technology is available, the business case gets approved, and the rollout happens. Then someone notices that adoption is low, the tool is not being used the way it was designed, or the workflows it was supposed to improve are still messy. At that point the conversation turns to the people, but by then the goodwill and the momentum are often already gone.
Via Kyndryl Report via GuruFocus, 2026
Why the Technology Was Never the Hard Part
This finding reinforces what XSIV has operated on since the beginning. 70% of AI adoption failures are human and process challenges, not technical ones. The tools work. The models are capable. The integrations are more accessible than they have ever been. None of that is where organisations are falling over.
They are falling over because the person who has done the same job for seven years is now being asked to work alongside a system they do not understand and were not consulted about. Because the workflow that AI was supposed to improve was never properly mapped before the tool went in. Because the team was told what was changing but not why, or not given time to adapt before it was live.
These are not technology problems. They are people problems. And unlike a software bug, they do not get fixed with a patch. They take time, communication, and genuine investment in your team’s readiness before the change happens.
The organisations outperforming their peers in the Kyndryl data are the ones that understood this in advance. They treated the workforce transition as the primary project and the technology deployment as a component of it, not the other way around.
The Default Human Response to AI Is Fear
Any honest conversation about workforce readiness has to start here. When people hear that AI is being introduced to their workplace, the first response for most of them is not curiosity. It is fear. Fear of not being good enough. Fear of being replaced by something they do not understand. Fear that the skills they have built over years are suddenly worth less than they were.
That fear is not irrational. It is a reasonable response to real uncertainty. And it does not go away just because a business owner sends an email saying AI is going to make everyone’s jobs easier. In fact, that kind of messaging often makes it worse, because it does not acknowledge what people are actually feeling.
The organisations that are seeing strong returns on AI investment are the ones that took this seriously. They had the honest conversations early. They involved their teams in identifying where AI could help, rather than presenting a finished solution. They gave people time to build familiarity before the tool was business critical. They acknowledged the uncertainty instead of papering over it.
That is not a soft skills exercise. It is how you protect your return on investment. A tool that your team does not trust or use properly is not delivering value regardless of how capable it is.
What Preparing Your People Actually Looks Like
Workforce readiness is not a training session the week before go live. That is the minimum viable effort, and the Kyndryl data suggests it is not enough to close the performance gap.
Real workforce readiness means understanding where your team is before you introduce the technology. It means knowing which roles are going to change the most, which people will find the transition hardest, and what support they actually need. It means mapping your existing workflows before you automate them, because AI does not fix broken processes. It accelerates them.
It means communicating the why clearly and early, not just the what. People can handle change when they understand the reason behind it and feel like they are part of the decision rather than subject to it.
It means building in time. Not the time the vendor’s implementation timeline allows for. The time your actual team needs to go from unfamiliar to capable to confident. Those are three different stages and they each take longer than most rollout plans account for.
And it means measuring the right things. Return on AI investment is not just productivity output. It is whether your people are using the tools, trusting them, and evolving how they work alongside them. If those measures are not tracking, the productivity numbers will follow eventually.
The Businesses That Will Win This Are Already Thinking About Their People
The Kyndryl findings reflect a shift that has been building for a while. AI adoption is no longer the differentiator. At 57% of organisations with AI embedded in core processes, adoption itself is table stakes. The differentiator now is whether that adoption is actually working, and the answer to that question runs almost entirely through the people.
In five years, the organisations that pulled ahead will not be the ones that moved first. They will be the ones that brought their people with them. That means starting the workforce conversation before the technology conversation. It means treating readiness as the investment, not the afterthought.
The good news is the window to do this properly is still open. The businesses starting this work now are not late. The question worth sitting with is this, what is the biggest gap between where your team is today and where it needs to be for AI to actually stick?
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