AI Isn’t a Tech Strategy. It’s a People Problem Hiding in Plain Sight.
Global AI spend is on track to hit $600 billion by 2028. The potential upside? $15 trillion in added economic value by 2030. The pressure on leadership teams to act, and show results, is enormous.
But the numbers tell a different story. Between 70 and 80 percent of AI projects fail to deliver meaningful business value. And it’s not the tech that’s holding them back.
“The true barrier isn’t employee readiness but a lack of leadership alignment on AI strategy, investment, and risk management.” — McKinsey, The Human Side of AI Transformation (2025)
Too many companies still treat AI like an infrastructure upgrade. They roll it out like CRM. The assumption is that if you buy the tools, train a few teams, and set up a steering committee, adoption will follow.
It doesn’t. And the fallout is expensive.
Our latest analysis outlined four consistent failure points, none of them technical.
First: Leadership Misalignment. Most organizations don’t have a clear vision for how AI will create value. Only 35% do, according to recent data from TXI. The rest are split across departments, priorities, and timelines. In large enterprises, this creates inertia—pilot projects never scale. In mid-sized firms, AI initiatives stall before they start. Leaders don’t have the time or fluency to drive them forward.
Second: Culture and Trust. Nearly half of employees believe AI could replace their jobs. Only a third trust AI-generated decisions. The result? Quiet resistance. Teams avoid the tools. People stay silent about what isn’t working. Adoption slows to a crawl.
“The absence of psychological safety prevents the adoption of a ‘fail fast, learn faster’ ethos.” — Agility at Scale, Human Side of AI Transformation (2025)
Executives often underestimate how much this fear shapes behavior. Without trust, people don’t experiment. They don’t share knowledge. They don’t help each other adapt. The return on investment vanishes.
Third: Skills Gaps. Only 1% of employees globally are considered AI experts. That’s not surprising when only 38% of organizations offer any kind of AI fluency training. The result is a workforce that’s being asked to use tools they don’t understand and can’t question.
Even at the top, leaders are underprepared. When senior decision-makers don’t know how to evaluate AI opportunities, they default to buzzwords and vendor promises. That’s not a strategy. It’s a liability.
Fourth: Weak Change Management. Most companies still bolt it on after the fact. When teams aren’t involved early, when communication is vague, and when training is shallow, adoption drops off. According to Gartner, 30% of GenAI projects are abandoned after proof of concept.
And yet, the pattern repeats. Leaders under pressure for fast ROI cut the very investments that would secure it: training, communication, coaching, reinforcement. The irony is obvious.
“The very drive for immediate ROI can thus undermine the long-term achievement of that ROI if the foundational human elements are neglected.” — AI Transformation Human Barriers Analysis (2025)
Treating AI like a tech rollout makes implementation slower and returns smaller. Treating it like a human-centered change makes the investment worth it.
So what does that look like in practice?
It starts with clarity. Leaders agree on what AI is supposed to enable, how it connects to strategy, and how they’ll communicate that consistently. No mixed messages. No side initiatives competing for attention. Clear signals from the top.
It continues with fluency. Not everyone needs to be a data scientist. But leaders do need to understand what AI can do, where it fits, and what makes it fail. Teams need to see the tools in action, try them, and ask questions without being punished for not knowing.
Then there’s culture. When people feel safe to test and fail, they learn faster. Organizations that reward experimentation, share learning, and normalize uncertainty move faster and adapt better. Those that punish mistakes or treat AI as a black box get resistance instead of results.
The companies that are pulling ahead aren’t always the biggest. They’re the ones that communicate clearly, invest in capacity, and treat AI as a capability, not a feature. They’re building systems for learning, not just deployment.
If your team is stuck in pilot mode or unsure how to move forward, you’re not alone. But you’re also not without options.
Invitation to our Interactive Online Conversation
We’re hosting a focused conversation for senior leaders across industries to discuss three human factors that quietly derail AI transformations, and what to do about them. Real talk with peers who are facing the same decisions.
Session details
Topic: Three challenges hiding in plain sight that will tank your AI initiative
Date: Thursday, June 19
Time: 11:00-12:00 EST | 17:00-18:00 CEST
If you’d like to participate live or receive the key takeaways from the conversation, comment “IOC” below and we’ll be in contact.
If you’re serious about AI, and even more serious about making it work, we’ll see you there.
