The AI investment paradox
Key Takeaways:
- Only 46% of workers trust AI systems at work and leaders are the reason that changes
- Why leadership capacity, not technology, is the real blocker to AI adoption
- What separates companies where AI works from the ones where it doesn’t
- Why human skills are becoming more valuable as AI scales
- How HR is leading the way (and why credibility is the currency)
Corporate America has poured $252.3 billion into AI in 2024 alone. Most companies still can’t point to a single meaningful business result.
The technology works. Employees are using it. But MIT found that 95% of generative AI pilots at companies are failing, and McKinsey reports that fewer than 1% of organizations describe their AI adoption as “mature.”
Massive investment, negligible return.
The real blocker isn’t the technology
Writing in Fortune, Torch CEO Heather Conklin argues that companies are treating AI as a technical problem when it’s actually a leadership capacity problem. “We’re asking leaders to drive massive change without equipping them to do it successfully,” she writes. “Until that gap closes, AI will remain stranded capital.”
The data supports this. A McKinsey report on AI in the workplace concluded that employees are ready for AI, but “the biggest barrier to success is leadership.” C-suite leaders were more than twice as likely to blame employee readiness than to look at their own role.
Meanwhile, Gartner found that 66% of CEOs say their own executive teams lack AI confidence. The people responsible for driving AI adoption don’t feel equipped to lead it.
HR is being forced to reinvent itself
In a recent Business Insider piece, Conklin described how getting workers to both trust and adopt AI has become one of HR’s most urgent challenges. “It’s forcing HR people to reinvent themselves,” she said. “And the ones I see succeeding are the ones who are going first.”
This means HR teams treating their own departments as testing grounds, experimenting with different tools and learning what works before asking the rest of the organization to follow.
“They can’t drive it across the company if they haven’t lived it,” Conklin said. “They need to drive it from a place of credibility.”
That credibility matters because trust is the currency. A Pew Research Center survey found that 32% of workers think AI will lead to fewer job opportunities for them in the long run. A University of Melbourne and KPMG study of over 48,000 people across 47 countries found that only 46% of respondents are willing to trust AI systems at work.
The human skills paradox
While companies pour money into AI tools and technical training, the skills that matter most aren’t technical at all.
Workday’s “Elevating Human Potential” report found that 83% of workers believe AI will make uniquely human skills even more valuable, not less. The skills deemed least likely to be replaced by AI are also considered the most valuable: ethical decision-making, relationship building, emotional intelligence, and conflict resolution.
The more AI automates routine work, the more human leadership capabilities matter.
This creates a gap that most organizations aren’t addressing. Gallup’s 2025 research found that when employees strongly agree their leadership has communicated a clear plan for integrating AI, they’re three times as likely to feel prepared to work with it and 2.6 times as likely to feel comfortable in their role.
Clear leadership communication changes everything. But most leaders don’t have a clear plan themselves.
What the successful 5% do differently
Harvard Business Review analyzed what separates companies where AI pilots succeed from the 95% where they fail. The differentiator isn’t the technology.
The winning companies do two things at once. They fund technical training and they fund developing leadership capacity for transformation. Their leaders can navigate fear and resistance across teams while creating strategic clarity and alignment. They build safety for experimentation and failure, and they model risk-taking in uncertainty.
That requires more than workshops. It requires individualized support for challenging situations. How to have conversations about readiness. How to create psychological safety when someone says they’re afraid of being replaced. How to navigate resistance when it shows up.
As Conklin puts it, “You can’t solve an adaptive challenge with a training program.”
The path forward
Companies are funding the wrong intervention. They’re treating AI as a training gap when it’s a leadership capacity gap.
Workday’s own AI adoption story illustrates what’s possible when you get this right. Their internal program achieved 85% employee adoption within six months by moving away from top-down mandates and focusing on peer-to-peer sharing. Employees demoed their own successful use cases. Leaders modeled curiosity instead of certainty.
At Torch, we’re building tools to help leaders practice these exact conversations. Spark, our AI coaching agent, gives leaders a safe space to rehearse high-stakes moments, from navigating resistance to creating psychological safety, before they face them with their teams.
The companies that get this right will build leadership capacity that outlasts whatever technology comes next.
The billions are already spent. The question is whether they turn into returns or stranded capital. That answer depends entirely on whether you’re willing to invest in the leadership gap, not just the technology gap.
Ready to build your leadership capacity for AI transformation?
