There’s a pattern emerging in corporate America. A company announces a bold AI strategy, markets respond favorably, and soon after, the company makes layoffs dressed up as efficiency gains. Employees aren’t fooled, and neither are customers.
That gap between what companies say about AI and what they’re actually doing is breaking down trust. And for enterprise leaders, it’s becoming a problem they need to address.
Employees are asking whether AI is being used to help them do better work or make them more expendable. Customers are questioning how their data is being collected, stored, secured, and used. The public is struggling to separate meaningful innovation from marketing theatrics. And business leaders are facing a reality that technology and money alone can’t rectify.
As a technologist, I believe deeply in the difference AI can make. In the IT world, where I come from, that opportunity is already visible. AI can help summarize tickets, prioritize alerts, surface vulnerabilities, and reduce the manual work required to keep systems secure and resilient. It can make teams faster and individuals less overwhelmed.
But that is not the story most people are hearing, or the reality that many are seeing.
A Turning Point
Contrary to popular belief, the companies that succeed with AI will not be the ones that exhaust their tokens the fastest. They will be the ones that prove AI can make people more valuable, not more disposable. They will use AI to reduce the rote work that burns people out while leaving space for judgment and expertise.
But getting there requires a different leadership mindset. Enterprise leaders must stop measuring AI success through rapid use-case adoption and sporadic implementation plans. If you’re asking your midlevel managers to report on three new ways they’re using AI every week, you’re missing the point. If you’re measuring AI adoption and benefit through token spend, you’re in for a rude awakening.
What business leaders should be asking is whether AI is helping employees produce better outcomes in their current roles. Are teams making better decisions sooner? Are customers getting better service? Are employees spending less time on administrative drag and more time on work that requires human insight?
Perhaps most importantly, leaders need to invest in their people as aggressively as they invest in new technology. Buying AI tools is easy. Building the skills, processes, and culture to use them effectively is harder. If employees aren’t equipped to succeed, that’s a reflection of poor leadership, not the workforce.
Technology vendors have a responsibility here as well. They should act not only as providers, but as partners and stewards. That means being transparent about data practices, human oversight, and the limits of automation. And it means resisting the temptation to overstate capabilities just because the market rewards AI momentum. Simply strapping an LLM to your product and upcharging on commercial token use for a gimmicky feature isn’t going to move the needle for customers. In-product AI enablement should make common tasks take less time to accomplish. That’s the metric your customers care about.
Keeping Humans at the Center
If every organization uses the same models and tools, technology alone will not be a differentiator. The real advantage will come from how people apply those tools, how they serve customers, how they solve problems, and how they build trust.
AI can make organizations more efficient, but efficiency without trust is not enough. Customers need confidence that companies are using AI responsibly. Employees need confidence that leaders see them as essential to the future, not a disposable commodity. And the public needs confidence that innovation is being guided by judgment, accountability, and real human values.
The organizations that succeed will be the ones that use AI to help people contribute more, create more, and solve bigger problems in less time. In the end, trust will be earned by those who use technology to amplify human capability rather than replace it.
