Bold claim: the real risk isn’t automation stealing jobs, it’s a slow, silent loss of trust as companies pour 93% of their AI budgets into technology and just 7% into the people who will actually use it. This is the core tension Deloitte’s CTO, Bill Briggs, highlights in a wide-ranging conversation about why the 93-7 split is a fundamental misstep and how to fix it.
In boardrooms around the world, leaders feel an anxiety that goes beyond fear of robots. They worry about buyer’s remorse—making a big bet on AI today only to watch a newer, better model arrive next week. Briggs points out that this fear is shaping a lopsided investment pattern, with most funds funneled into models, chips, and software, while the essential human elements—culture, workflows, and training—are left underfunded. He compares the situation to chasing a fancy ingredient without having a solid recipe: you may end up with something that looks like paella but tastes like cilantro.
Fortune sat down with Briggs at Deloitte’s New York office, amid holiday shoppers, to discuss Deloitte’s 17th annual Tech Trends report. Briggs has been a fixture of the Tech Trends project for nearly two decades. He recalls being a fresh Notre Dame graduate when tech was still a glimmer in the firm’s future, not the driving force it is today. The idea for Tech Trends emerged as Deloitte clients built CTO organizations and asked for guidance—so Briggs pressed the firm to formalize a CTO function, whether or not he personally took the lead.
Briggs travels frequently between Kansas City and New York, and the 93-7 split has become a surprising, quantifiable come-to-Jesus moment for him. He’s observed a persistent trap: organizations tend to layer new technology atop old processes instead of redesigning the processes themselves. This incremental approach, he warns, is the easy trap to fall into during every major tech wave.
While Briggs doesn’t declare whether AI budgets are too large or too small, he does observe what he calls institutional inertia—trying to fit AI into existing workflows as if it were just another bolt-on. He invokes Grace Hopper’s famous warning, that the most harmful phrase is “We’ve always done it this way.” To truly win the current revolution, Briggs argues, leaders must push beyond comfort zones, and the 93-7 ratio reveals a heavy bias toward old methods in a moment that demands new thinking.
These ideas align with a sweeping Protiviti survey released the same week as the Tech Trends report. Fran Maxwell, Protiviti’s HR leader, notes that HR itself will need to redesign roles—an undertaking that many functions aren’t built to handle yet. Her takeaway: you can’t solve today’s talent challenges with yesterday’s talent, which echoes Briggs and Hopper in spirit.
A major consequence of neglecting the human side is a growing trust gap. Briggs suggests reimagining AI as more than a set of tools; think of AI agents as new employees requiring their own HR-like governance. Questions about liability and performance management would become routine as AI creates its own generations of agents. If a misstep occurs in a later generation, who’s responsible—the human who created it, the line manager who deployed it, or the organization that let it proliferate? And how do you discipline or train a machine-based workforce?
The impact is already visible in today’s workplaces. Deloitte’s TrustID report shows that even with broader GenAI access, overall usage declined by 15%. A shadow AI problem is emerging: 43% of workers with GenAI access report using unapproved tools, bypassing IT and policies because those tools are faster or perceived as more accurate. This disconnect is fueling a steep drop in trust in GenAI—down 38% between May and July 2025. Yet training matters: employees who received hands-on AI education reported 144% higher trust in their employer’s AI capabilities than those who did not.
Another pressing fear for leadership is buyer’s remorse. Executives dread committing to a tool only to see a superior option released soon after. Briggs compares this hesitation to a pre-snap penalty in sports—paralyzing inaction that delays progress. His prescription: start somewhere now, even in a crowded market, because waiting only compounds the risk of becoming irrelevant.
The urgency grows with Physical AI—robots and autonomous systems moving from theory to real-world impact. Early wins are already surfacing: for instance, HPE later reported that data-to-decision cycles sped up by about 50% after deploying Zora AI.
Briggs’ closing message to the C-suite is clear: the technology is ready, but without a strong focus on people and culture, organizations risk investing in expensive solutions that no one trusts enough to use. The faster you begin, the sooner you’ll reach value—and the sooner you’ll avoid getting left behind.
In short, the debate isn’t whether to adopt AI—it’s how to balance technology with human capability and organizational change. The future belongs to those who transform culture as aggressively as they adopt new tools.
