Companies are defaulting to surface-level AI adoption, bolting tools onto existing workflows for incremental efficiency gains instead of restructuring operations around AI as a foundational capability.
Peter Blocker, Co-Founder and COO of IntoTexas, says hidden costs routinely excluded from ROI calculations include employee time spent training AI on company-specific processes and the higher labor rates required for human-AI hybrid roles that replace lower-cost positions.
AI transformation requires a multi-year strategic plan tied directly to P&L, with clear financial checkpoints for leadership, starting with small visible wins before committing to large-scale process changes.
Global enterprise AI investment has crossed $400 billion, yet fewer than 10% of enterprises report measurable ROI. The gap is not a technology problem but a planning problem. Boards are forcing AI adoption without a roadmap, leaders are deploying tools without restructuring work, and the real costs of training, role changes, and workforce transformation are consistently left out of the math.
Peter Blocker is Co-Founder and COO of IntoTexas, a consultancy focused on helping companies with the business side of AI integration. Blocker spent decades leading defense and unmanned systems programs at L3Harris Technologies, Sierra Nevada Corporation, and AAI/Textron, where he managed organizations through major technology transitions, new ownership structures, and large-scale operational change. He draws a direct parallel between AI adoption today and the decades-long arc of unmanned aircraft adoption in defense, where the technology proved itself long before institutions were willing to restructure around it.
"Leaders are being forced to bring AI in, they just don't know exactly what to do with it," Blocker says. "So they're bringing it in as a tool more than a foundation of their corporation, and that's where the problem starts."
The 10% illusion: When companies deploy AI as a tool, the efficiency gain is modest and misleading. "They just assume there's going to be some efficiency gain, let's say 10% across the corporation," Blocker says. But to capture real value, companies need to examine every position through three lenses: repetitive tasks AI can fully handle, work that only humans can do, and work where humans and AI collaborate. "When you start peeling off all the repetitive stuff, now you're changing everybody's job category. You're putting worry in all your employees."
The expense report test: Blocker uses a simple example to show the difference between tool-level and foundational AI. An expense report touches four people: the employee, a checker, an auditor, and a signature authority. Using AI as a tool means each person gets marginally faster. Using AI as a foundation means the checker and auditor roles are automated entirely, the signature authority's workload drops, and the employee gets AI assistance. "In that one little case, I went from a 10% increase in efficiency across four people to where I now eliminate two roles and reduce the workload on the other two."
Hidden cost, hidden role: Two costs are routinely excluded from AI ROI projections. The first is the employee time required to train AI on company-specific processes. "Every company has a unique way of doing things through their internal processes and liabilities. You have to train your AI on how to work with your company, and that cost is never taken into account," Blocker says. The second is labor rate escalation. Replacing three workers with one person in a human-AI hybrid role typically means a higher-salaried position. "I might replace three people with one person, but that one person is going to be at a higher salary rate. That's typically not taken into account either."
The execution path is a multi-year strategic plan tied directly to financials. Blocker compares the scale of change to enterprise database migrations, where the disruption is massive and the timeline always longer than expected. "People in general do not like change. AI is a huge change, and the problem is the boards are forcing the change," he says. The antidote is a roadmap with financial checkpoints that gives leadership something concrete to walk through.
Start small, plan big: Blocker's advice to any leader facing board pressure for quick AI results is to begin with a strategic plan, pick one or two visible wins from the roadmap, and demonstrate directional progress before committing to expensive process changes. "Pick out something simple, maybe the expense report. Show yes, we are making progress and headed the right direction. Then come back to the board when you need big money for the big process change."
Communicate and train: Change management is the central barrier, and Blocker says the only way through is transparency. "You've got to be open and upfront with your staff about what's going on. And you've also got to offer them AI-specific training, whether for this job or the next," he says. "If you take care of your employees, they'll do everything possible to stick around and take care of you."
The only metric that matters is one most companies have not yet figured out how to capture. "You can't just talk about headcount versus AI. That doesn't do it," Blocker says. "You've got to tie it back to the P&L. The only reason people don't know how to measure AI ROI yet is because AI is so new. But the companies that build the plan, tie it to the financials, and execute against the roadmap are the ones that will actually get there."
