Most organizations deploying AI are making the same mistake, and it is not a new one. They are automating before they are ready, moving fast before the foundation is solid, and wondering why the results do not match the ambition. Mark J. Concannon, founder of Concannon Business Consulting, is a transformation leader with experience spanning automotive, telecom, aerospace, and hospitality. He has spent his career solving exactly that problem. He believes AI does not fix broken processes. It accelerates them, and in the wrong environment, that acceleration makes things worse faster. “Don’t accelerate the broken process,” Concannon says, “and don’t build that process on incomplete, inaccurate, or unclean data.” The order matters more than most leaders want to admit, and getting it wrong is expensive.
Paving the Cow Trails
The failure pattern Concannon identifies is not new to AI. He saw the same dynamic play out with enterprise resource planning (ERP) and customer relationship management (CRM) implementations, and the lesson was the same then as it is now. Organizations rushed to automate before they had cleaned up the underlying processes driving their operations, and the result was what he calls “paving the cow trails.” As Concannon explains: “If you pave the cow trail, you end up with meandering processes because they were designed for the old world. AI just makes a broken process move faster.”
The fix requires working in a specific sequence that most deployment timelines resist: data first, process second, AI third. “One, data correct. Two, process correctly. Three, put in the AI.” When that order is respected, AI can move directly from step A to step C, eliminating the human intermediary steps that were only ever necessary because the process was inefficient. When it is not, those inefficiencies simply run at greater speed.
Bring the People Before You Deploy the Tools
The second failure point Concannon addresses is the one most organizations handle worst: change management. The technical work of cleaning data and redesigning processes is tractable. The human work of bringing teams through a fundamental shift in how they operate is where transformations most often stall or unravel.
“The more you don’t tell people what their role will be after the AI is in place, the more of the good ones will leave,” Concannon says. “They might just go to your competition.” His framework for managing this is built around clarity and communication, giving employees a specific picture of what their work will look like six or twelve months after deployment, including honest conversations with teams whose roles may be reduced.
“Give them a path forward, let them know what you’re going to do to make sure those impacted can stay until you’re complete, how you’re going to reward them for that loyalty, and help them find a path out.” For the rest of the organization, the message is equally deliberate: AI is here to remove the mundane and refocus work on purpose, not to replace the people doing it.
For those who still resist, Concannon is pragmatic. Education, clear governance, and visible leadership can overcome most reluctance. But at some point, he says, tough love is necessary. “AI is going to be part of everything we do in the future. Come with us, please. But if you’re not part of it, we’re not going to have a spot for you.”
Build for Hot-Swappable Intelligence
The third principle Concannon raises is the one most relevant to where AI is heading next. Agentic AI, systems that autonomously execute repeatable tasks within a designed process, is already being implemented across industries today. The question for business leaders is not whether to engage with it, but how to build infrastructure that does not trap them when the underlying models inevitably improve.
His answer is what he calls a hot-swappable logic engine. When data is clean and processes are designed for AI, the core intelligence layer, whether that is Claude, Gemini, or whatever model emerges next, can be replaced without dismantling the surrounding infrastructure. “The systems of record and the highways between each are what you build out to be robust today,” he says. “And then you remain logic-engine agnostic.” The lesson from legacy CRM and ERP implementations was that switching costs, data migration, retraining, and rebuilding became prohibitively high once an organization was locked in. The goal now is a digital infrastructure flexible enough that the intelligence at its core can evolve as the technology does.
The Work That Makes AI Real
AI transformation is not a technology project. It is a process discipline, a people strategy, and an infrastructure decision, all three of which have to be resolved before the technology can deliver on its promise. “AI supercharges our humans to allow us to focus on the rewarding work,” Concannon says. For organizations willing to do the foundational work first, that is exactly what it delivers. For those that skip it, the result is the same broken process, just moving faster than before.
Connect with Mark J. Concannon on LinkedIn or visit his website or company website for more insights on process design, AI adoption, and business transformation.
