AI tools are ready for real work, but most organizations face a readiness gap caused by hesitation, weak governance, poor data discipline, and unclear ownership.
Paul Neuman, Head of Go-To-Market Services at IT Services Demystified, draws on enterprise experience to explain why AI fails when leaders stall at pilots instead of deploying in real environments.
Hands-on deployment closes the readiness gap by pairing learning in production with clear governance, trusted data practices, and operational accountability.
AI tools are already powerful enough to change how work gets done, but most organizations are stuck at the starting line. Leaders talk about adopting AI while hesitating to experiment, deferring ownership, and over-indexing on abstract governance instead of rebuilding how work actually happens. That paralysis leaves teams to fill the gaps themselves, driving the rise of shadow AI as employees turn to consumer tools with no licensing, no anonymization, and no safeguards for sensitive information.
We spoke with Paul Neuman, the Head of Go-To-Market Services at IT Services Demystified and a veteran B2B technology executive. Drawing on a long career at HP, Neuman believes the current approach to AI adoption is deeply flawed. He's focused on dismantling the hype and exploring what he calls the "carnage in AI", the operational realities required to turn AI potential into a profitable, secure reality.
"AI isn’t being held back by what the technology can do. It’s being held back by leadership that delays decisions, avoids accountability, and hides behind governance instead of driving execution," says Neuman. He notes that the most common point of failure for AI initiatives often lies in the transition from a controlled proof-of-concept (PoC) to a live production environment. He points to the "PoC-to-prod" handoff, where consultant-led demos give way to operational reality, as the moment where a lack of effective governance and accountability proves terminal for most AI initiatives.
The myth of ideal: Neuman believes training matters more than theory. Proofs of concept often mislead because they showcase how a system works in controlled conditions, not how it survives real operations. He recalls a logistics company he advised skipping the lab entirely and running its PoC in a live environment. "The ideal environment will never exist," Neuman says. "Training AI only works when it happens in real-life conditions. Not in a lab, but in production environment, with dust, imperfect data and operational constraints. That’s how you move from PoC to production."
Don't hold your horses: From Neuman’s perspective, AI stalls when leadership and deployment lag behind the tools themselves. "We don’t have an AI capability problem. We have a leadership and deployment problem. Organizations are huddled at the start line while the tools are already powerful enough to transform daily operations."
The scaling surprise: But the sticker price of an AI model is often deceptive. Neuman warns that the true costs emerge from the deep, operational investments around the AI that are needed to support it in a production environment. "Anyone who has worked with cloud solutions knows the difference between PoC costs and the reality of production. It's the same with AI. When you scale up, you realize your operating costs are not the same anymore."
The disconnect between potential and reality is fueling widespread C-suite disappointment with AI's return on investment in the current spending frenzy, a fact Neuman attributes to leaders failing to grasp that AI simply amplifies existing data practices, for better or for worse.
The AI alibi: Neuman warns that frustration with AI results often pushes leaders to blame the technology itself, using it to justify workforce cuts and mask earlier strategic failures. The move may simplify the narrative, but it weakens brand trust and strips organizations of the human expertise required to make AI useful in the first place. "Companies use AI to excuse mistakes they made, and that sends the wrong signal," Neuman says. "You’re cutting off your future by cutting the people who create value. AI invents nothing. It only amplifies and accelerates."
For Neuman, the only sustainable path forward is creating what he calls an AI-friendly space, where strong data management, clear governance, and real accountability are built into everyday operations. The organizations that see lasting value are stepping back from the model race and redesigning how work actually gets done, so AI can scale without undermining trust. "The goal is to build an environment where AI truly supports human judgment and trust and becomes an assistant," Neuman says, "not a digital warden."
