Many leaders deploy AI as a “software project”: they buy licenses, conduct training, run internal communications, and measure usage, yet the results are vague. The cause is not the technology, but change management: businesses fail to redesign their work processes to harmonize human workers and AI.
- Author Travis Jones (Fast Company Executive Board) argues that AI is at a “historical turning point,” similar to when factories in the early 20th century electrified but did not reform their processes. The true value of AI emerges only when leaders “redesign how work gets done”: who does what, where decisions are made, and how humans and machines collaborate.
- AI is not just software that digitizes processes; it changes the structure of decision-making, the flow of information, and the way humans interact with technology. Therefore, change management must go beyond skills training: it must re-architect the organization to leverage AI.
Case Study: 60,000 Employees – Global Copilot Adoption A technology corporation deployed Microsoft Copilot to its sales force across more than 200 countries. Instead of merely distributing licenses, they built an “Adoption in a Box” program with detailed, role-specific guidance materials.
- Internal communication emphasized that Copilot is a tool to support humans, not replace employees.
- Dashboards and a “resistance scorecard” helped detect areas needing further training, creating continuous feedback through meetings and open support hours.
- Result: The sales team offloaded repetitive tasks to Copilot, focusing more on building customer relationships: a demonstration that AI only thrives when the business changes its way of working, not just changes its tools.
Three Golden Rules to Turn AI into Real Value
- Start with the process, not the tool: Identify key workflows and redesign them for AI to collaborate with humans, helping increase speed and accuracy.
- Empower employees: Provide role-based training, create practice opportunities, and establish a network of “AI champions” in each department to spread the energy of change from the bottom up.
- Combine speed and trust: Establish an AI governance mechanism from the start: simultaneously setting safe boundaries and encouraging rapid innovation, avoiding bureaucracy.
- Without change management, enterprises achieve only “cosmetic adoption”: they have licenses and users, but no real transformation, leading to “regret spend” or wasted investment.
Leaders need to measure success through cycle time, voluntary usage rates, and productivity gains, rather than the number of licenses distributed.
📌 Summary: Many leaders deploy AI as a “software project”: buying licenses, training, internal communications, and measuring usage, but the effectiveness is vague. The cause is not the technology, but change management: businesses fail to redesign their work processes to harmonize human workers and AI. It’s like electricity only revolutionizing manufacturing when factories redesigned their assembly lines. AI is not merely software that digitizes processes; it changes the structure of decision-making, information flow, and how humans interact with technology. Therefore, change management must go beyond skills training: it must re-architect the organization to leverage AI.
