Inc. argues that companies are making a fundamental mistake by treating AI transformation like a conventional technology rollout. The article, based on the views of Workforce Intelligence co-authors Michael Sullivan and Vinay Gidwaney, says organizations that simply deploy AI tools often achieve limited adoption—roughly 15–20% according to their assessment. The companies getting more value are instead redesigning work around collaboration between employees and AI.
One major problem is that AI is being measured as a technology rather than as a change in how work gets done. Companies often track licenses, usage and tokens, but those numbers do not necessarily show whether productivity, decision-making or customer outcomes have improved. The authors argue that leaders should ask a more fundamental question: Has anything actually changed about how the organization works because of AI?
The article also highlights the human side of AI productivity. When AI takes over routine analytical work, companies have a choice: use the resulting efficiency primarily to reduce headcount and costs, or give employees more time for higher-value activities such as client relationships, judgment, creativity and problem-solving. This turns AI adoption into a question of leadership and organizational values, not simply technology spending.
The broader takeaway is that AI's biggest challenge may not be choosing the right model, but redesigning the workforce around it. Companies that treat AI as another software deployment risk spending heavily without changing employee behavior or business processes. The organizations most likely to capture lasting value will be those that combine AI capabilities with reskilling, new workflows, clear roles and human judgment—essentially treating AI less like a tool being installed and more like a new kind of colleague being integrated into the workforce.
