Addressing Every Employee Concern Won’t Improve Productivity
Generative AI tools such as ChatGPT, Claude, and Gemini are rapidly being adopted across organizations in pursuit of higher productivity. Yet many companies continue to hear the same feedback: AI has made work more complicated rather than more productive, and actual usage remains low.
Why do so many enterprise-wide AI initiatives fail to deliver the productivity gains companies expect?
The problem rarely lies in the technology itself. More often, it lies in how organizations approach implementation.
Many companies assume that productivity improves by addressing every concern employees raise. In many AI implementation projects, requests to eliminate inconveniences, simplify processes, or add new functionality quickly accumulate.
Each request may be reasonable on its own. But accommodating them one by one can make the overall system increasingly complex . Individual problems may be solved, while the organization becomes less effective as a whole.
The fundamental problem is that AI is being added to existing ways of working rather than used as an opportunity to rethink them.
The ERP Mistake Is Being Repeated in the Age of Generative AI
This challenge is not unique to generative AI. Similar issues emerged during large-scale ERP (Enterprise Resource Planning) implementations in the early 2000s.
At the time, reflecting every request from frontline employees was widely regarded as best practice. As more and more requests were incorporated, ERP systems became heavily customized. The result was increasingly complex systems that created significant operational and maintenance burdens, while many organizations struggled to achieve the productivity gains they had expected.
Many of today’s generative AI initiatives are beginning to follow the same path.
Adding new capabilities to existing ways of working may make individual tasks easier. But unless the underlying structure of work and the organization itself changes, those improvements rarely translate into meaningful gains in organizational effectiveness.
The Real Question Is Not What to Add, but What to Remove
During ERP implementations, many Western companies took a fundamentally different approach. Instead of customizing systems to fit existing ways of working, they redesigned their operations around standardized systems. Doing so often required changes to roles, responsibilities, and organizational structures. Their focus was not on adding new capabilities to existing work, but on eliminating unnecessary activities and exceptions.
Productivity is not simply a matter of making individual tasks more efficient. Real productivity comes from deciding what should remain, what should be eliminated, and how work and organizations themselves need to be redesigned.
The same principle applies to generative AI.
Improving productivity with AI is not simply about deciding which tasks should be automated. It requires redesigning work with AI as a given. That, in turn, means management must make the decision to redesign roles, business processes, and organizational structures.
Employee Feedback Is the Starting Point, Not the Solution
Listening to feedback from frontline employees is essential. Yet understanding their concerns does not mean implementing every request they make. The real challenge is to look beyond individual requests and identify the structural issues they reveal.
Improving productivity with generative AI therefore requires more than collecting feedback from the frontline. It requires viewing those insights through the lens of the organization as a whole and making deliberate decisions about what to keep, what to change, and what to eliminate.
Ultimately, the success of generative AI will depend not on the technology itself, but on whether organizations can redesign the underlying structure of work and the organization itself with AI as a given.
How IGPI Can Help
Organizations often approach AI as a technology initiative. At IGPI, we help management teams approach it as an organizational transformation.
By identifying the structural changes required, rather than focusing solely on technological opportunities, we support companies in redesigning work, operating models, and organizational structures to realize meaningful productivity gains from generative AI.
