Leading in the AI Era: Efficiency or Transformation?
There are still many companies operating with traditional management models — not very dynamic and strongly based on control. Although, at first glance, these models may seem less attractive, the truth is that they continue to work for a significant number of people — often because they offer predictability and lower individual exposure to risk. Executing what is requested, within well-defined structures, can feel more comfortable than taking responsibility, making decisions, and dealing with uncertainty.
In this context, the recent evolution of Artificial Intelligence opens up a particularly interesting set of opportunities for this type of organization. Companies with more rigid and structured processes tend, somewhat paradoxically, to have a more favorable ground for adopting AI-based solutions, precisely because their workflows are more predictable and standardized. The integration of technologies such as Microsoft Copilot, Azure OpenAI Service, or Power Automate can enable significant gains in operational efficiency, execution speed, and consistency of results.
For example, repetitive and rule-based tasks — such as data processing, report generation, or first-line customer support — can be partially automated, reducing response times and minimizing human error. Tools like Microsoft Power BI also make it possible to transform large volumes of data into actionable insights, supporting more informed decision-making at the management level. For more traditional hierarchical structures, this combination of increased speed, greater accuracy, and potential reduction in operational costs tends to be particularly appealing.
On the other hand, there is a growing group of companies following a different path. Instead of using technology solely as a mechanism for efficiency, these organizations are rethinking their operating model more fundamentally. They invest in collaboration networks, less rigid structures, and a stronger appreciation of the human element — recognizing that people’s critical, creative, and adaptive capabilities remain a key differentiator.
In these cases, AI is integrated as a tool to amplify human capabilities, rather than simply replace them. Solutions such as Microsoft Teams, combined with generative AI and advanced analytics, enable the creation of more collaborative work environments, where knowledge sharing and distributed decision-making gain relevance. Technology supports — but does not replace — the organization’s collective intelligence.
The result is the coexistence of two distinct “rhythms” within the business landscape. On one hand, organizations that evolve by optimizing and automating their existing models; on the other, companies that structurally reinvent themselves to respond to an increasingly dynamic and competitive environment. In a way, these are two parallel leagues, moving at different speeds and driven by different ambitions.
Regardless of positioning, one point remains clear: there is still significant room for improvement, particularly in the services sector. More than a superficial adoption of “robots” or conversational interfaces, the real challenge lies in optimizing end-to-end processes — simplifying, eliminating redundancies, and redesigning workflows based on data and technology. Only then will it be possible to sustainably capture the true value that Artificial Intelligence has to offer.
by Rita Herédia Cordovil, Managing Partner at Luza
