True competitive advantage does not come from artificial intelligence, but from the ability to turn knowledge, processes and people into an organization capable of learning and continuously improving.
by Silvia Taretto — CEO of Impresoft Syscons
When people talk about artificial intelligence, the question I hear most often in boards of directors and executive committees is always the same: 'Which technology should we adopt?'. It's an understandable question, but it often starts from the wrong assumption. The reality I have observed for almost thirty years is different. Technologies today are more accessible, powerful and widespread than ever. Yet many organizations continue to achieve results below expectations despite significant investments. The reason is simple: technology amplifies what the organization already is. For this reason, the real question should not be which technology to adopt, but how ready the organization is to adopt it.
The Cartesian method invites us to break complex problems down into their essential components before taking action. This is exactly what should happen in digital transformation projects: understand, verify and share a process before attempting to automate it. It is also necessary to act with honesty of principles and intentions. In my experience, I have even suggested halting transformation projects when the prerequisites needed for their success were missing.
I remember a case in which customer master data was incomplete, and the introduction of the new system simultaneously required an organizational overhaul that the company was not yet ready to sustain. We proposed suspending the project, making its restart conditional on meeting two prerequisites: complete data and an adequate organization. It was the start of a journey lasting years that made it possible to successfully digitalize a process that until then had been managed almost exclusively through the knowledge of a few key people and the use of Excel, turning it into a shared asset that the entire organization could manage.
This experience taught us a fundamental lesson: technology almost always comes in at the end of the journey, not at the beginning.
Much of a company's knowledge does not reside in information systems, manuals or procedures. It lives in people's everyday experience, in decisions shaped in the field, and in practices developed over time.
Ikujiro Nonaka and Hirotaka Takeuchi called this type of knowledge "tacit", distinguishing it from explicit knowledge, that is, knowledge that is formalized and codifiable. In their model, one of the most critical steps for organizational learning is precisely turning tacit knowledge into shared, verifiable and transferable knowledge.
This is a step that precedes any effective automation initiative: before applying algorithms, workflows or artificial intelligence, the organization must understand and make explicit what today exists only in people's heads. AI can process already codified knowledge well; it is much harder to extract value from what the organization has not yet made understandable, structured and shared.
AI does not have direct experience, decision-making responsibility, or the understanding of context built over years of professional activity. The judgment that arises from integrating data, experience and organizational context remains human — not as a concession, but by definition. The risk is not that AI will decide in our place: it is that, by relying too much on its answers, we progressively stop training that critical capacity.
Introducing technology without providing time, training and context does not mean transforming the company. It simply means accelerating the existing disorder. It is therefore not surprising that many transformation initiatives fail to fully achieve their expected goals. As studies on organizational change have long shown, starting with the work of John Kotter, the main obstacles are almost never technological, but related to governance, organizational culture, change management and people's ability to adopt. AI does not replace those who judge, decide and build trust. It repositions their contribution toward higher-value activities.
The companies that will win the AI game will not necessarily be the ones that adopt the most advanced technologies first. They will be the ones that have developed the discipline needed to understand, govern and continuously improve their own processes, turning individual knowledge into organizational capital and change into a permanent capability. Because artificial intelligence does not turn disorganized organizations into excellent ones: it simply makes more visible what they have already become, and it makes it impossible to keep postponing the work of putting them in order.
Kotter, J.P. (1996), Leading Change.
Nonaka, I., Takeuchi, H. (1995), The Knowledge-Creating Company.