Spanish companies are in a very different moment than just a year ago when it comes to AI. AI has ceased to be an exclusively technological issue and has become a matter of business strategy and transformation.
Managers’ questions have also changed: they are no longer focused so much on the potential of AI as on how to get real value, scale, and avoid getting stuck in a succession of high-expectation, low-impact pilots. The challenge is, therefore, strategic, organizational and cultural.
Technology is no longer the competitive advantage. The speed of adoption yes. The models are increasingly accessible and scalable, so the difference will be in the ability of each organization to prioritize use cases, have reliable data, manage change and deploy operating models that allow AI to scale.
From experimentation to value capture
The first wave of AI was marked by curiosity: chatbots, conversational agents, productivity tools and numerous pilots. Today the question is different: where is the economic return? How to improve productivity? How to accelerate decision-making and transform entire processes?
AI is thus moving from being a technological project to becoming a sustainable business capability. And there appear the causes that explain why many initiatives fail to advance:
- There is no clearly defined business problem. You want to use AI without specifying what problem it should solve.
- The data does not have the necessary quality. AI does not correct inconsistent, scattered, or ungoverned data; can amplify your deficiencies.
- There are too many pilots and little real operation. The real challenge is not to demonstrate that a solution works, but to move from the pilot to its use at scale.
- Change management is insufficient. Adoption will depend on users seeing a real improvement in their daily work.
Therefore, the advantage will not be in having the technology, but in the speed with which an organization manages to integrate it into its real operations.
A bubble of expectations
Are we facing an AI bubble? Yes, but fundamentally expectations. As happened with the Internet, mobility or the cloud, the short-term impact has been overestimated while the long-term impact has been undervalued.
Promises of total automation, mass replacement of employees or immediate increases in productivity have generated expectations that have not always been met. However, everything indicates that in the coming years AI will be integrated into business processes as naturally as the Internet, mobile applications or the cloud.
There is also an important difference: AI accelerates its own evolution. The speed of change is much greater and that makes the adaptation capacity of organizations a critical factor.
Therefore, the question is no longer whether the technology works. It works and is powerful. The question is how and at what pace we manage to integrate it.
From flashy case to scalable case
If AI is going to become part of the organization, the question is where to start. The answer should not be the most spectacular use case, but one that combines impact, speed, low risk and the ability to adopt and scalable.
To evaluate each initiative, it is worth asking yourself:
- What economic impact can it generate and what metric do we want to improve?
- Do we have the necessary data?
- Can it be implemented quickly to generate first success?
- What operational or regulatory risks does it involve, including the AI Act?
- Can it be scaled to a significant part of the organization?
Processes such as personal productivity, software development, customer service, document management or the generation of corporate knowledge can be good starting points.
Ultimately, the best use case is not necessarily the most innovative, but rather the one that can become a repeatable capability on an enterprise scale.
AI requires new leadership
Many organizations continue to place AI under the responsibility of IT. Technical leadership is essential to manage models, architecture and integration, but the success of AI also depends on processes, people and business transformation.
Therefore, these initiatives must have the direct leadership of general management, who must sponsor them and actively promote the organization’s adaptation. More than with other technological disruptions, the speed of adoption now requires direct involvement from the management committee.
The AI Paradox
AI has quickly become an accessible and scalable technology for virtually any organization. Precisely for this reason, technology is no longer the differentiating element.
The real advantage will be whoever manages to industrialize AI first, effectively integrating it into their processes and turning it into a business capability.
The best use case is not necessarily the most innovative, but the one that can become a repeatable capability at enterprise scale.
This is the paradox: it has never been so easy to access such a powerful technology and, at the same time, so difficult to generate a competitive advantage based exclusively on it. When everyone has access to the same technology, the difference lies in the organization.
As happened with the Internet, mobility or the cloud, there will come a time when we will stop talking about AI because it will simply be part of the way we work. Organizations that have learned to industrialize it before will have built a competitive advantage that is difficult to achieve.
Fede Rousaud, Cloud Apps & Data Business Unit Managing Director at VISEO Iberia
