At trade shows, lectures, events, books, congresses, articles and, in fact, in any current business circle, the adoption of Artificial Intelligence (AI) has been widely discussed in the corporate world. Companies and leaders from different sectors are increasingly convinced that AI is essential to maintain their competitiveness and relevance in the market.
But while many companies recognize the value of AI, few are effectively integrating the possibilities brought by this type of technology in a strategic and transformative way.What we see, in most cases, are low-impact, one-off pilot projects that do not capture the true value that AI can offer.
Companies usually have the custom, in the face of any technological novelty, to adopt the “pilotar-evaluate-scale-maturate” model of project implementation. And with AI many organizations are, in fact, conducting tests and pilots in different departments and types of activity, using the same procedural logic. These experiments, in general, seek efficiency and productivity gains in specific areas, freeing time for employees to focus on higher value activities. Although important, these initiatives are often limited, not significantly impacting business strategy and often failing to generate value at scale.
The question that arises is: why do these pilots not evolve into broader, transformative initiatives? The answer lies in the lack of the strategic approach to AI within organizations, which needs to be guided by a clear vision and sustained by leadership & I.E.O.T often at board level, including.
How to move towards strategic use of AI
For AI to be truly revolutionary within companies, executives and leaders need to rethink the role of this technology in the context of their business.This goes far beyond implementing new software or automating specific tasks; it is a matter of reimagining processes, products and even entire business models from the perspective of AI.
Structuring a leadership for AI
One of the main barriers to a more strategic adoption of AI is the lack of leadership empowered to drive transformation. Companies that truly advance AI rely on executives and boards empowered to make informed decisions about this technology. Vice-presidencies dedicated to data and AI, expert advisors and innovation-focused governance are some examples of frameworks that can accelerate the adoption of AI at scale.
Culture change and employee training
AI is not only about technology, but also about people. In order for it to be widely adopted and integrated, it is critical that employees understand how technology can impact their routines and their sector. Continuous training and the promotion of a culture of innovation are essential for employees to feel part of the change and can actively contribute.
Robust data strategy adoption
AI relies on data to function effectively. Therefore, it is crucial that companies have a robust and well-structured data strategy. This includes collecting, storing, processing and analyzing data securely and ethically. Companies need to be prepared to handle large volumes of data and to explore machine learning and deep learning tools that allow them to extract valuable insights.
Examples from Big Tech
Big tech companies lead the transformation with AI and serve as a reference for the corporate sector. Meta, for example, uses AI to automate advertising processes, maximizing the reach and effectiveness of campaigns. Another example is Amazon, which applies AI at all points of its operation, from product recommendation to logistics management. These cases illustrate how AI, when used strategically, can transform not only internal processes, but also the customer experience and leverage financial results.
Integration with strategic objectives
To move beyond point pilots, it is important that AI initiatives are aligned with the strategic objectives of the company. AI should be seen as a tool that can help achieve these goals more efficiently and effectively. For example, if a company wants to increase customer satisfaction, it can exploit AI to offer real-time customization or to predict problems before they occur. AI should be incorporated into all major projects that constitute organizational strategic planning.
To capture the value of AI at scale, companies must overcome certain common challenges, such as resistance to change and technical complexity.This process requires the combination of visionary leadership, investments in data infrastructure, and a strategic approach that prioritizes long-term results.
Therefore, moving beyond point pilots with AI requires a change of mindset and structure within companies, which is not trivial. In order for AI to not just be a passing” chicken“, it is necessary for leaders to see it as a catalyst for real organizational transformation and to be willing to invest and completely reimagine their operations.
