At fairs, lectures, events, books, congresses, articles, and indeed in any current business circle, the adoption of Artificial Intelligence (AI) has been widely discussed in the corporate world. Companies and leaders from various sectors are increasingly convinced that AI is essential to maintain their competitiveness and relevance in the market.
However, 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 sporadic and low-impact pilot projects that do not capture the true value that AI can offer.
Companies typically follow the practice, when faced with any technological innovation, of adopting the "pilot-evaluate-scale-mature" model for project implementation. And with AI, many organizations are indeed conducting tests and pilots in different departments and types of activities, using the same procedural logic. These experiments, in general, seek efficiency and productivity gains in specific areas, freeing up time for employees to focus on higher-value activities. Although important, these initiatives are often limited, not significantly impacting the business strategy and often failing to generate value at scale.
The question that arises is: why don't these pilots evolve into broader and more transformative initiatives? The answer lies in the lack of a strategic approach to AI within organizations, which needs to be guided by a clear vision supported by leadership – often at the 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 businesses. This goes far beyond implementing new software or automating specific tasks; it's 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 toa The most strategic adoption of AI is the lack of qualified leadership to guide the transformation. Companies that truly advance with AI rely on executives and boards equipped to make informed decisions about this technology. Vice presidencies dedicated to data and AI, specialized advisors, and governance focused on innovation are some examples of structures that can accelerate large-scale AI adoption.
Culture change and employee training
AI is not just about technology, but also about people. For it to be widely adopted and integrated, it is essential that employees understand how the technology can impact their routines and their sector. Continuous training and the promotion of an innovation culture are essential for employees to feel part of the change and to actively contribute.
Adopting a robust data strategy
AI depends on data to function effectively. Therefore, it is crucial for companies to have a robust and well-structured data strategy. This includes the collection, storage, processing, and analysis of data in a secure and ethical manner. Companies need to be prepared to handle large volumes of data and to explore machine learning and deep learning tools that allow for extracting valuable insights.
Examples of Big Techs
Large technology companies lead the transformation with AI and serve as a benchmark 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 recommendations to logistics management. These cases illustrate how AI, when used strategically, can transform not only internal processes but also the customer experience and boost financial results.
Integration with strategic objectives
To move beyond pilot projects, it is important that AI initiatives are aligned with the company's strategic objectives. 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 leverage AI to offer real-time personalization or to predict problems before they occur. AI must be integrated into all major projects constituting the organizational strategic planning – and resources should be allocated for its use and enhancement.
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 a combination of visionary leadership, investments in data infrastructure, and a strategic approach that prioritizes long-term results.
Therefore, moving beyond pilot projects with AI requires a change in mindset and structure within companies, which is not trivial. For AI not to be just a fleeting "chicken flight," leaders need to see it as a catalyst for real organizational transformation and be willing to invest and completely reimagine their operations.
