In fairs, lectures, events, books, congresses, articles, and indeed in any current business circles, the adoption of Artificial Intelligence (AI) is being 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.
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 isolated pilot projects with low impact that do not capture the true value that AI can offer.
Companies typically have the practice, faced with any technological novelty, of adopting the “pilot-evaluate-scale-mature” model for project implementation. With AI, many organizations are indeed conducting tests and pilots in different departments and types of activities, following the same procedural logic. These experiments generally aim for gains in efficiency and productivity 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 do these pilots not evolve into broader and 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, as well.
How to advance towards strategic use of AI
In order for AI to truly be 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 through the lens of AI.
Structuring AI Leadership
One of the main barriers to more strategic adoption of AI is the lack of qualified leaders to guide the transformation. Companies that truly advance with AI have executives and boards qualified to make informed decisions about this technology. Data and AI dedicated vice presidencies, specialized advisers, and innovation-focused governance are some examples of structures that can accelerate widespread AI adoption.
Culture Change and Employee Empowerment
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 industry. Continuous training and the promotion of an innovation culture are essential for employees to feel part of the change and actively contribute.
Adopting a Robust Data Strategy
AI relies on data to function effectively. Therefore, it is crucial for companies to have a robust and well-structured data strategy. This includes the secure and ethical collection, storage, processing, and analysis of data. Companies need to be prepared to handle large volumes of data and to explore machine learning and deep learning tools that enable valuable insights to be extracted.
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 campaign reach and effectiveness. Another example is Amazon, which applies AI across all aspects 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 customer experiences and leverage financial results.
Integration with strategic goals
To move beyond ad-hoc pilots, it is important for AI initiatives to be aligned with the company’s strategic goals. AI should be seen as a tool that can help achieve these goals more efficiently and effectively. For example, if a company aims to increase customer satisfaction, it can explore AI to offer real-time personalization or to predict problems before they occur. AI should be embedded in all major projects comprising 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 change resistance 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 ad-hoc AI pilots requires a mindset and structural change within companies, which is non-trivial. For AI to not just be a fleeting trend, leaders need to see it as a catalyst for real organizational transformation and be willing to invest and completely reimagine their operations.
