At fairs, lectures, events, books, conferences, articles, and indeed in any current business circle, the adoption of Artificial Intelligence (AI) is widely discussed in the corporate world. Companies and leaders from different sectors are increasingly convinced that AI is essential to maintaining 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 technology in a strategic and transformative way. What we see, in most cases, are isolated and low-impact pilot projects that fail to capture the true value AI can offer.
Companies typically follow the ‘pilot-evaluate-scale-mature’ model when implementing projects for any new technology. With AI, many organizations are indeed conducting tests and pilots across different departments and types of activities, using the same procedural logic. These experiments generally seek efficiency and productivity gains in specific areas, freeing up employees’ time to focus on higher-value activities. Although important, these initiatives are often limited, failing to significantly impact business strategy and frequently falling short of generating value at scale.
The question that arises is: why don’t these pilots evolve into broader, 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 and supported by leadership—often at the board level, no less.
How to Advance Toward Strategic AI Use
For AI to be truly revolutionary within companies, executives and leaders must 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 about reimagining processes, products, and even entire business models through the lens of AI.
Structuring AI Leadership
One of the main barriers to the more strategic adoption of AI is the lack of capable leaders to guide the transformation. Companies that truly advance with AI have executives and boards equipped to make informed decisions about this technology. Dedicated vice presidencies for 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 Upskilling
AI is not just about technology but also about people. For it to be widely adopted and integrated, employees must understand how the technology can impact their routines and sector. Continuous training and fostering an innovation culture are essential for employees to feel part of the change and contribute actively.
Adoption of a Robust Data Strategy
AI depends on data to function effectively. Therefore, it’s 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 must be prepared to handle large data volumes and explore machine learning and deep learning tools to extract valuable insights.
Big Tech Examples
Major tech companies lead the AI transformation and serve as references 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 points of its operations, from product recommendations to logistics management. These cases illustrate how AI, when used strategically, can transform not just internal processes but also customer experience and financial results.
Integration with Strategic Objectives
To move beyond isolated pilots, AI initiatives must align with the company’s strategic objectives. AI should be seen as a tool that can help achieve these goals more efficiently. For example, if a company aims to increase customer satisfaction, it can leverage AI for real-time personalization or to predict issues before they occur. AI should be embedded in all key projects that constitute the organizational strategic planning—and resources must be allocated for its use and enhancement.
To capture AI’s value at scale, companies must overcome 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 prioritizing long-term results.
Thus, advancing beyond isolated AI pilots requires a shift in mindset and structure within companies, which is no small feat. For AI to not just be a fleeting ‘flash in the pan,’ leaders must see it as a catalyst for real organizational transformation and be willing to invest and completely reimagine their operations.
