Transforming Data Management: How IT Leaders Leverage AI for Revolutionary Impact

AI's Role in Improving Data Management

As organizations increasingly adopt AI technologies, including machine learning (ML) and generative AI (gen AI), the role of CIOs has evolved to incorporate these tools into data management strategies. However, while AI is often seen as a solution for enhancing operations, its application should be strategic, focusing on tangible business benefits.

For instance, Euronics, an international electrical retail association, has taken a data-driven approach to improve customer experiences and increase sales. The company's digital director, Umberto Tesoro, emphasizes using digital data to inform decisions and design better customer journeys. Euronics has implemented ML to enhance personalized content on its e-commerce platform, recommending products based on customer purchase history.

However, despite the growing interest in gen AI, Tesoro has opted not to implement it yet. He believes it doesn’t currently serve a functional role in Euronics’ retail activities. Instead, the company focuses on optimizing user experience (UX) and leveraging data to support decision-making, relying on best-in-class ML tools customized to its needs through technological partners.

Data Management for Nonprofits: Emergency Approach

In the nonprofit sector, data management is equally critical. Emergency, an Italian NGO, uses AI to ensure the security and efficiency of its data in war-torn regions. CIO Manuele Macario highlights how AI-supported encryption and cloud backup technologies have enhanced the security of their sensitive clinical data, which is collected in real-time from surgical centers in Afghanistan.

Emergency’s most significant AI-driven project, Amanat, involved digitizing over 10 million medical records and using AI to analyze this data, despite the challenges posed by handwritten, often incomplete documents. By partnering with Microsoft’s Azure OpenAI, Emergency was able to convert these records into usable data for medical analysis, helping improve care quality and resource allocation in conflict zones.

Selective Use of Gen AI

Both Euronics and Emergency are cautious about the broader application of gen AI. While Euronics tests its use in the Salesforce ecosystem for automating manual processes, Tesoro sees limited immediate impact on retail activities. Instead, he views AI as a tool to enhance productivity by reducing repetitive tasks and allowing employees to focus on more intellectually demanding work.

Similarly, Macario stresses that AI, including tools like ChatGPT, must be deployed within clearly defined boundaries. For Emergency’s Amanat project, gen AI was used strictly for analyzing medical records, and the team ensured that only reliable data was retained for analysis, discarding half of the records in their proof-of-concept (PoC) for accuracy.

Traditional AI Still Relevant

While gen AI holds promise, traditional AI techniques remain vital for many organizations. These established methods, such as optimization, simulation, and knowledge graphs, continue to be effective for business operations without the risks associated with gen AI. As Gartner suggests, CIOs should carefully evaluate whether AI use cases create value and are feasible before making significant investments in generative models.

Ultimately, the role of AI in data management is evolving, but its application should always be strategic and aligned with specific business goals. Balancing innovation with proven AI technologies is key to success in today’s data-driven landscape.