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Challenges and Opportunities of Generative AI in Industry: Our Experience at BNEW

Javier Campelo en BNEW 2024

At the recent BNEW – Barcelona New Economy Week event, we had the opportunity to participate in the panel “Applications of Generative AI in Industry.” In this session, Javier Campelo Piñón, our Head of Analytics & AI, shared the company’s vision alongside Sandra Arcos (Olistic Lab) and Roberto Maestre (BBVA), providing a comprehensive perspective on the challenges and opportunities of this emerging technology.

Generative AI, one of the most disruptive trends in today’s industry, promises to profoundly transform the way companies operate, but its implementation is not without challenges. During the discussion, some of the most relevant challenges were highlighted:

  1. Executive Team Knowledge and Expectations: It is crucial to align the executive team’s understanding of the real capabilities of Generative AI. Often, initial expectations do not match the possible outcomes or the associated cost-benefit, which can lead to disappointment if not properly managed.
  2. Data governance: A key aspect is having a robust data governance framework that ensures proper management in both quantity and quality. While the volume of data is essential for the success of AI, the quality and relevance of that data are equally critical to generating value.

aggity’s Proposal: DataLab Methodology

To overcome these challenges and ensure that Generative AI projects align with business objectives, at aggity we have developed the DataLab methodology. This approach aims to democratize the use of AI and generate tangible value through a strategic deployment in four key phases:

  1. Information and Expectations Alignment Phase: The goal is to ensure that all stakeholders involved understand the capabilities of AI and have realistic expectations regarding the outcomes.
  2. Design Thinking Phase: In this phase, the most relevant use cases are identified from a business perspective, focusing on those where data can truly make a difference and add value.
  3. Selection Phase: From all the identified initiatives, filtering and prioritization are carried out to determine which are the most feasible and relevant to execute in the initial stage.
  4. Execution Phase: Finally, a “Proof of Value” is launched—a pilot project that validates the use case in a controlled environment before scaling it across the entire organization.

This methodology allows us to adopt a pragmatic approach: “Think big, start small, and scale fast” , ensuring that Generative AI projects are not only viable but also scalable and aligned with the company’s strategic objectives.

aggity’s participation in BNEW has been an excellent opportunity to share our ideas and learnings with other industry experts, consolidating our position as leaders in the adoption of Generative AI in the industry.