Generative AI Generative AI is a popular buzzword, but it's important to recognize that it's not a one-size-fits-all solution. One of Tech Leaders Unplugged's recent guests, Ebenezer Schubert, VP of Engineering of Outsystems, emphasizes the significance of understanding the use cases and capabilities of generative AI. He also highlights the challenge of transitioning from a successful AI demo to actual productization and value generation. By gaining clarity on these aspects, organizations can effectively drive the initiatives and make a compelling business case for their implementation.
Balancing Transformation Priorities
Tullio Siragusa, the host of Tech Leaders Unplugged, raises the question of how organizations can balance generative AI with other ongoing initiatives like cloud migration and agile methodologies. Ebenezer suggests that organizations should assess the areas of their systems that can benefit from generative AI. Depending on whether it is a core competency or a supporting function, organizations can decide whether to build the capabilities in-house or partner with external companies offering generative AI solutions through APIs. Additionally, the maturity of an organization's digital transformation journey plays a role in determining where generative AI can be applied effectively.
The Importance of Data for AI Transformation
Data has a critical role in AI transformation initiatives. While AI technologies like generative AI may rapidly commoditize, the differentiation lies in the quality of data available. Before jumping into AI implementation, organizations need to focus on obtaining clean and relevant data specific to their use cases. This data can be used to fine-tune AI models or augment them for better performance and stability. Ultimately, data precedes AI in terms of importance, and organizations should prioritize data acquisition and quality for successful AI transformation.
Real-World Challenges and Communicating Realities
The discussion shifts to a real-world scenario where a CEO desires to implement generative AI but encounters challenges due to ongoing data integration and cloud migration efforts. Ebenezer Schubert explains the importance of understanding the limitations of generative AI and the need to align its implementation with the readiness of data. By identifying areas where generative AI can have an immediate impact without extensive data integration, organizations can gradually introduce and familiarize themselves with the technology. Collaboration with solution providers and partners can also be beneficial in navigating these challenges.
The Evolving Role of CTOs and CIOs
The conversation then explores how the role of CTOs and CIOs must adapt to the changing landscape. As technology becomes intertwined with every line of business, CTOs, and CIOs need to closely collaborate with business units and gain a deeper understanding of their challenges. The focus shifts from solving technical problems to designing engagement models and experiences across the organization. This requires a shift in mindset and acquiring skills beyond traditional technology expertise.
Wrapping Up
Engineering transformation and culture are crucial for organizations navigating the challenges of adaptation, generative AI, cloud migration, and agile methodologies. It is vital to understand the potential and limitations of generative AI, prioritize initiatives based on business needs and data availability, and foster collaboration between technology teams and business units.
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