According to a BCG Survey, 89% of C-suite executives rank AI and GenAI in their top three tech priorities for 2024. However, most organizations are yet to realize the benefits of their early investments. With such a rapid change in the GenAI technology landscape and the availability of varied solution options, customers need to find ways to fast-track technology adoption and build relevant capabilities quickly. Organizations exploring or adopting Generative AI face a multitude of challenges — How do we support data platform requirements for GenAI enterprise use cases while ensuring performance, reliability, and cost optimization? How can we involve SMEs and specialists as part of transactional and analytical workflows to provide additional context? How can we create a consolidated data hub to ingest, transform, and integrate data feeds from multi-modal sources? How can data be made discoverable and fit for training? How can the accuracy, stability, and transparency of model outputs be enhanced? How can we strengthen data security controls and data protection? Data strategy: Your differentiator A comprehensive Generative AI (GenAI) data strategy and an intelligent data foundation layer can accelerate the adoption of GenAI capabilities. There are several modalities such as text, code, audio, image, video and 3D/specialized data types in which we receive data. GenAI can guide business users to key insights in consumer behaviors by enabling them to combine data from various sources through natural language queries and summarizing issues to action without the help of dedicated analysts. Foundational models, which are the bedrock of GenAI applications, can be customized and finetuned with your organization’s proprietary data. This can deliver a differentiated experience when compared to out of the box foundational models. For example, a large grocery chain that tracks shopper preferences can customize a foundational model to produce a better recommendation engine that is highly differentiated from its competitors’ offerings. In the case of a healthcare provider, finetuning a foundational model with the enterprise’s multi-modal data can improve decision-making for a caregiver. Building such applications that are unique to your business needs requires the usage of your organization’s strategic data assets. Hence it is critical to secure, govern, and monitor data assets while generating tangible business value. GenAI in action In my experience at Eviden’s Data and Analytics team, our GenAI experts have developed a new offering tailored for such recent business realities — Eviden’s GenAI Unified Data Platform. This cuts across different clients’ business needs and technology capabilities to create a unified GenAI-enabled data platform. Designed to enable seamless connectivity, it also boosts integration across diverse structured and unstructured data sources. Overall, this integration helps with faster and a more effective realization of GenAI use cases across enterprise data entities. The value that these use cases provide include cost reduction, process efficiency, growth, innovation, discovery and insights, and a better consumer experience. With easy access to the right data and improved personalization, enterprises can gain from optimized productivity while developing new Generative AI use cases. Increased end-user adoption and trustworthy data quality enables them to discover and leverage the right data assets faster to derive insights. The multimodal semantic search enables search across multiple feeds such as texts, images and videos and faster compliance with regulatory and enterprise standards. This scalable and reliable data foundation can be used to generate contextualized insights from enterprise data and third-party data leveraging LLMs and GenAI. In pursuit of business benefits and better ROI, business leaders need to harness the potential that GenAI technology has to offer, and fast. Gear up to get ahead in the game today.