TLDR: A new report from Elets CIO emphasizes the critical need for enterprises to build AI-ready data foundations to unlock the full potential of artificial intelligence. Maurizio Garavello of Qlik notes that legacy data architectures are hindering AI ROI, with over 80% of AI projects failing to reach production. The solution lies in modernizing data infrastructure through real-time integration, governance, and the adoption of data lakehouses, which are seen as crucial for scalable and efficient AI initiatives.
In an era where artificial intelligence is poised to redefine enterprise operations, a recent Elets CIO report underscores the paramount importance of establishing a robust, AI-ready data foundation for future business success. Maurizio Garavello, Senior Vice President for Asia Pacific & Japan (APJ) at Qlik, highlighted in an exclusive interaction with Elets News Network (ENN) that many organizations are currently struggling to achieve a meaningful return on investment (ROI) from their substantial AI expenditures. This disconnect is largely attributed to outdated, legacy data architectures.
Garavello explained that these traditional systems fragment and isolate critical information, effectively blocking AI models from accessing the accurate and timely data they require to function effectively. The consequence is stark: more than 80% of AI projects reportedly never reach production, and only a quarter of organizations can demonstrate measurable returns on their AI investments. Specifically in India, the challenge is even more pronounced, with over half of enterprises unable to prepare their data for AI, and one in three leaders identifying poor data quality as their primary impediment. However, there is a silver lining, as four in ten Indian enterprises are already prioritizing data quality in anticipation of generative AI and retrieval-augmented generation (RAG) initiatives.
Modernizing the data foundation is presented as the essential antidote to these challenges. This involves integrating, governing, and enriching data in real-time, which effectively eliminates data silos, significantly reduces infrastructure costs, and establishes secure, discoverable data flows across the entire business. This unified approach allows AI models to train more effectively, scale at an accelerated pace, and ultimately generate outcomes that justify the initial investment.
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The report also positions data lakehouses as the future of enterprise data architecture. These innovative systems merge the strengths of traditional data warehouses and data lakes, providing enterprises with the capability to store, analyze, and leverage massive datasets with unparalleled speed and efficiency. By adopting open standards such as Apache Iceberg, data lakehouses decouple storage from compute, thereby eliminating vendor lock-in and enhancing overall flexibility. This architectural shift is particularly vital in markets like India and Australia, where most organizations now consider cloud migration indispensable for establishing real-time data pipelines and achieving comprehensive modernization.
