AI driving the growth of investment in modernization.

34 views 12:01 am 0 Comments May 30, 2024

Enterprises are projected to allocate an average of $35.5 million towards IT modernization in 2024, with over a third of this investment earmarked for AI initiatives. The drivers behind this strategy include the need to rapidly prototype and test new concepts, enhance employee efficiency, and capitalize on emerging business trends. According to Matt McDonough, SVP of product and partners at Couchbase, enterprises are just beginning to leverage the potential of AI, with a majority expressing intentions to integrate GenAI into their operations in 2024.

When utilized effectively, GenAI can play a pivotal role in addressing the challenges faced by organizations, such as meeting evolving user expectations and increasing productivity. However, its successful implementation is contingent on a robust data architecture capable of meeting GenAI’s demands. A significant proportion of enterprises, 54 percent, indicated that they currently lack all the components of a data strategy suited for GenAI.

Moreover, only 18 percent have a vector database that can efficiently store, manage, and index vector data. Concerns also abound regarding the rushed adoption of AI technology, with 64 percent of respondents believing that many organizations have embraced GenAI without a full understanding of its requirements for effective and safe usage. This hurried adoption may have resulted in a reallocation of resources, as 26 percent of enterprises disclosed that they had diverted funding from other areas, particularly IT support, maintenance, and security, to meet AI objectives.

McDonough emphasizes that investing in appropriate data management and infrastructure architectures is essential for unlocking the transformative potential of GenAI. He advocates for the adoption of adaptive applications that can harness GenAI to enhance specific end-user experiences, thereby achieving improved efficiency and faster time-to-market without the need for complex, all-encompassing applications or multiple costly databases.

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