AI Drives Nearly 30% Increase in IT Modernization Spend, But Businesses Are Unprepared for Data Demands

Sofa basea cloud database platform company, has released the findings of its seventh annual survey of global IT leaders.

The study of 500 senior IT managers found that investment in IT modernization will increase by 27% by 2024 as companies look to take advantage of new technologies such as artificial intelligence and edge computing, while meet ever-increasing productivity demands. There is a clear demand for technology modernization and investment: 59% are concerned that their organizations’ ability to manage data will not meet the demands of GenAI without significant investment. With the right approach to this investment, companies will be better prepared to overcome productivity challenges and satisfy end users who demand continuously improved experiences.

Companies plan to spend an average of $35.5 million on IT modernization by 2024. More than a third of that will be on AI, with the average company investing more than $21 million in the technology in 2023-24 and $6.7 million in generative AI (GenAI) specifically. The drivers for this are clear: quickly prototyping and testing new ideas, making employees more efficient, and identifying and capitalizing on new business trends. However, companies recognize that there are challenges ahead, from ensuring that AI can be used effectively and safely to having sufficient computing power and data center infrastructure.

Businesses have entered the age of AI, but so far they’re only scratching the surface, said Matt McDonough, vice president of product and partners at Couchbase. Almost every company we surveyed has specific goals to use GenAI by 2024, and if used correctly this technology will be key to managing the challenges facing organizations. From keeping pace with end-user expectations for adaptive applications to meeting ever-accelerating productivity demands, GenAI-powered applications can provide the agility and productivity that businesses need. Businesses must ensure that their data architecture can handle the demands of GenAI, because without high-speed access to accurate and tightly managed data, it can easily lead people and organizations through wrong way

Key findings include:

  • Businesses are unprepared for data demands: 54% do not have all the elements of a proper data strategy for GenAI in place. Only 18% of companies have a vector database that can efficiently store, manage and index vector data. Enabling capabilities such as controlling data storage, access and usage; the ability to access, share and use data in real time; the ability to use vector search to improve GenAI performance; and a consolidated database infrastructure to prevent applications from accessing multiple versions of data will be critical to building a strategy that meets GenAI’s data demands.
  • Reliance on legacy technology is holding back modernization: Despite increased investment in modernization, factors such as reliance on legacy technology that cannot meet new digital requirements are causing projects to fail, be delayed or scaled back, or prevented from ever happening. The result is an average investment of $4 million per year and an 18-week delay in strategic projects.
  • Targeted spending: Respondents are aware of how investment can help their GenAI capabilities. 73% are increasing investment in AI tools to help developers work more effectively and build new GenAI applications faster, while 65% say edge computing will be critical to enabling new AI applications reducing latency and pooling data and computing power.
  • The dangers of rushing into AI: 64% of respondents believed that most organizations have rushed to adopt GenAI without understanding what it takes to use it effectively and safely. It is worrying that this has been achieved by weakening other areas. 26% of companies diverted spending from other areas to achieve AI goals most often from IT support and maintenance and security.
  • Meet the productivity challenge: 71% of IT departments are under increasing pressure to do more with less. On average, companies need to increase productivity by 33% year-on-year simply to remain competitive. This could explain why 98% of respondents have specific goals to use GenAI by 2024.
  • Invest in infrastructure: 60% of respondents are concerned about whether their organization has enough computing power and data center infrastructure to support GenAI, while 61% say their corporate social responsibility and environmental responsibilities mean they cannot fully adopt GenAI if it is not based on a more efficient infrastructure. Some respondents may not be aware of potential solutions. 66% believe they would need to invest in multiple databases to get all the capabilities needed to support GenAI, despite the existence of solutions that support all multipurpose access needs.
  • Adaptability is key to meeting end-user demands: 61% of companies are under pressure to continuously deliver improved experiences for end users, with the average consumer-facing app falling behind expectations by 19 months, and the average employee-facing app by 20. For counter it, 45% of respondents say adaptability. the ability to change what the application provides to the user as needed will be the most essential attribute for applications.

Investing in data management and the right infrastructure architecture will help unlock the transformative potential of GenAI, McDonough continued. For example, organizations don’t need large, complex jack-of-all-trades applications to improve productivity and meet expectations, nor do they need multiple, expensive databases to meet their needs. An adaptive app that can use GenAI to improve a specific end user experience will be just as effective while having a much faster time to market. And a modern multipurpose database with all the necessary functionality will help keep architectures and costs as streamlined as possible.

Tags: AI, data, modernization

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