AI drives IT modernization spending to grow by nearly 30%, but companies are not prepared for data needs

sofa baseThe cloud database platform company has released the results of its seventh annual survey of global IT leaders.

A study of 500 senior IT decision-makers found that IT modernization investments are expected to increase by 27% in 2024 as enterprises look to take advantage of new technologies such as artificial intelligence and edge computing while meeting growing productivity needs. There is a clear need for modernization and technology investment: 59% are concerned that their organization’s ability to manage data will not meet the needs of GenAI without significant investment. With the right investment approach, businesses will be better able to address productivity challenges and satisfy end users who demand continually improving experiences.

Enterprises plan to spend an average of $35.5 million on IT modernization in 2024. Investing $6.7 million to be specific. The drivers are clear: rapid prototyping and testing of new ideas, improving employee efficiency, and identifying and capitalizing on new business trends. However, companies are aware that there are still challenges ahead – from ensuring that artificial intelligence can be used effectively and safely to having sufficient computing power and data center infrastructure.

“Enterprises have entered the age of artificial intelligence, but so far have only scratched the surface,” said Matt McDonough, senior vice president of products and partnerships at Couchbase. “Nearly every enterprise we surveyed had specific goals for using GenAI by 2024, and if Used correctly, this technology will be key to meeting the challenges organizations face, from meeting end-user expectations for adaptive applications to meeting ever-increasing productivity demands, GenAI-powered applications can deliver the agility and flexibility businesses demand. Productivity. Enterprises must ensure that their data architecture can meet the needs of GenAI, because without high-speed access to accurate, tightly managed data, it can easily lead individuals and organizations down the wrong path.

Key findings include:

• Businesses are unprepared for data needs: 54% do not have all the elements of a data strategy suitable for GenAI. Only 18% of enterprises have a vector database that can efficiently store, manage and index vector data. Ability to achieve control over data storage, access and use; Ability to access, share and use data in real-time; Ability to use vector searches to improve GenAI performance; A unified database infrastructure that prevents applications from accessing multiple versions of data Critical to developing strategies to meet GenAI’s data needs.
• Reliance on legacy technologies is hindering modernization: Despite increased investment in modernization, factors such as reliance on legacy technologies that cannot meet new digital requirements either cause projects to fail, suffer delays or curtailments, or are prevented from happening. The result is an average of $4 million in wasted investment each year and strategic projects delayed by 18 weeks.
• Target spend: Respondents understand how investments can help them improve their GenAI capabilities. 73% of respondents are increasing investment in AI tools to help developers work more efficiently and create new GenAI applications faster, while 65% say that by reducing latency and bringing data to the Integrated with computing power, edge computing is critical to realizing new artificial intelligence applications.
• The dangers of rushing into artificial intelligence: 64% of respondents believe that most organizations are rushing to adopt GenAI without understanding what it takes to use it effectively and safely. The worry is that this may be achieved by weakening other areas. 26% of enterprises are shifting spending from other areas to achieve AI goals—most commonly IT support and maintenance and security.
• Addressing productivity challenges: 71% of IT departments face increasing pressure to do more with fewer resources. On average, companies need to increase productivity by 33% annually to remain competitive. This may explain why 98% of respondents have specific goals to use GenAI in 2024.
• Infrastructure investment: 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 and environmental responsibilities mean they are unable to fully adopt GenAI. Unless based on more efficient infrastructure. Some respondents may not be aware of potential solutions – 66% believe that while solutions exist to support all multi-purpose access needs, they would need to invest in multiple databases to get all the necessary functionality to support GenAI.
• Adaptability is key to meeting end-user needs: 61% of enterprises are under constant pressure to provide an improved experience for end users, with consumer-facing applications lagging behind expectations by an average of 19 months and employee-facing applications falling behind expectations by an average of 20 months. One question, 45% of respondents said adaptability – the ability to change what an app provides users as needed – would be the most important attribute of an app.

“Investing in the right data management and infrastructure will help unlock the transformative potential of GenAI,” McDonough continued. “For example, organizations don’t need large, complex ‘one-size-fits-all’ applications to increase productivity and meet expectations, nor do they need multiple, expensive databases to serve their needs. GenAI can be used to enhance adaptive applications for specific end-user experiences Programs will be equally effective, while time to market will be faster. A modern multi-purpose database with all necessary functionality will help simplify architecture and costs as much as possible.

Label: Artificial intelligence, data, modernization