Organizations across sectors are preparing to increase their IT modernization spending in 2024, projecting a 27% rise compared to previous years. This surge largely stems from the integration of cutting-edge technologies, particularly artificial intelligence (AI) and edge computing.
Financial forecasts for 2024 indicate that companies plan to allocate an average of $35.5 million towards IT modernization.
Out of this substantial budget, AI technologies will receive over $21 million, highlighting the greater emphasis being placed on integrating advanced capabilities into business operations.
Generative AI (GenAI) technologies alone are slated for an investment of $6.7 million. Financial commitments are becoming a strategic priority that enterprises place on AI as a driver for innovation and efficiency within their IT frameworks.
Data management challenges
Data management is being seen as a major challenge in the evolving IT space, with 59% of senior IT leaders expressing concerns about their organization’s capacity to handle the demands imposed by GenAI technologies.
Leaders anticipate the need for greater investments to upgrade their data handling capabilities to fully leverage GenAI’s potential.
Organizational readiness to manage data suitable for GenAI appears lacking, as evidenced by 54% of businesses not having a comprehensive data strategy in place.
This highlights the pressing need for structured data management plans that can support advanced AI applications without disruptions.
A core aspect of data management that most organizations are still grappling with is the adoption of vector databases. Only 18% of enterprises have successfully implemented a vector database that can efficiently store, manage, and index vector data, which is key for operating sophisticated AI models that rely heavily on large datasets.
For businesses to successfully navigate the complexities of GenAI, they must focus on building robust data architectures. These should include capabilities for real-time data access and the use of consolidated database infrastructures that prevent the creation of siloed data environments.
Legacy technology and resource allocation
Effects of outdated technology
Organizations continue to wrestle with the challenge of outdated technology systems that hinder their modernization efforts. Legacy technologies become barriers to adopting new digital tools and lead to increased operational inefficiencies and risks.
Specifically, companies report an average financial waste of $4 million annually due to antiquated systems.
To further add to this challenge, strategic projects face considerable setbacks, with delays averaging 18 weeks. Delays and financial losses are spotlighting the growing need for enterprises to replace or upgrade their aging infrastructure to keep pace with technological advancements and market demands.
Funding reallocation concerns
In a bid to stay competitive and innovative, 26% of enterprises have shifted their financial priorities, diverting funds from critical areas such as IT support and security to bolster their AI initiatives.
While this reallocation underlines the strategic importance placed on AI technology as a transformative tool for business operations, it also raises concerns about the potential neglect of fundamental IT services and cybersecurity measures.
Executives must carefully balance their investment strategies to ensure that while they advance their technological capabilities, they do not compromise the foundational aspects of their IT infrastructure that safeguard and stabilize their business processes.
Strategic and risk management in GenAI adoption
Rapid adoption and risks
A notable 64% of IT leaders expressed concern that many organizations are hastily adopting generative AI without fully comprehending the requirements for its effective and safe application.
This rush towards embracing GenAI technologies may lead to implementation challenges, including inadequate training, potential misalignment with business objectives, and increased vulnerabilities.
These concerns suggest that while the appeal of GenAI is strong, due to its potential to dramatically enhance various business functions, organizations must adopt a more measured and informed strategy to mitigate risks associated with its deployment.
Growing investment in AI tools and infrastructure
To address these challenges, 73% of IT leaders are ramping up investments in AI tools, aiming to boost developer efficiency and accelerate the development of new GenAI applications.
Renewed focus on GenAI tools is intended to streamline development processes and to foster innovation within teams.
Alongside this, 65% of respondents recognize the key role of edge computing, particularly for its ability to reduce data transmission delays and bring computational power closer to the data source.
Synergy between AI and edge computing is important for enabling real-time processing and decision-making capabilities – improving the performance and responsiveness of AI-driven applications.
Productivity and corporate social responsibility
Increasing productivity demands
IT departments across various industries are under growing pressure to boost their productivity metrics by an average of 33% each year, a target set to maintain competitive parity in fast-evolving markets.
According to recent surveys, 71% of IT leaders report escalating demands to maximize efficiency and output with constrained resources.
There is a broader industry movement toward leaner operations, where doing more with less is both an ideal and necessary strategy for survival and growth in a technology-driven marketplace.
Infrastructure and responsibility concerns
Concerns about the sufficiency of compute power and data center infrastructure to support GenAI applications are prominent, with 60% of IT leaders acknowledging these apprehensions.
Rapid deployment and scaling of AI technologies requires robust and capable infrastructure to handle the increased load and complex data processing requirements.
Adding to this, 61% of executives consider corporate social responsibility and environmental impacts as core factors in their strategic decision-making processes for GenAI adoption.
This perspective is critical as businesses seek to align their technological advancements with sustainable practices and ethical standards, ensuring that their growth does not come at an excessive environmental or social cost.
User experience and application adaptability
Demand for improved user experiences
Organizations face challenges in keeping their consumer-facing and employee-facing applications up to date with current expectations. Research indicates that consumer applications tend to fall short of user expectations within 19 months, while employee applications lag within 20 months.
This places pressures on up to 61% of enterprises to continuously improve their software offerings to satisfy demanding user bases.
The growing need for continual improvement in application performance and functionality is due to the rapid pace of technological change and user preference evolution, making it a must for companies to remain agile in their software development practices.
Importance of application adaptability
Adaptability in applications is becoming increasingly essential, with 45% of IT leaders identifying it as a key attribute for modern software solutions. Focusing on adaptability allows businesses to modify and tailor their applications to meet evolving user needs dynamically.
In the context of infrastructure, there is a strong push for systems that are multipurpose while minimizing operational complexities and costs.
The preference for streamlined architectures that consolidate multiple functionalities into single platforms is evident, as it reduces the need for maintaining multiple databases, which improves operational efficiency and reduces associated costs.
Adaptability and efficiency is the focus here, and they are vital for businesses aiming to quickly respond to market changes and user demands without escalating their technological expenditures.
