Across the globe, artificial intelligence (AI) is rapidly becoming one of the most influential technologies in modern business. The eagerness surrounding AI innovation and implementation has reached new heights, with investors pouring billions of dollars into AI companies. However, while AI advancements continue to gain momentum, the enterprise adoption of these technologies is still in its nascent stages, as highlighted in the fourth annual Komprise 2024 State of Unstructured Data Management Report.
The Ground Reality of AI Implementation
Despite the massive influx of capital, 70% of organisations are merely experimenting with AI technologies, treating “preparing for AI” as a pivotal data storage and data management priority. This experimental phase is driven by the need to integrate AI within the confines of existing IT budgets, with only 30% of enterprises willing to increase their spending to support AI projects. This discrepancy between investment excitement and operational deployment illustrates the cautious approach of enterprises that are still figuring out how to harness AI’s full potential without breaking the bank.
Strategic IT Focus Areas and Data Storage Priorities
Currently, IT departments are not heavily regulating data usage, user access or tools concerning AI projects. Instead, they focus on constructing robust infrastructures and technology stacks that can effectively support AI applications.
Upgrading data storage and management technologies takes precedence as enterprises aim to improve security and governance across General AI (GenAI) and manage unstructured data more effectively. This focus is critical, given the growing necessity to address security skill gaps within IT teams.
Security and Governance
As enterprises build the technical groundwork for AI, they are simultaneously ramping up efforts in security and governance. Addressing governance and security issues is the foremost challenge in preparing data for AI applications, with data classification and tagging also presenting significant hurdles.
To tackle these challenges, a majority of companies are enhancing their data storage and management technologies. This focus underscores the critical role of a solid IT infrastructure in supporting a successful AI deployment.
Competing Needs and Unstructured Data Challenges
Modernising disaster recovery systems and backups is a primary concern for most, while others prioritise migrating data to the cloud without causing disruptions to users or applications. Furthermore, nearly half the enterprises face significant hurdles using AI to classify and segment data, underscoring the industry-wide shift towards integrating AI capabilities with strategic data management. enterprises are not exclusively focused on AI; they are equally committed to enhancing their overall data infrastructure to reinforce resilience and operational efficiency.
Prioritisation and Future Needs
The primary data storage priorities for the next year include cost optimisation and preparing data for AI. This is indicative of the broader challenge IT organisations face: balancing modernisation needs with operational requirements like disaster recovery and data migration to the cloud. In terms of future capabilities, AI data governance and security as the most crucial area for improvement. Additionally, there is a pronounced need for more personnel skilled in security, compliance, and data sensitivity.
The dichotomy between AI investment and implementation in enterprises underscores a broader trend: while businesses and technology vendors rapidly innovate, enterprise IT teams are methodically laying the groundwork for AI. This careful, strategic approach ensures that when enterprises are ready to fully embrace AI, they will have both the infrastructure and the strategies needed to do so effectively and securely.
IDS works with vendors like Komprise, which assists organisations in scaling and managing data across various storage solutions without the need for external interference, dedicated infrastructure, or alterations to the primary data or control paths. Komprise’s data management software sidesteps the costs, complexity, and disruptions associated with traditional data management methods by employing a modern architecture designed for simplicity and efficiency.
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