While 2023 was all about generative, AI-powered chatbots and search, 2024 introduced agentic AI – tools able to planning and executing multi-step actions in digital environments. From Devin's technical breakthroughs to Microsoft's early attempts at Copilot Vision, the innovations have been varied, but one constant has remained: the necessity to keep data infrastructure organized and reliable.
As corporations embraced advanced AI initiatives, several trends modified the best way data is managed, secured and used. Companies are increasingly turning to multicloud, open data and open governance strategies to avoid vendor lock-in and gain more flexibility. They also focused on unstructured data, turning data marketplaces into hubs that provide pre-trained AI models using proprietary datasets and apps. At the identical time, advances in vector and graph databases opened up latest possibilities and laid the muse for the long run.
Now, because the story of AI continues to unfold, industry leaders are sharing their predictions for the way its underlying data infrastructure will evolve in 2025.
1. Real-time, multimodal data will drive the intelligent data flywheel
“In 2025, corporations will fully embrace multimodal data and AI, transforming the best way they work and create value. At the center of this transformation is the Intelligent Data Flywheel – a dynamic cycle wherein real-time data enables AI-powered insights, driving continuous innovation and improvement. Today's dark data – images, video, audio and sensor output – shall be central to unlocking sharper predictions, smarter automation and real-time adaptability, ultimately resulting in a richer and more nuanced understanding of business reality.
“With the real-time data flywheel, AI will independently diagnose problems, optimize processes and generate revolutionary solutions. Companies will depend on AI agents to make sure data quality, generate insights, and develop strategies, freeing human talent to deal with higher-level tasks. This will redefine efficiency, speed up innovation and transform corporations into more dynamic and intelligent organizations.”
2. Chill factor: liquid-cooled data centers
“As AI workloads proceed to drive growth, pioneering organizations will move to liquid cooling to maximise performance and energy efficiency. Hyperscale cloud providers and huge enterprises will prepared the ground by deploying liquid cooling in latest AI data centers that house a whole bunch of 1000’s of AI accelerators, networks and software.
“Companies will increasingly decide to deploy AI infrastructure in colocation facilities reasonably than constructing their very own – also to cut back the financial burden of developing, deploying and operating intelligence manufacturing at scale. Or they rent capability as needed. These deployments will help businesses leverage the most recent infrastructure without having to put in and operate it themselves. This shift will speed up broader industry adoption of liquid cooling as a mainstream solution for AI data centers.”
3. Global data explosion results in storage shortage
“The world is generating data at an unprecedented scale. In 2028 as much as 400 zettabytes generated, with a compound annual growth rate (CAGR) of 24%. However, the storage install base is forecast to have a compound annual growth rate (CAGR) of 17% – significantly slower (growing) than the expansion in data generated. And constructing a harddisk takes an entire 12 months. This disparity in growth rates will disrupt the worldwide balance between storage supply and demand. As corporations turn into less experimental and more strategic of their use of AI, they are going to must create larger physical data center footprints and capability plans to make sure storage supply and fully monetize investments in AI and data infrastructure – while balancing financial, regulatory and environmental concerns.
4. AI factories will evolve to PaaS
“In 2025, AI factories will evolve beyond their initial phase of providing infrastructure-as-a-service with compute, networking and storage services to providing platform-as-a-service capabilities. While foundational services have been critical to accelerating AI adoption, the subsequent wave of AI factories will prioritize platforms that drive data affinity and deliver lasting value. This shift shall be key to creating AI factories sustainable and competitive in the long run.”
5. Companies will leverage their massive data sets but demand reliability
“Early AI applications were largely based only on base models trained on massive amounts of public data. As sophisticated RAG applications turn into mainstream and structured data generation products mature quickly, applications leveraging the vast repositories of personal enterprise data will begin to create real value. But the bar for these applications shall be high: corporations will demand reliability from AI applications, not only flash demonstrations.
“In addition, AI corporations deploying these models must work well with publishers and content providers to secure the long run of AI development. You must enter into licensing agreements with content providers to make sure they’re compensated for the extremely helpful data they provide. This must occur soon, before every thing becomes a large number of lawsuits and blocking AI crawlers.”
6. Corporate agents devour communications data
“In 2025, corporations will search through terabytes of communications data like emails, Slack messages, and Zoom transcripts using agents that deliver analytical insights, dashboards, and actionable decision support tools.
“This will result in significant productivity gains across industries.”
7. Data management and quality shall be the largest obstacles to successful and ethical AI adoption
“In 2025, data management, accuracy and privacy shall be the largest barriers to effective AI adoption. As corporations look to scale AI, they realize that successful AI outcomes are entirely depending on trustworthy data. Managing and preparing massive amounts of knowledge, ensuring compliance, and maintaining accuracy present complex challenges. Organizations must overcome these hurdles by investing in foundational data platforms that enable consistent management of diverse data sources.
“As a result, we’ll see greater emphasis on data stewardship roles and governance frameworks aligned with AI initiatives as organizations realize that unreliable data directly impacts the effectiveness of AI.”
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“In 2025, unified data statement platforms will turn into essential tools for giant enterprises, enabling comprehensive visibility into performance, quality, data infrastructure health, cost management and user behavior to deal with complex governance and integration challenges. By automating anomaly detection and enabling real-time insights, these platforms will support data reliability and streamline compliance efforts across industries.”
9. All hail the sovereign cloud
“In 2025 we’ll see an actual push towards sovereigns and personal clouds. We are already seeing the biggest hyperscalers pour billions of dollars into constructing data centers all over the world to supply these capabilities. It will take some time for this capability to come back online. Meanwhile, demand will skyrocket due to a wave of laws, mostly coming from the EU. Anyone who has a versatile, scalable and elastic cloud infrastructure will quickly find a way to adopt government or private approaches. Anyone who has a monolithic, rigid infrastructure will fall behind.”
10. Rise of Data processing at the sting
“I'm maintaining a tally of the potential expansion of edge computing, driven by the spread of 5G, which brings data processing closer to the source and reduces latency. This could help democratize AI. The query is: Can we develop efficient AI apps that run on mobile devices, perhaps without counting on cloud resources?
“If 5G is obtainable to field technicians, they may use AI to support their work – whether for medical professionals who provide diagnosis and treatment in disaster zones where 5G but no Wi-Fi is obtainable, or for engineers and scientists who On-site decisions work with AI-supported research and real-time calculations.”
11. Protecting unstructured data is becoming increasingly urgent
“Traditionally, data protection has focused on business-critical data because this data must be restored more quickly. But the landscape has modified, and unstructured data now accounts for 90% of all data generated within the last 10 years. The large petabyte-scale surface area of ​​unstructured data, in addition to its widespread distribution and rapid growth, make it extremely vulnerable to ransomware attacks. Cybercriminals can use the unstructured data as a Trojan horse to contaminate the corporate. Cost-effectively protecting unstructured data from ransomware will turn into a critical defense tactic, starting with moving cold, inactive data to immutable object storage where it can’t be modified.
“To this end, IT and storage leaders will look to unstructured data management solutions that provide automated capabilities to guard, segment and audit sensitive and internal data usage in AI – a use case that’s sure to grow as AI matures. Additionally, they need to create systematic ways for users to go looking corporate data stores, curate the fitting data, seek for sensitive data, and push data to AI with audit reports.”
In summary, 2025 guarantees significant advances in enterprise data infrastructure, starting from multimodal data flywheels to sovereign clouds. However, challenges comparable to data management and storage bottlenecks will remain. Success on this dynamic environment will rely on balancing innovation with trust and sustainability and turning data into an enduring competitive advantage.
