HYPER-CUSTOMIZABLE PLATFORMS
Agentic AI and intelligent automation are converging within data environments, observed Micha Kiener, CTO at Flowable. This enables the streamlining of workflows “by automating repetitive tasks, integrating data from multiple sources into a unified system, and using AI and machine learning to analyze that data, uncover patterns, and support smarter decision making.”
Potential Issues
Hyper-customizable platforms may face roadblocks from the outdated infrastructures that exist across the business landscape. “These outdated systems often lack proper governance frameworks or are not properly compatible with AI technology,” said Kiener. Tools and platforms that bridge these systems are essential.
Tangible Business Benefits
“Intelligent automation and agentic AI help teams work faster, smarter, and with fewer roadblocks,” said Kiener. “They cut down on repetitive tasks, speed up decision making, and give teams better visibility into what’s happening across the business.”
VECTOR DATABASES
A database that has arisen in the AI era, the vector database, is “transforming the way that data is stored,” said Matt Waxman, chief product officer at Arctera. Traditional databases cannot manage the complexity and size of data required for AI applications, he said. “By fundamentally reimagining how data is stored—and often by separating storage and compute in serverless implementations—vector databases are solving the challenges of storing, accessing, and updating information.”
Potential Issues
“When a mission-critical AI environment goes down, all agents go down,” Waxman cautioned. “And tasks may be impossible for team members to pick up again. Hackers know this and are putting AI infrastructure high on their target lists. Taking down vector databases in a ransomware attack can instantly bring an organization’s AI tools to an immediate standstill.” Implementing and running vector databases require plans for rapid recovery, he added.
Tangible Business Benefits
Vector databases deliver more scalable, reliable, and affordable foundations for AI, said Waxman. “By providing the structure to manage vector embeddings, they allow AI to create ‘long-term memory,’ while at the same time, preventing data sprawl across traditional—and often expensive—storage platforms.”
NEXT-GENERATION DATA STORAGE
While much of the focus these days is on the wonders of AI and agentic AI, there has been a revolution developing on the back end as well—in storage for the massive amounts of data needed to make it all work. “Mass-capacity data storage—often overlooked—plays an outsized role in big data operations and is undergoing spectacular technological progress,” said Ted Oade, director of product marketing at Spectra Logic. For example, as he explained, “The storage industry’s recent breakthrough of the long-established superparamagnetic limit is a microscopic barrier that dictates the maximum data density on disk and tape.”
Another back-end breakthrough is new LTO-10 tape technology, “which delivers major advances in capacity, density, and efficiency while enabling petabyte-to-exabyte-scale repositories,” said Oade.
Potential Issues
It is reported that LTO-10 drives cannot read or write LTO-9 or earlier tapes, requiring a complete migration of data from older tapes to LTO-10. This can be a significant undertaking for organizations with large archives.
Tangible Business Benefits
For AI-driven enterprises, tape can be a surprising game-changer. “As AI models become increasingly dependent on massive historical datasets, the ability to store this data economically and indefinitely is emerging as a strategic differentiator,” said Oade. “Performance on AI vision tasks increases with training dataset size, underscoring the advantage of richer, longer-retained datasets. LTO-10’s ultra-low power footprint, inherent air-gapped security, and multi-decade media life directly address the scale, sustainability, and security challenges of AI data pipelines. Those who can store everything will have the edge in AI, compliance, and digital innovation.” n