Enterprise

Work AI specialist Glean has today announced the launch of the Work AI Institute, a first-of-its-kind research initiative dedicated to decoding what actually drives results when companies commit to operating with AI projects at the core of their businesses.

The Work AI Institute brings together leading researchers from Stanford, Harvard, UC Berkeley, Notre Dame, University College London, Emory, and UNC Charlotte to answer the pressing question: What’s really working with AI at work? The Institute blends academic rigor with real-world data, experimentation, and end user insights to help enterprises separate signals from noise and accelerate meaningful AI impact.

A new report from Komprise shows that 85 percent of IT and data storage leaders are projecting an increase in data storage spend in 2026, while 74 percent are storing more than 5PB of unstructured data, a 57 percent increase over 2024.

To cope with these rising data volumes and outsized spending, enterprise IT infrastructure teams are looking to implement unstructured data classification. Survey respondents rank this as the top strategy to discreetly understand data for storage optimization, data governance, ransomware defense, security and AI curation needs. In parallel, classifying and tagging unstructured data is the top challenge in preparing unstructured data for AI.

The use of AI across modern enterprises in recent years has accelerated, with innovation at the forefront and APIs serving as the crucial enabler behind the scenes.

Now, agentic AI, capable of autonomous actions and decision-making, but this shift exposes several gaps in API documentation, drift in specifications and insufficient safety guardrails, all of which can lead to serious implications for organizations.

Most organizations lack the monitoring capabilities and governance policies needed to mitigate risks posed by shadow AI according to a new report.

The survey, of 600 IT leaders across North America, EMEA, and APJ, from Cato Networks finds that while 61 percent of respondents found unauthorized AI tools in their environments, only 26 percent have solutions in place to monitor AI usage. Nearly half (49 percent) of the respondents either don’t track AI usage at all or address AI on a reactive basis.

Only six percent of enterprise AI leaders say their data infrastructure is fully ready for AI according to a new report from CData Software.

The research exposes a divide in AI preparedness. 60 percent of companies at the highest level of AI maturity have also invested in advanced data infrastructure, while 53 percent of organizations struggling with AI implementations are hampered by immature data systems. The gap is costing companies time, money, and competitive advantage.

New research finds just two percent of organizations with 500+ employees report having no plans or interest in agentic AI. Indeed a significant portion of respondents are already using or interfacing with AI agents for both internal and external tasks.

But the study, from Enterprise Management Associates (EMA), reveals a critical, organization-wide inability to prepare for the identity and security challenges which these autonomous entities introduce.

Power constraints in the world’s largest data center hubs are now reshaping where hyperscalers plan their next wave of expansion, according to new analysis by DC Byte.

The analysis tracks activity across more than 8,000 facilities and looks at how the geography of hyperscale growth is evolving in response to mounting infrastructure pressure. Hyperscalers are now securing power and land up to 24 to 36 months before delivery as constraints intensify in markets such as Northern Virginia, Frankfurt and Singapore.

Vibe coding has surged in popularity in the last year. Tools like Lovable, Replit, and v0 are giving anyone the ability to generate apps without writing a single line of code. The experience is fast, intuitive, and surprisingly powerful, fueling a wave of innovation across both consumer and enterprise settings.

But as companies rush to adopt these tools, a new challenge has emerged. Employees are beginning to build their own AI-powered applications using whatever platforms they can find, often connecting them to live business data. It is a trend some experts are calling the rise of “shadow AI,” where software is created outside of established security and governance frameworks.

Agentic AI is rapidly moving from concept to reality, prompting organizations globally to rethink how they integrate these technologies into their business operations. The use of AI agents in daily workflows is set to rise dramatically in the coming years, raising questions over what organizations need to do to manage them effectively, and what might happen if they fail to do so.

We spoke with Ann Maya, EMEA CTO at Boomi, about the evolution of AI agents, the steps businesses should be taking ahead of deployment, and why the principles of human workforce management may hold the key to responsible use.

Although 74 percent of IT leaders expect budgets to rise in 2026 more than half say they still lack the internal resources to fix issues quickly or drive innovation.

A new report from DataStrike also finds 60 percent of organizations now rely on MSPs to manage data infrastructure, more than double the rate reported last year. This highlights a growing dependence on external expertise as teams tackle modernization and technical debt.

In October, one in every 44 GenAI prompts submitted from enterprise networks posed a high risk of data leakage, impacting 87 percent of organizations that use GenAI regularly.

A study from Check Point Research finds an additional 19 percent of prompts contained potentially sensitive information such as internal communications, customer data, or proprietary code. These risks coincide with an eight percent increase in average daily GenAI usage among corporate users.

Businesses are keen to deploy AI but doing so across hybrid and regulated environments, and managing the resulting workloads, remains deeply complex.

This is why Gcore is launching Everywhere AI, a deployment platform that allows enterprises to deploy, scale, and optimize AI workloads flexibly across on-premises, hybrid, and cloud environments while maximizing performance, efficiency, and revenue.

There’s been a boom in AI in recent years and the technology has found its way into more and more areas of commercial enterprise.

But in the rush to adopt AI is the quality of the underlying data being ignored? We spoke to Krishna Subramanian, co-founder and COO of Komprise to find out why good data governance is key to implementing AI successfully.

It’s known as an ‘ohno-second’ that moment in time when you realize you’ve clicked send on something you shouldn’t have. But it’s no laughing matter, a new survey of more than 300 security and IT professionals from Abnormal AI highlights the growing threat and business impact of legitimate email messages sent to the wrong recipient.

These misdirected emails can result in data breaches, regulatory violations, remediation costs, and reputational damage. The research shows 98 percent percent of security leaders consider misdirected email as a significant risk when compared to other data loss risks like malware and insider threats.

New research finds that business trust in autonomous AI is growing, with 57 percent of organizations saying they’re ‘very confident’ in the technology’s reliability in core business processes.

Yet, despite this increasing trust, implementation is lagging. The survey from Insight Enterprises shows that six in 10 organizations are stuck in pilot or experimental phases. Most are deploying AI in low-risk, narrowly defined areas, with only 24 percent using it in production for clearly scoped use cases.