Enterprise

Despite record AI spending, 87 percent of enterprises still miss their revenue targets according to new research from Clari Labs, the research arm of Clari + Salesloft.

The findings suggest that AI acceleration has outpaced enterprise data governance and control. More than half (55 percent) report conflicting pipeline signals from disconnected data sources, and nearly half (48 percent) admit their revenue data isn’t AI-ready. As a result, 57 percent say AI agents still haven’t been fully deployed across revenue operations.

The adoption rate of artificial intelligence in the enterprise shows no signs of slowing down, but while organizations have been focused on which of the many models to use, the playing field is beginning to level out.

We talked to Saket Saurabh, CEO and co-founder of Nexla, to discuss ‘context engineering’ and why he believes it will be key to gaining competitive advantage.

While AI promises lightning-fast transformation, many organizations are running into problems when outdated, complex networks bog down deployments, forcing costly redesigns and leaving businesses waiting months instead of weeks to implement AI at scale.

We spoke to Amir Khan, CEO at Alkira, to learn why fixing the network has become foundational to unlocking real AI value.

The FBI reports that complaints around deepfake AI videos have more than doubled, and financial losses have nearly tripled this year.

Agentic AI is set to accelerate this process, making it even easier to commit fraud with deepfakes. We spoke to Alix Melchy, VP of AI at Jumio, to discuss the threat and how to combat it.

Artificial intelligence has been driving much of the technical agenda for the last couple of years and is still evolving rapidly, finding its way into more and more areas.

Here some industry experts look at what we can expect to see from the AI space in 2026.

IT leaders are unhappy with their productivity current setup and are forced to manage an average of over nine different tools. This is why, according to a new study, 87 percent of IT leaders will consider changing from their current productivity suite to adopt a more unified and secure platform.

The research from JumpCloud in collaboration with Google Workspace is based on a survey of 250 US IT leaders from enterprise organizations. Its findings show that IT teams are urgently searching for a new platform to make work simpler and more secure.

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.