Enterprise AI

SUSE has released SUSE Linux Enterprise 16, which it describes as the first enterprise Linux distribution to integrate agentic AI through the Model Context Protocol (MCP). The update aims to let operating systems interact more directly with AI models and external data sources.

The Model Context Protocol, introduced in 2024 by Anthropic, is an open standard for connecting large language models to tools and data. It defines a simple client-server structure that allows AI systems to request information or perform tasks across different applications in a consistent way. SUSE’s adoption of MCP is meant to make those capabilities available at the operating system level, without depending on a single AI provider.

Although enterprise AI investment continues to accelerate, executive confidence in the strategies guiding this transformation is falling according to a new report.

The research from Akkodis, looking at the views of 500 global Chief Technology Officers (CTOs) among a wider group of 2,000 executives, finds that overall C-suite confidence in AI strategy dropped from 69 percent in 2024 to just 58 percent in 2025. The sharpest declines are reported by CTOs and CEOs, down 20 and 33 percentage points respectively.

AI is reshaping how companies protect themselves against cyber threats, according to new research from ISG Software Research.

Enterprises are building layered cybersecurity defense systems that combine access controls, endpoint monitoring, and data recovery, and artificial intelligence is helping these systems adapt faster and work more effectively.

Updates to the Starburst data platform for apps and AI are designed to accelerate enterprise AI initiatives and support the transition to a future-ready data architecture built on a data lakehouse.

At the heart of these changes are Starburst AI Workflows, a purpose-built suite of capabilities that speed AI experimentation to production for enterprises. AI Workflows provides a link between vector-native search, metadata-driven context, and robust governance, all on an open data lakehouse architecture.

New research shows that enterprises are going beyond AI experimentation and into large-scale production but that return on investment is taking a back seat in the process.

The report from Domino Data Lab shows that while 88 percent of organizations report improved ability to move AI from experimentation to production, nearly 60 percent expect less than 50 percent ROI in the rapidly changing areas of machine learning (60 percent) or Gen AI (57 percent).

New research shows that 71.7 percent of workplace AI tools are high or critical risk, with 39.5 percent inadvertently exposing user interaction/training data and 34.4 percent exposing user data.

The analysis from Cyberhaven draws on the actual AI usage patterns of seven million workers, providing an unprecedented view into the adoption patterns and security implications of AI in the corporate environment.

Agentic AI has been in the news quite a bit of late, but how should enterprises expect it to impact their organizations?

We spoke to Mike Finley, CTO of AnswerRocket, to discuss Agentic AI's benefits, use cases and more.

A new report from Zscaler reveals a 3,000 percent year-on-year growth in enterprise use of AI/ML tools, highlighting the rapid adoption of AI technologies across industries to unlock new levels of productivity, efficiency, and innovation.

This surge in adoption also brings heightened security concerns though. According to the study enterprises blocked 59.9 percent of all AI/ML transactions, indicating awareness around the potential risks associated with AI/ML tools, including data leakage, unauthorized access, and compliance violations.

Artificial intelligence is finding its way into more and more areas and that presents a challenge for businesses who need to keep things secure. Add in shadow use of AI and the problem becomes worse.

Californian start up Singulr.ai is launching a new enterprise AI governance and security platform to help organizations stay on top of AI adoption.

Despite the excitement around AI as a transformative force, many enterprises are struggling to adopt the technology in meaningful ways, according to a new survey from Unily.

This has resulted in a growing gap between AI 'haves' and 'have nots,' where enterprises adopting AI tools for their people are making quicker gains than those without. At the same time employees who are open to using AI tools increasingly want more exposure to them and may even choose employers who are early AI adopters over those who are slower to adapt.

Businesses are often under pressure to deliver AI agents, but development teams are struggling with siloed tools, fragmented governance and limited functionality that makes promising prototypes unfeasible in production.

According to a survey of over 1,000 enterprise technology leaders released today by Tray.ai, 42 percent of respondents need access to eight or more data sources to deploy AI agents successfully -- which is impossible when SaaS app agents are restricted in scope by the integrations to which their host applications have access.

As more businesses turn to AI, they face a number of challenges around integrating it effectively and obtaining the best value while still ensuring that their data remains secure. It's also important that they select the right AI provider for their needs.

We spoke to Naren Narendran, chief scientist at database specialist Aerospike, to discuss the strategic considerations and concerns enterprises face as they incorporate AI into their operations.

CrowdStrike has announced its integration of the Falcon cybersecurity platform with NVIDIA NIM Agent Blueprints, aimed at helping developers securely utilize open-source foundational models and accelerate generative AI innovation.

Developing enterprise-grade generative AI applications involves a complex process that requires blueprints for standard workflows—such as customer service chatbots, retrieval-augmented generation, and drug discovery—to streamline development. Ensuring the security of these models and the underlying data is essential for maintaining the performance and integrity of generative AI applications.

Almost one in 10 decision-makers in both the UK (eight percent) and US (seven percent) are planning to spend over $25 million on AI this year.

A survey from Searce, of 300 C-Suite and senior technology executives at organizations with more than $500 million, finds that for US decision-makers, data privacy and security are ranked as the number one hurdle to adopting AI (20 percent), whereas UK decision-makers rank lack of qualified talent as their number one challenge (19 percent).

Platform-as-a-Service (PaaS) provider Rafay Systems is launching new capabilities for its enterprise PaaS for modern infrastructure to support graphics processing unit- (GPU-) based workloads.

This makes compute resources for AI instantly usale by developers and data scientists but still with the enterprise-grade protections.