Enterprise AI

Dell Technologies and NVIDIA are enhancing their AI partnership with updates to the Dell AI Factory with NVIDIA platform at SC25. These enhancements streamline AI workload deployment and management, addressing scalability complexities. Key integrations include NVIDIA’s NIXL library for faster inferencing and support for NVIDIA RTX Blackwell GPUs. The platform now includes Dell Automation Platform for pre-tuned deployments and expanded AI PC options. These updates aim to transition organizations from AI pilots to production deployments with greater confidence and efficiency, leveraging infrastructure, automation, and data tools.

IBM’s report identifies data silos as the primary obstacle to enterprise AI adoption, hindering seamless integration and collaboration. Fragmented data across departments leads to prolonged data cleansing projects, delaying insights and ROI. The report suggests distributed data architectures like data mesh and fabric, alongside “data products,” to improve accessibility. Talent shortages and governance complexities also pose challenges. Success hinges on breaking down silos, democratizing data literacy, and treating data as a strategic asset to scale AI across the organization.

Baidu’s new ERNIE-4.5 model rivals GPT and Gemini in multimodal AI, focusing on enterprise data, including visual formats like schematics and video. Its lightweight architecture activates only 3 billion parameters, reducing inference costs. ERNIE excels at interpreting non-textual data, solving complex visual problems, and automating tasks. Benchmarks show competitive performance in visual question answering. ERNIE aims to bridge the gap from perception to automation, enabling structured data extraction from visuals and integration with business systems, though substantial hardware is required. It’s available under the Apache 2.0 license.

A new review of 445 LLM benchmarks raises concerns about their validity and the reliance of enterprises on potentially misleading data for AI investment decisions. The study highlights weaknesses in benchmark design, including vague definitions, lack of statistical rigor, data contamination, and unrepresentative datasets. It urges businesses to prioritize internal, domain-specific evaluations over public benchmarks, focusing on custom metrics, thorough error analysis, and clear definitions relevant to their unique needs to mitigate financial and reputational risks.

Druid AI introduced its Virtual Authoring Teams at Symbiosis 4, aiming to revolutionize AI automation with AI agents that autonomously create, test, and deploy other agents. Druid claims its system can accelerate enterprise-grade AI agent development tenfold, offering orchestration, compliance, and ROI tracking. The platform includes Druid Conductor for central control and a marketplace for industry-specific agents. While competitors like Cognigy, Google, and Microsoft also explore agentic AI, Druid emphasizes explainability and control, seeking to bridge the gap between AI experimentation and scalable business transformation.

Google Cloud launched Gemini Enterprise, an AI platform designed to streamline AI agent development and deployment, aiming to transform enterprise workflows. It features Gemini models, a no-code workbench, pre-built agents, and seamless data integration with systems like Salesforce and Microsoft Teams. Virgin Voyages and Macquarie Bank are early adopters, demonstrating productivity gains and widespread AI training. Gemini Enterprise emphasizes governance, security, and an open ecosystem. Pricing starts at $21 per seat for smaller businesses and $30 for larger organizations.

Google Cloud has launched Gemini Enterprise and Business, subscription-based AI agents targeting enterprises of all sizes. Priced at $30/$21 per user monthly, these subscriptions enable businesses to create custom AI agents integrating with platforms like Box and Salesforce, and leverage pre-built Google agents. Model Armor, a built-in security feature, addresses data privacy concerns. Google aims to automate entire processes, differentiating from OpenAI’s plugin approach. Analysts emphasize security and continuous model upgrades (Gemini 3.0) as critical for wider adoption.

Anthropic is aggressively expanding its global enterprise AI footprint, driven by surging international demand for its Claude models. The company plans to triple its international workforce and quintuple its applied AI team in 2025. Nearly 80% of Claude’s activity originates outside the US, with adoption rates exceeding US levels in countries like South Korea. Anthropic is establishing its first Asia office in Tokyo and scaling operations throughout Europe, focusing on industry-specific solutions and data sovereignty. This expansion intensifies competition with OpenAI, Microsoft, and Google.

Samsung has introduced TRUEBench, a novel AI benchmark specifically designed to evaluate large language model (LLM) performance in real-world enterprise contexts. Addressing the limitations of traditional benchmarks, TRUEBench assesses AI across diverse business tasks, multilingual capabilities, and the ability to understand unstated user intents. It leverages a comprehensive suite of metrics across 10 categories and 46 sub-categories, based on Samsung’s internal AI deployments. Through its open-source platform on Hugging Face, Samsung aims to establish TRUEBench as an industry standard for AI productivity measurement.

C3 AI shares plummeted 14% after announcing Stephen Ehikian as CEO and releasing disappointing fiscal first-quarter results. Revenue declined nearly 20% year-over-year to $70.3 million, with a widening GAAP net loss of $0.86 per share. The CEO change, triggered by former CEO Thomas Siebel’s health issues, adds to existing concerns regarding restructuring strategies and recent sales underperformance, which Siebel attributed to the reorganization’s disruptive effects. Ehikian expresses confidence in capturing a larger share of the Enterprise AI market.