# enterprise AI Published articles for enterprise AI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance DevFeed: [PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance]() Original publisher: [Read original article]() Author: Asif Razzaq Published: 2026-09-18T18:06:36Z Content type: news Language: en Sources: [MarkTechPost]() Topics: [Bonsai](), [qwen3](), [Language models](), [releases](), [gpt-oss](), [llama.cpp](), [quantization](), [MLX](), [vllm](), [computer-use]() Tags: [ai-infrastructure](), [ai-shorts](), [apache](), [applications](), [artificial-intelligence](), [benchmarks](), [bonsai-2-27b](), [coding-agents](), [computer-use](), [editors-pick](), [enterprise-ai](), [for-devs](), [language-model](), [large-language-model](), [llama-cpp](), [machine-learning](), [mlx](), [new-releases](), [quantization](), [qwen3](), [releases](), [staff](), [tech-news](), [technology](), [uncategorized]() ### AI overview PrismML has released Ternary Bonsai 2 27B, a ternary-weight version of Qwen3.8 27B. The 5.93 GB model supports text and images, a 262K-token context, and deployment on a 16 GB laptop or a single 24 GB GPU using PrismML's llama.cpp fork or MLX runtime. PrismML reports 98.2% of the parent model's average across 20 benchmarks. ### Source excerpt PrismML has released Ternary Bonsai 2 27B, a ternary-weight version of Qwen3.8 27B. The language model occupies 5.93 GB, against 53.80 GB in FP16. PrismML reports that it keeps 98.2% of the parent model's average across 20 benchmarks. The model accepts text and images and supports a 262K-token context. PrismML demos it driving Cline coding [...] The post PrismML Releases Ternary Bonsai 2 27B: A 5.9 GB Apache 2.0 Model Retaining 98.2% of Qwen3.8 27B Performance appeared first on MarkTechPost. ## What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25 DevFeed: [What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25]() Original publisher: [Read original article]() Author: Victoria Gayton Published: 2026-09-18T13:57:37Z Content type: opinion Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [Automation](), [Model Context Protocol](), [business logic](), [Command-line interface]() Tags: [agentforce](), [agentforce-operations](), [agentic](), [agentic-ai](), [agentic-enterprise](), [ai](), [ai-agents](), [ai-security](), [artificial-intelligence](), [automation](), [cloud](), [command-line](), [connected-ai-workflows](), [cube-event-coverage](), [customer-360](), [customer-experience](), [data-360](), [data-360-headless](), [data-cloud](), [data-governance](), [digital-transformation](), [dreamforce](), [dreamforce-2026](), [dreamforce26eventpage](), [enterprise-ai](), [enterprise-applications](), [generative-ai](), [kishan-chetan](), [model-context-protocol](), [news](), [salesforce](), [salesforce-help-agent](), [thecube](), [tools](), [workflow-orchestration]() ### AI overview The article previews analysis of Dreamforce and examines how Salesforce is positioning connected AI workflows and the agentic enterprise. It focuses on integrating AI agents with business data, established workflows, application environments, permissions, and governance, including the use of APIs, Model Context Protocol tools, and a command-line interface. ### Source excerpt Enterprises are moving beyond standalone AI tools as they look for ways to weave agents into the work employees and customers already do. The shift toward connected AI workflows raises broader questions about how agents access data, interact with people and operate across established business processes. Salesforce Inc. is positioning the agentic enterprise as the [...] The post What to expect from theCUBE's analysis of Dreamforce: Tune in Sept. 25 appeared first on SiliconANGLE. ## Context and Coordination: The Missing Layer in Enterprise Automation DevFeed: [Context and Coordination: The Missing Layer in Enterprise Automation]() Original publisher: [Read original article]() Author: Anand Krishnan, Executive VP, Persistent Systems Published: 2026-09-18T12:28:43Z Content type: opinion Language: en Sources: [Unite.AI]() Topics: [Artificial Intelligence](), [Automation](), [context](), [data](), [scheduling]() Tags: [agentic](), [agentic-ai](), [ai-governance](), [ai-strategy](), [automation](), [context](), [context-engineering](), [data](), [digital-transformation](), [enterprise](), [enterprise-ai](), [enterprise-automation](), [process-automation](), [scheduling](), [system-coordination](), [thought-leaders](), [workflow-optimization]() ### AI overview The article argues that enterprises need to engineer context and coordination around Agentic AI to turn automation pilots into business outcomes. It contrasts task automation with outcome automation, emphasizing unstructured data, exception handling, and cross-system coordination. An appointment-scheduling example illustrates how predictive models can reduce patient no-shows and improve operational results. ### Source excerpt Most enterprises have plenty of automation and not much transformation. The pilots worked. The bots run. The productivity curve flattened anyway. It is the most repeated story in the enterprise, and the next wave, Agentic AI, can finally break that pattern, for the enterprises willing to engineer the context these systems run on. Almost no one treats that as the strategy. It is. A large US healthcare provider network discovered that the real constraint wasn't clinical capacity. It was... ## Using AI vs. Building Around It: Where's the Line? DevFeed: [Using AI vs. Building Around It: Where's the Line?]() Original publisher: [Read original article]() Author: Michael Abramov, Founder & CEO, Scryon Published: 2026-09-18T10:47:28Z Content type: opinion Language: en Sources: [Unite.AI]() Topics: [Artificial Intelligence](), [Automation](), [Large Language Model](), [Software]() Tags: [ai](), [ai-automation](), [ai-implementation](), [ai-native](), [ai-strategy](), [artificial-intelligence](), [business-automation](), [developers](), [enterprise-ai](), [generative-ai](), [llm](), [machine-learning](), [product](), [software-architecture](), [tech-leadership](), [thought-leaders]() ### AI overview The article distinguishes ordinary AI use from being AI-native by asking what would happen if AI were switched off. It contrasts companies where AI mainly improves employee efficiency with businesses whose operating models depend fundamentally on AI-driven systems and agents. ### Source excerpt We use the word AI to describe all sorts of different things, from talking to ChatGPT in a browser to running entire businesses where operations would simply cease to exist in their current form without language models. Where is the line between using AI automation and AI being at the core of a business? At what point does a fundamental shift occur? And most importantly, should companies even strive to put AI center stage? When Is a Business AI-Native? Today, even if management has never... ## How Development Teams Can Use AI Strategically Through Digital Accessibility Practices DevFeed: [How Development Teams Can Use AI Strategically Through Digital Accessibility Practices]() Original publisher: [Read original article]() Author: Dylan Barrell, CTO, Deque Systems Published: 2026-09-18T10:37:30Z Content type: opinion Language: en Sources: [Unite.AI]() Topics: [Artificial Intelligence](), [Development](), [digital accessibility](), [Accessibility](), [Risk]() Tags: [accessibility](), [ai](), [ai-code-generation](), [ai-economics](), [compliance](), [development](), [digital-accessibility](), [engineering](), [enterprise-ai](), [harness-engineering](), [roi](), [rules-based-tools](), [software-development](), [technical-debt](), [thought-leaders](), [token-costs]() ### AI overview The article argues that enterprise AI adoption should be guided by measurable ROI rather than indiscriminate use. It uses digital accessibility as an example of a rules-driven area where teams should prevent issues early, reduce technical and accessibility debt, and choose deterministic tools when they are more consistent, faster, or cheaper than AI. ### Source excerpt The economics of AI integration have changed dramatically as the reality of token cost has set in. Tokenmaxxing may have been fun while it lasted, but innovation without ROI is not sustainable. Now that we're beyond the cheap-token era, AI must justify itself with real, measurable business outcomes across the enterprise. That means no more throwing AI at every challenge. Organizations need to be far more strategic, using AI for what it's good for, and deploying other approaches when there's a... ## Enterprise AI desperately needs a lifecycle for context DevFeed: [Enterprise AI desperately needs a lifecycle for context]() Original publisher: [Read original article]() Author: Soham Mazumdar Published: 2026-09-18T10:00:00Z Content type: opinion Language: en Sources: [CIO]() Topics: [context](), [Artificial Intelligence](), [App](), [data]() Tags: [ai](), [artificial-intelligence](), [artificial-intelligence-software-deployment-software-development](), [contributor](), [enterprise-ai](), [information](), [mcp](), [product](), [software-deployment](), [software-development]() ### AI overview Enterprise AI applications depend on business context such as changing revenue definitions, customer policies, product information, and exceptions. The article argues that organizations need lifecycle management for this context because updates are distributed across documents, prompts, code, repositories, and applications. ### Source excerpt Every successful enterprise AI deployment begins long before anyone types the first prompt. Teams spend weeks defining business metrics, documenting policies, connecting enterprise systems, and explaining the rules and exceptions that allow an AI application to answer questions correctly. Then the application launches and the business keeps moving. Finance changes how it recognizes revenue, sales introduces new pricing, legal revises a customer policy, or product launches a new capability. Each decision changes something an AI application needs to know, yet those changes rarely reach every application that depends on them. A definition updated in one system remains six months old in another, while a policy legal replaced last week continues guiding a support agent. Enterprises already know how to keep data accurate and secure, but the meaning attached to that data often lives across documents, prompts, code, and individual applications. AI depends on both, and enterprises have spent far more time building infrastructure for data than for the business knowledge required to interpret it. Companies know how to manage data, but they don't yet know how to manage context Context captures what data means inside a particular company, including how finance calculates revenue, which customer policy applies to a situation, and when an exception overrides a rule. Data can tell an AI what happened, while context tells it how the business understands what happened. Revenue offers a simple example. A customer record might show $500,000 in annual revenue, and the number might be completely accurate, but an AI application still needs to know whether the company considers that ARR, recognized revenue, bookings, or contracted value. It needs to know which products count and whether finance recently changed the definition. Those answers come from decisions the business has made about how to interpret the number. The same problem runs through customer policies, product information, cont ## Salesforce Agentforce's production tooling for enterprise AI agents DevFeed: [Salesforce Agentforce's production tooling for enterprise AI agents]() Original publisher: [Read original article]() Author: Jean-marc Mommessin Published: 2026-09-18T07:34:06Z Content type: article Language: en Sources: [MarkTechPost]() Topics: [Artificial Intelligence](), [Agent Harness](), [Testing](), [Orchestration](), [Tooling](), [reliability](), [CI/CD](), [Model Context Protocol (MCP)](), [ai-coding](), [Deployment]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-coding-tools](), [autonomous](), [autonomous-agents](), [claude-code](), [cursor](), [editors-pick](), [enterprise-ai](), [guardrails](), [new-releases](), [optimization](), [orchestration](), [production](), [real-time](), [scale](), [staff](), [stress](), [synthetic](), [testing](), [tooling](), [vibe-coding]() ### AI overview The article describes Salesforce Agentforce as an enterprise platform for operating autonomous AI agents in production. It highlights synthetic stress testing, regression testing through CI/CD, real-time prompt optimization, dynamic interfaces across multiple channels, and deterministic guardrails. ### Source excerpt Building an AI prototype is easy, but operating autonomous agents at scale requires production-grade tooling. Salesforce Agentforce bridges the gap from "vibe coding" to enterprise reliability by combining synthetic stress-testing, real-time optimization, dynamic agentic UIs, and deterministic guardrails--as proven by Southwest Airlines' 7x ROI. The post Salesforce Agentforce: Bridging the Enterprise AI Gap from 'Vibe Coding' to Battle-Tested Orchestration appeared first on MarkTechPost. ## How AI agents are changing audit evidence and accountability DevFeed: [How AI agents are changing audit evidence and accountability]() Original publisher: [Read original article]() Author: Jonathan Anthony Published: 2026-09-17T20:22:32Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [audit](), [audit trail](), [ai-governance](), [Risk](), [data]() Tags: [accounting](), [agents](), [ai](), [ai-agents](), [ai-governance](), [alison-kosik](), [amplify](), [amplify-2026](), [artificial-intelligence](), [audit](), [audit-trail](), [compliance](), [cube-event-coverage](), [data](), [data-management](), [enterprise-ai](), [enterprise-reporting](), [esg](), [finance](), [financial-reporting](), [governance](), [grc](), [josh-robinson](), [krista-case](), [netsuite](), [news](), [risk-management](), [security](), [sustainability](), [thecube](), [vast-space-llc](), [workiva]() ### AI overview This report examines how AI agents are changing audit assurance by making human judgment and decision evidence harder to trace. It discusses applying completeness and accuracy tests to multistep agent workflows, maintaining accountability, and addressing data silos. ### Source excerpt Enterprise audit teams are finding that the evidence trail they depend on does not survive contact with AI agents. Financial and operational data still sits across NetSuite, human resources platforms and data lakes, but the judgment calls once recorded in email threads, Slack messages and tick marks are increasingly made by software moving faster than [...] The post AI agents erase the paper trail, reshaping audit assurance appeared first on SiliconANGLE. ## Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data DevFeed: [Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data]() Original publisher: [Read original article]() Author: Eric Mboizi Published: 2026-09-17T17:46:19Z Content type: news Language: en Sources: [TechRepublic]() Topics: [Artificial Intelligence](), [Google](), [Google Cloud Platform (GCP)](), [Cloud](), [data]() Tags: [agentforce](), [ai](), [ai-agents](), [artificial-intelligence](), [cloud](), [crm](), [data](), [enterprise](), [enterprise-ai](), [enterprises](), [gemini](), [gemini-enterprise](), [google](), [google-cloud](), [hyperforce](), [infrastructure](), [integration](), [news](), [salesforce](), [tableau](), [tech-industry](), [workflows]() ### AI overview Google Cloud and Salesforce are connecting Gemini Enterprise, Agentforce, Hyperforce, and Tableau with CRM data to reduce integration friction in enterprise AI workflows. ### Source excerpt Google Cloud and Salesforce are connecting Gemini Enterprise, Agentforce, Hyperforce, and Tableau to reduce integration friction for enterprise AI workflows. The post Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data appeared first on TechRepublic. ## AI governance moves closer to the workflow: theCUBE Insights at Amplify DevFeed: [AI governance moves closer to the workflow: theCUBE Insights at Amplify]() Original publisher: [Read original article]() Author: Chad Wilson Published: 2026-09-17T16:15:14Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [ai-governance](), [AI Agent](), [human review](), [data](), [Risk](), [audit]() Tags: [accounting](), [ai](), [ai-agents](), [ai-governance](), [amplify](), [amplify-2026](), [artificial-intelligence](), [auditability](), [compliance](), [cube-event-coverage](), [data](), [data-management](), [data-readiness](), [enterprise-ai](), [enterprise-reporting](), [esg](), [finance](), [financial-reporting](), [governance](), [grc](), [human-review](), [news](), [reporting](), [risk](), [risk-management](), [sustainability](), [thecube](), [trust](), [video-exclusive](), [workiva]() ### AI overview The article examines how AI governance must adapt as AI agents perform consequential work in reporting, audit, compliance and finance. It emphasizes traceability, approval records, reliable data, clear ownership and risk-based decisions about when agents may act independently or require human review. ### Source excerpt AI governance now has to account for systems that perform work rather than merely assist employees. That shift is especially relevant for Workiva Inc., which operates in reporting, audit and compliance workflows where errors carry serious consequences. As companies introduce AI agents into finance and other regulated functions, plausible output is no longer enough. Businesses [...] The post AI governance moves closer to the workflow: theCUBE Insights at Amplify appeared first on SiliconANGLE. ## Trust, not budget: Why AI adoption in finance comes down to governance DevFeed: [Trust, not budget: Why AI adoption in finance comes down to governance]() Original publisher: [Read original article]() Author: Ryan Stevens Published: 2026-09-17T16:03:53Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [AI Strategy](), [trust](), [Software](), [data]() Tags: [accel](), [accel-entertainment-inc](), [accounting](), [ai](), [ai-adoption](), [ai-governance](), [alison-kosik](), [amplify](), [amplify-2026](), [artificial-intelligence](), [auditing](), [christie-kozlik](), [compliance](), [cube-event-coverage](), [data](), [data-management](), [enterprise-ai](), [enterprise-reporting](), [erp-systems](), [esg](), [finance](), [financial-reporting](), [gaming-industry](), [generative-ai](), [governance](), [grc](), [internal-controls](), [krista-case](), [news](), [risk-management](), [software](), [sustainability](), [thecube](), [trust](), [workiva]() ### AI overview AI adoption in corporate finance is constrained more by governance and trust than by budget. The article describes using defined inputs, expected outputs, documented reviews, and early auditor involvement to evaluate AI in reporting and ERP workflows, with ROI measured against operational metrics. ### Source excerpt AI is spreading across corporate finance from the inside out, coming from the software, reporting platforms and the ERP systems that accounting teams already run. That makes governance, not budget, the gating factor on how fast teams can move. In regulated industries the calculation is sharper, because every number eventually lands in a filing that [...] The post Trust, not budget: Why AI adoption in finance comes down to governance appeared first on SiliconANGLE. ## New Ways to Scale and Apply Enterprise AI with Neo4j DevFeed: [New Ways to Scale and Apply Enterprise AI with Neo4j]() Original publisher: [Read original article]() Author: terri schlosser Published: 2026-09-17T12:46:00Z Content type: news Language: en Sources: [Graph Database & Technology | Neo4j Blog]() Topics: [Artificial Intelligence](), [Neo4j](), [AI Development](), [Graphs](), [data]() Tags: [ai](), [ai-applications](), [aura-graph-analytics](), [auradb](), [data-patterns](), [enterprise-ai](), [financial-crime](), [financial-services](), [fraud-detection](), [graph-data-science](), [graph-database](), [neo4j](), [news](), [preview](), [production](), [virtual-graph]() ### AI overview Neo4j describes updates for scaling enterprise AI applications in production, including GraphAware Financial Crime Intelligence for graph-based detection and investigation, and Neo4j Virtual Graph for zero-copy graph capabilities across existing data systems. Virtual Graph is currently in Preview and is expected to move to General Availability in the next couple of months. ### Source excerpt As AI initiatives grow beyond a first use case, the conversation starts to change. It's no longer just about getting a model working. Questions about running AI at scale and applying it to complex problems like financial crime become just... Read more -> ## The CIO-Legal partnership will define the future of enterprise AI DevFeed: [The CIO-Legal partnership will define the future of enterprise AI]() Original publisher: [Read original article]() Author: Dan Clarke Published: 2026-09-17T10:00:00Z Content type: opinion Language: en Sources: [CIO]() Topics: [Artificial Intelligence](), [ai-governance](), [Security](), [Risk](), [scaling]() Tags: [agents](), [ai](), [ai-governance](), [artificial-intelligence](), [artificial-intelligence-business-operations-c-suite-cio-data-governance-data-management-it-gov](), [business-operations](), [c-suite](), [cio](), [compliance](), [contributor](), [data-governance](), [data-management](), [enterprise-ai](), [it-governance](), [it-leadership](), [legal](), [roles](), [security]() ### AI overview The article argues that CIOs and legal teams must work together as enterprises expand AI adoption. It focuses on governance, security, privacy, compliance, litigation risk, and the challenges created when AI agents and workflows are built by nontechnical employees. ### Source excerpt AI is moving quickly across organizations. Companies are scaling existing use cases, introducing new tools and giving more employees access to AI as they look for ways to improve efficiency and stay competitive. That expansion is making AI governance a shared responsibility. CIOs are responsible for helping their organizations adopt the technology effectively and securely. Legal teams are increasingly focused on the litigation, regulatory, privacy and compliance risks associated with its use. As AI reaches more parts of the business, these teams need to work closely together. Each has visibility and expertise the other needs, and both have an important role in helping the organization scale AI responsibly. AI has changed the relationship between IT and legal AI touches nearly every part of a business. It can involve company and customer data, influence decisions, interact directly with employees or customers, and perform work through agents. Agents, in particular, have changed the governance equation over the past year. When companies were primarily piloting AI, much of that experimentation happened within IT. Those teams were accustomed to working within security, data and compliance requirements. Now organizations are under pressure to scale AI beyond those controlled environments. That shift is already well underway. McKinsey's 2025 State of AI survey found that 88% of respondents said their organizations regularly use AI in at least one business function, up from 78% the year before. I see that pressure from both sides. As a CEO and a member of several boards, I push companies to use AI because I believe failing to adopt it effectively poses a serious competitive risk. But that same push for adoption makes governance more important, not less. CIOs are being asked to expand AI use while simultaneously managing risks that become harder to see as adoption spreads. The strength of AI agents is that nontechnical employees can use them to build workflows and automate ## OpenAI Releases a Model Misalignment Disclosure Framework With 3 Review Tracks and 6 Incident Reports From RL Training DevFeed: [OpenAI Releases a Model Misalignment Disclosure Framework With 3 Review Tracks and 6 Incident Reports From RL Training]() Original publisher: [Read original article]() Author: Michal Sutter Published: 2026-09-17T07:35:37Z Content type: news Language: en Sources: [MarkTechPost]() Topics: [OpenAI](), [incident](), [Monitoring](), [Security]() Tags: [ai-ethics](), [ai-governance](), [ai-shorts](), [applications](), [artificial-intelligence](), [data-governance](), [editors-pick](), [enterprise-ai](), [incident](), [monitoring](), [openai](), [reports](), [security](), [staff](), [tech-news](), [technology]() ### AI overview OpenAI has released a framework for tracking, investigating, and publicly disclosing model misalignment. The framework defines disclosure criteria, deadlines, review tracks, and coverage across training, evaluation, testing, and deployment, alongside six initial incident reports. ### Source excerpt OpenAI can disclose misalignment before fixes exist. Its 6 initial reports include fabricated data and leaked API keys. The post OpenAI Releases a Model Misalignment Disclosure Framework With 3 Review Tracks and 6 Incident Reports From RL Training appeared first on MarkTechPost. ## Ex-Infosys chief Vishal Sikka's Hang Ten raises $53M more for enterprise AI services DevFeed: [Ex-Infosys chief Vishal Sikka's Hang Ten raises $53M more for enterprise AI services]() Original publisher: [Read original article]() Author: Duncan Riley Published: 2026-09-16T23:38:53Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [AI Development](), [AI Strategy](), [AI-assisted coding](), [Development](), [Software Engineering]() Tags: [agentic-ai](), [agentic-code-generation](), [ai](), [ai-services](), [ame-cloud-ventures](), [applied-ai](), [aramco](), [aramco-ventures](), [code-generation](), [engineering](), [enterprise](), [enterprise-ai](), [funding](), [hang-ten-systems](), [infosys](), [infrastructure](), [intel](), [jerry-yang](), [lip-bu-tan](), [mahdi-aladel](), [mayfield](), [micron-technology](), [news](), [phil-inagaki](), [platform](), [sanjay-mehrotra](), [sap](), [saudi-arabian-oil](), [seed-funding](), [siemens-energy](), [siemens-gamesa](), [startup](), [systems-integration](), [temasek-holdings](), [the-latest](), [vianai-systems](), [vinod-philip](), [vishal-sikka](), [xora-innovation]() ### AI overview Hang Ten Systems, an enterprise AI services startup founded by former Infosys chief Vishal Sikka, raised $53 million in a second seed round. The company provides advisory, transformation and applied AI services using agentic code generation, domain expertise and a reusable skills library to build enterprise software. Its customers include Saudi Aramco and Siemens Gamesa. ### Source excerpt Enterprise artificial intelligence services startup Hang Ten Systems Inc. today announced it has raised $53 million in a second seed round, less than three months after announcing its first. Hang Ten provides advisory, transformation and applied AI services, and its customers are large enterprises trying to get AI working inside their businesses. The delivery model [...] The post Ex-Infosys chief Vishal Sikka's Hang Ten raises $53M more for enterprise AI services appeared first on SiliconANGLE. ## How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI DevFeed: [How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI]() Original publisher: [Read original article]() Author: Scott Nyberg Published: 2026-09-16T17:23:42Z Content type: article Language: en Sources: [Salesforce Engineering Blog]() Topics: [Artificial Intelligence](), [context](), [data-platforms](), [data](), [distributed-systems](), [databricks](), [Streaming]() Tags: [ai](), [artificial-intelligence](), [context](), [data](), [data-platforms](), [databricks](), [distributed-systems](), [enterprise-ai](), [snowflake](), [streaming]() ### AI overview Salesforce describes Data 360 as a shared runtime foundation for compiling current, relevant, and authorized enterprise evidence into trusted context for humans, applications, and AI agents. The approach combines ingestion and Zero Copy connections across structured and unstructured data, knowledge graphs, streaming signals, and external platforms while applying access, masking, residency, consent, freshness, latency, token, and cost controls. ### Source excerpt By Raveendrnathan Loganathan and Tobias Muehlbauer. How do you compile the smallest sufficient body of current, relevant, and authorized evidence for every agent turn but without loading the entire enterprise data estate into every prompt? That is the context problem at the center of enterprise AI. Putting everything into every prompt consumes tokens, increases latency [...] The post How Data 360 Builds Trusted Context: The Enduring Layer for Enterprise AI appeared first on Salesforce Engineering Blog. ## Enterprise AI Accuracy: Building a More Trustworthy RAG Application DevFeed: [Enterprise AI Accuracy: Building a More Trustworthy RAG Application]() Original publisher: [Read original article]() Author: Scott Nyberg Published: 2026-09-09T22:30:06Z Content type: article Language: en Sources: [Salesforce Engineering Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [Large Language Model](), [Parsing]() Tags: [ai](), [artificial-intelligence](), [engineering](), [enterprise-ai](), [parsing](), [product](), [rag](), [retrieval]() ### AI overview This engineering article examines why RAG applications can perform well on clean benchmarks yet fail on complex enterprise documents. It traces lost meaning across parsing, chunking, enrichment, embedding, retrieval, and answer generation, and discusses improving accuracy by inspecting the context pipeline rather than blaming the language model alone. ### Source excerpt By Sivakumar Shanmugam, Kartik Muktinutalapati, and Palani Ramanathan. Your retrieval-augmented generation (RAG) application passes its benchmarks, reaches production, and begins confidently giving customers the wrong answers. Financial tables lose their headers at page breaks. Coverage matrices become garbled. Retrieval selects an article that appears relevant but belongs to the wrong product. Meanwhile, the clean, text-based [...] The post Enterprise AI Accuracy: Building a More Trustworthy RAG Application appeared first on Salesforce Engineering Blog. ## Enterprise AI transformation relies on the end-to-end platform: Azure was built for this moment DevFeed: [Enterprise AI transformation relies on the end-to-end platform: Azure was built for this moment]() Original publisher: [Read original article]() Author: Jeremy Winter Published: 2026-09-03T19:00:00Z Content type: opinion Language: en Sources: [Microsoft Azure Blog]() Topics: [Azure](), [Artificial Intelligence](), [AI Development](), [Cloud](), [Architecture & Design](), [dev-tools]() Tags: [agents](), [ai](), [ai-applications](), [ai-plus-machine-learning](), [analytics](), [azure](), [cost](), [critical](), [data](), [databases](), [developer](), [developer-tools](), [efficiency](), [enterprise-ai](), [hybrid-plus-multicloud](), [infrastructure](), [integration](), [internet-of-things](), [management-and-governance](), [models](), [operations](), [performance](), [platforms](), [production](), [risk]() ### AI overview Microsoft argues that enterprise AI value depends on an integrated platform connecting models, infrastructure, data, applications, agents, security, operations, and developer tools. The post presents Azure as supporting multi-model deployments, faster delivery, reliability, risk management, cost control, and scalable AI applications, while citing 2026 Gartner and Forrester recognition. ### Source excerpt The recognition for Microsoft over the past couple of weeks comes down to models, infrastructure, data, applications, and developer tools working as one system when AI moves into production. The post Enterprise AI transformation relies on the end-to-end platform: Azure was built for this moment appeared first on Microsoft Azure Blog. ## The knowledge layer for enterprise AI: Operating structure DevFeed: [The knowledge layer for enterprise AI: Operating structure]() Original publisher: [Read original article]() Author: Jocelyn Hoppa Published: 2026-08-28T08:45:00Z Content type: article Language: en Sources: [Graph Database & Technology | Neo4j Blog]() Topics: [Artificial Intelligence](), [Neo4j](), [Graphs](), [structure](), [data](), [Code]() Tags: [ai](), [code](), [data](), [developer](), [enterprise-ai](), [enterprise-architecture](), [genai](), [graph](), [knowledge](), [neo4j](), [semantic-layer](), [structure]() ### AI overview This chapter explains how to model an enterprise operating structure as a graph-based knowledge layer for AI agents. Using a fictional bank, it covers business units, teams, roles, reporting lines, and people, with Neo4j used to load and query the model so agents can identify responsibility, handoffs, and escalation paths. ### Source excerpt Business units, teams, roles, reporting lines. The structure behind the work, built as a graph you can load, query, and explore. New to the enterprise knowledge layer? Jesús Barrasa has written the manifesto. The knowledge layer for enterprise AI - Neo4j... Read more -> ## How Graph Technology Connects Data for Enterprise AI Applications DevFeed: [How Graph Technology Connects Data for Enterprise AI Applications]() Original publisher: [Read original article]() Author: Enzo Htet Published: 2026-08-19T07:00:00Z Content type: article Language: en Sources: [Graph Database & Technology | Neo4j Blog]() Topics: [Artificial Intelligence](), [AI Agent](), [Graphs](), [Databases](), [graph theory](), [Query (disambiguation)]() Tags: [ai](), [database](), [enterprise-ai](), [graph](), [graph-database](), [graph-technology](), [relationships]() ### AI overview Graph technology stores and analyzes connected data so AI applications can traverse relationships across entities such as orders, products, parts, and suppliers. The article explains how graph databases, graph analytics, and knowledge graphs provide context for questions that span multiple data relationships. ### Source excerpt You ask an AI agent a question whose answer lives in a table or a chunk of text, like "What's this customer's balance?", and it instantly provides the correct answer. But then you ask it a question that cuts across... Read more -> ## Rethinking Scanning for the AI Era: Wiz's Agentic Code Security System DevFeed: [Rethinking Scanning for the AI Era: Wiz's Agentic Code Security System]() Original publisher: [Read original article]() Author: Amir Lande Blau Published: 2026-07-30T16:47:57Z Content type: article Language: en Sources: [Wiz Blog | RSS feed]() Topics: [Application Security](), [Artificial Intelligence](), [Vulnerabilities](), [Security](), [AI-assisted coding](), [Cloud]() Tags: [ai](), [appsec](), [cloud](), [code-security](), [coding-agents](), [enterprise-ai](), [models](), [product](), [security](), [wiz-code]() ### AI overview The article argues that enterprise AI application security requires a layered system rather than a powerful model or one-off scanner. It describes continuous broad scanning, targeted deep analysis, and the use of application, cloud, runtime, and risk context to balance speed, cost, and depth. ### Source excerpt Enterprise AI AppSec requires more than powerful models. It requires a system that balances speed, depth, and cost across the software lifecycle. ## The knowledge layer for enterprise AI DevFeed: [The knowledge layer for enterprise AI]() Original publisher: [Read original article]() Author: Alyssa Di Pasqualucci Published: 2026-07-20T15:32:41Z Content type: opinion Language: en Sources: [Graph Database & Technology | Neo4j Blog]() Topics: [Artificial Intelligence](), [context](), [Large Language Model](), [data](), [systems](), [Orchestration]() Tags: [agentic-ai](), [agents](), [ai](), [context](), [enterprise-ai](), [governance](), [grounding](), [llms](), [orchestration]() ### AI overview This manifesto argues that enterprise AI often fails because agents lack the business meaning and institutional knowledge encoded in enterprise data, application logic, and people's expertise. It proposes a shared, governed knowledge layer that connects data and agents through an ontology, grounding data, policy enforcement, source selection, and decision-trace memory. ### Source excerpt A manifesto for context-rich enterprise AI When enterprise AI fails, it's not because of the model. Frontier LLMs arrive every few months, cheaper, better, and in everyone's hands. Enterprise AI doesn't fail because of the scaffolding either. The harness has... Read more -> ## Why AI-Ready Data Is The Real Advantage DevFeed: [Why AI-Ready Data Is The Real Advantage]() Original publisher: [Read original article]() Author: Published Published: 2026-07-08T13:53:41Z Content type: article Language: en Sources: [The Next Platform: In-depth coverage of high end computing]() Topics: [AI-Ready Data](), [data](), [Artificial Intelligence](), [AI Factory](), [Inference](), [NVIDIA DGX](), [Nvidia]() Tags: [agentic](), [ai](), [ai-factory](), [ai-ready-data](), [data](), [data-platform](), [enterprise-ai](), [everpure](), [gpu](), [nvidia](), [pipeline](), [production](), [storage](), [store]() ### AI overview A podcast discussion explains why enterprise AI projects often stall because their data is fragmented, ungoverned, stale, or otherwise unprepared for production. It also presents Everpure Data Stream as a capability intended to discover, transform, and deliver data to AI workloads. ### Source excerpt Many ambitious enterprise AI projects stall before they ever reach production, and according to Nvid ... ## The Next Workspace Isn't Just For Humans DevFeed: [The Next Workspace Isn't Just For Humans]() Original publisher: [Read original article]() Author: Ruslana Zbagerska Published: 2026-07-01T01:34:20Z Content type: opinion Language: en Sources: [Desktop and Application Streaming]() Topics: [Artificial Intelligence](), [Computing](), [Cloud](), [systems](), [Software](), [Job]() Tags: [agent](), [ai](), [ai-agents](), [cloud](), [computing](), [end-user-computing](), [enterprise-ai](), [identity](), [infrastructure](), [permissions](), [policies](), [trust](), [workspaces]() ### AI overview The article argues that enterprise AI agents are moving from conversation into execution across software, infrastructure, claims, vendor onboarding, and finance workflows. It presents the trusted environment where agents run--the enterprise agentic workspace--as a defining architectural and IT management question, emphasizing identities, permissions, policies, and accountability. ### Source excerpt Picture a global manufacturer that finds a critical defect in one of its products. Within minutes, a hundred things have to happen at once: orders paused, suppliers and customers notified, inventory reallocated, regulatory paperwork prepared, executives briefed. Even a year ago, that meant pulling hundreds of people into virtual war rooms, each logging into a [...] [Next page]()