1235 summarised stories about Enterprise AI, each linking back to the original source. Browse all topics →
Product Hunt 1 month ago 37
Doneit launched version 3.2 with an updated Doneit Assist, adding photo attachments for task planning and new help features for turning notes into tasks. It is the company’s 8th launch, and the update is launching today. Task creation now includes subtasks, suggested reminders/attributes, brief task-list summaries, key-task highlights, and upcoming-task previews.
Product Hunt 1 month ago 27
Slashy launched Slashy Assistant, an AI-native email client feature that drafts replies in a user's voice and handles follow-ups and inbox triage by connecting to email, calendar, CRM, and meeting notes. The assistant is described as starting to help within five minutes. As a result, users can set plain-English automations and use Slashy inside the app or via iMessage, Slack, or phone to reduce time spent managing email.
SiliconANGLE 1 month ago 37
Supio described long-horizon AI agents and a “Firm OS” approach meant to orchestrate multi-day legal workflows across case systems and communications instead of handling single tasks. The company says about two-thirds of case work involves communication with external parties other than the client, driving the need for an agentic operational layer. The result is a shift in law-firm AI from point solutions that improve task efficiency to systems that can document and manage work over time while escalating judgment points to attorneys.
Fortune 25
Black Lake Technologies founder Yuxiang Zhou tested an AI sales coach against his company’s top sales leader, and most senior salespeople backed the machine’s advice. Seven out of 10 of the most experienced salespeople voted for the AI. The results pushed Black Lake to keep exploring AI-led decisions while also emphasizing that human salespeople still provide motivation and a “trust journey” for adoption.
Fortune 23
The adoption of AI in workplaces is producing employees’ “grief” about lost professional identity, trust, and job prospects, which leaders are sometimes dismissing rather than addressing. A Gallup study in August 2026 found employees in organizations deploying AI extensively were significantly more likely to report a worsening workplace culture. Companies responding by listening to employees’ fears are shifting toward mentorship, clearer authorship and transparency norms, and transition support instead of forcing optimism.
The New Stack 1 month ago 7 ● 3 sources
Cohere released North Small Translate, an open-weight mixture-of-experts machine translation model aimed at enterprise deployments. It reports a WMT26 All Languages score of 83.60 for North Small Translate, versus 81.56 for Qwen 3.5 397B A17B and 68.20 for Google Translate. The company positions this as a shift toward smaller, more specialized translation models that can run inside customers’ environments and handle longer, sensitive documents with more control.
Product Hunt 1 month ago 15
Duvi launched this week as a SaaS that lets people build AI agents by describing what they want. The page lists 22 followers. As a result, Duvi positions voice, chat, and actions in one agent that can work across a business’s website and phone/WhatsApp without separate experiences for each channel.
The New Stack 1 month ago 46
The article describes how AI agent deployments can pass CI and automated evals while still producing wrong answers for customers, leaving teams with an “observability gap” where failures can’t be alerted on as “wrong.” The Dynatrace 2026 State of SRE and Platform Engineering report, based on 919 enterprise leaders surveyed globally, found that 77% embed observability in some services but only 40% fully integrate it across all deployments. It argues that teams must add run-time evidence beyond traces—especially retrieval inputs/filters, tool-call/tool-result trajectories, and linked evaluation results—so missing retrieval preconditions and other causes can be reproduced and fixed with regression tests and version-validity checks.
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20 October 2026
Today’s AI story isn’t just about better models—it’s about who gets to run them, where, and at what cost. The Trump administration’s Department of Homeland Security ordered DeepSeek’s AI technology removed from all U.S. federal civilian agencies, citing “multiple threat vectors” and directing agencies to delete DeepSeek-affiliated products and stop buying its services. That federal clampdown echoes closer-to-the-ground moves: New York City Mayor Zohran Mamdani, after banning AI use in elementary and middle schools, argued that deployment isn’t inevitable and that local pressure should force accountability rather than wait for federal policy gaps to close.
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