Big idea: More businesses are moving from experimenting with public chatbots to building private LLMs and retrieval-augmented generation (RAG) systems that power secure, task-focused AI copilots. Vendors like OpenAI, Anthropic, and Google pushed enterprise-grade models and tools in 2024–2025, and organizations respond by combining models with vector databases, access controls, and workflow agents to keep […]

Short summary The EU AI Act is the world’s first comprehensive law to regulate artificial intelligence. It groups AI systems by risk (unacceptable, high, limited, minimal) and sets strict rules for “high-risk” systems — including requirements for risk management, data governance, transparency, human oversight, and technical documentation. Some uses (like certain biometric social scoring) are

Short summary Companies are rapidly adopting retrieval-augmented generative AI (RAG) to build private, enterprise copilots that can read internal documents, CRM notes, SOPs and databases — then answer questions, draft responses, and automate workflows. Instead of relying only on a base large language model (LLM), businesses combine LLMs with indexed company data (via vector databases

What’s happening Companies are increasingly using Retrieval-Augmented Generation (RAG) and vector databases to build private AI assistants and automate reporting. Instead of relying only on general-purpose LLMs (which can hallucinate or lack context), RAG lets models fetch exact, company-specific facts from internal documents, CRM records, SOPs, and databases. This trend is powering smart customer support

Quick summary: Retrieval-augmented generation (RAG) — pairing large language models (LLMs) with vector databases that store company documents, product data, and past conversations — is moving from experiments into everyday business use. Instead of asking an LLM to answer from memory (which can lead to hallucinations), RAG fetches relevant, company-specific facts and feeds them into

Quick summary Enterprises are increasingly combining private large language models (LLMs) with Retrieval-Augmented Generation (RAG) and vector databases to build secure, accurate, and actionable AI solutions. Instead of sending sensitive content to public APIs, businesses store indexed documents as embeddings (in Pinecone, Weaviate, Milvus, etc.), retrieve the most relevant bits on each query, and feed

A clear trend: more companies are moving from public chat tools to private, company-hosted large language models (LLMs). These private LLMs run on-premises or in controlled clouds and are designed to protect sensitive data, comply with regulations, and deliver better, business-specific answers by learning from internal documents. Why this matters for business leaders – Data

Quick take: AI “agents” — autonomous, connected AI tools that can plan, execute, and follow up on tasks across apps — are moving from demos into real business use. Major vendors (Microsoft, Google, Anthropic and growing open-source ecosystems) plus new agent frameworks are making it fast and affordable to automate end‑to‑end processes: customer triage, sales

AI update: Autonomous “AI agents” are moving from research demos into real business use. Rather than answering a single prompt, these agents can plan, fetch data, take multi-step actions, and loop with humans. Companies are already piloting them for tasks like personalized sales outreach, automated reporting, cross-system orchestration, and first‑line customer support. Why this matters

AI agents—systems that combine large language models (LLMs), tool use, and retrieval-augmented generation (RAG)—are no longer just demos. Over the past year we’ve seen companies move from pilot projects to production-grade agent-driven automation. These agents can read your CRM, query internal knowledge bases, run reports, and even trigger workflows—cutting time from tasks that used to