Quick summary Generative AI and autonomous AI agents are being combined with robotic process automation (RPA) to create “intelligent process automation.” Instead of simple scripted bots, modern automation can understand documents, decide next steps, interact with systems through natural language, and learn from feedback. Major RPA and cloud vendors are already embedding large language models […]

Trending now: businesses are accelerating adoption of autonomous AI agents — software that can plan, act, and interact on behalf of users. Driven by improvements in large language models, retrieval-augmented generation (RAG), and agent frameworks (e.g., LangChain-style agents and vendor copilots), companies are piloting agents for customer support, procurement, accounts reconciliation, sales outreach, and internal

Quick summary Companies are rapidly moving from experimenting with chatbots to building enterprise “copilots” — AI assistants that pull knowledge from your systems to answer questions, draft messages, and automate tasks. The fuel behind this shift is Retrieval-Augmented Generation (RAG) and vector databases (Pinecone, Milvus, Weaviate and others). Together they let large language models (LLMs)

AI is moving from chatbots to autonomous agents that can plan, act, and follow through on multi-step business tasks. Over the last year, more companies have begun piloting agent platforms to automate sales outreach, generate recurring reports, triage support tickets, and coordinate simple procurement tasks. These agents can connect to CRMs, calendars, and internal databases

Quick update: AI agents — autonomous, multi-step assistants powered by large language models (LLMs) and connected tools — are moving from labs into the enterprise. Tech vendors (think Copilot-style assistants, custom GPTs, and open-source agent frameworks) and startups are shipping agent toolkits that can research, draft, and execute tasks across systems. That means businesses can

Autonomous AI agents — software that can plan, act, and complete multi-step tasks with little human direction — are moving from research demos into real business use. Over the past year many vendors and startups have released agent frameworks and low-code tools that let companies automate workflows across sales, customer service, finance, and operations. That

Big idea (what’s trending) AI “agents” — task-focused AI that combines large language models, retrieval-augmented generation (RAG), and tool integrations — have moved from labs to real business use. Companies are deploying agents that can draft sales outreach, summarize customer histories, run PO approvals, and auto-generate compliance reports. The result: faster decisions, fewer manual steps,

Short summary Apple introduced “Apple Intelligence” at WWDC 2024 — a system-level suite of generative AI capabilities built into iPhone, iPad and Mac. The features include on‑device processing for faster responses, multimodal understanding (text, voice, images), deeper integration with native apps, and personal context access to help with writing, summarizing, search, and assistant tasks. Why

Autonomous AI agents — software that can plan, act, and use company tools to complete tasks — have moved from labs into real business use. Over the past year we’ve seen more organizations pilot agents that do things like automate report generation, triage support requests, and coordinate multi-step supply chain tasks. Combined with Retrieval‑Augmented Generation

Big idea: AI agents — autonomous, tool-using AI that can read documents, query systems, and take actions — are moving from pilot projects into mainstream business use. New LLM capabilities, better retrieval (vector databases/RAG), and agent frameworks mean companies can build copilots for sales, finance, HR, and operations that actually save time and reduce cycle