AI Post

Big news: the European Union has reached a political agreement on the AI Act — a first-of-its-kind, risk-based law that will reshape how companies develop, deploy, and buy AI across industries. Why this matters for business leaders – The AI Act classifies AI systems by risk (unacceptable, high, limited, minimal) and puts stricter rules on […]

Quick summary (what’s happening) – More companies are pairing large language models (LLMs) with retrieval-augmented generation (RAG) and vector databases to build accurate, up-to-date AI assistants. – Instead of relying only on a model’s internal knowledge, RAG pulls relevant documents, product specs, and CRM data at query time. That reduces hallucinations and makes answers traceable.

A growing trend in AI is the rise of autonomous AI agents — systems that can plan, act across tools, and complete multi-step tasks with minimal human input. Businesses are using agents for research summaries, sales outreach, report automation, customer triage, and repetitive back-office work. For leaders, agents promise faster workflows, lower costs, and new

Quick summary RAG (Retrieval‑Augmented Generation) is a fast‑growing way businesses combine large language models (LLMs) with company data. Instead of asking a model to rely only on its pretraining, RAG pulls specific documents (from FAQs, CRM notes, SOPs, contract libraries, etc.) into the prompt via embeddings and a vector database. The result: answers that are

AI is moving from experiment to execution. Over the past year, major vendors (OpenAI, Google, Microsoft) and a wave of startups have pushed “AI agents” and retrieval-augmented generation (RAG) into mainstream business use. These agents combine large language models with connectors to company systems and vector databases so the model can fetch up-to-date, internal data

AI agents—small, task-focused systems powered by large language models—are moving fast from labs into everyday business tools. In recent months, major cloud and software providers have embedded agent-style features into productivity apps and developer platforms. The result: companies can automate multi-step tasks, coordinate across systems (CRM, ERP, email, calendars), and create lightweight “digital assistants” that

What’s happening now AI “agents” — small, goal-directed systems built with large language models that can access your data, call APIs, and run multi-step workflows — have moved from proofs-of-concept into real enterprise pilots and early production. Major cloud vendors and open-source frameworks made it easier in 2024–2025 to connect LLMs to CRMs, ERPs, BI

Short summary AI “agents” — software that can plan, take multi-step actions, call tools, and learn from results — are moving fast from labs and hobby projects into real business use. Platforms and frameworks (think tool-enabled LLMs, agent libraries, and orchestration layers) now let companies automate end-to-end tasks: prospecting emails, invoice reconciliation, IT ticket routing,

Quick summary There’s a growing trend in enterprise AI: teams are combining large language models (LLMs) with vector databases to power Retrieval‑Augmented Generation (RAG). Instead of asking an LLM to memorize everything, companies store company documents, CRM records, product data, and SOPs as embeddings in a vector database. When an employee asks a question, the

Why this matters now AI-powered “copilots” are moving from proofs-of-concept into everyday business tools. These copilots combine large language models (LLMs) with company data (via retrieval-augmented generation, vector search, and connectors to CRM/ERP systems) to help people write emails, pull live reports, resolve customer issues, and automate routine processes — all inside the apps teams