AI Post

Short summary The EU’s new AI regulations (commonly called the EU AI Act) are reshaping how businesses can use generative AI, decision systems, and high-risk AI tools. The rules set clear expectations for transparency, data quality, human oversight, and risk mitigation — and they create obligations for providers and users of certain AI systems. For […]

Autonomous AI agents — virtual assistants that plan, act, and iterate — are moving from experiments into real business processes. Major platforms (from cloud providers’ copilots to purpose-built agent frameworks) are now being used to qualify leads, route support tickets, automate procurement tasks, and generate operational reports. For leaders, this shift means faster cycle times,

A major shift in AI governance is here: regulators in the EU are moving from debate to enforcement with the EU AI Act and similar global initiatives. That means companies using AI—especially high-risk systems—face new legal, operational, and documentation rules. For business leaders, this is both a risk and an opportunity: get compliant, and you

Short summary AI “agents” — task-focused systems built from large language models (often called custom GPTs) — are quickly moving from experiments to business tools. Companies can now build agents that read your documents, call your APIs, summarize meetings, draft emails, and even run parts of routine workflows without heavy engineering. This trend is lowering

Recent trend snapshot Many companies — from startups to major cloud vendors — are pushing “AI agents”: small, goal-driven systems built on large language models that can act across apps, pull data, and complete multi-step tasks without constant human prompting. These agents are being used today for things like intelligent sales outreach, automated contract review,

AI agents — small, goal-driven software that plan, act, and use tools on your behalf — are moving from experiments to real business value. Over the last year, companies have layered agents on top of large language models, RAG (retrieval-augmented generation), and API integrations so these systems can handle tasks end-to-end: from drafting customer replies

The trend AI “agents” — autonomous, goal-driven software that can read, write, schedule, and call APIs — are moving fast from lab demos into real business use. Over the last year we’ve seen more enterprise-grade agent platforms, tighter integrations with CRMs and ERPs, and better tools for giving agents safe access to company data (think

AI agents — small, goal-driven AI programs that can plan, act, and learn — are moving from labs into real business work. Over the past year we’ve seen more companies pilot agents that handle tasks like customer follow-ups, report generation, lead qualification, and cross-system automation. Big platform vendors and open-source frameworks are making it easier

Short update for business leaders: AI “copilots” are no longer an experiment. Major vendors are embedding generative AI into everyday apps—CRMs, analytics tools, service desks, and productivity suites—so employees get contextual, task-ready help inside the tools they already use. That means faster decision-making, fewer manual tasks, and more consistent outputs across teams. Why this matters

Quick summary AI agents — autonomous systems that combine large language models (LLMs) with tools — are moving from demos to real business use. When you connect agents to company data using retrieval-augmented generation (RAG) and vector databases, they can answer complex questions, generate reports, and run multi-step processes while staying grounded in your documents