Ron Mitchell

Short summary Apple’s WWDC 2024 introduced “Apple Intelligence,” a set of AI features built into iPhone, iPad, and Mac that embed generative AI across apps and workflows. The system mixes on‑device models for privacy-sensitive tasks and cloud processing for heavier jobs, and it’s designed to surface personalized summaries, help automate routine tasks, and make interfaces […]

Short summary Autonomous AI agents — software that can plan, act, and learn to complete multi-step tasks with little human supervision — are no longer just lab experiments. In 2023–2024 we saw a wave of products and toolkits that make it easier to deploy agentic workflows for things like lead qualification, sales outreach sequences, data

Quick update for business leaders: over the last 12–18 months, companies and cloud vendors have accelerated adoption of Retrieval‑Augmented Generation (RAG) and vector databases to make large language models (LLMs) reliable for real business use. RAG combines your company data (documents, CRM records, SOPs) with powerful LLMs so answers are accurate, up-to‑date, and auditable —

Businesses are increasingly moving from public chatbots to private LLMs combined with Retrieval-Augmented Generation (RAG). Instead of exposing sensitive data to public APIs or relying on generic answers, companies use RAG to pull context from their own documents, then generate accurate, up-to-date responses with a private or hybrid LLM. This trend is gaining fast traction

Quick summary Companies are increasingly pairing large language models (LLMs) with vector databases to build retrieval-augmented generation (RAG) systems. Instead of asking an LLM to answer from its training data alone, RAG gives the model relevant company documents — product specs, support tickets, contracts, knowledge bases — at query time. This approach is powering faster,

AI agents — autonomous, task-focused systems that combine large language models with connectors, tools, and real-time workflows — are moving from labs into everyday business operations. Over the past year we’ve seen a surge in agent platforms (user-built “GPTs,” vendor agent suites, and RPA + generative AI combos) that can draft outreach, run data reconciliations,

AI agents — autonomous, goal-driven software that combines large language models, retrieval (RAG), and API automation — are moving quickly from demos to real business value. Organizations are using agents to triage support tickets, qualify leads, generate reports, and run repetitive back-office workflows — freeing people to focus on judgment and strategic work. Why business

Recent trend snapshot Major cloud vendors (Microsoft’s Copilot Studio, Google’s Duet AI and similar platforms) have pushed enterprise-ready AI agents — sometimes called “copilots” — into mainstream use. These agents can connect to company data, call APIs, and run multi-step workflows. The result: faster knowledge work, automated repetitive processes, and new opportunities to reduce costs

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.