Ron Mitchell

AI trend summary Regulators, customers, and IT teams are moving from experimentation to control. As companies deploy large language models (LLMs) and AI agents into sales, support, finance, and operations, new priorities are emerging: model monitoring, explainability, data lineage, and incident response. Recent industry reports and guidance from regulators highlight that unmanaged AI can create […]

Quick summary (for business leaders) Retrieval‑Augmented Generation (RAG) plus vector databases is one of the clearest, most practical AI trends for companies today. Instead of relying on an LLM’s memory alone (which can hallucinate or be out of date), RAG reads your internal documents, manuals, CRM records, and reports in real time and feeds that

Quick summary RAG (Retrieval-Augmented Generation) combined with vector databases is one of the fastest-growing enterprise AI trends. Instead of asking a large language model (LLM) to guess answers from raw training data, businesses store their documents, product specs, and policies in a vector database, retrieve the most relevant pieces for each query, and feed those

Quick take: The AI assistant trend — led by custom “GPTs” and low-code agent tools — is moving from demos into real business use. Companies can now build tailored assistants that connect to internal systems (CRM, knowledge bases, ticketing) and automate routine tasks. This creates major productivity and service wins — but also raises questions

AI is moving from experiments to core operations. One of the fastest-growing trends for businesses is the adoption of private LLMs (large language models) combined with Retrieval‑Augmented Generation (RAG) and vector databases. This approach gives teams fast, accurate answers from company data while keeping sensitive information private and auditable. Why it matters for business leaders

Big trend right now: companies are pairing large language models (LLMs) with retrieval systems — called Retrieval-Augmented Generation (RAG) — and storing embeddings in vector databases. That combo makes AI answers anchored to a company’s own documents, CRM records, and product data. The result: faster, more accurate, and more secure AI for sales, support, and

AI trend summary AI “agents” that combine large language models with retrieval-augmented generation (RAG), vector databases, and app/tool integrations have moved from labs into real business use. These systems can pull exact answers from company documents, run multi-step tasks across apps (calendar, CRM, ticketing), and keep responses grounded in real data — reducing hallucinations and

Quick summary AI “agents” — systems that take goals, plan steps, use tools (APIs, databases, calendars, RPA), and act with minimal human direction — have moved fast from research demos to real enterprise pilots. Companies are already using agents to auto-generate and distribute reports, triage customer requests, orchestrate multi-step workflows across CRM/ERP systems, and speed

Quick update: AI agents — autonomous, multi-step AI “workers” that can act on behalf of users — went from experimental to enterprise-ready in 2024. Improved large models, agent orchestration tools (low-code + APIs), and tighter CRM/ERP integrations mean companies can now automate complex workflows, not just single tasks. Why this matters for business leaders –

Quick summary – Recent traction in Retrieval-Augmented Generation (RAG) plus vector databases is turning general LLMs into dependable “knowledge workers” for businesses. – Instead of relying only on a model’s internal memory, companies are connecting LLMs to their documents, manuals, CRM records, and databases via vector search. That reduces hallucinations, speeds answers, and unlocks new