Ron Mitchell

AI agents—autonomous software that can read, decide, and act across apps—are moving from proofs-of-concept into real business use. Companies are combining large language models (LLMs) with RPA and API integrations to automate end-to-end workflows: qualifying leads, routing support tickets, auto-generating reports, reconciling invoices, and updating CRMs without manual handoffs. Why business leaders should care – […]

Quick summary Enterprises are increasingly pairing private large language models (LLMs) with Retrieval-Augmented Generation (RAG) and vector databases to make internal documents searchable, actionable, and secure. Instead of relying only on third-party chat tools or public internet knowledge, companies are building AI systems that retrieve facts from their own contracts, manuals, CRM records, and reports

Quick take Retrieval-augmented generation (RAG) — using vector databases and embeddings to connect large language models to your company data — has moved from research demos to real-world deployments. Businesses are now building secure, private “AI copilots” that answer staff questions, automate reporting, and streamline workflows without exposing sensitive data to public models. Why it

AI trend summary: Enterprises are rapidly moving from one-off chatbots to private LLM deployments that use Retrieval-Augmented Generation (RAG) and vector databases. Instead of relying solely on a single model’s memory, businesses now store company documents, CRM records, and SOPs as embeddings in a vector DB. At query time the system pulls the most relevant

Short summary Retrieval-Augmented Generation (RAG) — using vector databases to feed LLMs with your company’s own documents — is one of the clearest, fastest-growing ways businesses are getting real value from generative AI. Instead of hoping a base model “remembers” your policies, product specs, or CRM notes, RAG pulls the exact context from your private

Short summary Retrieval-Augmented Generation (RAG) paired with vector search is rapidly becoming the go-to pattern for companies that want the power of large language models without risking data leaks or poor answers. Instead of relying on a single, general-purpose model to recall everything, RAG systems fetch relevant, company-owned documents (using vector embeddings + a vector

AI agents — autonomous, workflow-driven systems that combine large language models with connectors, tools, and business data — are moving from proofs-of-concept into real, revenue-driving deployments. From automating lead qualification and personalized outreach to orchestrating cross-team processes, these agents are reshaping how companies run sales, support, and operations. Why this matters now – Tooling maturity:

Short summary Companies are increasingly using Retrieval-Augmented Generation (RAG) and vector search to give AI instant access to internal documents, reports, and knowledge bases. Instead of relying on a static model’s memory, RAG finds the most relevant documents (using vector embeddings) and feeds them into an LLM to produce accurate, up-to-date answers. This shift is

AI trend in focus AI agents—autonomous, task-oriented AI that can carry out sequences of actions, query systems, and learn from feedback—are moving from labs into everyday enterprise use. Major vendors now offer “agent” features inside Copilot-style products and workflow tools, and startups are building specialized agents for sales outreach, customer support resolution, procurement, and IT

The trend: AI agents — autonomous, tool-using AI that can complete multi-step tasks — are moving from experiments to production. Over the past year, vendors and startups have launched agent platforms that let LLMs call APIs, query knowledge bases, schedule meetings, run reports, and trigger systems — all without human step-by-step prompting. Companies are piloting