Ron Mitchell

Big story in brief AI “agents” — small, autonomous AI programs that can carry out multi-step tasks (think: qualify a lead, draft a proposal, run weekly sales reports, or coordinate a vendor escalation) — are moving from lab demos into real business workflows. Companies are now embedding these agents into CRMs, ticketing systems, and analytics […]

Quick summary AI agents — autonomous, goal-oriented software that can read systems, take actions, and follow up — are no longer just experiments. Over the past year we’ve seen more companies move AI agents from one-off pilots into day-to-day operations for sales, customer service, and internal reporting. These agents are being used to qualify leads,

Quick summary AI “agents” — autonomous assistants that combine language models, data access, and automation — are now being adopted beyond R&D teams and into real business workflows. Companies are using them to run sales outreach, triage customer support, prepare reports, and automate recurring processes. The tools that make this practical are retrieval-augmented generation (RAG),

Quick summary AI “agents” — autonomous, multi-step AI assistants that can research, plan, and act across apps — are moving from demos into real business use. Major AI platforms now include agent features, and startups are packaging them for sales, finance, and operations teams. That means businesses can automate complex workflows (example: gather data, generate

The story in a nutshell AI agents — software that can act on behalf of people (read email, pull data, update systems, draft messages, schedule meetings) — are moving from experiments into everyday business use. Companies are wiring agents into CRMs, customer support tools, and reporting pipelines so these systems do more than surface information:

Quick summary Autonomous AI agents — systems that take actions on behalf of users (e.g., qualify leads, draft emails, produce reports, or trigger workflows) — have moved from demos to practical pilots across sales, ops, and finance. Companies are pairing large language models with connectors to CRMs, BI tools, and automation platforms so agents can

Quick summary Autonomous AI agents — software that can research leads, draft outreach, book meetings, update CRMs, and produce reports — are moving from experiments to real business use. Companies are using these agents to automate repetitive sales tasks, accelerate response times, and free reps to focus on high-value conversations. Why this matters for your

Summary AI agents — small, goal-directed AI programs that can act across apps, pull data, and carry out multi-step tasks — have moved from experiments into real business pilots. Advances in large language models, prebuilt connectors, and orchestration tools mean companies can now automate end-to-end processes: follow-up outreach, meeting summaries with action items, regular sales

Quick summary Autonomous AI agents — small software “workers” that can read data, take actions, and follow up without constant human prompts — are no longer just tech demos. Over the past year we’ve seen rising adoption across sales, customer support, and operations as businesses use agents to handle repetitive workflows: lead qualification, order updates,

Summary AI “agents” — autonomous workflows driven by large language models — have moved from experiments into real business use. These agents can read emails, pull CRM data, qualify leads, build weekly performance reports, and even book meetings with minimal human input. Improvements in LLMs, agent frameworks (LangChain-style toolkits), and connector ecosystems that link agents