Ron Mitchell

Too many teams chase volume and ignore one simple fact: the best growth comes from qualified conversations. Quick follow-up and solid lead qualification turn inquiries into qualified appointments—then into closed deals. Why it matters: fewer wasted calls, better use of leadership time, and a predictable sales pipeline that supports real forecasting. Booked meetings that fit […]

When a lead is contacted quickly, they’re far more likely to answer and agree to a meeting. That simple fact is reshaping how teams approach outbound sales and lead qualification. Faster follow-up turns interest into booked meetings, creates more qualified appointments, and keeps your sales pipeline predictable. Why it matters: leadership time is limited. You

What’s happening Autonomous AI “agents” — systems that can perform multi-step tasks, talk to apps, and take actions without constant human prompting — have moved from demos to real deployments. Over the last 12–18 months we’ve seen vendor platforms and open frameworks make agents easier to build and connect to business systems (CRMs, ERPs, BI

The story in one line Over the past year, low-code agent builders and tighter integrations from major cloud and SaaS vendors have pushed AI agents out of experiments and into real business use — automating tasks end-to-end from lead research to weekly reporting. Why it matters for your business – Faster, more consistent work: Agents

Summary AI “agents” — autonomous, task-focused AI assistants that connect to your systems (CRM, email, calendars, databases) — have moved from experiments to production in many companies. Instead of one-off chatbots, businesses are now building agents that can draft personalized outreach, schedule meetings, update records, pull and summarize reports, and even run multi-step workflows across

Story summary Large organizations are moving beyond single-chat bots and isolated generative AI pilots to deploy AI agents — systems that can act across apps, pull data, take actions, and run multi-step processes on their own. These agents combine retrieval-augmented generation (RAG), connectors to CRM/ERP systems, and simple orchestration to automate tasks like lead qualification,

Quick summary AI agents — autonomous systems that act on your behalf across apps (CRM, email, analytics, chat) — moved from experiments to real business tools over the last year. Companies are now using these agents to automate repetitive sales tasks, generate near-real-time reports, and run multi-step workflows (e.g., prep a sales call, update the

Quick take There’s a clear, accelerating trend: businesses are starting to deploy AI agents — connected, task-focused AI assistants that can act on your data and systems — to automate sales and ops work. These agents can handle lead qualification, schedule meetings, update CRMs, and produce routine reports. The effect is less manual busywork and

Quick summary AI “agents” — autonomous workflows that combine large language models, tools, and data connectors — have moved from research demos into practical business use. Thanks to better agent frameworks, prebuilt integrations (CRMs, email, cloud storage) and safer tool controls, companies can now automate tasks like lead qualification, multi-step reporting, routine customer responses, and

What’s happening AI agents — autonomous software that can read, act, and learn across apps — are moving from experiments to real business use. Sales teams are using agents to qualify leads, draft and A/B test outreach, update CRMs automatically, and generate real-time pipeline reports. Operations teams use agents to stitch data from multiple systems