AutomateAI

I had a blast giving a quick AI primer to my ProVisors home group of Forsyth County (affectionately, FoCo). When you work in your own bubble (AI, in my case), it’s easy to forget that not everyone lives in that world. You get so used to being surrounded by people who do what you do that you forget it’s actually a specialty. Right now, even smart leaders are still sorting out the AI basics: What’s real. What’s hype. What’s risky. What actually matters. That’s one of the things I love about my PV group, it brings together people from completely different circles, and everyone walks away sharper and better supported. Different industries, different perspectives, and everyone’s genuinely trying to help each other get better. If your team, company, or networking group could use a 30 to 60 minute AI primer (no buzzwords, no sales pitch, just clear answers about what's real and what's hype), let me know. Happy to come share what I'm seeing on the ground.

ADKAR isn't theory. It's the checklist for whether your AI pilot survives contact with Monday morning. Awareness. Desire. Knowledge. Ability. Reinforcement. Service firms have thin management bandwidth. Without a simple adoption plan, "AI tools" become shelfware and resentment. Your change program is as important as your tool choice.

If you can't explain the control points in plain English, you're not ready for agents. Not ready to deploy? Start with process boundaries and permissions. Map who touches what data. Define what an agent can and can't do. Then audit it. This is the checklist nobody talks about. But it's the one that keeps you out of trouble.

Before you deploy agents: define the outcome, map the work, and decide what the human owns. Agents don't fix unclear handoffs. They just make them faster. We're seeing service firms copy what bigger companies do with AI agents—but without the operating system. No clear ownership. No measurement. No controls. That's where agent projects fail in week three. The hard part isn't the agent. It's the process it's supposed to improve.

You deployed the automation. Now what? Most teams treat AI and automation like a "set it and forget it" thing. Deploy, hope it works, move on. Then six months later, you realize it's been running suboptimally the whole time. Or it broke silently. Or it's solving yesterday's problem, not today's. AI needs an ops cadence. Not a big one. Just a rhythm. Here's what works: **Weekly ritual (30 minutes):** **Monday: KPI Check** – Is the automation performing? (One metric. One number. Is it moving the right direction?) **Tuesday: Exception Review** – What broke or surprised us? (Pull the weird cases. Are there patterns?) **Wednesday: Process Tweak** – Should we change the workflow based on what we learned? (Small adjustments. Not big redesigns.) **Thursday: Automation Tweak** – Should we adjust the rules, thresholds, or logic? (Iterate based on exceptions.) That's it. Four 7-minute conversations. One person leads. Everyone else listens and contributes. The teams that do this? They catch problems early. They iterate continuously. They stay aligned on what's working and what's not. The teams that skip it? They deploy once and wonder why adoption stalls or performance drifts. If you're rolling out AI or automation and want to build an operating system around it, let's talk about the cadence. That's where consistency happens. What's your current rhythm for reviewing automation performance?

Agentic automation is the new buzzword. And like most buzzwords, it's being oversold. The pitch: "Deploy an AI agent to handle decisions and orchestration across your systems." The reality: If your workflow is messy, an agent will just make the mess *faster*. Here's what we see: teams have 3–5 SaaS tools with brittle handoffs. Data doesn't match between systems. Exception paths are unclear. Then they drop an agent on top and hope it figures things out. It doesn't. It amplifies the chaos. The rule is simple: **Don't automate decisions you haven't standardized.** Before you deploy an agent, you need: 1. **Stable workflow** – What are the steps? What does "done" look like at each stage? 2. **Clear definitions** – What is "qualified"? What is "billable"? What triggers an exception? 3. **Consistent data** – Does the same thing mean the same thing across all your systems? 4. **Narrow scope** – What's the smallest, most repeatable decision the agent should make? Only then does an agent add value. It handles the narrow, repeatable stuff. Humans handle judgment calls and exceptions. Most teams skip steps 1–3 and jump to step 4. Then they're surprised when the agent makes bad decisions or creates new problems. The fix: stabilize the workflow + data definitions first. Then add agents for the narrowest repeatable steps. Then monitor and adjust. If you're thinking about agents and want to talk through the process side first, let's connect. That's where the real work happens.

Agentic automation is the new buzzword. And like most buzzwords, it's being oversold. The pitch: "Deploy an AI agent to handle decisions and orchestration across your systems." The reality: If your workflow is messy, an agent will just make the mess *faster*. Here's what we see: teams have 3–5 SaaS tools with brittle handoffs. Data doesn't match between systems. Exception paths are unclear. Then they drop an agent on top and hope it figures things out. It doesn't. It amplifies the chaos. The rule is simple: **Don't automate decisions you haven't standardized.** Before you deploy an agent, you need: 1. **Stable workflow** – What are the steps? What does "done" look like at each stage? 2. **Clear definitions** – What is "qualified"? What is "billable"? What triggers an exception? 3. **Consistent data** – Does the same thing mean the same thing across all your systems? 4. **Narrow scope** – What's the smallest, most repeatable decision the agent should make? Only then does an agent add value. It handles the narrow, repeatable stuff. Humans handle judgment calls and exceptions. Most teams skip steps 1–3 and jump to step 4. Then they're surprised when the agent makes bad decisions or creates new problems. The fix: stabilize the workflow + data definitions first. Then add agents for the narrowest repeatable steps. Then monitor and adjust. If you're thinking about agents and want to talk through the process side first, let's connect. That's where the real work happens.

Your CRM says the deal is "done." Your project tool says it's "complete." Your accounting system says it's "billable." Which one is right? All of them. And none of them. This is the integration pain nobody talks about. You've got 3–5 SaaS tools. Each one has its own definition of what "done" means. So you end up with: - Duplicated data entry - Manual exports and imports - Mismatched numbers - "Why doesn't this match?" conversations every week Then you try to integrate them. You build a connector. Data flows. And suddenly you're reconciling discrepancies because the definitions don't align. Here's the rule: **Definition comes before integration.** Before you connect anything, answer these questions: - What does "done" mean? (Not in each system. In the business.) - What does "qualified" mean? - What does "billable" mean? - What's the source of truth for each field? Write it down. Get agreement. *Then* integrate. Most teams skip this and jump straight to "let's connect the systems." That's why integrations fail or create more work than they save. The teams that do it right spend a week on definitions and save months of reconciliation. If you're drowning in manual data work and thinking "we need better integration," the conversation should start with definitions, not connectors. What's the biggest data mismatch you're dealing with right now?

CEOs and COOs are drowning in exception handling. You're buried in status chases, escalations, and "what should we do about this?" conversations. So the pitch for decision automation is seductive: "Let AI handle the routing and prioritization." But here's the danger: **Automate decisions last.** If your workflow is inconsistent, automating decisions will just automate the wrong answers faster. Here's the order: 1. **Remove ambiguity in the workflow** – What are the steps? What triggers each one? What's the exception path? 2. **Define escalation rules** – When does something need human judgment? What's the criteria? 3. **Use AI to summarize and route** – Not to decide. To surface the right information to the right person. 4. **Humans make the call** – For anything that requires judgment or carries risk. Most leaders skip steps 1–2 and jump to step 3. They want AI to "make decisions." But if the underlying process is inconsistent, you're just automating inconsistency. The teams that win? They clean up the workflow first. Then they use AI to reduce noise and surface what matters. Then humans decide. You'll still have exceptions. But they'll be *clear* exceptions. And you'll spend less time chasing status and more time on actual decisions. If you're considering decision automation and want to talk through the workflow side first, let's connect. That's where the real relief happens. What's the decision or routing that's taking up the most of your time right now?

Governance is not a committee. It's a workflow. Most small teams hear "AI governance" and think: ethics board, compliance checklist, bureaucracy. So they skip it. Then one automation touches customer billing wrong, or an agent approves something it shouldn't, and suddenly you're spending weeks untangling the mess. Governance doesn't have to be heavy. It just has to be *there*. Here's what lightweight governance looks like: **Approval** – Who signs off before the automation goes live? (Not everyone. One person.) **Monitoring** – What metric tells you if it's working? (One number. Check it weekly.) **Rollback** – What triggers a pause or shutdown? (Define it upfront: if error rate > X%, stop.) That's it. Three swimlanes. One workflow. Map it to your process: - Step 1: Automation touches customer data → needs approval - Step 2: Weekly KPI check → is it performing? - Step 3: If performance drops → who pulls the plug? The teams that do this right don't spend time on governance. They spend time on *outcomes*. Because they know the guardrails are in place. The teams that skip it? They spend months investigating what went wrong. If you're deploying automation or AI and haven't mapped governance into the workflow, that's the conversation to have first. What's your biggest governance concern right now?