Red Hawk CEO Matt Strippelhoff joined Dynamics Corner Episode 517 with a message every business leader needs to hear: stop starting with AI. Start with your most expensive problem.
Everyone’s talking about AI. Fewer people are doing it right.
The most common mistake we see mid-market business leaders make isn’t choosing the wrong tool. It’s starting with the tool at all. They hear about an AI product, get excited, buy a subscription, and then try to retrofit it onto their business — hoping something sticks.
In a recent episode of the Dynamics Corner podcast, our CEO Matt Strippelhoff flipped that approach on its head. His core message was simple but easy to miss when you’re surrounded by AI hype: take AI completely out of the conversation first, and start by mapping your most expensive workflows. Only after you understand where the real friction, waste, and risk live should you ask whether AI, automation, or agents can help.
This blog captures the key ideas from that conversation — for business leaders who are genuinely ready to take the first real step.
The Real Starting Point: Map Your Workflow from Opportunity to Cash
Before you open a single AI tool, Matt’s advice is to sit down and map your most critical workflow from end to end — from the moment an opportunity enters your business all the way through to cash in the door.
As you trace that path, you’re looking for three specific things:
- Where are the handoffs? Every time work passes from one person, system, or team to another is a point where things slow down, get lost, or get inconsistent.
- Where is the friction? What steps require more effort than they should? Where do people manually re-enter data, chase approvals, or work around broken processes?
- Where are the mistakes costing you money? Errors, omissions, rework — what’s the real price tag of your status quo?
Only after you have honest, specific answers to those questions should AI enter the conversation. At that point, you’re not asking “what can AI do?” You’re asking a much more powerful question: “Can AI, agents, or automation solve this specific, expensive problem?”
“Forget models, frameworks, and token counts. Start by mapping your most expensive workflows. Where are the handoffs? Where’s the friction? Where are the mistakes costing you money? Only then do you ask whether AI can help.” — Matt Strippelhoff, Dynamics Corner Episode 517
That flip in sequence — problem first, tool second — is what separates AI initiatives that create real ROI from ones that generate activity without results.
Not sure where AI fits in your business? Our free AI Opportunity Decision Matrix walks you through the thinking — helping you identify the right problems before you start evaluating solutions.
The Tool Overload Problem: Dozens of Options, Zero Clarity
One of the biggest real-world challenges the episode tackled is one every business leader is quietly dealing with right now: there are dozens of AI tools available, and most of them are marketing themselves as the answer. ChatGPT, Copilot, Gemini, Claude, Perplexity, industry-specific tools, platform add-ons — the list is overwhelming and grows every week.
So how do you choose?
Matt’s framing cuts through the noise. If you’ve already mapped your workflow and identified your most expensive problem, the question of “which tool” becomes dramatically easier. You’re no longer comparing features on a spec sheet — you’re asking which tool actually addresses the specific friction you’ve identified. That’s a very different — and much more answerable — question.
Without that anchor, you’re just picking based on whatever’s loudest in the market that week. With it, you have a filter.
Shadow AI Is Already Happening. Don’t Ignore It.
Shadow AI is something the episode addressed directly that most leadership teams don’t want to say out loud: your employees are already using AI. Personal ChatGPT subscriptions, free Copilot tiers, browser extensions, AI-powered tools tucked inside other software, it’s happening whether you have a policy for it or not.
This isn’t a moral failing. It’s human nature. People find tools that help them do their jobs faster and they use them. The problem is what they might be doing with those tools without guidance: pasting sensitive customer data into a public model, generating outputs that contradict company policy, or making decisions based on AI-produced content that nobody has reviewed.
The answer isn’t to ban AI use and hope for the best. The answer is to get ahead of it, which means your organization needs to address two things directly.
1. An AI Acceptable Use Policy
Do you have one? If not, you’re already behind. An AI acceptable use policy doesn’t have to be a 30-page legal document. It needs to answer the basics: What tools are approved for use? What categories of data can and cannot be entered into AI systems? Who is accountable for AI-generated outputs? What does human review look like?
The goal isn’t to lock everything down. It’s to give your team a clear, reasonable framework so they can use AI productively without inadvertently creating legal, security, or compliance exposure for the business. Start with checking out our sample AUP.
2. Ownership: Who Leads AI in Your Organization?
Someone needs to own AI strategy in your business. Not as a side project. Not informally. This is the concept of an “AI czar” — a designated leader, ideally someone technical enough to understand the tools and senior enough to drive adoption — who is responsible for evaluating tools, setting standards, and guiding the team through the inevitable questions that come up as AI becomes embedded in daily work.
In smaller organizations, this might be the CEO, the COO, or a trusted technical lead. In larger ones, it could be a dedicated role. The title matters less than the accountability.
The “14 Agents and Billions from the Beach” Reality Check
If you spend any time on LinkedIn or in AI marketing content, you’ve seen this: promises that you can deploy a handful of AI agents, step away from your business, and watch the revenue pour in. The episode called this out directly — and it’s worth saying clearly here too.
That’s not how this works.
AI agents are genuinely powerful. They can automate repetitive tasks, work across systems, handle routing logic, and execute processes faster than any human team. But they operate within the structure your business provides. If your workflows are poorly defined, your data is inconsistent, or your processes are full of exceptions and tribal knowledge, agents will amplify those problems — not solve them.
The businesses that will actually see transformative results from AI agents are the ones that first do the hard, unglamorous work: documenting their processes, cleaning up their data, reducing unnecessary complexity, and building clear accountability into their operations. That’s not as exciting to post about, but it’s what actually works.
Sustainable AI adoption is built on a solid operational foundation — not on shortcuts.
What This Looks Like in Practice
Based on the framework Matt laid out in the episode, here’s a practical starting sequence for any mid-market business that’s serious about AI:
- Step 1 — Map your workflow end to end. Pick your most critical business process — usually the one closest to revenue. Walk it from start to finish. Document every step, every handoff, every tool involved.
- Step 2 — Find the friction. Where does work slow down? Where do errors happen? Where are people doing work that feels like it should be automated? These are your candidates.
- Step 3 — Quantify the cost. What does that friction actually cost you in time, labor, errors, and missed opportunities? Putting real numbers on the problem changes the conversation from “should we do something?” to “how fast can we move?”
- Step 4 — Now ask the AI question. With a specific, quantified problem in front of you, evaluate whether AI, automation, or a custom solution addresses it. This is when tool selection becomes meaningful.
- Step 5 — Build your governance foundation. Before you deploy anything, make sure you have a basic AI use policy, clarity on data boundaries, and a designated owner for AI initiatives in your organization.
This isn’t the fastest path to having something to show at your next all-hands. But it’s the surest path to building AI that actually changes the trajectory of your business.
We built the AI Opportunity Decision Matrix specifically for this moment. It's a free download that walks you through the process of identifying where AI is actually a good fit in your business — before you commit to anything.
Ready to Find Your Starting Point?
Red Hawk works with mid-market businesses to identify the workflow friction that costs the most, evaluate whether AI or custom software can solve it, and build solutions that are maintained and supported as long-term assets — not one-time projects.
If you want to work through the workflow mapping exercise Matt describes, our free DIY Tech Innovation Workshop tools are a great place to start. Download them at redhawk-tech.com/downloads, or schedule a call with our team to dig into your specific situation.
And if you haven’t watched the full episode yet, we strongly recommend it — especially if you’re a business leader or an advisor helping clients navigate this moment.
▶ Watch Dynamics Corner Episode 517
ABOUT THIS EPISODE
Dynamics Corner Episode 517: “Flip the Script: Start with Your Business Problem, Not the AI Tool” features Matt Strippelhoff, CEO of Red Hawk Technologies, in conversation with hosts Kris Ruyeras and Brad Prendergast. The episode is designed for business leaders who keep hearing about AI but aren’t sure where to begin — and for partners trying to help their customers navigate the same question. Published May 12, 2026.
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