Mid-Market's AI Playbook: Episode 04 With Caleb Gawne

How mid-market companies can find the right AI opportunities, avoid scattered experimentation, and use automation to actually improve operations

Featuring Caleb Gawne, CEO and co-founder of Kye.

Mid-market leaders are being told to "do something with AI."

The pressure is real. Teams are experimenting with ChatGPT, Claude, Copilot, internal tools, and AI agents. Vendors are promising faster work, lower costs, better service, and more scalable operations.

But the real challenge isn't whether middle-market companies should use AI. It's knowing where AI actually creates business value, and having the discipline to build toward it instead of just throwing mud at the wall.

In this episode of Field Notes, Caleb Gawne, who has spent over a decade building technology for complex, manual industries like logistics, healthcare, insurance, and financial services, shares the playbook he uses to help mid-market companies find their operational bottlenecks and fix them with AI in weeks, not quarters.

The Playbook

AI isn't a strategy. It's a tool that should be pointed at a specific business outcome.

The mid-market companies making real progress aren't the ones deploying the most tools or running the most experiments. They're the ones who start with a goal, find the bottleneck standing in the way of it, and fix that bottleneck in a way that's measurable, repeatable, and built to last.

For mid-market leaders, the useful question isn't "how do we use more AI?" It's "where is our business actually getting stuck, and what would it be worth to fix it?"

1. Start With a Business Goal, Not a Tool

AI for the sake of AI is, as Caleb puts it, a solution in search of a problem.

Before deploying anything, mid-market leaders need to get specific about what they're actually trying to achieve. Margin improvement? The ability to scale without adding headcount? Faster response times to win more market share? Each goal points toward a different starting point and a different solution.

Without that clarity, AI efforts drift. Teams adopt tools and activity increases, but nothing ties back to a metric that actually matters to the business. Before you pick a tool, get clear on the number you're trying to move, and treat AI like any other investment: if it doesn't move that number, it's not a priority.

2. Just "Doing AI” can create more silos

Chat tools like Claude and Copilot are a good first step. They build excitement and get people thinking about what's possible. But on their own, they tend to create individual gains, not organizational ones.

Caleb's warning is that this can quietly recreate the exact problem mid-market companies spent the last decade trying to escape: hundreds of disconnected spreadsheets, each one tuned to a single person or team. Except now it's happening faster, and it looks a lot cleaner on the surface.

In businesses where the work is genuinely individual, like a small dev shop or a solo legal practice, that's fine. But in operations where success depends on coordination across sales, ops, and customer service, scattered individual AI use doesn't move the business. It just makes the silos faster.

The fix is top-down coordination. Someone needs to own a clear view of where AI gets deployed across the organization, rather than leaving it to wherever a person happens to try it first. That's what turns early excitement into an actual advantage instead of a faster version of the same fragmentation.

3. Use Small Wins To Build Real Momentum

The instinct in a lot of organizations is to leap straight to the big swing: replace the ERP, rebuild the CRM, transform everything at once. Caleb advises strongly against it.

Instead, start with tasks that are lower-value, well-defined, and already close to being documented, the kind of work you'd traditionally hand to a BPO. Quoting, order entry, and administrative processing are good places to start because you likely already understand the workflow well enough to isolate it.

Those wins matter for two reasons. First, they build a measurable track record leadership can point to. Second, they build organizational muscle and credibility. Staff start to trust that this will help them, not replace them, before you ask them to change how they work at a deeper level. That trust is what makes the bigger transformation possible later.

4. Not Everything Needs AI

One of the sharpest points in the conversation is that mid-market leaders should actually want to minimize how much they rely on AI.

Caleb's framing: Would you rather have 100,000 geniuses with no process, or 100,000 normal people with clear roles, defined workflows, and the right tools? The answer is obvious, and it's the same answer for AI. Repeatable processes, clean handoffs, and traditional automation work 100% of the time. AI doesn't.

In practice, Caleb has seen this break down into roughly a 40/40/20 split on the gains that actually move a business: 40% pure AI, 40% better automation and software that AI helped build faster and cheaper, and 20% simply from understanding the business well enough to redesign the process itself.

He compares it to the shipping container, one of the most economically transformative inventions of the last 50 years, and it had nothing to do with intelligence. It was just a well-defined interface everyone could build around. The takeaway for mid-market leaders: before reaching for AI, ask whether the real problem is actually a process problem or a definition problem. Fixing that first often gets you most of the way there.

5. Mid-Market Has a Real Window, But It's Closing

Large enterprises carry more resources, but also more friction: more systems, more approval layers, more organizational change required to shift how work gets done. Mid-market companies have flatter structures and simpler tech stacks, so if ownership commits, they can move dramatically faster.

Caleb frames this through the venture lens of "default alive" versus "default dead." Many mid-market companies have historically been default alive: grow a couple points a year, no urgency required. That's changing. Margins are eroding, market share is harder to hold, and financing is tighter. For those companies, adopting AI well isn't optional anymore. It's existential.

His estimate is that mid-market companies have roughly two to three years where early adoption creates a real, defensible advantage. After that, the field levels out, the technology becomes standard, and the edge shifts from whoever moved first to whoever executes best.

The takeaway: the risk of moving isn't nearly as high as it feels. Most of the perceived risk comes from how abstract this still is, not from the actual cost or complexity of getting started. A specific pilot with a measurable ROI is a much smaller bet than most leaders assume, and the companies that treat the next two to three years as a real window will be the ones setting the pace in their category.

Rapid Fire, From the Episode

A few of Caleb's fastest, sharpest answers:

• Most dangerous assumption companies make about AI: That it's a panacea, a silver bullet that solves everything with minimal effort to deploy.

• Who should own AI inside a mid-market company: The CEO or COO.

• Fastest way to tell if an AI initiative is working: Adoption first. Impact second, measured against the metric that actually matters to your business, not an arbitrary one.

• A skill that matters more because of AI: Management. Getting value from AI takes the same skills as managing people well: decomposing work, giving clear context, and knowing when something isn't working.

Watch the Full Episode

In the full conversation, we cover:

• Where mid-market companies should start with AI

• Why scattered AI adoption can create more silos, not less

• How small wins build organizational confidence

• Why not every workflow needs AI

• How to decide between AI, automation, software, and process improvement

• Why mid-market companies may be able to move faster than large enterprises

• What's actually driving margin and retention gains in distribution today

Final Takeaway

The useful question isn't "how do we use more AI?"

It's "where is the business getting stuck, and what would it be worth to fix it?"

Start with the bottleneck. Define the business outcome. Then decide whether the answer is AI, automation, software, process improvement, or some combination of all four.

AI can change operations, but only when it's connected to the work that actually needs to change. For mid-market companies, the window to make that change count is open now, not indefinitely.

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