Hey, it's Martin.
Traditional consulting has a physics problem.
Revenue scales with hours. Hours scale with headcount. Headcount scales with payroll. The whole model is linear. You grow by adding people, which adds cost, which means you need more revenue, which means more people.
We started our AI consulting biz with a different bet: what if we designed from day one for leverage instead of labor?
Not "let's use ChatGPT sometimes." Actual structural leverage. Automations that run whether we're awake or not. Agents that handle the stuff humans shouldn't waste cycles on.
Here's what that looks like in practice.
When a new client signs, an onboarding workflow triggers automatically. It scaffolds the Google Drive folder structure. Spins up a Notion workspace with our standard docs. Kicks off a research workflow that pulls context on the client's industry, competitors, recent news. By the time we sit down for the kickoff call, there's already a grounding document waiting.
We have an assistant named Fred (not a person) who monitors our inbox for signed contracts. Archives them. Logs the details. No one touches it.
Expense management runs the same way. Receipts get categorized, tagged, and logged without anyone thinking about it.
We've built scouts that watch specific topics across the web and generate summary reports on a schedule. Research that used to take hours now just... arrives.
None of these are revolutionary on their own. Each one saves maybe 20 minutes here, an hour there. But they compound. And they never need a day off.
The unsexy truth about internal automation: tiny wins accumulate into structural advantage. You don't notice the shift until you realize your admin overhead is a fraction of what it used to be. And that freed-up time goes straight into higher-leverage work.
Here's the tension though. Every hour spent building internal systems is an hour not spent on client delivery. That tradeoff is real. It's also exactly why most consulting firms never build this stuff. They're too busy doing the work to change how the work gets done.
We treat internal R&D as non-negotiable. Not a side project. A core function.
And now we're building toward something bigger.
All these micro-systems generate context. Client docs, call transcripts, project artifacts, internal comms. Right now that context is scattered. Useful, but fragmented.
The next layer is what I've been calling the Shared Memory Layer. One unified system that ingests everything, structures it semantically, and makes it retrievable at operational speed. Persistent context across every project, every client, every workflow.
Why does this matter? Because agents without memory are assistants. Agents with memory become autonomous. They can pick up where they left off. They can learn across projects. They can execute, not just respond.
That's where we're headed in 2026. Not just automations that save time, but systems that compound knowledge. The foundation for a consulting firm that actually scales.
The linear physics of services is breaking. The firms that figure out leverage-first will operate at a fundamentally different level.
We're building ours in public. More on what we've learned working with clients on AI adoption next week.
Until next week,
Martin
PS: feel free to connect with me on LinkedIn and say hi! It’s always fun to chat with new readers.
PS2: want to accelerate AI adoption in your business? Book a free 30-min disovery call here.