Digital Twins: Test your decisions before they cost you

Every consultant knows the feeling. A client makes a big call—hiring, inventory, pricing, vendor change—and six months later, the numbers tell them whether it worked. By then, the money's already spent. The opportunity's gone either way.

What if you could run that decision first? Not as a guess. Not as a forecast in a spreadsheet. But as a living test, against real business data, before anything gets signed.

That's what a digital twin does.

What a digital twin actually is

Growing up on my family's farm in Greece, I learned early that systems don't forgive carelessness. The irrigation had to account for soil type, water pressure, timing, and weather—all at once. Miss one variable, and the harvest suffered. There was no "undo."

Modern businesses work the same way. Your pricing affects your inventory. Your vendors affect your cash flow. Your hiring affects your capacity. Everything connects.

A digital twin is a virtual replica of that connected system. It mirrors your actual business—using your real data—and lets you ask questions like: What happens if we raise prices 10%? What if our main supplier delays by two weeks? What if we double our ad spend next quarter?

The difference between a digital twin and a regular simulation is that it doesn't sit frozen in time. It updates continuously. It learns as your business changes. Think of it less like a model and more like a practice field where you can run plays before the real game.

Why this matters for consultants

Here's the reality: most business decisions are still made by looking backward. Dashboards show what happened last month. Reports summarize last quarter. Leaders make their next move based on patterns they hope will repeat.

A digital twin flips that entirely. Instead of recording what already happened, it lets you explore what could happen next. Your clients stop reacting and start anticipating.

For consultants, this changes the conversation. You're no longer just analyzing past performance—you're helping clients test future strategies in a space where mistakes cost nothing. The expensive lessons happen virtually, not in the real world where they eat into margins and morale.

This is what I call "the cost of being wrong." Every decision carries hidden risk. A digital twin lets you absorb those risks in simulation before they become line items on a P&L.

A practical example

A retail client I worked with faced a familiar problem: how to manage inventory without tying up too much cash or running out of key products. They also had vendor discounts available for bulk orders, but taking them meant committing capital upfront.

We built a digital twin of their inventory system—nothing exotic, just their sales data, vendor terms, delivery schedules, and payment conditions, all connected in a way that showed how each piece affected the others.

The twin revealed something the spreadsheets had hidden: by shifting their order timing by just a few days and consolidating certain purchases, they could capture the vendor discounts without straining cash flow. It wasn't a dramatic pivot. It was a small adjustment that the connected data made visible.

That's the real power here. Digital twins don't always produce fireworks. Sometimes they just show you the quiet, obvious move you couldn't see because your data was scattered across too many places.

What a digital twin is Not

It's worth being clear about what you're not building.

A digital twin isn't an AI agent that makes decisions for you. It's a thinking tool, not an autopilot. You're still the one interpreting the results and advising your client.

It's also not a recommendation engine. Netflix suggests movies based on patterns; a digital twin shows you consequences—what happens if you do X versus Y, given everything else in the system.

And it's not a static simulator with fixed rules. Old-school simulations are like flight simulators: useful, but disconnected from reality. A digital twin stays connected to live data, so it evolves alongside the actual business.

Getting started without overcomplicating it

Here's how to actually build your first digital twin—no infrastructure, no months of setup.

Step 1: Gather the data you want to explore. Pick one area of your client's business. Maybe it's sales and inventory. Maybe it's client projects and team capacity. Export the relevant spreadsheets, reports, CRM data—whatever exists. Messy is fine. You're not building a perfect system yet.

Step 2: Feed it to an LLM and have a conversation. Upload your data to Claude, ChatGPT, or whichever tool you use. Then ask it questions: What patterns do you see in this data? What's the relationship between X and Y? Summarize the key trends. See if it understands what you gave it. If the answers don't make sense, clarify the context and try again.

Step 3: Let the machine show you what you're missing. This is where it gets interesting. Ask the AI to find patterns, anomalies, and connections you haven't noticed. Human eyes see what they expect. The machine sees what's actually there—correlations across hundreds of rows that you'd never spot manually.

That's your MVP. A working digital twin you can query, built in an afternoon.

Step 4: Scale when you're ready. Once you've proven the value with a small dataset, you can expand. Build a proper knowledge base to hold all your client's data. Create an analysis framework so the AI knows how to interpret different types of information. Set up a process to feed new data continuously—daily, weekly, whatever fits the business rhythm.

This part takes more work, but it's not hard if you've already done step one. You know what questions matter. You know what the data reveals. Now you're just making it systematic.

Want help building your first digital twin?

If you'd rather skip the trial-and-error and get it right the first time, let's talk. I'll walk you through exactly how to set this up for your business or your clients.

The shift in thinking

What I find most valuable about digital twins isn't the technology. It's the shift they force in how you think.

Most consulting work is about bringing clarity to complexity—helping clients see what's actually happening so they can act with confidence. A digital twin takes that further. It lets you move from "here's what happened" to "here's what could happen if..."

That's strategic foresight. It's the difference between reading the terrain and scouting the road ahead.

And here's the part that matters most for how I work: the goal is always to leave your client more capable than when you started. A digital twin should become their tool, not yours. If they can't understand it, run it, and evolve it without you, it's not finished.

That's the real win. Not dependency. Ownership.

This article expands on ideas from the 3Nuggets video: