Lesson 4: Become a Business–Tech Translator

You now hold both halves: outcome-first and tech-smart. Put them together and you become something companies are desperate for — a translator who can sit between the business people and the technical people and make them actually understand each other. This is the MIS superpower.

The three fluencies of a great translator

Charlie Warren says the best founders building AI companies share three traits. They're really the job description for any business–tech translator:

Domain fluency — you deeply understand the business and the customer.
Model fluency — you know what the technology can realistically do (Lesson 3).
Operational rigor — you care about how the work actually gets done: process, throughput, where things break.

"You're selling to skeptical buyers and often regulated spaces. You have to bleed credibility."

— Charlie Warren, Y Combinator

Credibility comes from being fluent on both sides. Engineers trust you because you understand the tech; business leaders trust you because you understand the outcome. You become the bridge everyone has to cross.

Translation = removing the magic

Here's the most practical definition of translation you'll ever get, courtesy of Sabrina Ramonov. To get AI (or any team) to do an expert's job, you have to drag the expert's invisible "I just know" intuition out into clear, explicit steps:

"Remove the magic from your expertise… break down your intuition into concrete steps so that you can explain it to AI."

— Sabrina Ramonov

She gives a brilliant test: "If a junior intern followed you around all day, what would they observe you doing?" Writing those steps down is exactly what a translator does between a business expert (who works on instinct) and a technical team (who needs explicit logic). You convert tacit know-how into something a system — or a developer — can actually build.

Try it: tech-speak → outcome-speak

A translator constantly converts jargon into what people actually care about. Tap each row to reveal the human version:

"We'll implement a RAG pipeline over your knowledge base."

"Your help docs will answer customer questions automatically, so your team stops repeating themselves."

"We need to reduce data latency in the reporting layer."

"Managers will see today's numbers this morning instead of waiting until next week."

"Let's add a human-in-the-loop quality gate."

"A person double-checks the tricky cases, so we don't embarrass ourselves when the AI is unsure."

"The model is hallucinating on edge cases."

"The AI sometimes makes things up on unusual questions — here's where we can't trust it yet."

"We should treat the app as ephemeral and protect the data layer."

"The app is easy to rebuild; your customer data and know-how are the irreplaceable asset, so let's guard those."

This translator role has a name in the working world: business analyst, systems analyst, product manager, MIS professional. It's the heart of the field. Companies are full of business people who can't write a clear requirement and engineers who build exactly what they were told (not what was needed). The person who stands in the gap — turning outcomes into specs and specs into outcomes — is often the most promoted person in the building. That's the job you're training for.

Where humans add the most value

As AI does more of the routine work, where do people stay essential? Tom Blomfield's answer is a perfect description of the translator's home turf:

"The humans sit around the edge… interfacing with the real world. It's where this intelligence makes contact with reality. Human beings reach into places the models can't go yet."

— Tom Blomfield, Y Combinator

Novel situations. Judgment calls. High-stakes, high-emotion moments. The messy human conversation where someone doesn't even know what they want yet. That edge — between the clean logic of the system and the messy reality of people — is exactly where a translator lives.

Quick check

The marketing team wants "personalization." The dev team asks "based on what data?" You're in the room. What's your translator move?

"Personalization" is vague magic. You remove the magic: "Marketing wants returning visitors to see products related to their last purchase — that needs purchase history linked to the account." Now both teams are talking about the same concrete thing. That's translation.

How you actually get fluent: do the work

Translation isn't a personality type — it's a skill you build by getting your hands dirty. DoorDash's Tony Xu, who personally did deliveries for years, says it best:

"There's no better way to be the expert than just to do the work. And you might be surprised at how quickly you get to become the expert."

— Tony Xu, DoorDash CEO

Sabrina says the same about AI: automate one of your own tasks first, "because then you'll understand how this stuff actually works." Build a tiny automation. Make a simple dashboard. Use the AI tools yourself before you recommend them. You don't become a translator by reading about both languages — you become one by speaking them, badly at first, until you don't.

🧠 Think like an MIS consultant

Two teams, zero shared language

Operations says: "The new system is useless, it doesn't do what we need." Engineering says: "We built exactly what the ticket asked for." Both are frustrated. You're brought in. Where do you start?

Both teams are right, which means the breakdown is in translation — the ticket captured a solution, not the real outcome. You go sit with operations and watch the actual workflow (the "follow you around for a day" move from Lesson 2). You "remove the magic" — write down the real steps and the real need in plain language. Then you translate that into something engineering can build, and translate engineering's constraints back into trade-offs operations can choose between. You didn't pick a side; you rebuilt the bridge.

✍️ Your turn

Take an expert skill you have (even a hobby). If a junior intern followed you for a day, what 3–4 steps would they see you do that you've never actually written down?

Translate this into outcome-speak: "We migrated the database to the cloud and added an API."

Key takeaway: The business–tech translator combines domain fluency, model fluency, and operational rigor to remove the "magic" between people and systems. It's the core of the MIS profession — and you build it by doing the work, not just reading about it.

← Previous3 · Stay Tech-Smart Anyway Next →5 · What Problems Will You Solve?