AI communication works best when it sounds like it was written for real people, not for a software demo. That lesson started for me long before ChatGPT, agents, or automation were part of the conversation.

My first professional boss was David Gallagher .

Dave owned Gallagher Printing and the Buffalo Rocket newspapers, and he was one of the most important people in my early career.

I still quote him, still have the utmost respect for him, and still talk fondly of him for the way he gave me a start, handed me responsibility, and helped shape how I think about communication.

Every week, Dave wrote a column in the paper under the pen name Joe Bortz.

This was back in the 90s. I was editing the papers, and Dave would come in with his printed-out column, hand it to me, and almost apologize for it before I even had a chance to read it.

"You're probably going to have to edit it all," he'd say.

And almost every week, I would tell him the same thing.

"Nope. I'm not changing a word."

Dave didn't give himself enough credit as a writer. But he was an outstanding communicator.

There's a difference.

Respecting the Reader

He wasn't trying to impress people with how polished he sounded. Instead, Dave wrote directly to the people he served.

He wasn't writing to win awards, and he wasn't trying to sound like someone else. Most importantly, he knew exactly who he was writing for.

That was the magic.

More importantly, it worked.

People read Joe Bortz because it sounded like someone they knew. It sounded like someone who understood North Buffalo and Buffalo's West Side: the people, the small businesses, the churches, the schools, the arguments, the humor, the personalities, and the everyday rhythm of the community.

Writing for Real People

I used to tell him, "Everyone reads your column. That's why they read the paper."

And I meant it.

Over time, I got to know Dave's voice so well that I'll admit something now: there were probably a half-dozen times over the six years I worked there when he was out of town, and I wrote the Joe Bortz column myself.

Learning the Audience

At the time, nobody knew.

Not because I was doing anything brilliant. Instead, it worked because I had spent years listening to Dave, editing around Dave, talking with Dave, reading Dave, and understanding the audience Dave was trying to reach.

I knew how Joe Bortz sounded because I knew who Joe Bortz was talking to.

Ultimately, that lesson has stayed with me for my entire career.

In practice, communication is not about saying the smartest thing.

It is about saying the right thing in the way your audience is most likely to receive it.

That matters in newspapers.

It also matters in marketing.

It matters in leadership, too.

And, of course, it absolutely matters in AI.

The Gap Between Tech Language and Real Life

At Momentum AI , a lot of my work sits at the intersection of two very different audiences.

On one side, there are the tech people: builders, developers, and the AI-native crowd. In other words, these are the people who say things like "workflows," "agents," "grounding," "retrieval," "RAG," and "implementation layer" as casually as they would order coffee.

On the other side, there are business owners, chamber executives, nonprofit leaders, association teams, staff members, consultants, salespeople, marketers, and very smart professionals who do not spend their day talking like a software release note.

To be clear, they are not less sophisticated.

They are busy.

They are also practical.

Meanwhile, they have customers, members, boards, employees, invoices, follow-ups, events, newsletters, sales goals, and too many tabs open.

For example, they do not wake up in the morning thinking, "I need to optimize my workflow."

They wake up thinking:

"I have to get this newsletter out."

"I forgot to follow up with that prospect."

"Where is that spreadsheet?"

"Why are we still doing this manually?"

"How do we get more people to register?"

"How do we stop dropping the ball?"

"How do we grow without adding more chaos?"

"Why are my salespeople not selling more?"

Ultimately, that is the kind of language most of us need around AI if it is going to matter in our real world.

Why "Workflow" Isn't Always the Right Word

For instance, one of the funny things about working with AI is that it loves certain words.

Every time I ask it to design me a flyer or give me Powerpoint slide copy about the power of Codex, it wants to tell people they can "create workflows."

Technically, that is true.

However, it is not always the best description possible.

Because it is not how most people talk.

If I put "learn how to create workflows" in an ad, some people will understand it. But a lot of people will glide right past it because it sounds like software language.

If I say, "learn how to stop doing the same annoying task over and over again," now we're getting somewhere.

It is the same idea.

However, it uses different language.

As a result, it creates a better connection.

So in honor of all the tech terms getting tossed around right now, I thought it would be fun to put together a quick translation guide for humans who don't spend all day buried in code.

AI Terms Translated for Humans

Workflow

AI translation: A repeatable process.

Plain-English translation: The way work gets done today – and where AI can step in to handle the repetitive parts so your team isn't stuck doing the same steps over and over.

Automation

AI translation: A task that runs without manual effort.

Plain-English translation: The thing you keep doing over and over that AI can now handle for you so you can go do something else.

Dashboard

AI translation: A visual interface for tracking information.

Plain-English translation: AI pulls everything you need together so you don't have to go hunting for it.

Integration

AI translation: Connecting tools or systems so they work together.

Plain-English translation: Making your systems talk to each other so AI can actually use your data without you having to feed it every time.

Prompt Engineering

AI translation: Structuring instructions to get better AI output.

Plain-English translation: Clearly and effectively telling AI what you want so it gives you something useful the first time.

Grounding

AI translation: Giving AI the right source material or context so it can produce a more accurate answer.

Plain-English translation: Feeding AI your real data so it stops guessing and starts sounding like it actually works here.

Use Case

AI translation: A specific way technology can be applied.

Plain-English translation: A real task in your business where AI can save time, reduce mistakes, or help you move faster.

Hallucination

AI translation: When AI produces information that sounds true but is not.

Plain-English translation: What happens when AI hasn't been given the right information and fills in the blanks anyway.

Person in the Loop

AI translation: A person reviews, approves, or guides the AI's work.

Plain-English translation: AI does the grunt work, but a human still makes the final call.

Context

AI translation: The background information AI needs to understand the request.

Plain-English translation: The details you give AI so it can respond like it understands your business, not just the question.

Agent

AI translation: An AI system that can take steps toward completing a task.

Plain-English translation: AI that doesn't just answer questions, but actually helps get the work done.

Retrieval

AI translation: Pulling relevant information from documents, databases, or sources before answering.

Plain-English translation: AI checking your files and data before it responds so it's not just making things up.

RAG

AI translation: Retrieval-augmented generation.

Plain-English translation: AI that looks up your information first, then answers based on that instead of guessing.

API

AI translation: A way for software tools to communicate with each other.

Plain-English translation: The connection that lets AI pull from and push to your other systems without copying and pasting everything yourself.

Data Hygiene

AI translation: Keeping information clean, accurate, consistent, and usable.

Plain-English translation: Cleaning up your data so AI doesn't get confused and give you bad results.

Optimization

AI translation: Improving a system or process for better results.

Plain-English translation: Tweaking how AI is used so it gets faster, more accurate, and more useful over time.

Knowledge Base

AI translation: A structured collection of information AI or people can reference.

Plain-English translation: The place AI pulls from so answers are consistent, accurate, and not dependent on one person's memory.

Implementation

AI translation: Putting a strategy, system, or tool into actual use.

Plain-English translation: The moment AI stops being an idea and starts actually doing work inside your business.

The Real Opportunity Is Translation

That last one may be the most important.

Because the future of AI in business will not be won by the people who can spew the most jargon.

It is going to be won by the people who can translate possibility into progress.

That means looking at a business, chamber, nonprofit, association, or team and asking:

Where are we wasting time?

Where are we losing opportunities?

Where are we making simple things harder than they need to be?

Where are we depending on one person's memory instead of a system?

Where are we using five tools when one better process would do?

Where are we using five people when one better process would do?

Where are we talking about growth, but still operating in ways that keep us stuck?

That is where AI gets useful – in the translation from tech to real life.

Our Mission at Momentum AI

Dave Gallagher understood something long before AI existed.

He understood that communication starts with respect for the audience.

You do not talk down to people.

You do not try to impress them with language they would never use.

You meet them where they are, in words they recognize (and would use themselves), around problems they actually have.

That is still the job.

At Momentum AI, we have trained more than 2,500 organizations on AI since November 2022, when ChatGPT was released to the public – and we have trainings and speaking engagements already lined up through the end of the year.

Why?

Because we are the translator.

Because we focus on giving people solutions, insights, and guidance that will yield results in the real world – in every training we do and every tool we build.

Because my favorite thing in my professional life right now is when someone I worked with comes back to me six months later and says they took our training, followed the advice, and solved a real problem in their organization.

Keeping Up with the Pace of AI

This stuff is moving so quickly that it is impossible to keep up with everything.

Our job is to curate the best of it and deliver it to our audiences in a way that is accessible, achievable, and useful.

That matters because tech speak can make AI sound impressive while still leaving people lost as to what to do next.

And most people I know do not have time for that.

They are trying to run businesses, serve members, lead teams, communicate better, follow up faster, solve problems, and find practical ways to grow.

They do not need every term.

They need the right idea, explained clearly enough that they can use it.

Whether you are reading a neighborhood newspaper column, a business newsletter, a chamber email, a sales message, a training description, or an AI strategy, you deserve the same thing:

Clear communication that meets you where you are.

Respect for what you already understand.

Plain language that helps you grasp what matters.

And something you can use right away.

That is what Dave Gallagher did every week as Joe Bortz.

And that is what we try to do at Momentum AI.

Take the complicated stuff and find the good stuff in it.

Then deliver it in a way that helps real people gain real understanding in the real world.

Originally published on LinkedIn as "What Joe Bortz Taught Me About AI."

FAQ: AI Communication

Why does AI communication matter for business?
AI communication matters because people are more likely to use AI when the value is explained in language connected to their real work, not in technical jargon.

What is plain-language AI training?
Plain-language AI training translates concepts like workflows, grounding, automation, and agents into practical examples people can use in everyday business decisions.

How can leaders make AI easier to understand?
Leaders can make AI easier to understand by starting with familiar problems, showing practical use cases, and explaining what AI changes in language their audience already uses.

If your team needs help translating AI into practical business action, explore Momentum AI training or start with an AI opportunity evaluation.