The World Is Drowning in Jargon

You’ve seen it. You’ve cringed at it. Maybe, just maybe, you’ve even used it.

Corporate jargon is everywhere. Especially on LinkedIn, where people use words like synergy, disrupt, and circle back like they’re casting spells. Instead of saying “Let’s have a meeting,” someone insists on aligning key stakeholders for a value-driven conversation.

Even AI isn’t innocent. If left unchecked, it spits out the same nonsense, stuffing sentences with cutting-edge and game-changing fluff. But could AI also fight this nonsense? Could it help us separate real communication from corporate gobbledygook?

We decided to find out.

LinkedIn & The Land of Corporate Speak

LinkedIn is like the Wild West of corporate jargon. Some people truly believe that making things sound complicated makes them sound smart. Others just get carried away.

Exhibit A: James Watt, co-founder of BrewDog, who announced his engagement on LinkedIn by calling it a “long-term contractual agreement poised for future growth.” Congratulations, James, on your marriage startup! Investors are watching.

It’s a perfect example of how corporate speak can go from trying to sound impressive to accidentally hilarious.

So, we wanted to see if AI could sniff out this nonsense and make it make sense.

Our Experiment: Can AI Call Out Jargon?

Step 1: Gather Real-World Jargon

We collected examples of actual corporate mumbo-jumbo from LinkedIn, startup pitches, and AI-generated content.

Step 2: Feed It to AI

We made AI read the nonsense and asked it to translate it into plain English.

Step 3: Judge the Results

We checked whether AI could:

✅ Spot the jargon.

✅ Suggest a better, clearer version.

Results: AI vs. Business Buzzwords

Example 1: The Corporate Gobbledygook

Original Post:

“We must optimize our revenue-generating frameworks and enhance our go-to-market initiatives.”

Prompt Used:

“Identify any corporate jargon in the following sentence and suggest a clearer way to say it.”

AI’s Fix:

“We need a better sales strategy.”

Translation: Just say what you mean!

Example 2: Startup Pitch Hype Machine

Original Pitch:

“Our groundbreaking SaaS platform is revolutionizing synergy in the enterprise ecosystem.”

Prompt Used:

“Find the buzzwords in this sentence and rewrite it in plain English for a general audience.”

AI’s Fix:

“Our software helps companies work together better.”

Translation: You don’t have to sound like a TED Talk.

Example 3: AI Calls Itself Out

To be fair, AI sometimes creates this nonsense too. So, we tested if it could recognize its own jargon.

We gave it this prompt:

Prompt Used:

“Describe our company’s innovative solutions.”

AI’s First Attempt:

“We provide cutting-edge solutions that leverage advanced technologies to deliver unparalleled value.”

We then asked AI to analyze its own response:

Prompt Used:

“Find any corporate jargon in your previous response and rewrite it in simple terms.”

AI’s Fix:

“We use new technology to provide great services.”

Translation: Even AI gets carried away sometimes.

What We Learned

AI can detect jargon – It successfully identified and simplified business fluff.

But humans still need to step in – Sometimes AI oversimplifies, so human judgment is key to keeping meaning intact.

AI isn’t just a chatbot toy – This experiment highlights that AI is only as good as the people using it. Setting it up properly, understanding its strengths and limitations, and fine-tuning its responses requires real expertise. AI isn’t just some magic button that spits out perfect content—it’s a powerful tool that needs careful handling.

Not as simple as it looks – Many people think AI is just about typing in a question and getting an answer. But as we’ve seen, if AI isn’t trained properly, it can contribute to the problem rather than fix it. Knowing how to structure prompts and interpret AI’s responses is just as important as the AI itself.

AI can help us fight corporate BS, but it needs a human touch. Otherwise, we risk replacing one kind of nonsense with another.