10 Ways to spot AI-generated consulting content in 2026

It bothers me. A lot. I used to go on LinkedIn to learn something. Now all I learn is that more and more of the content is AI generated. For instance, I’ve never known humans use this symbol “—“ so much. Have 90% of the people suddenly started doing that or are all these posts I am seeing generated by the same bot?

In the software and management consulting world, the primary product is advice. But nowadays, that advice is being increasingly packaged by LLMs. While AI is an incredible accelerator, the "pure" AI output often lacks the grit, the nuance, and the specific horror experience that a human consultant brings to a project.

If you’re reviewing a proposal, a technical audit, or a strategy piece, this post might be helpful.
I’ve worked with three different AIs to create the 10 tips below. Using them in combination may help you understand if the content you are reading is generated by an AI and perhaps not reviewed by a human.

1. The "Tapestry & Delve” language
Although this will likely change over time, it seems like the AI models currently have "favourite" words that they use with statistical frequency.

• How to tell: Watch for words like tapestry, delve, orchestrate, seamless, robust, and transformative.
• The reality: Real consultants use simpler, more functional language e.g. they don't "delve into the tapestry of digital transformation"; they "review the legacy API documentation."

2. The "Rule of Three" structure
AI is trained on "balanced" writing. It loves to group benefits or features into sets of three.

• How to tell: "Our approach ensures scalability, security, and efficiency."
• The Signal: If nearly every bulleted list or concluding sentence has exactly three rhythmic parts, it’s probably a machine seeking symmetry.

3. The "Moralising" conclusion
AI models are programmed to be helpful and optimistic. They almost always end an article or a post by zooming out to a "grand vision" of the future.

• How to tell: Phrases like, "Ultimately, by embracing [X], organisations can not only survive but thrive in the ever-evolving digital landscape"
• The human approach: A real consultant’s conclusion is usually a call to action or a warning about a specific risk, not a generic conclusion about the "digital era"

4. Absence of failure or stories
AI can explain how a system works, but it struggles to describe how it fails in the real world.

• How to tell: The writing is "clean." It lacks mentions of specific, messy human experiences, like for example that one time RabbitMQ wouldn’t start because someone rotated a certificate without telling anyone else.
• The test: If the article contains no "uncomfortably specific" examples, it’s likely synthetic.

5. The "It’s Not Just X, It’s Y" Pivot
This is a specific rhetorical phrase that I find the easiest to spot right now. I am told AI models use it to sound profound.

• How to tell: "It’s not just about writing code; it’s about crafting solutions."
• The signal: This pseudo-sophistication is a classic AI shortcut for creating a transition without having to provide a real, data-backed insight.

6. Perfect Paragraph Symmetry
Look at the article from a distance. Do all the paragraphs look to be about the same length?

• How to tell: AI generates "blocks" of text that are visually consistent (usually 5 or 6 lines).
• The human approach: Human writing is not as consistent. We write a long, complicated explanation which may then be followed by a short, punchy sentence. Like this.

7. Over-reliance on "Moreover" and "Furthermore"
While these are used in a grammatically correct way, statistically they are used a lot more by the machine than by a typical human.

• How to tell: A high frequency of formal transitions (Moreover, Consequently, Additionally, In addition).
• How humans write today: In 2026, professional B2B writing has moved toward a more direct, "TL;DR" style. Heavy use of academic transitions is a sign of an LLM trying to "glue" ideas together.

8. The "Voice of God" perspective
AI bots speak from a position of total certainty and universal truth. That is unless you point out a mistake and then they are forced to issue an apology.

• How to tell: A lack of "I," "We," or "In my experience."
• The signal: If the article states, for example, that "AI-first workflows are the only path to ROI" as a definitive fact rather than a debated strategy, it’s a model hallucinating consensus.

9. Predictable Analogies
AI offers analogies that are either too basic or too obvious. I guess on the flip side they are safe metaphors.

• How to tell: Watch out for obvious analogies e.g. comparing management to a "chess match," a "symphony," or a "journey."
• What a human might do: A human consultant might compare a messy legacy migration to "trying to change the tires on a car while it’s doing 70mph on the M1." In contrast it’s specific, localised, and non-standard.

10. The "Non-Existent" reference
In professional services, and perhaps in any respected field, we cite reports (Gartner, McKinsey, etc) or reputable publications. But the AI bot doesn’t seem to need to do that.

• How to tell: The AI might cite a "2025 study on software agility" that sounds plausible but doesn't actually exist, or it might attribute a real quote to the wrong person.
• What to do: Always check the specific "Year/Report" name. If it’s slightly off (e.g., "Google's Global Innovation Pulse"), then it’s probably just a synthetic hallucination.

Why it matters
In a world where anyone can generate a 2,000+ word article in 10 seconds, the value of originality has never been higher. If your content looks like it meets some of the criteria in the list above, then your clients will subconsciously (or perhaps even consciously) know it, and assume you are taking shortcuts with your other work too.
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What should you do instead? Sure, use AI to draft, but then "break" the machine's patterns. Read, change the language so you can be proud of it, then read again. Add your own 3 AM stories, use a real world metaphor, and please, please, delete the word "tapestry."