How leveraging human inputs created a growth flywheel that outperformed traditional demand spend.
Most AI-generated content begins with a simple prompt. While the output might be acceptable enough to post, it more than likely will not create trust or meaningful outcomes.
Copy.ai chose a different path for its content strategy. Rather than basing it on a few prompts, they anchored their process on human inputs that fueled marketing and sales.
This approach generated $50M in pipeline from only $37K of spend. Here is how they did it.
Why AI-Generated Alone Falls Short
Purely prompt-driven content often reads generic. Sure, it’s tuned for keywords, but it’s thin on substance.
Customers these days are savvy. They can tell when a company is publishing for volume instead of trying to add value. And this can affect the reputation of the brand.
It’s important for business leaders today to care about more than just producing content at scale.
Simple prompt-driven workflows eat up cycles without compounding into durable growth. You get words on a page, but not trust or authority.
How AI-Synthesized Content Works
Copy.ai took a different approach than the standard prompt and post. They recorded sales calls, leadership meetings, and product discussions. And then they pulled the transcripts to create a content goldmine.
These transcripts served as the input for all of their sales and marketing content. Content that reflected what the company was actually doing in its own words.
This is the essence of AI-synthesized content. The AI is not generating something from nothing.
It is structuring real human voice and insight into content that reflects the company’s unique perspective based on their employees’ real words.
Placing Humans Where They Add Value
Kyle Coleman, CMO at Copy.ai, explained in a podcast why this approach matters. Creating AI-synthesized content allowed his voice and perspective to stay intact.
While AI accelerated the drafting, he still owned the final pass. This preserves authenticity while efficiently scaling output at the same time.
He stressed the best approach is not to remove people from the process entirely. Instead, put them at the stages where insight and judgment matter most.
Why Natural Language Wins in Visibility
Every company today is scrambling to figure out how to get its company mentioned in AI-generated summaries and recommendations from ChatGPT. This challenge has been dubbed GEO, or Generative Engine Optimization.
In the past, companies focused on SEO in order to rank in search results. Now SEO efforts are supplemented with GEO in order to be included in an AI-generated response.
Keyword lists work in SEO, but they do not work in GEO. Generative engines instead prioritize the way people actually phrase questions and describe problems. (And if you want to win at GEO, here are 10 things you can do in under a day.)
This is where AI-synthesized content has a clear advantage. Because it can be built from customer conversations and leadership insights, it mirrors the language buyers use.
That makes it more likely to be surfaced in generative answers and more credible when it appears.
Growth Without an Advertising Budget
Copy.ai chose not to spend on paid search. Instead, they built a loop that reinforced itself.
Conversations created transcripts. Transcripts became content. That content drove discovery and sparked new conversations.
The system compounded over time. With $37K invested, it produced $50M in the pipeline. Treating content as infrastructure rather than filler made the difference.
The Operator’s Takeaway
The principle extends well beyond marketing. AI delivers results when it is integrated into durable workflows.
Those workflows must start with authentic inputs and be reinforced with structure and guardrails.
AI then handles the synthesis, while people apply judgment.
Whether you are rebuilding after an exit, leading a growth-stage company, or evaluating portfolio execution, the lesson applies, AI cannot fix weak processes.
Strong systems can be multiplied by it.
. . .
Nick Talwar is a CTO, ex-Microsoft, and a hands-on AI engineer who supports executives in navigating AI adoption. He shares insights on AI-first strategies to drive bottom-line impact.
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