Inside the AI-Powered Sales Engine: How I Built a 25,000-Hour Customer Intelligence System Without Writing a Line of Code
When I talk to executives about AI, I remind them that you can’t delegate transformation. In this new era, leaders who understand how to build with AI — not just talk about it — will separate the disruptors from the disrupted.
That’s the premise behind the How I AI episode I recently recorded with Claire Vo. Unlike most conversations about AI, this wasn’t theory. It was a blueprint for how we built a system at Suzy, my company, that transformed 25,000 hours of customer conversations into a living intelligence engine without writing a single line of code.
This isn’t an engineering story. It’s a playbook for how business leaders can use no-code tools, AI orchestration, and real-world data to create a self-learning organization that runs at the speed of the customer.
Finding the Problem Worth Solving
Every great AI project starts with a simple question: what problem is holding you back?
At Suzy, our sales and customer success teams kept saying the same thing:
“We can’t find what we need to help customers fast enough.”
They had access to mountains of data — call notes, CRM records, Slack threads, and customer surveys — but no way to synthesize it.
When I looked deeper, I realized the solution was already hiding in plain sight. For years, every customer interaction had been recorded and transcribed in Gong, a platform that captures sales and support calls. Over time, that created a library of over 25,000 hours of conversations — arguably the most authentic and unfiltered source of customer truth we had.
The question was: how could we unlock that data so every employee — not just analysts — could use it to make smarter, faster decisions?
Turning 25,000 Hours of Calls into Actionable Data
We didn’t have the luxury of a dedicated AI team or engineers waiting for instructions. So I decided to build the workflow myself.
The system started with a trigger: every time a new Gong call finished, it kicked off an automation through Zapier, the no-code orchestration platform I’ve used for years.
Here’s how it worked:
Browse AI scraped the Gong transcript as soon as it was available.
Zapier cleaned and formatted the text, stripping out HTML.
A delay ensured the data pipeline didn’t break when hundreds of calls came in at once.
GPT-4 Turbo, via OpenAI’s API, analyzed the transcript, summarized key insights, and generated sentiment scores for customer satisfaction.
The insights were posted to a Slack channel called Customer Health, visible to the entire company in real time.
Suddenly, every call was being summarized, scored, and categorized — automatically.
The “Churn Early Warning System”
One of the earliest benefits of this automation was pattern recognition.
We trained the system to assign each customer interaction a sentiment score from 1 to 10, where ten signaled strong health and one indicated a risk of churn.
If a call fell below a score of seven, the system would automatically post an alert to a private Slack channel called Churn Early Warning System.
Within weeks, we began seeing the power of that visibility. In one case, an account manager dismissed a customer’s frustration as minor. But the AI flagged the call as high-risk. Three weeks later, the customer indicated they were considering a competitor. We intervened immediately, solved their issue, and retained the account.
When we looked back across the data, we found something remarkable: the AI’s sentiment scoring had been more predictive of churn than our CRM pipeline.
From Conversation to Marketing Fuel
Once the AI started summarizing calls, I realized it wasn’t just a customer success tool — it was a marketing engine.
Every call contained valuable insight into what customers were searching for, how they described their challenges, and what language resonated.
So we added another layer of automation:
The AI began extracting keywords and themes from each transcript.
Those keywords were automatically appended to our Google Ads campaigns, allowing us to target prospects using the same vocabulary as our best customers.
The system also created anonymized summaries of use cases — e.g., “a financial services brand exploring product naming research” — which were automatically turned into blog posts published to Suzy’s content hub.
Each post was fully scrubbed of confidential information, optimized for SEO, and scheduled to go live three weeks after the call.
Within months, we had over 10,000 new blog posts — each one based on a real customer interaction, optimized for organic search, and supported by dynamic Google Ads.
For the first time, our marketing engine was literally powered by the voice of the customer.
AI as the Sales Coach
Next, we used the system to improve individual performance.
After each call, the AI automatically sent a short report directly to the rep who hosted it. The report included:
A performance assessment (“You dominated 72% of the call; consider letting the customer speak more.”)
Positive behaviors to reinforce (“You handled objections efficiently and connected value to outcomes.”)
Areas for improvement (“Avoid repeating the same talking point twice.”)
A pre-written follow-up email draft, ready to personalize and send.
For high performers, the system became a personal coach. For managers, it became a data source for reviews.
More importantly, it leveled the playing field. Not every rep has a great mentor, but every rep now had a feedback loop that made them better.
The Power of No-Code Building
When people see this system, they assume it took months to build. It didn’t.
The first version took two weeks. It wasn’t perfect, but it worked — and that was the point.
There were bugs, failed zaps, and moments where I questioned if I’d lost my mind trying to wire Gong, Zapier, Browse AI, Slack, and OpenAI together. But every obstacle taught me something new about how data moves through an organization.
And the truth is, you can’t outsource that learning curve.
If you tell your engineers to “go build AI,” you’ll go nowhere. The only way to lead through this moment is to get your hands dirty — not because you’ll become an engineer, but because you’ll finally understand how the engine works.
When you build, you learn. And when you learn, you lead.
AI is a Data Strategy, Not a Tool Strategy
Most companies get AI wrong because they start with tools, not data.
The key lesson from our experience was this: the value isn’t in the applications — it’s in the information that flows through them.
Gong gave us the data. Zapier and Browse AI unlocked it. OpenAI made it intelligible. Slack made it visible.
But the real breakthrough was realizing that every business — regardless of size or industry — already has its own version of that 25,000-hour dataset. For some, it’s call transcripts. For others, it’s chat logs, customer reviews, or emails. The question is whether you’re treating that data as a strategic asset or as a digital landfill.
Designing for Human + Machine Collaboration
AI doesn’t replace humans; it removes friction so humans can do what they do best.
At Suzy, the system doesn’t send emails or make decisions without human oversight. It simply surfaces what matters most.
The rep still writes the final follow-up. The manager still interprets the customer’s tone. The CEO — me — still asks why a trend is emerging before acting on it.
That’s what I call human-in-the-loop intelligence. It’s not about replacing judgment; it’s about amplifying it.
And it’s why every great AI strategy must be built around people, process, and data — in that order.
The Rise of the Go-To-Market Orchestrator
Building this system forced me to think differently about organizational design.
In the age of AI, the most valuable role inside a company isn’t an engineer or an analyst. It’s what I call the Go-To-Market Orchestrator — a cross-functional operator who understands how marketing, sales, and customer success data connect.
They don’t just analyze what happened. They build workflows that make insights flow automatically across the organization.
At Suzy, that role sits at the intersection of growth, automation, and creativity. It’s part strategist, part technologist, and part psychologist. And it’s becoming the blueprint for how modern teams are structured.
Lessons for Every CEO
Looking back on this project, a few truths have become clear:
AI success starts with a single problem, not a dozen tools.
Don’t chase shiny objects. Find the one friction point that slows your growth, then build around it.Data is your moat.
Anyone can access OpenAI, but only you own your call transcripts, customer feedback, and internal documents. That’s your competitive advantage.No one is coming to save you.
Don’t wait for an “AI team.” Be the AI team. Build one workflow yourself. It’ll change how you think about your entire business.Human trust is the ultimate currency.
Automate processes, not relationships. Keep people in the loop wherever judgment, empathy, or creativity matter.
The CEO as Builder
In Generation AI, I wrote that the next decade belongs to builders. That includes CEOs.
We’re entering an era where leaders can’t hide behind PowerPoint decks or strategy memos. The only way to understand AI is to use it — and the only way to lead through it is to build something that works.
When I started this journey, I wasn’t trying to create an automation empire. I was trying to solve a human problem: giving my team clarity. What I ended up building was a self-learning company — one where the customer’s voice fuels every decision we make.
That’s what AI is really about. Not replacing people, but empowering them with truth at scale.
The Age of the Super Individual Contributor
The modern workforce is changing fast. I said on Claire Vo’s show that this is the age of the super-IC — the individual contributor who can wield AI to create exponential output.
In the past, leaders managed headcount. In the future, they’ll manage capability.
The people who thrive won’t be the ones who wait for direction. They’ll be the ones who open their laptops, connect a few APIs, and say, “I fixed it.”
That’s what I want every leader to understand about AI: it’s not about automating your company. It’s about unlocking your people.
Final Thought: Start Small, Build Fast, Learn Relentlessly
If you take one thing from this story, let it be this:
Start small. Build fast. Learn relentlessly.
You don’t need to be an engineer to build an intelligent system. You need curiosity, persistence, and a willingness to get your hands dirty.
Because in this new era, the companies that win won’t be the ones with the most engineers. They’ll be the ones with the most builders.
And that starts with you.
