From Marketing Automation to AI: Andre Yee’s Vision for the Future

I recently had a fascinating conversation with Andre Yee, founder of Tiga AI and former CEO of Triblio, about where AI is heading in the marketing and sales space. Having built and sold multiple successful marketing technology companies, Andre has a unique perspective on how AI is reshaping the landscape.

What stood out most was his insight on something many marketers are overlooking in the AI conversation: the power of custom buyer interest signals.

The Conflation of Demand Creation and Demand Capture

One of Andre’s most compelling observations is how marketers have conflated demand creation and demand capture for the past 15 years. He explains:

“I think what’s becoming more apparent now is that you actually have to think differently about each of those. What I see in the market is that the idea of this really long nurture is over. People don’t need to be nurtured for a long time because when they’re ready to buy, they look up whatever they need and they just get on with it.”

This distinction is crucial for understanding where AI can provide the most value:

• Demand creation encompasses building your brand, creating content like podcasts, and helping people understand your proposition
• Demand capture focuses on finding people already in market, reaching them effectively, and doing so as efficiently as possible

The challenge is that most companies invest heavily in demand capture (up to 80% of marketing resources by some estimates) while underinvesting in demand creation – largely because the latter is harder to measure and attribute.

The Problem with Generic Buyer Intent Signals

When it comes to demand capture, most companies rely on what the industry calls “intent data” – signals that supposedly indicate a prospect is ready to buy. But Andre points out a fundamental problem with this approach:

“There’s been so much hype around intent data in the last few years, but I’ve noticed people are getting disillusioned. It’s overpromised and under-delivered.”

The issue? Most intent signals are too generic to be truly valuable. Consider this example Andre shared:

“A very common signal is when a company receives funding. They then get reached out to by every single company trying to sell them something. That’s a legitimate signal, but it’s not particular to your business.

Now let’s say you’re a managed security services company. A more meaningful signal might be that one of your prospects is hiring security professionals. That’s a good signal because they clearly have a need, and they might choose to outsource security management to your firm instead of hiring.”

The most valuable buyer signals are custom signals – ones specific to your business and your buyers’ unique behaviors. And until now, finding these signals has been entirely manual, with salespeople and marketers combing through websites, social profiles, and news sources.

How AI Is Changing the Signal Discovery Process

This is where Andre sees the most transformative potential for AI in marketing. Rather than just automating content creation (which everyone’s focusing on), he’s built Tiga AI to autonomously discover custom buyer interest signals and execute outbound motions based on those signals.

Some examples of these custom signals include:

1. Champion movement: When your sponsor or power user moves from a current client to a new company
2. Project announcements: If you sell project management tools, new project announcements from target accounts
3. Expansion signals: For retailers, new store openings that indicate growth
4. Technical investments: Implementation of complementary technologies that pair with your solution

The breakthrough is using AI to find these signals in unstructured content – not just structured data points from platforms like ZoomInfo or Crunchbase. This allows for real-time signal identification from announcements, social posts, and even long-form content like SEC filings, analyst reports, and podcast interviews.

The Enterprise AI Adoption Challenge

Despite the potential, Andre acknowledges that AI adoption among marketers remains low. “Most marketers are only just now signing up for ChatGPT Plus,” he noted. The reason? Many lack the technical background or time to become prompt engineers.

“I think you have this rare blend of technical ability and marketing know-how,” Andre told me. “Most mainstream marketers cannot be prompt engineers to get the best out of AI.”

This is driving the next wave of AI tools – purpose-built applications for specific roles rather than general-purpose AI like ChatGPT. Andre likens ChatGPT to “the Excel of generative AI” – incredibly powerful but not optimized for any specific workflow.

“Eventually, there will be purpose-built AIs that don’t require you to be a prompt engineer. They’ll be built for a day in the life of your workflow,” he explained.

What This Means for Your Career

Andre’s perspective on the future job landscape is both optimistic and sobering:

“I think of it as a pyramid. AI just pushes automation up the pyramid a lot more aggressively. The unique part that humans fill starts to become the very apex of the pyramid and getting smaller all the time.”

However, he sees tremendous opportunity for those who adapt:

“In the future, a 10-person company will operate like a 50-100 person company today. This opens the door for entrepreneurship in a massive way. One of the biggest hurdles to being an entrepreneur is raising capital. If you can truly run with a much smaller footprint of full-time employees, more people can pursue their dreams.”

When I asked about Sam Altman’s prediction that 95% of marketing jobs could be eliminated, Andre was measured: “I don’t know if he’s right, but even if he’s half right, that’s a lot of people. I think it causes an issue that we have to deal with as a society.”

Where to Focus Your AI Learning

Based on my conversation with Andre and my own experiences with AI, here’s where I recommend focusing your attention:

1. Start with what you know: Look for repetitive tasks in your daily workflow that follow clear patterns – these are prime candidates for AI automation
2. Build your signal intelligence: Identify the custom buyer signals that matter most for your business
3. Think strategically about demand creation: As demand capture becomes increasingly automated, your ability to create demand through brand building becomes more valuable
4. Embrace AI as an assistant: Rather than viewing AI as a replacement, see it as an assistant that handles lower-value work while you focus on strategy

The marketers who will thrive in this new landscape won’t be those who bury their heads in the sand or who blindly automate everything. They’ll be the ones who understand what signals truly matter to their business and use AI to discover and act on those signals at scale.

If you’re interested in learning more about implementing AI marketing strategies in your business, I’d love to hear about your experiences with custom buyer signals and how you’re using AI to identify them.