Directors Are More Lost in AI Than Their Teams

What we talked about.

Alejandro Romeia, co-founder of Silicon Valley Certification Hub, joins Sunny Ray to explain why executives, not their teams, are the biggest blocker to AI adoption. An economist by training, Alejandro spent fifteen years building growth engines at Bain and Co, Time Inc, and DiDi, the Chinese Uber, before writing the book Alexa Make Me Rich on applying AI to digital marketing. Two years ago he and Daniel Gomez founded Silicon Valley Certification Hub to certify executives on AI fluency, later pivoting to a B2B model that aligns company strategy with AI adoption across teams. He shares a personal story about AI agents accidentally taking down his company website, explains why 80 percent of AI projects stall due to director level discomfort rather than technical failure, and discusses how politics and team size often outweigh productivity gains in leadership decisions. Alejandro also details why the company remains bootstrapped, growing through revenue sharing partners instead of venture capital or full time hires, and predicts AI readiness certification will eventually matter as much as ISO standards.

An economist turned AI certifier explains why executives, not their teams, are the real bottleneck in AI adoption.

The questions, and the answers.

What is Silicon Valley Certification Hub and why are you building it?

Executives feel pressure to adopt AI, but the tools are not the problem, they already work well. What is missing is leadership understanding of where to apply AI in processes, what metrics matter, and how to make decisions. We help executive teams align AI and LLMs with their actual strategy instead of just handing tools to their staff.

What is the biggest growth lesson from your time at DiDi moving hundreds of millions of rides?

I moved into global growth marketing without deep experience in it, and had to learn how TikTok, SEO, Facebook, and Google Ads worked as a kind of black box, each with different data. I had to analyze that and make strategic decisions worth millions of dollars to keep DiDi growing across Latin America and globally.

What did you see in AI that made you write Alexa Make Me Rich before most executives could even spell LLM?

The book mixes strategy, channels, and AI. I start with strategy because knowing how to run ads means nothing without direction. Then I cover how each marketing channel works. Finally I show how AI can replace what used to take a team of ten people, doing the same work across channels with AI agents instead.

Was there a moment when you realized AI was going to be your life's work rather than just a tool?

I was running AI agents to improve my company's SEO over a weekend, scraping the site and rewriting content, and I accidentally shut the whole website down. My co-founder was already upset with me that day, and I had to ask him to fix it. He solved it in two hours, but it showed me how fast and messy this new way of working really is.

You said directors are more lost in AI than their teams. When did you first notice that, and why does nobody talk about it?

When we were pivoting, we talked to a lot of directors and near sea level executives, and about 80 percent of them just handed AI tools to their teams and said 'figure it out.' Around 90 percent say AI is a priority, but 80 percent of AI projects never launch because mid managers and directors do not understand the risk or the pipeline well enough to feel comfortable releasing them.

After training over 700 executives, what is the most common blind spot you see when a C-suite executive walks in?

Politics is a huge stopper, often tied to team size. If someone has a team of 20 or 50 or 300 people, that size matters to them personally, even if AI could do the same work with fewer people. It is not just about whether AI makes business sense, it is about whether it threatens their position, and that takes time to work through.

Why partners instead of headcount, and why no venture capital?

Partners share revenue with us, so incentives are aligned on both sides, and many are professors or advisors already working with companies who use our frameworks and branding. On VC, we have talked to many investors and gotten good insight, but we are still finding product market fit. Taking millions now would mean scaling before we are ready.