From Predictive AI to Human-Guided Scale: Inside Aerospike's Global Marketing Strategy

Building a Global Marketing Engine

Jared: Aerospike is used in some of the world's most data-intensive environments. How global is your customer base today?

Gaurav: We have customers across North America, Europe, Israel, India, and even parts of Africa, truly a worldwide footprint. What's especially interesting is that nearly 60% of our revenue and about half of our customers use Aerospike for AI and machine learning inference at scale. When I joined, one of my priorities was to make that story known: to bring those customer successes and our technology's unique value proposition to market.

Jared: You mentioned rethinking go-to-market when you came on board. How do you define that?

Gaurav: I look at marketing and sales as one motion. I call it AMORE, like the Italian word for love. It stands for Accounts, Meetings, Opportunities, Revenue, and Efficiency.

We've focused heavily on account-based marketing, roughly 80% of our outreach is account-based, and it's paying off. We doubled pipeline from Q2 to Q3 and expect another significant jump in Q4.

The AI Marketing Stack

Jared: What tools are powering that engine?

Gaurav: We use Marketo for marketing operations, Salesforce as our core CRM, and Qualified for conversational engagement. We're adding Clay and Drippify next quarter, along with LinkedIn and a few AI-driven systems for account intelligence.

Jared: Speaking of AI, how is your team applying it in practice?

Gaurav: We're using AI across multiple fronts. Our digital team uses it to generate first-cut content before refining it. Tools like Keyplay help us identify accounts with the highest intent. Reo.dev flags developers engaging with our site and downloading our products. Glean supports internal research so our reps can prepare more intelligently. Each use case is about improving precision and freeing up time for higher-value work.

Amanda: AI has expanded what's possible for us. It's not just about efficiency, it's about seeing new possibilities that weren't visible before.

The Confident Toddler & The Stubborn Teenager

Jared: You've described AI with two metaphors, the confident toddler and the stubborn teenager. Can you explain?

Gaurav: (laughs) Sure. AI today swings between two personas. The confident toddler thinks it knows everything but often gets things wrong. The teenager ignores feedback and insists it's right. That's the stage we're in, probably the first or second inning of a nine-inning game. AI moves fast, but it still needs human guidance. Used correctly, it can accelerate production and decision-making; left alone, it can go off the rails quickly.

Jared: What skills do you look for as you build your marketing team for this next phase?

Gaurav: People who can create their own ideas and use AI as an accelerator, not a substitute. We view AI as productivity improvement, not headcount reduction.

Amanda: Exactly. It enables us to do more and uncover new ways to work. Our team culture is very data-driven. We have real-time updates flowing through Slack from our tools so everyone sees what's happening. That visibility helps us make faster, smarter decisions.

Intent Data & Human Judgment

Jared: Intent data is another theme that comes up in almost every CMO conversation. How do you approach it?

Gaurav: Intent data is a bit of a four-letter word in marketing. Everyone claims to have great intent data, but it's often oversold. For us, first-party intent is most valuable. We connect signals from reo.dev website downloads, and engagement activity to triangulate real buying intent. Automation helps, but we still rely on human judgment to interpret it correctly.

Zero to One vs. One to a Million

Jared: You've talked about the zero-to-one challenge in AI. How do you see that shaping your team's work?

Gaurav: I borrow from Peter Thiel's Zero to One. Zero to one is human innovation, people like Amanda and me experimenting, creating, and testing new things. One to a thousand or a million is AI, scaled, repeatable, human-guided. You still need people to design the system and ensure context doesn't get lost. Without those guardrails, you get the kind of mistakes we've all seen, like a chatbot promising free cars.