94: Ryan Gunn: HubSpot cheat codes, AI features, attribution and documentation

Humans of Martech

What’s up folks, today we’re joined by Ryan Gunn, Director of Demand Gen & Marketing Ops at Aptitude 8.

Summary: HubSpot is not just a user-friendly CRM but also a forward-looking tool in the rapidly evolving world of AI and martech. While it's not a substitute for a dedicated data warehouse for complex queries, it serves well as a real-time connector to other systems via CRM cards. Gaining practical skills from HubSpot's developer portal is critical—certifications alone won't cut it. If keeping up with martech changes overwhelms your in-house team, specialized consultancies offer a reservoir of constantly updated expertise. Sound documentation serves as the bedrock of your internal processes, setting you up for long-term success. Don't just read about it; listen to the podcast episode for deep, actionable insights into leveraging HubSpot for AI integration and data quality.

About Ryan

• Ryan started his career by getting his feet wet freelancing in design and social media projects
• He took on the role of Inbound Marketing Account Exec at Boyle public affairs where he got to wear a bunch of different marketing hats, including his first taste of Hubspot
• He later became Senior Digital Marketing Manager at WealthForge, a fintech company where he owned marketing automation and lead gen
• Ryan the took on the challenge of Head of Marketing at Array, an event technology startup where he built their marketing department from the ground up in two years
• Today, Ryan works at Aptitude 8, an Elite HubSpot partner consultancy where he started in a client facing consulting role helping clients with big hairy migration projects like migrating Marketo and Pardot into Hubspot and marketing attribution projects
• Today he’s Aptitude 8’s Director of Demand Gen and MOPs responsible for growing the consultancy’s services business and brand awareness

HubSpot's Emerging AI Landscape and Market Adoption

We started by asking Ryan about his experience with HubSpot's new AI tools and their current usage in the market, he offered a comprehensive view. HubSpot is rolling out two significant tools: Content Assistant and ChatSpot. Content Assistant serves as an internal ChatGPT, letting users draft blog posts or emails directly within HubSpot's interface. ChatSpot, while more complex, operates as an external system linked to your CRM data, generating reports through natural language prompts.

However, these tools are still in the nascent stage. Ryan revealed that the implementation rate is relatively low at this point. Despite the curiosity among clients to explore these features, the tools haven't fully integrated into business processes yet. But don't let that deter you; HubSpot is ahead of the curve in the AI game. According to Ryan, HubSpot has already laid out a roadmap for AI-based tools that will extend far beyond just Content Assistant and ChatSpot. We're talking about reporting assistants, automation assistants, and even an AI-powered website builder.

This isn't a mere extension of existing features; it's a reimagination of what a CRM can be. HubSpot is not stopping at providing the basic CRM tools; they're layering AI functionalities on top, touching every aspect of their platform. While current adoption may be slow, Ryan sees this as an indicator of an inevitable, transformative change in how businesses will interact with CRMs.

Key Takeaway: The adoption rate of HubSpot's new AI tools may be in its infancy, but that's more a function of market readiness than a comment on the tools' potential. With an expansive AI roadmap, HubSpot is setting the stage for a future where AI isn't just an add-on; it's intrinsic to the CRM experience. It's worth keeping an eye on HubSpot's next moves, as they'll likely set the pace for the industry.

The AI Integration Dilemma for Emerging Tech Founders

When Ryan was asked about the hesitation some tech founders have regarding AI integration into their products, his stance was unequivocal: it's early days, but progress is rapid. A mere six months ago, AI was barely a blip on most of our work radars. Now, it's becoming integral. Founders find themselves at a crossroads, forced to make a pivotal decision. Either integrate AI into their software or offer the option to connect their software with AI tools via third-party platforms like Zapier.

But this isn't a decision to make lightly. According to Ryan, it boils down to whether the company aims to be a comprehensive platform or a specialized point solution. Opting for the latter means the pressure is on to excel in that niche. If they don't, larger platforms like HubSpot are poised to scoop up those features, layer AI functionalities over them, and package it as a part of their already established CRM systems. These integrated solutions may not be better, but they offer convenience by residing in an ecosystem clients are already invested in.

So what's the crux of the issue? To integrate or not isn't just a technical decision; it's a strategic one that could define a company's future. Choose to stay specialized, and you need to be the best in that realm to stay relevant. Integrate AI, and you may not outshine the giants, but you become a part of a broader, rapidly evolving landscape.

Key Takeaway: Hesitation to integrate AI into your product could lead to missed opportunities. You're choosing between being a specialist in a niche or part of a wider, faster-evolving tech ecosystem. Each has its merits, but understand this: indecision is a decision in itself, and the pace of AI development waits for no one.

The Vital Role of Data Structure in AI Adoption

When Ryan was asked about the practicalities of implementing AI tools in CRM systems like HubSpot, he was quick to pinpoint the critical role of data structure. It's simple: your AI experience is only as good as the data you provide. If you've got a shaky foundation, don't expect the sophisticated algorithms to correct your mistakes. AI isn't a magic wand that turns bad data into insightful outcomes; it's a magnifier that accentuates the quality—or lack thereof—of your existing information.

This isn't a new phenomenon. Ryan compares the situation to current reporting structures within organizations. How many times have you heard, "I don't trust this report" or "These numbers aren't right"? Often, the blame doesn't lie with the reporting tool but with the underlying data or its flawed structuring. Just like you wouldn't blame a mirror for how you look in the morning, pointing fingers at AI for poor results steers the attention away from the actual culprit: bad data.

This brings us to an important realization: if you're going to integrate AI into your processes, you need to take the time to audit, clean, and structurally organize your data. AI isn't forgiving; it doesn't make bad data better, it makes it obvious. And in the realm of business where data-driven decisions are pivotal, shoddy data is not just an inconvenience—it's a handicap.

Key Takeaway: Before even thinking about adopting AI into your CRM or any business process, ensure your data is clean and well-structured. Anything less and you're setting yourself up for failure. AI amplifies the quality of your data; it doesn't fix it. Make this your first step in any AI implementation journey.

The Tug-of-War Between All-In-One Solutions and Niche Expertise

When asked about the consolidation of martech tools, particularly in platforms like HubSpot, Ryan offered a clear-cut viewpoint. The future belongs to either all-encompassing platforms or specialized point solutions catering to niche markets. There's a thinning middle ground, and if you're neither a giant like HubSpot nor focused ...