20Growth: The Death of Growth Teams? | How Hubspot Use AI to...

Full Title

20Growth: The Death of Growth Teams? | How Hubspot Use AI to Triple Email Conversion | The Future of AI SEO | Why Prompt Engineering is the New Coding | What Every CMO Needs to Know About AI in 2025

Summary

This episode explores how AI is fundamentally transforming growth strategies and go-to-market functions, suggesting traditional growth teams may evolve into AI innovation pods focused on cross-functional efficiency. Kieran Flanagan from HubSpot discusses the practical applications, challenges, and future implications of AI in marketing and customer engagement, emphasizing the critical role of data and continuous experimentation.

Key Points

• AI is poised to make traditional growth teams redundant, shifting their focus towards "AI innovation pods" that integrate AI across the entire go-to-market spectrum, including sales and customer support, beyond just product-led growth.
• AI has demonstrated significant value in personalizing outbound communications, with HubSpot seeing triple conversion rates in email through deep personalization using structured and unstructured data.
• Off-the-shelf AI tools for go-to-market often struggle due to their inability to customize prompts effectively for diverse company needs, highlighting the superior performance of internally tailored AI solutions.
• The "content collapse" is a major threat to organic content distribution, as AI overviews in search (like Google's AI mode) absorb content without generating direct website visits, diminishing the incentive for publishers.
• All business functions, including marketing and sales, are expected to become significantly smaller due to AI automating mundane tasks, allowing humans to be redeployed to higher-value, more creative work.
• Memory serves as a crucial network effect and retention mechanism for large language model (LLM) providers like ChatGPT, making users highly sticky due to personalized historical context.
• Prompt engineering is increasingly vital for differentiating AI results, and AI models themselves can be leveraged to generate more effective prompts.
• AI search optimization diverges from traditional SEO, requiring companies to create hundreds of micro-versions of product pages tailored to the conversational nature of AI assistants.
• Europe's restrictive AI regulations are viewed as counterproductive, potentially hindering innovation and placing European companies at a disadvantage in the global AI race.
• The future CMO's role will prioritize building influence through hyper-personalized, AI-enabled marketing, including creator-led content and scaled creative testing in paid advertising.
• While LLM referral traffic is growing (5x for HubSpot), it remains a small fraction (less than 1%) of overall traffic, indicating that "share of voice" in AI-generated answers is becoming a more important metric than direct traffic.
• The quality and breadth of data, particularly unstructured data like sales transcripts and chat logs, are paramount for the success of any AI initiative.
• Multimodal chat agents, capable of seamless switching between text, voice, video, and screen-sharing, represent a significant, yet largely untried, future for B2B customer journeys.
• Dynamic context windows, which allow AI to intelligently retrieve and discard relevant data from connected systems as needed, are a highly anticipated advancement for enhancing AI efficiency.

Conclusion

Successful AI implementation requires adopting a "growth project" mindset, prioritizing rapid experimentation, continuous learning, and agile deployment rather than traditional software rollouts.

Companies must focus on building a robust data foundation, including effectively leveraging unstructured data, to unlock the full potential of AI for personalization and go-to-market efficiency.

The future workforce will favor "super ICs" and "tastemakers" who can harness AI to elevate their output, engage micro-audiences, and creatively navigate evolving digital distribution landscapes.

Discussion Topics

• How can businesses best prepare their internal data infrastructure to maximize the effectiveness of AI adoption in go-to-market strategies?
• Given the predicted "content collapse" and shift in search behavior, what innovative strategies will emerge for brands to maintain and grow their online visibility and influence?
• What new skills and organizational structures will be most crucial for marketing and growth teams to cultivate in order to thrive in an AI-first business environment?

Key Terms

Multimodal agent

An AI agent capable of conversing via various modalities like text, audio, and video, and interacting by seeing and guiding users through screens.

Prompt

The input text or instruction given to an AI model to generate a specific output or behavior.

AI Overviews

Google's feature that provides AI-generated summaries directly within search results, aiming to answer user queries without requiring clicks to external websites.

Share of voice

A brand metric measuring the visibility of a company or product within a specific channel (e.g., search results, social media mentions) compared to competitors.

IC (Individual Contributor)

A role in an organization where a person performs tasks and delivers results directly, rather than managing other employees.

Dynamic context windows

An anticipated AI capability where an AI model can automatically retrieve and incorporate relevant data from connected systems into its working memory for a specific task, and then discard it afterward.

Timeline