Don't Get Left Behind: Your AI Growth Playbook

The current generative AI platform shift is happening much faster than previous cycles, compressing what used to take 8-10 years into 3-5 years. It also feels more chaotic due to everyone having a megaphone on various platforms, amplifying opinions and making navigation challenging.

Organizations should start by identifying their company and team goals and the biggest challenges they want to solve, rather than focusing on technology for its own sake. AI should be used to reimagine and solve old problems in new ways, driving specific business outcomes.

The most important mindset for leaders is an explorer and experimentation mindset, embracing that mistakes are part of the journey. In this era, there's no clear map, so organizations must learn by doing, pivot quickly, and be agile, celebrating when hypotheses are proven wrong as much as when they are right.

Effective AI use cases include AEO (Answer Engine Optimization) for showing up in LLM answers, data and prospecting agents for enriching CRM data and personalizing outreach, sales assistants for call note-taking and follow-ups, and customer agents for resolving support tickets. These applications help solve old problems with new solutions.

AI is blurring the lines between sales and customer success, requiring a continuous process of helping customers get value rather than distinct handoffs. Leaders should hire for curiosity, an experimentation mindset, and a willingness to learn and adapt to change, rather than fearing it.

While AI makes coding easier, core organizational growth systems like payroll or ERP should still be bought due to the complexity of maintenance, integration, and adherence to best practices. Custom building with AI is more suited for agentic automation on top of existing platforms, leveraging specific organizational context to drive outcomes.

HubSpot transformed its product building by first using copilots for assisted coding, then leveraging agentic coding with tools like Cursor and Claude, leading to significant engineering velocity improvements. For growth, they implemented demand, inbound, and prospecting agents, and customer agents for support, handling 82% of website questions and 60% of support tickets.