To overcome the sentiment that AI is just hype, Snowflake's CEO advocates for building and using internal AI agents daily. He personally uses a sales agent on his phone in executive meetings, demonstrating its practical value which drives both internal adoption and external credibility.
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To drive internal change like adopting coding agents, Snowflake's CEO combines top-down goals with bottoms-up enthusiasm. He finds and elevates passionate early adopters—like a founder who fell in love with coding agents—whose influence proves more effective at driving change than executive mandates alone.
Generic use cases fail to persuade leadership. To get genuine AI investment, build a custom tool that solves a specific, tangible pain point for an executive. An example is an 'AI board member' trained on past feedback to critique board decks before a meeting, making the value undeniable.
While empowering employees to experiment with AI is crucial, Snowflake found it's ineffective without an executive mandate. If the CEO doesn't frame AI as a top strategic initiative, employees will treat it as optional, hindering real adoption. Success requires combining top-down leadership with bottom-up innovation.
The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.
An organization's progress in AI adoption is directly proportional to its CEO's personal engagement with the technology. Companies with CEOs who actively experiment with tools like ChatGPT, rather than merely delegating, foster a culture that enables much faster and deeper transformation.
When selling AI, effectiveness shifted from pure sales craft to demonstrated expertise in using AI tools. Salespeople must act as 'AI ambassadors,' and their personal use of the technology builds the authenticity and trust needed to sell a new way of working, not just a product.
The key to driving AI adoption at Block was leadership by example. CEO Jack Dorsey and CTO Danji Prasana use their internal AI tool, Goose, daily. They argue this hands-on approach provides more insight into organizational workflow changes than any top-down mandate or analysis of industry reports.
Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.
Snowflake's former CRO offers a pragmatic view of AI, calling it a 'task automator.' He stresses that for enterprise adoption, AI tools can't just be 'cool.' They must deliver a clear return on investment by either generating revenue or creating significant cost savings, like the 418 hours per week saved by their support team.
Snowflake moved beyond basic AI tools by building proprietary agentic models. One agent analyzes campaign data in real-time to optimize ad spend and ROI. A second 'competing agent' provides on-demand talking points for sales and marketing to use against specific competitors, solving a massive enablement challenge.