To prepare for a future of human-AI collaboration, technology adoption is not enough. Leaders must actively build AI fluency within their teams by personally engaging with the tools. This hands-on approach models curiosity and confidence, creating a culture where it's safe to experiment, learn, and even fail with new technology.

Don't just replace human tasks with AI. Deploy AI agents to handle leads your sales team ignores, like small deals or low-scored prospects. This untapped segment, as SaaStr found with a 15% ticket revenue lift, represents significant growth potential by filling a gap in your GTM process that humans create themselves.

AI's power is not in creating successful strategies from scratch, but in scaling your existing best practices. An AI agent cannot make a broken process work. First, identify what messaging and campaigns are effective, then use AI to execute them at a near-infinite scale, 24/7.

While consolidating tools seems efficient, using specialized, best-in-class AI agents for each GTM function (one for outbound, one for inbound) yields superior results. The depth and focus of specialized tools enable more powerful and nuanced use cases, justifying the management overhead of multiple systems.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

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.

You can't delegate AI tool implementation to your sales team or a generalist RevOps person. Success requires a dedicated, technical owner in-house—a 'GTM engineer' or 'AI nerd.' This person must be capable of building complex campaigns and working closely with the vendor's team to train and deploy the agent effectively.

Don't use AI to generate generic thought leadership, which often just regurgitates existing content. The real power is using AI as a 'steroid' for your own ideas. Architect the core content yourself, then use AI to turbocharge research and data integration to make it 10x better.

To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.