Review your organization's incentive structure for AI. Are employees only rewarded for executing known use cases faster, or are they encouraged to experiment and share lessons? Without explicit rewards for exploration, companies risk stifling innovation and missing out on transformative AI applications that come from experimentation.

Finding transformative AI use cases requires more than strategic planning; it needs unstructured, creative "play." Just as a musician learns by jamming, teams build intuition and discover novel applications by experimenting with AI tools without a predefined outcome, letting their minds make new connections.

The real power of AI in a shared space like Slack is not just individual productivity. When colleagues observe each other's prompts and workflows, it creates a viral learning loop. This public interaction spreads best practices and up-levels the entire organization's AI competency.

Incentivizing high AI token usage is not waste, but a form of R&D. In the new agentic paradigm, there are no best practices. Mass experimentation, even with failures, is the only way to discover future workflows and avoid being left behind.

Contrary to traditional efficiency models, leaders should allow teams to build similar AI tools or agents. In this early stage, widespread hands-on experimentation and learning are more valuable than preventing redundant work. The goal is to get everyone testing, not to achieve premature standardization.

Contrary to popular belief, Anthropic's internal analysis revealed that the employees using the most tokens were not the company's most productive people. This suggests that 'token maxing' is a flawed metric for performance and that thoughtful, efficient AI interaction is more valuable than sheer volume.

Individual AI use is often a siloed, one-to-one experience. To foster collective learning, create a dedicated "AI Playground" Slack channel. This gives team members a space to share successful prompts, interesting outputs, and even failures, turning individual experimentation into a shared team asset.

By launching their internal agent in a single company-wide Slack channel, Perplexity enabled employees to see each other's prompts and use cases. This created a powerful cross-pollination of ideas and accelerated learning on how to best leverage the new tool for collaborative work.

Despite fears of runaway costs from "token maxing," enterprises are overwhelmingly encouraging more AI model consumption. A developer survey found 7x more companies were told to increase spending. The value gained from experimenting on AI's rapidly expanding capability frontier currently outweighs the push for cost optimization.