Many leaders test AI with simple, surface-level experiments. But modern AI is so advanced that these small tests create a false sense of understanding. According to Braze CPO Kevin Wang, genuine value is only revealed when AI is applied to complex, multi-team business problems and real-world workloads.

AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.

Leaders often expect AI to magically solve complex issues like data harmonization without considering the foundational work required, such as building an ontology. This shortcut-seeking mindset leads to poor decision-making and ineffective AI deployment, highlighting the need to involve technical experts early.

Simply instructing engineers to "build AI" is ineffective. Leaders must develop hands-on proficiency with no-code tools to understand AI's capabilities and limitations. This direct experience provides the necessary context to guide technical teams, make bolder decisions, and avoid being misled.

The quality of a leader's own AI usage directly impacts their team's success with the technology. When CEOs are the most adept users, they set realistic expectations, avoid under or over-estimating capabilities, and inspire more effective organizational adoption.

Unlike past tech shifts like the cloud, becoming “AI-first” requires leaders to have a deeper technical understanding. They must grasp concepts like AI memory and accuracy to evaluate costs versus returns and identify where the technology can be realistically applied.

Unlike past tech (e.g., GPS) that trickled down from large institutions, generative AI is consumer-first. This leads leaders to mistake playful success (e.g., writing a poem) for enterprise readiness, causing them to stumble on the 'jagged edge' of AI's actual, limited business capabilities.

There is a significant gap between how companies talk about using AI and their actual implementation. While many leaders claim to be "AI-driven," real-world application is often limited to superficial tasks like social media content, not deep, transformative integration into core business processes.

True AI leadership requires moving beyond superficial use, like treating LLMs as a better Google. To avoid being left behind, leaders must get their hands dirty with the underlying technology. This deeper understanding is what enables them to identify real business opportunities and drive meaningful adoption.