The impulse to make all historical data "AI-ready" is a trap that can take years and millions of dollars for little immediate return. A more effective approach is to identify key strategic business goals, determine the specific data needed, and focus data preparation efforts there to achieve faster impact and quick wins.

The path to adopting AI is not subscribing to a suite of tools, which leads to 'AI overwhelm' or apathy. Instead, identify a single, specific micro-problem within your business. Then, research and apply the AI solution best suited to solve only that problem before expanding, ensuring tangible ROI and preventing burnout.

For startups adopting AI, the most effective starting point is not a massive overhaul. Instead, focus on a single, high-value process unit like a bioreactor. Use its clean, organized data to apply simple predictive models, demonstrate measurable ROI, and build organizational confidence before expanding.

Snowflake's CEO advises against seeking a huge ROI on the first AI project. Instead, companies should run many small, inexpensive experiments—taking multiple "shots on goal"—to learn the landscape and build momentum. This approach proves value incrementally rather than relying on one big bet.

Begin your AI journey with a broad, horizontal agent for a low-risk win. This builds confidence and organizational knowledge before you tackle more complex, high-stakes vertical agents for specific functions like sales or support, following a crawl-walk-run model.

The classic 'pick two' project management triangle (fast, cheap, good) is altered by AI. You can achieve all three, but only by focusing on an extremely narrow use case or a 'thin slice' of data. Prove product-market fit on this small scale first, then expand once you get strong customer validation.

To navigate the high stakes of public sector AI, classify initiatives into low, medium, and high risk. Begin with 'low-hanging fruit' like automating internal backend processes that don't directly face the public. This builds momentum and internal trust before tackling high-risk, citizen-facing applications.

Instead of treating a complex AI system like an LLM as a single black box, build it in a componentized way by separating functions like retrieval, analysis, and output. This allows for isolated testing of each part, limiting the surface area for bias and simplifying debugging.

In sectors like finance or healthcare, bypass initial regulatory hurdles by implementing AI on non-sensitive, public information, such as analyzing a company podcast. This builds momentum and demonstrates value while more complex, high-risk applications are vetted by legal and IT teams.