One thing I have been reflecting on recently is how painful AI and technology transformation can become when it starts as a software purchase. In a few client conversations, I have heard different versions of the same story: a company wanted to modernize an important part of its operations. Leadership was serious. They brought in a vendor, spent a meaningful budget, and had custom software built for the team. On paper, it sounded like the right move. But after the system went live, adoption was weak. The tool did not fit naturally into how work was actually done. Some workflows had been misunderstood. Some operational exceptions were missed. Instead of reducing friction, the new system created more of it. Eventually, the project became a painful lesson. Not just because money was spent, but because it made future transformation harder. Once a team has lived through a failed digital project, leadership has to rebuild trust before asking people to change again. I am not saying this as someone who has all the answers. I could be biased because I am seeing this through the lens of the work we are doing now. But after hearing multiple stories like this, I have become more convinced that AI transformation cannot be treated as simply buying an AI product. McKinsey & Company (https://www.linkedin.com/company/mckinsey/) discussion on digital and AI transformation stayed with me: “It should always start with the business problem you want to solve.” That sounds obvious, but I think it is skipped more often than people admit. I also see a similar pattern in many recent AI startups, including several from YC. The strongest ones are not starting with “AI.” They are starting with a painful workflow inside an organization, then asking how AI can make that workflow meaningfully better. That feels directionally right to me. The value is rarely created by technology alone. It is created when technology changes how work actually gets done. Before a company changes the tool, it has to understand the work: how the process actually runs, where people rely on judgment or informal knowledge, and what behavior needs to change after the system is introduced. This is where AI transformation becomes more complex than a technology decision. AI can improve analysis, automate work, and help teams make better decisions faster. But it can also expose unclear ownership, inconsistent data, broken handoffs, weak incentives, and workflows that were never formally written down. That is why I think the real work often starts before implementation and continues after the purchase. Buying AI is the easier part. Helping people change how they work is the harder part.