I have developed internal AI use policies, created operational frameworks that utilize AI, and acted as the project designer/lead for a few nonprofit SaaS platforms utilizing AI. Here are the three most important considerations I've learned from implementing AI at organizational scale: 1. Always start with process clarity, and don't jump straight to technology. Before introducing any AI tools, map your existing workflows and identify specific pain points THEN think about if AI can add measurable value. I've seen organizations fail because they implemented AI solutions for problems they didn't clearly understand. Define success metrics upfront such as what exactly will AI help you accomplish that you can't do efficiently now? This foundation prevents expensive technology investments that don't drive real outcomes. 2. Build your internal capacity while you integrate technology AI implementation is a change management initiative. Investing in training your team to work effectively with AI tools and understand their capabilities and limitations is as important as investing in the tool itself. When we developed our food systems planning tool, CO-FARM, we spent equal time on stakeholder education and technical development. Your people need to become "AI-literate" to maximize the technology's potential and avoid costly mistakes from misuse. 3. Establish your governance framework first. Create clear policies around data use, decision-making authority, and quality control before AI becomes embedded in critical processes. This includes compliance considerations, ethical guidelines, and accountability structures. Governance frameworks prevent problems much more effectively than trying to fix them after implementation. Above all treat AI as an enhancement for your people, not a technology upgrade that exists in a vacuum.