- The author argues that most leaders misunderstand the concept of “AI-First,” focusing on buying tools instead of changing how the organization operates.
- After a few months of implementation, many AI platforms are abandoned or misused because the underlying processes were not ready to adopt new technology.
- The author, CEO of an AI-core non-profit serving over 1,100 adults in Washington and Oregon, states that AI was applied from the start due to limited resources, not a budget surplus.
- The first lesson is to document workflows before automating. If current processes are inefficient, AI only helps amplify that inefficiency faster.
- Upon reviewing operations, the organization found that less than one-third of processes were formally documented; the rest existed in employee memories or old email chains.
- The author suggests this is the main reason many AI projects fail: tools generate results, but no one knows what the next step is.
- Citing research by McKinsey & Company, the article notes that only about 21% of organizations using generative AI actually redesign their workflows, despite this being the strongest predictor of business value.
- The second lesson is to build a culture of verification before deploying AI. The speed of content creation makes it easy for employees to view AI output as a finished product, ignoring the verification step.
- The author recounts a case where AI generated content for a grant application with a statistic attributed to the wrong source, which was caught thanks to the review process.
- The third lesson is that the best process improvements often come from front-line staff rather than tech teams or senior leadership.
- By redesigning processes based on real-world feedback, the organization manages and analyzes data for over 1,100 participants while keeping operating costs under 7.5% of the budget.
- The author concludes that success with AI does not require a massive digital transformation budget but a solid operational foundation, clear processes, and consistent quality control.
📌 AI-First is actually an operational strategy, not a technological one. Experience from an organization serving over 1,100 people shows three key factors for success: documenting processes before automation, building a culture of verifying AI results, and empowering front-line staff to participate in redesigning work. While only about 21% of businesses actually change their processes to leverage AI, those that do are the ones capable of creating sustainable value from AI instead of just chasing new tools.
