HOW TO GET READY FOR AI AGENTS
Find use cases in your organization 🗂️ Catalog existing processes Document current workflows throughout your organization, paying special attention to processes with manual steps and activities people repeat frequently, pain points where people struggle, and bottlenecks that slow down work. Create a list of candidate processes. For each one, write down what the process does, who performs it, how often, and the time and cost involved. ⚖️ Evaluate against criteria Evaluate each process against the criteria you learned earlier: - Does it involve complex decision-making with multiple variables, shifting context, and edge cases? - Does it involve unstructured data like emails, documents, or images? - Can you define clear success criteria? - Can you provide tools through Make modules or APIs? - If business-critical, can you include a human in the loop for review? ❌ Eliminate mismatches Remove processes that don't fit according to the criteria above. Eliminate processes that follow clear, fixed steps and conditions, for these use traditional Make automation instead. Also remove processes that need guaranteed outcomes, high-stakes decisions without review, vague goals, or tasks where you cannot provide necessary tools. 📊 Calculate potential ROI For each remaining process, estimate current costs including time, errors, and delays. Study implementation costs including building and testing in Make, and compare benefits against costs. Target processes that offer significant time savings and assess feasibility based on available data and integrations. Also, consider organizational readiness and potential resistance. 💪 Start small with quick wins Begin with lower-risk opportunities with clear metrics for success. Build capabilities and confidence with each implementation while learning from your first agent before building the second. Remember that the best agent implementations start with a clear problem to solve, so look for problems that AI agents can solve effectively