Step 4: Build It Out
The 4-Step Methodology for AI Experimentation
Identify Outcomes & Tasks: Determine the goals you want to achieve and the specific tasks where AI might help.
Create a Gold Standard & Gather Data: Establish how you'll measure success and collect the necessary data.
Experiment & Pilot: Test different AI models on your chosen task.
Build & Deploy: Integrate the successful AI solution into workflows. You can complete steps 1-3 without engineering.
Let's dive into step 4.
If your pilot is successful, the next stage involves scaling the solution:
Collaborate: Work with engineers, product managers, and other relevant teams to refine requirements and estimate the effort needed to build a robust application.
Design Integration: Plan how the AI capability will fit naturally into existing workflows. This might involve designing user interfaces or even developing AI agents that can perform sequences of tasks.
Develop & Evaluate: Start with a Proof of Concept (POC) implementation and evaluate its effectiveness in a real-world context.
Deploy: If the POC is successful, move towards implementing the full application or agent workflow into production, including monitoring its ongoing performance.
The Best Way To Learn is To Experiment
Following a structured approach—identifying tasks, creating evaluation standards, experimenting systematically, and then building thoughtfully—demystifies AI adoption. It allows you to learn quickly, demonstrate value, and build confidence in using AI to solve real problems. Reach out to us if you want more detailed guidance on this methodology of experimentation.
