We've open-sourced a 30-day playbook for SMBs (10-100 people) looking to build their own internal AI capability. It's a free document with reference Python notebooks, available at [github link].
After 12 engagements with SMBs in Asia and the US, we kept seeing the same three failure modes: teams treating AI as a magic box, starting with tools instead of problems, and attempting to boil the ocean. This playbook is our attempt to address those patterns.
The document structures a month-long rollout across four weeks: problem scoping and data inventory (week 1), model selection and prototyping (week 2), integration and workflow design (week 3), and team scaling and governance (week 4). It includes four reference notebooks covering data preparation, prompt engineering, model evaluation, and deployment patterns. Three case study writeups show how teams applied this framework to real problems in customer support and operations.
What's not included: no SaaS platforms, no tool recommendations, no promises of "AI transformation." We're not selling anything—just sharing what's worked in the field.
The playbook comes from 12 engagements across different industries. The sample size is small, and we're sharing it to get feedback from others doing this work. We expect it will need refinement as more teams experiment with this approach.
We're particularly interested in hearing about patterns we've missed, assumptions that don't hold in other contexts, and alternative approaches that have worked for others. The GitHub repo includes an issues section for structured feedback.
This piece is from our notes on helping SMBs (10-100 people) build their first in-house AI teams. If your team is exploring this — quick feedback and questions welcome in the comments.
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