March 1, 2026
A Practical Framework for ML in SMBs
Most AI advice targets enterprises. Here's what actually works at SMB scale, based on what we've shipped.
March 1, 2026
Most AI advice targets enterprises. Here's what actually works at SMB scale, based on what we've shipped.
Most advice on AI adoption is written for enterprises with dedicated data teams and six-figure tool budgets. That doesn't translate well to small and medium businesses. Machine learning can deliver measurable results at SMB scale, but the approach needs to be different.
Here's what we've found works in practice, across engagements in New Zealand and Australia.
The useful question is "what's costing us time, money, or accuracy?" — not "how do we use AI?" The best ML projects start with a specific operational problem and work backwards to the right technique.
Production-grade models don't require millions of rows. We've shipped models trained on modest datasets. What matters more is data quality and thoughtful feature engineering.
Off-the-shelf tools handle generic tasks well. For anything specific to your business logic and data, a purpose-built model will outperform. Most SMBs benefit from a custom model deployed into their existing systems and maintained over time.
Bias, explainability, and data privacy apply at every scale. Addressing these early is cheaper than retrofitting, and it builds trust with both your team and your customers.
Start small. Pick a real problem. Work with people who have shipped models into production — the gap between a proof-of-concept and a reliable production system is where most projects stall.