For SaaS companies, AI has moved from a differentiator to an expectation. The challenge is rarely deciding to add it; it is doing so while the existing roadmap keeps moving.
Start where your data is unique
The AI features customers value most use data only you have: your catalogue, your history, your customers' own records. An assistant that answers questions from that data is far harder to copy than a generic writing tool.
Build a thin layer, not a rewrite
Most AI features can sit on top of existing services through an API layer that handles prompts, model calls, logging and cost. That keeps the core product stable and lets you switch models as the market changes.
Gate by plan and track cost per customer
AI features have running costs that grow with use. Gating them by subscription tier and logging cost per session turns AI from a cost centre into a reason to upgrade.
Ship with evaluation, not just tests
Traditional tests check that code runs. AI features also need evaluation: a set of real questions with expected answers, run on every change, so quality does not quietly drop.
Add capacity, not distraction
A small AI feature team working alongside your core engineers can ship the first feature in weeks and then keep improving it, while your team stays focused on the roadmap.