One Strategy, Three Answers: Test Planning for Hybrid AI Programs | 11th New York Meetup
Many teams now ship apps that mix regular software with AI parts like chat assistants, classifiers and retrieval pipelines. The basics of test planning still apply. But scope, risk and release decisions get harder, especially where the AI and non-AI parts hand work to each other.
At The Test Tribe’s 11th New York Meetup, Jackie McDougall will show how she plans testing for these hybrid programs with one strategy.
Session: One Strategy, Three Answers: Test Planning for Hybrid AI Programs
Speaker: Jackie McDougall, QA Leader
Jackie will go through a standard test strategy, from scope and risk to exit criteria and reporting. She’ll set one quality goal for the whole program and look at it from three sides: the traditional parts, the AI parts, and the handoffs between them, where a lot of failures happen.
What she’ll cover:
Using one strategy template, with a separate answer for traditional, AI and handoff components
Entry and exit criteria that include AI thresholds and handoff checks
Why change control has to cover more than deployed code, such as vendor model updates and content changes
Which risks traditional testing already handles, what needs to change, and what AI adds
She’ll also share the hybrid test strategy template she uses.
About Jackie
Jackie has spent 25+ years building and modernizing QA teams in regulated and commercial industries. These days her focus is testing AI-enabled systems. She has spoken at EuroSTAR, CAST, UCAAT, QEF, TestingMind, The Test Tribe and the Scottish Testing Group, and has written for Quality Matters, Software TEST magazine and the EuroSTAR Newsletter. In 2025, the State of New Jersey and the Quality Engineering Foundation recognized her work in quality engineering.
Event Details
Date: 02 December 2026
Time: 06:00 PM – 08:00 PM ET
Venue: 307 W 38th St #1401, New York, NY 10018, USA
Stay back to connect with fellow QA and Engineering professionals, exchange experiences, and discuss how testing strategies can evolve to support increasingly complex hybrid AI programs.
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