Most enterprise AI assistants are already live. Most have never been properly tested. Watch this session to find out what happens when they're not — and what to do about it.
Your team built it. Your stakeholders signed it off. It went live on schedule. But internal testing was not built for the way conversational AI actually behaves in the wild. Right now, your assistant is handling real customers with incomplete visibility into whether it is performing correctly.
The number of real-world behavioural failures that manual testing typically misses in enterprise conversational AI deployments.
Source: VUX client testing dataA failure that should have been caught pre-launch. Real customers, real consequences — and no warning before it happens.
Issues that could have been caught before go-live become regulatory exposure once the assistant is live in a regulated environment.
A rollback that sets the programme back by months — and damages confidence in AI internally, just when momentum matters most.
Manual QA and happy-path testing were not designed for the non-linear, context-dependent nature of conversational AI. See exactly what falls through the gaps.
Real examples of the failure types that only surface under structured simulation — and how to recognise them before they become incidents.
A practical walkthrough of the testing methodology VUX Consulting uses with enterprise clients — and what changes when you move from manual to structured evaluation.
How to build a repeatable, structured QA programme that keeps pace with your AI development cycle and gives you confidence before every release.
of enterprise AI failures are preventable. They are not caught because teams are using the wrong testing approach for the technology they have built.
Built for AI Product Owners, GenAI Engineers, Heads of Conversational AI, and CX leaders at enterprise organisations. If you are responsible for shipping an AI assistant that has to perform reliably in production, this session is for you.
