AI is increasingly being embedded in enterprise systems used to determine how technology products are launched, adopted, and scaled. Predictive models and automated workflows have started to influence decisions traditionally made by humans, such as customer prioritization, activation sequencing, and intervention timing. While these systems improve efficiency, many organizations struggle to achieve reliable scale in production. Metrics apapear healthy, automation is in place, yet outcomes remain inconsistent. The underlying issue is often not model performance or data availability, but activation – the point at which users reliably reach value.
This webinar reframes activation as a systems level bottleneck in AI enabled go-to-market (GTM) environments. Rather than treating activation as a binary milestone, it is considered as a probabilistic, signal driven process that directly affects scalability. When activation signals are weak, delayed, or poorly governed, automation amplifies variance and prevents systems from scaling predictably.
The session introduces a practical systems framework that integrates predictive activation signals, decision boundaries, privacy regulations and governance mechanisms to support reliable scaling. Emphasis is placed on engineering principles, measurement integrity and human oversight rather than business tactics or tool selection. The webinar is relevant to engineers and technical leaders designing AI systems that operate at scale under real-world constraints, where reliability, accountability, and interpretability matter as much as performance.
Speaker(s): Jagbir Kaur
Agenda:
6:25 to 6:35 PM – Open for participants to enter and network.
6:35 to 6:40 PM – IEEE LM and CTCN Business meeting and to introduce speaker.
6:40 to 7:55PM – Formal Program and Q&A.
Virtual: https://events.vtools.ieee.org/m/538139
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