February 2026 meeting is virtual only

Scaling Bottleneck of Activation in AI Enabled Go-To-Market System

Speakers

Jagbir Kaur of Google
Email: jagbirkaurjk3@gmail.com

Jagbir Kaur works at the intersection of AI, predictive analytics, product strategy and large scale product activation. Her work focuses on launching and governing AI assisted systems that operate in uncertain conditions, regulatory constraints, and real world complexity. She has led cross functional initiatives involving predictive modeling, measurement frameworks, and operational governance in technology driven environments.

Currently she is a Strategy and Operations Manager for Global Product Activation at Google.  She holds a Bachelor of Technology in Computer and Software Engineering degree from Punjab Technical University and a Master of Science degree in Business Analytics and Project Mangement from the University of Connecticut.

Presentation

URL TBD

Recording

URL TBD

Event Description

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 appear 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.

In this webinar, we will learn:

1. Why activation, not demand or automation, is often the primary constraint to scaling AI enabled systems
2. How activation can be modeled as a probabilistic system using leading indicators rather than binary events
3. Common failure modes that prevent AI driven systems from scaling reliably
4. How decision boundaries and governance improve system stability under automation
5. Practical patterns for integrating human oversight without sacrificing scale (real life industry lessons from Fortune 100 companies

Registration

https://events.vtools.ieee.org/m/532931

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