HUMAN + AI Interaction Science Lab ny

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Customer relationships are built over time, not in a single interaction. In this session, we explore how predictive intelligence, behavioral science, and real-time learning can improve customer interactions across the full lifecycle, from discovery and early engagement to activation and long-term retention. We’ll unpack how businesses can reduce friction, build trust, drive first-use, and strengthen loyalty by delivering the right interaction at the right time, based on where each customer is in their journey.

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Join us for a forward-looking discussion on the organisational patterns that survive AI-assisted development at scale, shared blueprint libraries, architectural governance without centralised bottlenecks, and the Deterministic Spine as the single reference that multiple teams generate from independently.
This discussion will address the specific failure mode where fast-moving teams drift apart structurally because each team's AI is generating from a different implicit understanding of the system.

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Balance enquiries are one of the most common interactions customers have with their telecom provider. Yet they are often treated as purely informational moments. In this session, we explore how AI and predictive models can transform balance checks into opportunities for contextual recommendations.

Using real-time signals such as usage patterns, recharge history, location, and time-based behavior, telecom providers can present relevant bundles or offers exactly when customers are most likely to need them.

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Most businesses still rely on a single model trained on population data to predict customer behavior. While this may appear personalized, it often produces generalized outcomes. In this session, we explore the model-per-customer approach and how it enables truly contextual predictions by training models on individual interaction histories.

We’ll unpack the technical realities of operating millions of models, including real-time learning, cold start challenges, and scalable infrastructure required to support this shift in machine learning systems.