As organizations accelerate automation, a new challenge is emerging: not everything should be automated the same way.

Some decisions are deterministic and repeatable, perfect for rules. Others are ambiguous, contextual, and evolving, better suited for AI models. Most enterprise workflows now require a combination of both, with humans playing a critical role in oversight, exception handling, and continuous learning.

Yet many organizations struggle to define where rules end and models begin. The result is over-engineered systems, misplaced trust in AI, or rigid processes that fail to adapt.

This session introduces a practical framework for designing AI-augmented decision systems that balance automation, intelligence, and human judgment.

Drawing on real-world implementations, we’ll explore:

How to distinguish between rule-based automation and model-driven decisioning

Where AI adds value, and where it introduces unnecessary risk
Patterns for combining rules, models, and human input in a single workflow

How to design human-in-the-loop systems for validation, escalation, and learning

Governance approaches to ensure transparency, accountability, and control

Attendees will leave with a clear, actionable approach to building decision systems that are not only automated but adaptable, trustworthy, and aligned to real business outcomes.