Why Your AI Is Flying Blind — And How Process Context Fixes It

 The Blind Spots: What We Have  Seen in the Field
AI is everywhere, yet outcomes disappoint. Tease the "why" without revealing the answer yet. Strong hook moment.The speaker walks through 1–2 real patterns: customers who deployed Copilot, agents, or LLMs on SAP/Oracle and got technically correct but operationally useless outputs. Name the blind spots specifically — this is the PS credibility moment.

Enter the Context Layer: The Missing Piece Between Data and AI 

Introduce the Context Layer (PI Graph / OCDM) as the fix   a governed semantic layer of objects, events, relationships, event logs, and variants, sitting above data platforms (Snowflake/Databricks/Fabric/SAP/Oracle) and below the AI/Agent layer, turning raw records into auditable "process truth." Celonis doesn't replace those platforms — it makes them legible to AI.

Before & After: Customer Scenario
Walk through one concrete before/after — same AI, same prompt, different outcome once process context is layered in. Make it feel real and transferable to the audience's own environment.

Live Demo: Before vs. After
Same AI/agent scenario shown twice — once flying blind on raw data, once grounded in the Context Layer — highlighting the operational difference. Use an India-relevant industry scenario: Supply Chain (Order Management/Procurement/Inventory) or Automotive Order-to-Delivery.
 

What "Good" Looks Like Post-Fix ?
Concrete before/after outcomes PS has observed once context was added: fewer false positives, actionable recommendations, conformance enforced, trust restored in AI outputs.

How to Get Started
Path for attendees to pilot or request early access.