Too many rush into AI without clarity and fail, we reveal the foundation that makes it work
Dhanusha
Mark
Lance
CEO @ POTENZA
COO @ POTENZA
Founder @ Model Citizn
CAN YOU AFFORD TO PUT THE CART BEFORE THE HORSE WITH AGENTIC AI?
CAN YOU AFFORD TO PUT THE CART BEFORE THE HORSE WITH AGENTIC AI?
“95% failure rate”
The problem isn’t AI, it’s the lack of clarity and foundations
What is Agentic AI ?
Specific independent goals and actions defined with human-like decision-making in an autonomous manner.
It adapts to changing conditions in a multi-step workflow manner.
“Agentic AI uses digital agents that adapt, reason, and act in context not just follow rules.”
Agentic AI: Beyond Reactive Automation
Structure brings clarity & confidence
Unstructured models = chaos
Bad data = hallucinations
No clarity = no business value
Watch Outs & Risk Patterns in
Agentic AI – Problem
Structure brings clarity & confidence
Scenarios reveal options & risks
Baselines create measurable value
From Chaos to Clarity: Structured Models Win
From Models to Data: What Fuels the Engine?
“Once you have models, the next question is: what data fuels them?”
Garbage In → Garbage Out
Poor data = flawed outcomes
Compliance & trust at risk
Readiness is non-negotiable
The Golden Layer:
The Data Foundation You Can Trust
Bronze = Raw, unprocessed
Silver =Cleansed, partial transformation
Gold=Trusted, harmonised, ready for AI
Lessons from Azure Copilot Projects
Adoption failed where Golden Layers were missing.
Sandboxing Data
Mitigation Strategies
Architecture: Guardrails, containment, throttling.
Golden Layer: maintain harmonized datasets.
From Data Chaos to a Single Source of Truth
Consistent reporting
AI-ready data
Lower compliance risk
From Readiness to Responsible AI at Scale
“Once the data foundation is in place, AI can be applied responsibly and at scale.”
Is Your Organisation Ready for Agentic AI?
Lessons from Azure Copilot Projects
Users lost trust due to hallucinated outputs
Sandboxing issues
Over promising and delivery
Mitigation Strategies
Scope Control: start narrow, scale gradually
Fail-safe & Rollback: always retain human validation etc
Monitoring: drift detection, human-in-loop review
The Risks of Rushing Into AI Without Data Readiness
Wasted Budgets
Compliance Risks
Flawed Outputs
Responsible AI = Guardrails Before Growth
Oversight
Compliance
Business Alignment
From Pilots to Enterprise:
Scaling AI the Right Way
What the C-Suite Really Cares About
3 Steps to Get Ready for Agentic AI
Assess data maturity: quality, accessibility, governance.
Align AI initiatives to business goals.
Build the Golden Layer (single version of truth)
Roll out in phases: �augmented → supervised → agentic.
Master Agentic AI Before You Start
Thank You & Q&A