Unlocking IoT Potential: Strategic Approaches to Sensors and Communication
Date: Thursday, October 10 2024
Time: 10:00 PM (PST)
Location: Virtual
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Leveraging AI to Strengthen Master Data Quality and Improve 'Mean Time to Resolution' (MTTR) in Oil & Gas Operations
Duration
2 Hrs
Preferred Date & Location
- Houston - 05-Mar-2026 - 12:00 PM CST
- Calgary - 12-Mar-2026 - 12:00 PM EST
- Dallas - 19-Mar-2026 - 12:00 PM CST
Time
12:00 PM
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About the Workshop
Workshop Objective
This workshop helps engineering, operations, and supply chain leaders understand how AI can materially reduce Mean Time to Resolution (MTTR) — and why reliable master data is the critical enabler.
Participants learn how AI-driven triage, diagnosis, and decision support depend on trusted asset, material, vendor, and location data. The session shows where AI breaks down when master data is fragmented, how a single source of truth enables AI to reason accurately, and how organizations can move from reactive resolution to predictive and guided operations.
The outcome is a clear, business-led 90-day plan to make data AI-ready and unlock faster, more confident resolution across operations
Who Should Attend
Strategic Operations & Supply Chain Executives
Digital Transformation & IT Leadership
Asset Performance & Reliability Leaders
Data Governance & Master Data Specialists
Enterprise Architecture & Analytics Teams
Why You Should Attend
Understand how AI actually reduces MTTR
See why AI initiatives fail without a single source of truth
Connect MTTR directly to data and decision speed
Learn what “AI-ready master data” looks like in practice
Leave with a practical 90-day path forward
Key Takeaways
Understand how AI reduces MTTR and why it fails without trusted master data
Connect operational delays directly to specific master data gaps in your environment
Learn what "AI-ready master data" looks like for real maintenance operations
Identify your highest-impact data fixes for measurable MTTR improvement
Leave with a 90-day action plan to activate AI-ready master data
Agenda
Purpose
- Set MTTR as a leadership KPI, not an IT metric
- Establish AI as a decision accelerator, not a reporting layer
Key Discussion Points
- Why MTTR stays stubbornly high despite automation and analytics
- The shift from manual resolution → AI-assisted resolution
- Where AI helps immediately and where it fails without trusted data
Outcome
- Leadership alignment that MTTR improvement requires AI + data readiness
Purpose
- Move from hype to real operational value
Topics Covered
- AI-assisted triage and incident classification
- AI-driven root cause hypothesis generation
- Decision support across engineering, maintenance, and supply chain
- Predictive signals vs reactive firefighting
Key Message
- AI reduces MTTR by compressing decision time — not by replacing people.
Outcome
- Clear understanding of where AI saves time in the resolution lifecycle
Purpose
- Create urgency for fixing the data foundation
Topics Covered
- Conflicting asset identities across systems
- Missing or inconsistent material and vendor data
- Broken hierarchies and unclear ownership
- AI amplifying confusion when data lacks context
Outcome
- Leaders recognize their own failure patterns
- AI failure is reframed as a data trust issue, not a model issue
Purpose
- Position MDM as an AI enabler, not a governance exercise
Topics Covered
- What “AI-ready master data” really means
- Golden records for assets, materials, vendors, and locations
- Semantic consistency and context for AI decisioning
- Governance that allows AI to trust data over time
Key Message
- A single source of truth is how AI knows what is real.
Outcome
- Clear understanding of how MDM unlocks reliable AI outcomes
Purpose
- Make the problem tangible and organization-specific
Activity
- Select one recurring resolution delay (incident, maintenance, supply disruption)
Identify:
- The decision that slows resolution
- The data AI would need to accelerate it
- The master data gap blocking AI today
- Estimate time lost due to data friction
Outcome
- Shared visibility into high-impact AI-readiness gaps
Purpose
- Give leaders a clear, sponsor-ready vision
Future State Characteristics
- One trusted identity per asset, material, vendor, location
- AI systems reasoning over consistent master data
- Faster triage, better decisions, fewer handoffs
- Seamless alignment across ERP, EAM, CMMS, historians, and field systems
Outcome
- Executives can clearly picture success and justify investment
Purpose
- Move from insight to execution
Topics Covered
- Top 3 master data fixes that unlock MTTR improvement
- Quick-win data harmonization opportunities
- Governance roles required for AI trust
- KPIs to track MTTR improvement and AI effectiveness
Outcome
- A pragmatic, phased plan leadership can act on immediately
Key Takeaways
- AI reduces MTTR only when it can trust the data
- Master Data is the foundation for AI-driven resolution
- MTTR is the metric that proves AI value
Optional Next Step
- MTTR AI-Readiness Diagnostic / Data Foundation Assessment
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