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Webinar
How to make AI transformation work - Examples from Oil, Gas and Chemicals
Trusted by Industry Leaders for Delivering Cutting-Edge, AI-Driven Solutions That Drive Success.
abstract
These days, the media is rife with reports of AI transformations not working, or not having a clear return on investment. This is in part because AI transformations are historically pushed as a technology, rather than a strategic imperative. In this talk, we will examine how to make AI transformation work, and ensure a tangible return on investment. McKinsey and MIT opine that over 80% of AI projects are not working out the way companies intend them to.
This presentation examines why AI implementation so frequently fails in the industry. Examples from oil, gas, and chemicals sector are presented, as well as what it takes to succeed. Drawing on MIT and BCG research showing that the vast majority of generative AI pilots yield no measurable return, the talk argues that technical capability is rarely the bottleneck, nor is it the data readiness (which are important problems nonetheless). Instead, value from AI is the product of three compounding factors: whether the tool works, whether it is actually used, and — most critically — whether it is applied to the right problems. Using historical parallels like the slow adoption of electricity in manufacturing, the presentation frames AI as a transformation requiring workflow redesign and cultural change, not just technology deployment. It outlines a structured digital strategy framework covering why, what, and how to digitalize, and emphasizes that AI initiatives must be anchored in corporate strategy and linked to measurable organizational outcomes.
webinar Details
Date
Wednesday, July 1, 2026
Time
10:00 AM EST (New York)
5:00 PM KSA (Riyadh)
6:00 PM GST (Dubai)
duration
1 Hour
SPEAKER
Krishnan Sankaranarayanan, PhD, MBA
Krishnan Sankaranarayanan, PhD, MBA, is a seasoned executive with over 24 years of experience in the chemical and energy industry. He obtained his PhD in Chemical Engineering at Princeton University, after which he spent 1 2 years at ExxonMobil, followed by 12 years at SABIC, in a host of different roles. Most recently, he was responsible for the AI transformation at SABIC across all domains and geographies. Krishnan, also teaches at the University of Houston (AI for Chemical Engineers, and Chemical Industry Strategy), and Columbia University (How to make AI implementations for) for executives.
Key Takeaways
This session is designed to provide practical clarity, not just conceptual understanding. Participants will leave knowing how to:
Why AI Transformations Fail
Understand why the majority of AI initiatives struggle to deliver measurable value despite significant investment and executive attention.
Strategic Alignment Matters
Learn why AI must be anchored to business strategy and organizational priorities rather than driven solely by technology adoption.
Choosing the Right Problems
Discover how selecting high-impact business challenges is often the single biggest determinant of AI success.
The AI Value Equation
Explore the three factors that drive outcomes: whether AI works, whether people use it, and whether it is applied to the right problems.
Escaping Pilot Purgatory
Examine why promising AI pilots fail to scale and how governance, ownership, and accountability enable enterprise adoption.
Driving Organizational Adoption
Understand the critical role of change management, trust, training, and workflow integration in realizing AI value.
Following the Money
Learn how to identify AI opportunities with the clearest path to financial impact, operational improvement, and business value.
Building an ROI-Focused AI Strategy
Develop a practical framework for prioritizing, implementing, and scaling AI initiatives that deliver sustainable and measurable returns.
Who Should Attend
Risk, Governance, and Compliance Professionals
Professionals responsible for enforcing guardrails, defining policies, and ensuring AI systems operate within acceptable risk boundaries.
AI Engineers & Architects
Technical practitioners building and deploying AI systems who need to understand model orchestration, routing strategies, and production-grade reliability.
CXOs and Senior Executives
Leaders evaluating AI as a strategic capability and needing clarity on how to structure accountability, control costs, and scale adoption responsibly.
IT and Digital Transformation Leaders
Leaders driving enterprise-wide transformation initiatives who must ensure AI systems are integrated, governed, and aligned with business processes.
AI & Data Science Teams
Teams transitioning from experimentation to production who need clarity on scaling models, managing costs, and maintaining system performance.
Product, Operations, and Business Leaders
Decision-makers responsible for AI-enabled workflows across functions who need to define ownership, escalation paths, and real-world deployment readiness.
CIOs, CTOs, and CDOs
Technology and data leaders responsible for defining enterprise AI architecture, platform strategy, and long-term governance models.
Enterprise Architects
Professionals designing system-wide architectures who need to integrate AI components into existing technology ecosystems in a structured and scalable way.
If you are evaluating how to generate measurable business value from AI and avoid the pitfalls that cause most transformation initiatives to fail, this session is for you.
