By Jagadish Gattu, CEO of UptimeAI
It was the final question asked to the panelists in AI Potential: Hype vs. Reality, in the final hours, of the final day of CERAWeek. Jake Yang, CTO at S&P Global Energy, posed the question “For CEOs and technology and operational leaders, what’s your single most actionable piece of advice?” My answer was simple: Pick the problems that are impactful. Then find the solutions that are scalable.
This panel followed a packed week of conversations about the future of the energy industry where AI was not merely an agenda topic, but a pervasive thread woven into every discussion. It became clear that the industry is no longer proving that AI can add value, but the focus has shifted to how well can it scale? and can it do so at a rate that outpaces the ongoing expertise exodus? It was the perfect time, and the perfect place to announce UptimeAI’s launch of our AI Reasoning Agents for Operational Excellence.
Agentic AI & the Expertise Crisis
“If you don’t have a P&L owner who will then get margin accretion from the activity you’re doing, I think it’s really hard for it to be successful.” – Ben Wilson, AWS
UptimeAI’s Solution Consulting Lead joined a panel on Wednesday with AWS and Honeywell focused on Where Agentic AI is Now, and What Comes Next. Two insights really stuck with me from this panel:
- From Ben Wilson of AWS – You need a P&L owner, who is getting margin accretion from AI activities that you’re doing, or it won’t be successful. We’ve seen this time and time again in early-stage sales conversations. People want to test on something low risk or simple, but if you pressure test AI against the repeatable, high-impact problems, the ROI won’t be there to justify growing the solution.
- From Cody who called out the common objection that ‘our data isn’t ready for AI’ with the fact that these operating company engineers are solving problems in plants with incomplete data all the time, today. When there’s an issue on a piece of equipment, they don’t go out there and throw their hands up in the air and say’ hey, I don’t have the sensors I can’t diagnose this come back when you’ve got the knowledge graph built.’ That’s not the reality we’re living in and AI should reason in the same way.
Human experts have overcome the shortfalls of data and tools for decades. It’s those reasoning capabilities, the ability to connect the dots and fill in the blanks, that is the missing ingredient in many AI projects—not a years’ long data infrastructure project.
Products Built for the Problems that Matter
Our launch at CERAWeek focused on four high-value problems that our AI Reasoning Agents are designed to address across industrial operations. These organization-wide challenges have historically remained unsolved due to limitations in expert decision capacity.
- Root Cause Diagnosis
- Maintenance Schedule Optimization
- Process Optimization
- Process Hazard Analysis (e.g. HAZOP)
And we didn’t dream up these problems on our own. Our Reasoning Agent roadmap is created in partnership with global top 10 leaders in energy, chemicals, and cement. Like our new friend from AWS recommended, we doubled down on the levers that moved the needle for our customer’s P&L. For our energy and heavy industry customers, this is driving 2-5% EBITDA margin growth by reducing maintenance and repair costs and increasing throughput and yield.
What’s Next
We’re so excited about the mark this first batch of AI Reasoning Agents is making on the industry, and to start to measure the impact of the agents currently in our development pipeline.
The expert decision bottleneck is tightening, and the only way to unlock the margins being pinched is to scale that expertise. It’s exactly what we’ve been building for.
