During the pandemic, I became involved with a company that was rethinking the work of the quality professional at fabrication shops. The traditional practice was to dispatch a company person to the fabrication business to see what was being built to make sure it conformed to the specification. The pandemic rendered site visits hard to pull off, but the quality standards could not be relaxed.
Such equipment manufacturers have to comply some of the strictest standards of any sector. It’s still surprises me that many companies manage the delivery of quality through spreadsheets, paper documents, disconnected folders, and manual processes. Systems like these are what technical people call fragile. They don’t cope well with change, they’re a pain to maintain, expensive to run, and are dangerously dependent on the knowledge of a handful of insiders.

When a defective part turns up, particularly if it has caused an upset, it’s then a five alarm fire trying to find the cause. Inspection records, supplier documents, and production histories may be scattered across multiple systems. And heaven forbid that the quality manager retires, or leaves for better opportunities.

You might not be surprised that the fragile quality system environment is attracting the attention of AI as a solution. Modern quality management platforms can connect previously disconnected processes, automate documentation, track corrective actions, and make quality information available through natural language conversations. Rather than hunting for the elusive records related to an obscure part, quality managers can just ask their systems questions, issue instructions, and focus on improving operations.

In this episode, I’m speaking with Onega Ulanova, co-founder of QMS2GO, and a former American Petroleum Institute auditor. We discuss the shortcomings of traditional quality management, the potential of AI-powered quality systems, and how better compliance can improve reliability, productivity, and cash flow.

👤 About The Guest

Onega Ulanova is the co-founder of QMS2GO, a company developing AI-powered quality management solutions for industrial manufacturers and service providers.

Her career includes roles as a quality manager, consultant, and lead auditor for the American Petroleum Institute, where she audited more than 200 facilities worldwide. She has extensive experience with API Q1, API Q2, and ISO 9001 quality management standards.

Together with her husband and business partner, Konstantin Dolgan, she is applying this experience to make quality management more accessible, automated, and effective.

Artificial intelligence has been part of the oil and gas industry's technology toolkit for years. Predictive models identify deteriorating equipment, flag production anomalies, and help engineers make better decisions. These tools generate valuable insights across exploration, production, and maintenance operations.
But knowing something is wrong and doing something about it are two different things. Someone still has to interpret the information, determine the appropriate response, and organize the work. Frustratingly, highly skilled specialists spend enormous amounts of time gathering and cleaning data, the most experienced operators are rapidly approaching retirement, and industrial facilities remain difficult and expensive to inspect and maintain.
I’ve speculated in my books that AI could someday move beyond generating predictions to actually helping carry out the work. Finally, that day is here.
In this episode, I speak with Alex Knowles, Industry Chief Technology Officer at DataRobot, about the emergence of agentic AI in oil and gas. Drawing on his experience in the US Navy, oil and gas operations, and enterprise AI, Alex explains how intelligent agents can help bridge the gap between insight and action.

• compressing months of work into days

• preserving the knowledge and experience of senior people

• optimizing maintenance across complex industrial assets

• collaborating with Chevron to develop AI-enabled autonomous inspections

• using physics models and sensor data to predict hazardous gas plumes and guide robotic inspections

I'm most interested in where agentic AI is already delivering value, why annoying tasks such as cleaning well data and seismic metadata deserve more attention, and what happens when AI begins to interact with the physical world of oil and gas.

⚒️ Additional Tools & Resources

📚 My books: https://geoffreycann.com/book/

🎬 Go backstage and check out my studio:https://geoffreycann.com/mystudio/

🎓Take my one-day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

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🔗 Connect with Me

📘Blog series: https://digitaloilgas.substack.com/

🎧 Podcast: https://geoffreycann.com/podcast/

💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🐦 X: https://x.com/geoffreycann👉 https://geoffreycann.com/contact/

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⚠️ Disclaimer

The views expressed in this podcast are my own and do not constitute professional advice.

How Infrared Spectroscopy Is Changing Industrial Water Diagnostics, an interview with Graham Roeber of Orono Spectral SolutionsOil and water don't mix, but in oil and gas operations, they frequently end up together. Refineries, processing plants, and other industrial facilities routinely test their water streams for hydrocarbon contamination. Historically, this testing has been driven by environmental compliance. The objective is to demonstrate that discharged water meets regulatory standards, rather than to improve the performance of the facility.The problem is that traditional oil-in-water testing can be slow, labour-intensive, and expensive. Results can take hours, making the technology poorly suited to diagnosing leaking equipment or monitoring process performance. Meanwhile, oil escaping into wastewater represents lost product, increased treatment costs, and potentially costly equipment failures. For operators trying to improve efficiency without adding capacity, these losses matter.Infrared spectroscopy offers a different approach. By capturing oil from water samples on a specialized membrane, the technology can measure hydrocarbon concentrations in minutes rather than hours. More intriguingly, the spectral fingerprint of the captured oil can be compared against reference data to help identify the type of oil present. That makes it possible to narrow down the source of contamination, detect emerging problems, monitor process trends, and make better operational decisions.In this episode, I speak with Graham Roeber, CEO of Orono Spectral Solutions (OSS), about how this technology is transforming industrial water diagnostics. We discuss how chemical fingerprints can help maintenance teams locate leaking equipment, why faster testing changes the economics of process monitoring, the challenges of gaining regulatory acceptance, and how AI is making sophisticated chemical analysis more accessible. We also explore emerging applications in desalination, ammonia, corrosion monitoring, and other industrial processes.👤 About the GuestGraham Roeber is CEO of Orono Spectral Solutions (OSS), a company specializing in infrared spectroscopy technology for oil-in-water analysis and industrial water diagnostics.OSS develops analytical solutions that help industrial operators measure hydrocarbon contamination, identify oil types, and gain actionable insights into the performance of their equipment and processes.Website: https://www.ossmaine.com/Graham on LinkedIn: https://www.linkedin.com/in/grahamroeber/#DigitalOilAndGas #InfraredSpectroscopy #WaterTechnology #IndustrialInnovation #OilAndGas #OronoSpectralSolutions-----------------------------------------------------⚒️ Additional Tools & Resources📚 My books: https://geoffreycann.com/book/🎬 Go backstage and check out my studio:https://geoffreycann.com/mystudio/🎓Take my one-day digital strategy training course for oil and gas: https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185-----------------------------------------------------🔗 Connect with Me📘Blog series: https://digitaloilgas.substack.com/🎧 Podcast: https://geoffreycann.com/podcast/💼 LinkedIn: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/🐦 X: https://x.com/geoffreycann👉 https://geoffreycann.com/contact/-----------------------------------------------------⚠️ DisclaimerThe views expressed in this podcast are my own and do not constitute professional advice.

Oil and gas companies have invested billions in SCADA, sensors and other technologies to understand what's happening with their equipment. Yet field workers still spend huge amounts of time driving to remote assets simply to inspect them. Experienced operators can see, hear and sense things that conventional instrumentation misses.

Those blind spots have consequences. Equipment problems can go unnoticed until they become failures. Workers make unnecessary truck rolls. Maintenance becomes reactive. And with experienced field workers becoming harder to find, the industry needs a better way to understand what's happening across thousands of pieces of equipment.

Physical AI offers another approach. New edge computing technologies can continuously interpret sound, vibration, vision, temperature and other signals right at the equipment. Add AI decision-making and the result is much greater visibility into equipment condition, earlier warning of problems, fewer unnecessary field visits and more targeted maintenance.

In this episode, I'm in conversation with Alex Hawkinson, founder and CEO of BrightAI. We talk about stick-on AI sensors, edge computing, connected-worker technology, predictive maintenance and robot dogs. We also explore where this could ultimately lead: an oil field that continuously observes its own condition, decides what needs attention and eventually sends robots to take action.

👤 About Alex Hawkinson

Alex Hawkinson is the founder and CEO of BrightAI, a Physical AI company focused on the infrastructure and essential services that modern society depends on. Alex previously founded SmartThings, the connected-home technology company acquired by Samsung. With BrightAI, he is applying his experience in connected devices, AI and edge computing to energy, power, water, transportation and other physical infrastructure.

BrightAI: https://bright.ai/

BrightAI for Oil & Gas: https://bright.ai/industries/oil-gas/

Stateful OS: https://bright.ai/stateful-os/

Connect with Alex Hawkinson: https://www.linkedin.com/in/ahawkinson/

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
🎓 Take my one day digital strategy training course for oil and gas:
👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
🎧 Podcast: https://geoffreycann.com/broadcast/
📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

The views expressed in this podcast are my own and do not constitute professional advice.

#PhysicalAI #IndustrialAI #OilAndGas

AI can help us quickly when we need to make decisions. But when executives are making high-stakes decisions about pipelines, energy infrastructure, capital investments or geopolitical risk, a quick answer isn't necessarily a good answer. Insight into the decision is fragmented across organizations, external information is difficult to assess, and misinformation, competing interests and human bias all complicate the picture.

General-purpose AI tools can sort of help solve this problem. I find that they are designed to produce a fast answer, not necessarily to work systematically through a complex problem using rigorous analytical techniques. Strategic decisions need something more thorough: clearly defined problems, credible sources, competing hypotheses, scenario analysis, bias checks, traceable reasoning and the ability to revisit an assessment as circumstances change.

Structured AI offers a different approach. Instead of replacing the human analyst, it combines AI with formal intelligence methodologies and human judgement. The result is analysis that can be challenged, explained and refreshed as conditions evolve. It's so much more satisfying than an AI-generated answer.

In this episode, I talk with Chris Coupal of Foresight Reports about Nexus, the company's structured intelligence platform. I wanted to know why traditional approaches to intelligence are faring with today's complicated information environment, how structured AI differs from tools such as ChatGPT, why deliberately searching for contradictory evidence matters, the continuing importance of the human analyst, and how this approach could change strategic decision-making across energy and other industries.

Chris Coupal is Chief Operating Officer of Foresight Reports. His background includes 16 years in the Canadian military as a signals officer, including service in Afghanistan, as well as experience in mining and oil and gas.

At Foresight Reports, Chris works with a team of analysts focused on intelligence and forecasting. The company has traditionally specialized in activism intelligence and social licence to operate and is now applying structured analytical techniques and AI to a broader range of strategic problems through its Nexus platform.

💼 Chris Coupal 👉 https://www.linkedin.com/in/chris-c-828702/
🌐 Foresight Reports 👇
https://www.linkedin.com/company/foresight-reports/

https://foresightreports.com

📧 engage@foresightreports.com

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
🎓 Take my one day digital strategy training course for oil and gas:
👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
🎧 Podcast: https://geoffreycann.com/broadcast/
📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

The views expressed in this podcast are my own and do not constitute professional advice.

Digital innovations are creating huge value for oil and gas, but digital landscape is setting the industry up for problems down the track.

Think of them as digital time bombs. Quantum computing will expose encrypted data that companies believe is safely protected. Legacy on-premise software is fast becoming difficult and costy to support as investment capital moves elsewhere. And the industry's shrinking talent pipeline will leave companies without enough people, particularly people with the digital skills needed to replace retiring workers.

In this episode, I look at four digital time bombs facing oil and gas: decrypting stolen data, hacking live business communications, aging software, and the shrinking talent pool. The exact timing of these problems is uncertain, but that's the nature of a time bomb. Companies that recognize them early still have time to mitigate the impacts, or perhaps defuse them entirely.

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
🎓 Take my one day digital strategy training course for oil and gas:
👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
🎧 Podcast: https://geoffreycann.com/broadcast/
📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

The views expressed in this podcast are my own and do not constitute professional advice.

 Energy superpower is a fine marketing slogan. In fact, lots of countries have stated their ambition to become the next energy superpower, but oddly, no one spells out what that actually means.

Fortunately, there is a rigorous academic definition for energy superpowerhood, which ranks the EU, the U.S., and China as the world's true energy superpowers. All others are pretenders, Canada included, despite its obvious oil and gas resources.

What would it take to become a true energy superpower, and is now strategically the right time to pursue such a game plan?

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
🎓 Take my one day digital strategy training course for oil and gas:
👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
🎧 Podcast: https://geoffreycann.com/broadcast/
📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

The views expressed in this podcast are my own and do not constitute professional advice.

Industrial AI is into a new phase. Agentic AI is software that can plan, reason, learn, and act with far greater independence than traditional automation. While the technology is advancing quickly, the oil and gas industry faces unique technical, regulatory, and operational challenges that make full autonomy far less straightforward.

In this episode, I share what agentic AI actually is, why it differs from conventional automation, and why the industry's aging assets, fragmented data, safety requirements, cybersecurity concerns, and accountability standards create real barriers to adoption.

I also share where I believe agentic AI can deliver the greatest value today: reducing the cognitive burden on engineers by assisting with planning, monitoring, coordination, and decision preparation while keeping humans squarely in the loop. The future is a function of humans plus AI, instead of the gloomy either/or we hear.

⚒️ Additional Tools & Resources:

🎬 Go backstage and check out my studio:
👉 https://geoffreycann.com/mystudio/
🎓 Take my one day digital strategy training course for oil and gas:
👉 https://www.udemy.com/course/digital-oil-and-gas/?referralCode=0161D4D49AB75735A185

🔗 Connect with Me:

🖥️ Blog series: https://digitaloilgas.substack.com/
🎧 Podcast: https://geoffreycann.com/broadcast/
📇 Linkedin: https://www.linkedin.com/in/advocate-digital-innovation-for-energy/

🤬 X: https://x.com/geoffreycann

⚠️ Disclaimer:

The views expressed in this podcast are my own and do not constitute professional advice.