Human + Machine: The Real Story of AI in Oil & Gas: From Rigs to Real-Time Intelligence (feat. Steve Senterfit)
Digital transformation in oil and gas blends AI, data, and domain expertise to optimize operations, while human judgment remains critical for decisions.
In this insightful interview, Steve Senterfit, President of SmartBridge, shares his extensive experience in digital transformation, especially within the oil and gas industry. The discussion covers industry-specific challenges, the role of AI, and practical strategies for successful technology adoption.
Podcast
Digital Transformation in the Oil & Gas Industry: Where Data Meets Deep Domain Expertise — on Apple and Spotify.
The oil and gas industry is often perceived as traditional—anchored in physical infrastructure, field operations, and decades-old engineering practices. But beneath that surface, a significant shift is underway. What used to be a largely mechanical and intuition-driven industry is steadily becoming one of the most data-intensive sectors in the global economy.
Conversations with leaders like Steve Senterfit reveal that this transformation isn’t primarily about adopting new tools. It’s about rethinking how decisions are made, how operations are run, and how value is created across the lifecycle of energy production.
Transformation Starts With the Business, Not Technology
A common misconception is that digital transformation begins with technology selection—AI platforms, analytics tools, or automation systems. In reality, especially in oil and gas, it starts with the business itself.
As highlighted in your discussion , early transformation efforts—often called the “digital oil field”—were focused on a simple but powerful objective: improving production outcomes. The goal was to extract resources more efficiently, reduce costs, and enhance safety. That fundamental objective hasn’t changed. What has changed is the sophistication of the tools available to achieve it.
But technology alone doesn’t transform an organization. Companies often struggle not because their strategy is flawed, but because they underestimate the complexity of execution—aligning teams, managing change, and ensuring adoption.
Why Oil & Gas Is Fundamentally Different
One of the reasons transformation in oil and gas is so challenging is that the industry itself is deeply specialized. Unlike software-driven sectors where solutions can be reused across domains, oil and gas operations are tightly coupled with physical environments and geological realities.
A well in Texas behaves differently from one in Pennsylvania. Offshore drilling introduces an entirely different set of constraints compared to onshore operations. Even when processes appear similar at a high level, the underlying conditions—temperature, pressure, chemical composition—require tailored approaches.
This is why domain expertise matters so much. You can’t simply apply a generic transformation playbook. The systems, the data, and even the decision logic are often unique to the field, the basin, or the asset.
The Hybrid Nature of Transformation
Another distinguishing feature of oil and gas is that transformation isn’t purely digital. It exists at the intersection of physical and digital systems.
Modern operations rely on sensors embedded deep within wells, fiber optics capturing real-time data, and drones inspecting pipelines across vast geographies. These physical technologies feed into software systems that analyze, interpret, and act on the data.
This creates a layered ecosystem where operational technology and information technology converge. Transformation, therefore, isn’t about upgrading software alone—it’s about orchestrating an entire system that spans the field and the cloud.
AI: Evolution, Not Revolution
There’s a tendency to frame AI as something entirely new, but in oil and gas, that’s not quite accurate. Machine learning and predictive models have been in use for years, particularly in areas like equipment maintenance and production forecasting.
What’s changed recently is accessibility. With the rise of generative AI and more user-friendly platforms, the barrier to entry has lowered significantly. Organizations can now experiment and deploy solutions faster than before.
A compelling example from your discussion is chemical injection optimization. Traditionally, engineers relied on experience and historical data to decide how to treat wells for issues like corrosion or scaling. Today, AI systems can analyze years of sensor data and lab results simultaneously, generating recommendations that are far more comprehensive than what a human could process alone.
And yet, the final decision still rests with people.
The Enduring Role of Human Judgment
This is where one of the most important insights emerges. Despite advances in AI, human expertise remains central.
AI systems can identify patterns, generate recommendations, and even automate certain workflows. But they are not infallible. They depend on data quality, can drift over time, and occasionally produce incorrect outputs. In a high-stakes environment like oil and gas, where decisions can have safety and financial implications, that margin of error matters.
The most effective approach, as emphasized by Steve Senterfit, is to keep humans in the loop. AI augments decision-making rather than replacing it. Over time, feedback from human decisions helps improve the system, creating a continuous learning cycle.
The Real Bottleneck: Adoption
Interestingly, the biggest challenge isn’t building these systems—it’s getting people to use them effectively.
Organizations often invest heavily in technology but fall short on training and integration. Tools are deployed, but workflows remain unchanged. Employees revert to familiar methods, not because they resist innovation, but because they haven’t been shown how to incorporate new tools into their daily work.
This gap between capability and usage is where many transformation efforts stall. It’s not enough to provide a tool; companies must also build the skills and habits required to use it.
Alignment and Execution
Another recurring theme is the difficulty of maintaining alignment within large organizations. Transformation initiatives typically span multiple functions—engineering, operations, IT—and each comes with its own priorities.
Even when leadership agrees on a roadmap, execution can drift. Teams may interpret priorities differently, or short-term pressures may override long-term goals. Without strong governance and clear ownership, progress slows and outcomes fall short.
This is particularly pronounced in oil and gas, where operations are complex and interdependent. Success depends not just on technology, but on coordination across the entire organization.
Looking Ahead
The future of oil and gas is not about replacing traditional operations but enhancing them. We are moving toward systems that are more connected, more predictive, and more adaptive.
Data will continue to play a central role, but it will be the combination of data, technology, and human expertise that defines success. Companies that understand this balance—those that invest not just in tools but in people and processes—will be the ones that lead the next phase of transformation.