The new tech operating model
The energy industry has reached a turning point: technology is no longer just supporting the workforce—it is shaping it. [i]
Where companies once had their people following processes using data and supported by technology, today’s reality is reversing that equation. Processes are driven by data, enabled by technology, and orchestrated by people. [ii]
This shift demands a fundamental rethink of how energy businesses operate, how work gets done, and what the workforce of the future looks like.
In earlier papers in this five-part series—Challenges ahead, Opportunities ahead, and Energy transition—we explored the mounting pressures facing the workforce and the possibilities awaiting those ready to adapt. [iii] [iv] [v]
In this edition, we describe how technology is becoming the primary architect of operational models, with humans situated in the middle—orchestrating, supervising, and optimizing the systems that now drive performance.
From human execution to human supervision
For decades, the energy industry deliberately designed operations to rely on human execution. In part, this reflected limitations in the ability of the infrastructure to operate autonomously. In addition, this approach was rooted in a deep and abiding concern for the safety of workers and the environment.
Today, that operating model struggles to respond to labour shortages, greater operational complexity, and rapid technological advancement. Human-executed workflows are increasingly unsustainable at scale. [vi]
The new human-in-the-middle model ensures that humans remain vital where they always have been—handling edge cases, managing exceptions, responding to changing customer demands, and adapting as technology advances asynchronously—while distributing the routine and mundane to machine-led execution.
Robotic process automation (RPA) technology illustrates this concept. A leading energy producer deployed RPA to automate repetitive, manual administrative tasks in operations management. [vii] Administrative cycle times improved by 30% along with a reduction in error rates and lower risk. More importantly, scarce human staff were reallocated to higher-value oversight and decision-support roles, and employee satisfaction rose as the mundane and repetitive tasks were offloaded.
This model frees up the most scarce resource—time—so that energy workers have greater opportunity to drive the kind of critical, creative thinking that automated systems cannot replicate.
Smarter assets, smarter systems
For most of its history, oil and gas operated with remarkably low-tech pumps, compressors, tanks, and heaters. At best, these assets provided analogue data through physical gauges and dials, requiring manual inspection to assess operating conditions.
A huge proportion of today’s installed base is still brownfield, offline, and effectively invisible unless a worker physically visits the site. That model is not obsolete yet—and will not be anytime soon. The economics to retrofit a running asset with modern technology are cost prohibitive. Older assets remain in service until end-of-life, with only minimal adaptations.
The newest generation of assets, however, is different:
- Digital twins mirror asset performance in real-time.
- Sensors and IoT platforms deliver continuous, high-frequency data streams.
- AI and machine learning detect anomalies and optimize operations without waiting for human intervention.
For the foreseeable future, the industry will run a hybrid model, consisting of brownfield assets lightly upgraded with add-on tools where feasible, and modern greenfield assets designed from the ground up for intelligent operations.
An LNG facility in Australia illustrates this future. [viii] Rather than rip and replace its analogue pumps and gauges, the operator mounted smart cameras aimed at the gauges without interrupting processing. AI interpreted the dial readings, fed the data into a planning system, and dispatched operators only to the pumps that required attention. The pumps stayed dumb—but the system around them got smart.
In complex environments, meaningful progress isn’t driven by rigid control—but by the ability to sense and respond in real time.
This layered approach—augment where you can, design smart where you must—will be the hallmark of tomorrow’s successful smart operators.
A back office ripe for intelligent automation
Much like operations, back office business functions in energy, including accounting, supply chain, and HR, were designed for a human-centric era, based on plentiful low-cost administrative workers. As with operations, this model is no longer sustainable.
Intelligent automation is redefining how energy companies manage these critical support systems:
- AI-enabled procurement predicts supply needs, negotiates pricing, and flags contract risks before they escalate.
- Automated inventory systems dynamically adjust stocking strategies based on real-world consumption data.
- Smart logistics platforms optimize routing, scheduling, and vendor selection without manual intervention.
A case in point is an energy producer that deployed automation tools to streamline its global IT service desk ticket handling, reducing carbon emissions, saving staff time, and managing compliance. [ix]
The automation of the routine decision points across supply chains and back-office operations frees up human capacity for strategic work—contract optimization, supplier partnerships, risk management—and with leaner, smarter teams.
Workforce implications: managing systems not just assets
The future energy workforce will still operate the myriad low-tech pumps, vessels, and compressors in the energy landscape. Increasingly, however, they will also manage intelligent systems that operate themselves.
This shift—from human execution to human supervision—reshapes the entire profile of skills, capabilities, and competencies that energy companies must attract, develop, and retain. This worker profile is scarce and in high demand.
The future energy workforce will have the following attributes:
- System thinking: The ability and interest in understanding how interconnected assets, energy flows, and digital systems interact—not just within a plant, but across entire energy ecosystems.
- Interpretation and judgment: Strength in validating, prioritizing, and acting on AI-driven insight—bringing critical judgment to machine-driven recommendations.
- Management of the exception: Courage and motivation to intervene when automated systems face outliers, multi-variable disruptions, or novel operational scenarios.
- Continuous optimization: Discipline to both maintain operations, and the drive to optimize asset performance, and improve workflows dynamically.
- Human–machine collaboration: Comfort in collaborating with AI, automation, and robotics—supervising and shaping digital systems as active partners, not as passive users.
- Adaptability and digital fluency: Proficiency in rapidly adapting to evolving platforms, interfaces, and data systems—learning continuously, not cyclically.
- Ethical and regulatory oversight: Rigorous in ensuring that automation aligns with safety, environmental standards, and ethical operations frameworks.
- Resilient mindset: Orientation towards innovation and opportunity-seeking, strategic thinking, collaboration, communications, and action.
The next generation of workers won’t just run systems—they’ll shape them.
The reality is that it will take years for educational systems to realign and deliver new cohorts of digitally skilled energy professionals. [x] Up-skilling and re-skilling the existing workforce—today—is the only viable path to staying competitive.
Conclusion: a workforce shaped by technology
The energy sector’s future will not be built on a bigger workforce—it will be built on a smarter, more agile and strategically designed one. With technology now defining work, indeed doing much of the work directly, energy companies will rethink their approach to operations, workforce design, and organizational structure.
In this future, companies start to design workflows and processes around intelligent systems—with humans placed at the center as orchestrators, decision-makers, and stewards of continuous improvement.
Those who adapt will scale smarter, operate leaner, innovate faster, and create new value.
Those who cling to human-centric execution models will find themselves unable to compete in an industry that demands speed, intelligence, and continuous reinvention.
Because in the energy sector’s next era, it will not be the biggest companies that win.
It will be the smartest.
This article was co-written by the following two experts:
| Geoffrey Cann, Advocate for Digital Innovation in Energy | Peter Warren, Vice-President, Global Industry Lead, Energy & Utilities, CGI |
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If you’d like more information on our work in this area, feel free to contact Peter.
References
[i] Techstep.io. 2024. “The Rise of the Gen Z Workforce: Addressing Mobile Technology Needs of Digital Natives.” January 15, 2024. https://www.techstep.io.
[ii] Akter, Shahriar, Saida Sultana, and Samuel Fosso Wamba. 2024. “Tackling the Global Challenges Using Data-Driven Innovations.” Annals of Operations Research, February 5, 2024. https://link.springer.com/article/10.1007/s10479-024-05851-2.
[iii] Warren, Peter and Geoffrey Cann. 2025. “Workforce of the Future: Challenges Ahead.” https://www.cgi.com/canada/en-ca/article/energy-and-utilities/workforce-future-energy-challenges-ahead.
[iv] Warren, Peter and Geoffrey Cann. 2025. “Workforce of the Future: Opportunities Ahead.” https://www.cgi.com/canada/en-ca/article/energy-and-utilities/workforce-future-energy-opportunities-ahead.
[v] Warren, Peter and Geoffrey Cann. 2025. “Workforce of the Future: Energy Transition”
[vi] Careers in Energy. 2024. “Canada’s Energy Workforce: National Labour Market Outlook to 2035.” March 14, 2024. https://careersinenergy.ca/our-work/labour-market-intelligence/national-energy-labour-market-outlook-to-2035/.
[vii] “An Interview with Cory Bergh and Michele Taylor,” interview by Geoffrey Cann, Transcript, October 21, 2019, geoffreycann.com/interview-cory-bergh-michele-taylor.
[viii] Eltringham, Emma, Leon Burgin, and Shawn Fernando. 2023. “Sense, Insight, Action and Automation – Remote Operations Using a Digital Twin.” Paper presented at LNG2023 Conference, Vancouver, Canada, July 10–13, 2023. https://cdn.asp.events/.../Eltringham_Emma_Woodside-Energy_8XARKWWN96.pdf.
[ix] CGI. Client Success Story: Shell Service Process Automation. PowerPoint presentation. Accessed April 30, 2025.
[x] Cambridge University Press & Assessment. 2023. “Curriculum & Assessment Reform: Learning Loss to Long-Term Resilience.” https://www.cambridge.org
