Oil and gas CIOs are in a prime position to boost operational performance through the judicious adoption of AI. As the industry increasingly relies on data-driven insights, these executives can unite operational technology (OT) with IT systems, creating more transparent and efficient workflows. AspenTech delivers software solutions that empower asset-intensive industries to optimise asset design, operation and maintenance. Heiko Claussen, Co-chief Technology Officer at AspenTech, discusses how CIOs can measure progress on AI adoption and track sustained benefits, ensuring these transformations deliver lasting value.
Firstly, tell me more about AspenTech and the service it provides?
Aspen Technology is a global software leader helping industries at the forefront of the world’s dual challenge meet the increasing demand for resources from a rapidly growing population in a high performing and sustainable manner. AspenTech’s solutions address complex asset-intensive environments – from chemicals, energy and engineering to oil & gas – where it is critical to optimise the asset design, operation and maintenance lifecycle.
Incorporating software designed for sustainability and decarbonisation, AspenTech’s solutions in this space assist with emissions reduction, microgrids, carbon capture and the hydrogen economy. The company partners with a wide range of asset-intensive industries by delivering innovative technology, including Industrial AI, plant digitalisation and digital twins. For example, AspenTech leverages its predictive analytics tools to deliver downtime reduction for the connected enterprise.
What challenges do oil and gas CIOs face?
Oil and gas CIOs face the challenge of integrating legacy systems with modern AI and digital solutions to boost operational performance. They must unite operational technology (OT) and IT systems, enabling real-time analytics and Machine Learning across various operational layers. At the same time, ageing infrastructure, tightening regulations and a changing workforce that lacks institutional knowledge add pressure.
CIOs are tasked with transforming scattered data into actionable insights while ensuring robust data quality and security. They also need to balance near-term production gains with long-term operational resilience, which requires deliberate risk management strategies and smart investments in new technologies. In short, these leaders must manage the complexities of modernising infrastructure, cultivating technical talent and building strategic partnerships to maintain asset reliability and enhance decision-making, all while keeping pace with an evolving regulatory environment and rapidly-shifting market conditions.
What are the primary risks you foresee when integrating AI into existing OT environments?
Asset intensive OT environments have a clear requirement for a safety-first mindset. Also, decisions in this environment are often mission critical and have high stake consequences. Therefore, like any other OT technology in this space, AI applications need to ensure guardrails, need to be robust and trusted by the operator.
Guardrails refers to provable adherence to safety relevant constraints that are often based on first principles and engineering knowledge. Robust operation means that the AI systems must operate reliably, even if setpoints change and the system operates in a state where no training data was available. Finally, the operator needs to always stay in control and be enabled to address unforeseen situations. If there is a lack of trust in an AI system, e.g., due to its black box nature, the operators will not adopt the technology and therefore potentially leave significant operational efficiency gains on the table.
How can CIOs establish strong partnerships – internal and external – to support transformation initiatives?
Successful Digital Transformation hinges on robust, integrated partnerships both within and outside the organisation. Internally, CIOs must break down silos by fostering close collaboration between IT and operational technology teams.
This unified approach enables a seamless data fabric that supports real-time analytics and Machine Learning, driving continuous improvement and agile decision-making. Establishing internal cross-functional teams and investing in workforce upskilling further ensures that domain expertise and technical excellence guide transformation efforts.
Externally, CIOs should proactively engage with strategic partners, including technology vendors, academic institutions and industry regulators. These collaborations bring specialised expertise and resources that are critical for adopting secure, scalable data ecosystems and advanced industrial AI applications. Early engagement with external partners facilitates the alignment of shared objectives, standardisation of data protocols and the creation of innovative, compliant solutions.
By nurturing both internal and external relationships, CIOs can drive transformation initiatives that not only optimise asset performance and operational resilience but also deliver sustained business value in an increasingly data-driven world.
In what ways can organisations balance near-term results with a long-term vision for AI-driven progress?
Balancing near-term gains with a long-term vision for AI-driven progress requires a dual approach that delivers quick operational wins while building a robust, scalable foundation. In the short-term, organisations can focus on targeted projects, such as optimising asset performance and predictive maintenance, that demonstrate clear value and build confidence in AI capabilities. These initiatives not only improve production and safety today but also serve as practical proofs of concept that encourage broader adoption.
At the same time, it’s essential to invest in an integrated data management platform that unifies IT and OT systems. This ‘data fabric’ enables seamless data access, contextualisation and real-time analytics, forming a strong foundation for sustained model performance and future innovation.
By breaking down data silos, contextualising their data and establishing secure, maintainable connections, companies can ensure that near-term applications feed into a long-term, continuously evolving AI strategy. Additionally, nurturing technical talent and forging strategic partnerships are critical to navigating evolving regulations and workforce challenges, ensuring that immediate results drive lasting, enterprise-wide value.
What best practices can help close the skill gap for operational staff working with AI-driven systems?
Closing the skill gap for operational staff working with AI-driven systems starts with practical, hands-on training that ties advanced technology directly to industry challenges. I believe it’s essential to blend technical education with real-world applications to ensure teams understand not just the ‘how’ but also the ‘why’ behind AI tools. This approach helps staff see tangible benefits in their daily operations, which builds confidence in adopting new technologies.
Facilitating cross-functional collaboration between IT and operational teams is another key practice. Such collaboration fosters a shared understanding of data management and process integration, making it easier to implement AI solutions effectively. Additionally, deploying user-friendly, centralised platforms can streamline data access and reduce the need for complex manual integrations.
Finally, continuous learning through regular training sessions and targeted certifications is vital. This ongoing support ensures that skills remain current as technology evolves. Together, these best practices build a more agile, knowledgeable workforce that can fully leverage the advantages of AI-driven systems.
How might industry regulators support the safe and effective rollout of AI in critical oil and gas operations?
Industry regulators play an essential role in ensuring the safe and effective adoption of AI in critical oil and gas operations. Clear guidelines that define robust cybersecurity measures, data management practices and model validation protocols are vital. By establishing standards that require centralised, secure and accessible data infrastructures, regulators can accelerate model building and rollout of AI functionalities while minimising vulnerabilities caused by fragmented or siloed data sources.
Furthermore, fostering collaboration among technology providers, operational experts and end-users is key to developing best practices that address the unique challenges of integrating AI with legacy systems. This co-operative approach can help mitigate risks associated with technical debt and the complexities of merging AI with existing operational technology environments.
Ultimately, a proactive regulatory framework that emphasises security, transparency and collaboration will not only minimise risks but also drive innovation. By setting these benchmarks, regulators can enable the industry to confidently harness AI to optimise operations, enhance safety and maintain operational resilience in an increasingly complex landscape.
What’s next for AspenTech?
At AspenTech, our focus remains on delivering breakthrough value through innovation and operational excellence. We’re doubling down on our industrial AI and data fabric strategies to seamlessly integrate IT and OT, ensuring that our customers benefit from secure, context-rich data that drives smarter, real-time decisions.
In line with this, we will continue to focus on driving customer value with tangible ROI. That means combining AI with first principles engineering and domain expertise to address challenges such as feedstock volatility, demand changes and sustainability goals.
Equally, it involves enhancing user experience for example, leveraging Generative AI to solve the ‘blank page’ problem, providing a first draft to edit and iterate. Finally, it means automating routine tasks to free up engineers for more critical activities. This provides the potential to improve productivity and accuracy, from model development to decision-making.
