CXO INSIGHT
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.
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, realtime decisions.
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.
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. x
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