Does the Transformative Nature of AI and AI Agents provide an Inflection Point opportunity for Asset Management?

The integration of artificial intelligence (AI) offers unprecedented opportunities for enhancing asset performance. However, many organizations may be attempting to solve the wrong problem. Instead of leveraging AI to its full potential, we often find ourselves confined within existing paradigms that separate work execution management from reliability domains. This separation limits our ability to harness AI for transformative change.

Traditionally, asset management has operated within distinct silos. The work execution management domain focuses on the efficiency of tasks, while the reliability domain emphasizes the effectiveness of asset performance. This division is not merely organizational; it extends to processes and technology, creating barriers that inhibit holistic improvement. Currently, efforts to integrate AI often aim to enhance productivity, improve analytical accuracy, or provide deeper insights—yet these initiatives remain tethered to the constraints of existing domains.

What’s missing in this approach is a thorough assessment of the challenges that operations face in real-time without a consideration of the current domains. Asset managers, operations managers, and information officers must critically evaluate how their current practices align with the overarching goals of performance improvement and cost management. The reality is that the execution of work on assets is where performance and costs are ultimately determined. The outcomes we experience are directly linked to the quality of execution, which encompasses both efficiency and effectiveness.

The relationship between asset performance and work execution is intricate. The tasks performed are influenced by the comprehensive operations and the holistic care plan for the asset, which may include monitoring asset and process health. Furthermore, the organizational culture plays a pivotal role in shaping how work is executed. A culture that prioritizes continuous improvement can significantly enhance the effectiveness of asset management strategies.

Given these dynamics, it becomes evident that a paradigm shift is necessary. Rather than merely seeking to improve existing processes through AI, asset managers should consider an execution-led asset performance improvement strategy. This approach emphasizes the importance of work execution in driving business outcomes and fosters a culture of improvement that leverages data and technology.

AI has the potential to disrupt the asset management discipline by enabling a more integrated approach. By breaking down the barriers between work execution and reliability, AI can facilitate real-time monitoring and analysis and enhance decision-making by providing actionable insights that are directly tied to and integrated into the work execution process, thereby aligning asset management practices with broader business objectives. This means going beyond automated work order creation for detected degradation of a single asset or component and seeks to truly integrate these insights into the current holistic work plan that is in flight for a system.

To realize the full benefits of AI, organizations must embrace a more pragmatic approach that focuses on tangible performance improvements. This involves not only adopting advanced technologies but also fostering an organizational culture that values collaboration and continuous learning. By creating an environment where data-driven decision-making is the norm, organizations can effectively leverage AI to connect asset management to business outcomes.

In conclusion, there is a “once in a career” opportunity for asset managers, operations managers, and information officers to leverage A.I. and reconsider their approach asset performance improvement. By shifting the focus from isolated enhancements to a holistic, execution-led strategy, organizations can unlock the transformative potential of AI. This change in perspective could be the key to driving meaningful improvements in asset performance, ultimately leading to enhanced operational efficiency and reduced costs. The future of asset management lies in our ability to adapt and innovate, embracing AI as a catalyst for change rather than a mere tool for optimization.

Jason Apps

-------

Jason Apps is an Executive Level Asset Management Consultant, providing support to organisations in pursuit of high-reliability world-class asset management practices. He is the Author of ASMx: Asset Strategy Management - A Leaders Guide to Reliability Transformation in the Digital Age, a regular presenter, workshop facilitator and trainer.

Jason has delivered significant performance improvement, cost reduction, and risk management to global, blue-chip clients, for the last 20+ years. With a proven, unique, pragmatic approach to identifying improvement initiatives, implementing and structuring for enduring success.

Email Jason at Jason.apps@exar-am.com or visit exar-am.com