Integrating AI into Asset Performance Management: It’s All About Data

Imagine a future where artificial intelligence (AI) works seamlessly with existing supply chain solutions to redefine how organizations manage their assets. If you’re currently using traditional AI, advanced analytics, and intelligent automation, aren’t you already gaining deep insights into your asset performance?

Without a doubt. But what if you could optimize even further? This is the transformative promise of generative AI, which is beginning to revolutionize business operations in groundbreaking ways. This could be the solution to finally breaking through the functional silos of business units, applications, data, and people, constraints that are costing the company dearly.

However, as with any new technology, there are learning costs for early adopters and challenges in preparing and integrating existing applications and data with the new technologies that enable these new technologies. Let’s look at some of the challenges to generative AI for asset performance management.

Challenge 1: Reconciling relevant data

The journey to generative AI begins with data management. According to the Rethink Data Report, 68% of the data available to businesses is not being utilized. This is your opportunity to leverage the wealth of information gathered from within and around your assets and put it to good use.

Enterprise applications serve as repositories for extensive data models that include historical and operational data from various databases. Generative AI-based models learn on large amounts of unstructured and structured data, but orchestration is critical to success. It requires a mature data governance plan, integration of existing systems into current strategy, and collaboration across business units.

Challenge 2: Preparing data for AI models

AI is only as trustworthy as the data that supports it. Preparing data for any analytics model is a technically and resource-intensive task, requiring careful attention from a large team with both technical and business unit knowledge.

Critical issues that need to be addressed include operational asset hierarchies, reliability standards, meter and sensor data, and maintenance standards. A collaborative effort is needed to lay the foundation for effective AI integration in APM and to deeply understand the complex relationships within an organization’s data environment.

Challenge 3: Design and deploy intelligent workflows

Integrating generative AI into existing processes requires a paradigm shift in how many organizations operate. This transformation includes embedding AI advisors and digital workers, who are fundamentally different from chatbots or robots, to scale and accelerate the impact of AI with trusted data across businesses and applications. And it’s not just technological change.

AI workflows must support accountability, transparency, and “explainability.”

Unlocking the full potential of AI in APM requires cultural and organizational change. Fusing human expertise and AI capabilities is the cornerstone of intelligent workflows, promising increased efficiency and efficiency.

Challenge 4: Building sustainability and resilience

The initial deployment of AI in APM is not the last stop on the road. A holistic approach helps build sustainability and resilience into the new enterprise AI ecosystem. Increasing managed services agreements across the enterprise is a proactive step to ensure ongoing support for evolving systems.

Transforming an aging asset reliability workforce with a wealth of knowledge presents both challenges and opportunities. Sustaining effective deployment of embedded technologies may require “outside the box thinking” as organizations manage new talent models.

As generative AI evolves, we must remain alert to changing regulatory guidelines and adhere to local and global ethics, data privacy, and sustainability standards.

travel preparation

Generative AI will impact organizations across most business functions and imperatives. So consider these challenges as interconnected milestones and leverage the capabilities of each to streamline processes, improve decision-making, and increase APM efficiency.

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