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

Undoubtedly. But what if you could optimize even further? That’s the transformative promise of generative AI, which is beginning to revolutionize business operations in game-changing ways. It may be the solution that finally breaks through dysfunctional silos of business units, applications, data and people, and moves beyond the constraints that have cost companies dearly.  

Still, as with any emerging technology, early adopters will incur learning costs, and there are challenges to preparing and integrating existing applications and data into newer technologies that enable these emerging technologies. Let’s look at some of those challenges to generative AI for asset performance management.

Challenge 1: Orchestrate relevant data

The journey to generative AI begins with data management. According to the Rethink Data Report, 68% of data available to businesses goes unleveraged. Here’s your opportunity to take that abundant information you’re collecting in and around your assets and put it to good use. 

Enterprise applications serve as repositories for extensive data models, encompassing historical and operational data in diverse databases. Generative AI foundational models train on massive amounts of unstructured and structured data, but the orchestration is critical to success. You need mature data governance plans, incorporation of legacy systems into current strategies, and cooperation across business units.  

Challenge 2: Prepare data for AI models

AI is only as trusted as the data that fuels it. Data preparation for any analytical model is a skill- and resource-intensive endeavor, requiring the meticulous attention of (often) large teams with both technology and business-unit knowledge.  

Critical issues to resolve include operational asset hierarchy, reliability standards, meter and sensor data, and maintenance standards. It takes a collaborative effort to lay the foundation for effective AI integration in APM and a deep understanding of the intricate relationships within your organization’s data landscape.

Challenge 3: Design and deploy intelligent workflows

Integrating generative AI into existing processes requires a paradigm shift in how many organizations operate. This shift includes embedding AI advisors and digital workers—fundamentally different from chatbots or robots—to help you scale and accelerate the impact of AI with trusted data across your business and your applications. And it’s not just a technology change.

Your AI workflows should support responsibility, transparency, and “explainability.”

To fully leverage the potential of AI in APM requires a cultural and organizational shift. Fusing human expertise with AI capabilities becomes the cornerstone of intelligent workflows, promising increased efficiency and effectiveness.

Challenge 4: Build sustainment and resiliency

The initial deployment of AI in APM isn’t the last stop on the road. A holistic approach helps you build sustainment and resiliency into the new enterprise AI ecosystem. Increasing managed services contracts across the enterprise becomes a proactive measure, ensuring continuous support for evolving systems.

With their wealth of knowledge, the transition of the aging asset reliability workforce presents both a challenge and an opportunity. Maintaining the effective deployment of embedded technologies may require your organization to “think outside the box” when managing new talent models.

As generative AI evolves, you’ll want to stay vigilant to changing regulatory guidelines and stay in tune with local and global ethical, data privacy and sustainability standards.

Prepared for the journey

Generative AI will impact your organization across most of your business capabilities and imperatives. So, consider these challenges as interconnected milestones, each harnessing capabilities to streamline processes, enhance decision-making, and drive APM efficiencies.