AI is rapidly developing, and its evolution is changing the ways manufacturers handle asset management. Our expert panel weighs in.
How is artificial intelligence (AI) transforming asset management decision-making?
Brian Fortney: Artificial intelligence (AI) is changing how organizations manage and optimize production assets by enabling better, faster and more confident decision-making across the entire asset life cycle. Instead of reacting to failures or relying on fixed maintenance schedules, AI helps anticipate issues, prioritize the right actions and extract greater value from assets every day.
Ben Swisher: One of the biggest challenges of the digital transformation revolution has been trying to make good use of the mountains of data coming in from the sensing devices that are now ubiquitous in the field. Teams have more access to data than they’ve ever had before, but with a shifting workforce, the expert personnel necessary to decode that data and turn it into actionable insights are becoming increasingly scarce.
Modern industrial AI can parse tremendous amounts of data in a fraction of the time a human can. That has lowered the bar for entry into a successful predictive maintenance program – if a team implements the right tools. Bolt-on solutions typically add more complexity, both in engineering connections to external systems and in training users to drive value from the systems. Fortunately, today’s most advanced automation suppliers are helping organizations navigate this challenge by engineering industrial AI directly into the tools reliability teams use every day. Edge environments, asset monitors and even wireless vibration monitors use on-board AI and pattern recognition to help cut through the complexity of raw data and instead deliver actionable information to personnel.Â
In addition, machinery health software at both the plant and enterprise level is incorporating an increasing amount of AI-powered predictive and prescriptive maintenance technology. The AI is built into existing software instead of executed on an external system, making it an intuitive extension of the reliability team’s existing workflows. When done right, AI insights are seamless.
Scott Campbell: Nearly every organization is exploring this question and testing different methods to drive productivity with AI. GenAI chat capabilities using large language models are the most common starting point. It is providing a new way to interact with asset and work order data, get quick answers to previously difficult maintenance questions and operation trends. Ironically, these AI interactions are also helping organizations realize, in many cases, they lack quality data. This inhibits their ability to go beyond inquiry into the higher value insights and automation. It has shifted the focus for many to a data first approach for leveraging AI innovations.
The latest innovations tied to agentic AI workflows has the promise to unlock the paths for condition-based, predictive and financially optimized maintenance strategies, which can deliver tremendous value to operations.
What role will autonomous maintenance play in the future of manufacturing?
David Tishmack: Autonomous maintenance will increasingly mean automated detection, verification and workflow execution — so routine checks don’t rely on manual rounds. As devices provide self-diagnostics and continuous monitoring indicators, systems can trigger alerts, recommend actions and automatically generate documentation. The near-term impact is fewer unnecessary site visits, improved safety and more consistent compliance. Longer term, plants may move toward self-optimizing maintenance schedules based on actual asset condition.
Brian Fortney: Autonomous maintenance will play a critical role in helping manufacturers achieve more reliable, efficient and resilient operations as assets, processes and workforce models become increasingly complex. Rather than replacing people, autonomous maintenance enables teams to prevent problems earlier, respond faster to emerging issues and focus on higher‑value work, while digital intelligence handles routine monitoring and decision‑making.
Scott Campbell: Eventually, it will play a major role for of the decision points that require human knowledge in operations today. But there are still many inhibitors to overcome.
Among those inhibitors are things like trust in the underlying data leveraged for triggering work events and auto approvals.
System integration between maintenance and MES systems for example, where plant operations and maintenance must be working in unison. For true autonomous operations, maintenance decisions need the intelligence to understand production impact, safety, inventory levels, supply chain and the full financial risk for stopping a production line to avoid asset failure.
Additionally, people will still be required for the foreseeable future to execute maintenance on machines. They will still require training and high levels of expertise focused on higher value jobs. Process flows must also be streamlined. Automating a bad maintenance process makes thing worse, not better.
How are cloud-based asset management platforms changing operations?
Scott Campbell: First, it is the most common way organizations are moving to an asset centric approach, treating assets as a system of record. It allows multiple plants to centralize their asset data on a common platform, serviced by the cloud. This is typically the first step toward consolidation of bespoke systems and drives asset hierarchy and data management as key focus areas.
Second, it allows IT organizations to shift their focus away from managing infrastructure, software deployments, patches and upgrades. Instead, they can focus on the business operations side, working to improve data quality and connectivity at the edge where technicians need the data. It helps to bridge gaps between operations and information technology (IT) organizations, by removing the lower value tasks and focusing on higher business value tasks.
Eric Uutala: Conceptually, the benefit is remote visibility of asset integrity and preservation status, which helps prepare for future work.
David Tishmack: Cloud-based asset management platforms are expanding visibility from single-device checks to fleet-wide insight. They make device diagnostics, verification actions and documentation accessible remotely, which helps standardize workflows, speed decision-making and reduce manual collection and reporting. In practice, cloud orchestration shifts effort from “finding data” to “acting on data,” enabling safer and more efficient maintenance with better traceability across the installed base.
What emerging technologies are most likely to disrupt asset management in the next five years?
Eric Uutala: Drone inspections have made a tremendous impact in the last five years and that will likely continue. It started with drones being able to do external inspections on oil and gas modules and piping systems and we now have robots that can perform floors scans in aboveground storage tanks. This type of technology will grow and spread, allowing for increased efficiency and accuracy in asset management.
David Tishmack: The biggest disruptors will be technologies that make device intelligence easier to access and use at scale: always-on self-diagnostics and condition indicators, in-situ verification with automated reporting and cloud services that centralize results and connect them to maintenance workflows. Modern industrial Ethernet and Ethernet-APL will also accelerate adoption by enabling richer, faster access to instrument data — so advanced diagnostics and monitoring are no longer “specialty features,” but inputs to everyday operations and reliability decisions.
Brian Fortney: As asset management becomes more autonomous, AI-driven systems will proactively optimize reliability, cost and performance across the asset life cycle, enabling higher availability and scalability with fewer resources. Technologies such as agentic AI and digital twins will have a significant impact on asset management decision-making, shifting from reactive to proactive operations.
How do you see asset management evolving in the era of industry 5.0
Brian Fortney: In Industry 5.0, asset management is evolving from purely digital optimization to a human-centric, resilient and sustainable value discipline. The focus is no longer just efficiency — it’s about how assets support people, profitability and purpose across their entire life cycle.
