Inside Honeywell's roadmap to autonomous reliability

4. AI could help operators with alert fatigue and prioritization. 

"When you try to move to be more proactive, you naturally get a lot more alerts." 

As predictive technologies become more sophisticated, maintenance organizations may face alert fatigue. Sayeed described AI surveillance as one of the critical workflows supporting autonomous reliability programs. Future systems will likely need to prioritize alerts, identify which require immediate attention, and filter out noise before personnel become overwhelmed. Like patients are triaged in the ER, AI can prioritize alerts as they come in and provide guidance on response times.

5. AI may help automate root cause investigations.

"We would allow the agent to actually perform a 5-Why or perform a fishbone and present the evidence to a human being." 

Root cause analysis remains one of reliability's best practices, but it can be time intensive. Honeywell envisions AI agents collecting relevant evidence, organizing information, and conducting preliminary analyses using established methodologies. Human experts would still validate conclusions, but much of the investigative groundwork could become automated. For small reliability teams, this capability could significantly reduce administrative burden.

6. Prescriptive maintenance is the next frontier.

"Basically, transitioning from 'when it's going to break' to 'what should I do about it.' " 

Knowing that a failure is likely to occur is only part of the equation. Moving from preventive to prescriptive, systems will increasingly focus on recommending specific corrective actions based on the anomalies detected. Rather than simply generating alarms, these systems could guide technicians toward the most effective interventions and the most necessary fixes. For organizations seeking greater consistency across sites and shifts, prescriptive capabilities may be the answer.

7. Reliability and production decisions can no longer happen in silos.

"Being able to make the trade-offs between the reliability of the asset and the process requirement, and evaluate that quickly to make decisions, we think is really important as far as extending asset operation.”

Maintenance decisions often involve competing priorities. Running equipment harder may increase short-term production but accelerate degradation. Taking equipment offline for maintenance can reduce operational risk but affect output targets. Sayeed argued that future systems must evaluate these trade-offs dynamically, allowing organizations to balance asset health against operational demands in real time.

8. Autonomous operations begin with maintenance fundamentals.

"The first step is to have a foundation in place." 

Despite the discussion surrounding AI agents and autonomous action, Sayeed repeatedly returned to the basics. Organizations need to identify critical assets, standardize work processes, centralize reliability information, and establish robust predictive programs before pursuing higher levels of autonomy. Autonomous operations are no shortcut for reliability best practices. They're built on them.

Autonomy starts with the basics

One of the most interesting aspects of Sayeed's presentation was that it framed autonomy as the latest stage in reliability's long evolution. It’s not a sudden technological leap that facilities will take without preparation.

The journey from reactive maintenance to preventive programs took decades. The shift toward predictive maintenance is still underway in many facilities today. Autonomous asset optimization may eventually follow, but only for organizations willing to invest in the people, processes, and data infrastructure needed to support it.

For maintenance and reliability professionals facing labor shortages, aging assets, and increasing expectations around uptime, the future is not about removing humans from the equation. Autonomy won’t replace reliability fundamentals. It will be the long-term result of putting them into practice.