From System of Record to System of Action: Interloc’s AI Journey

At Interloc, our AI journey began with the operational challenges facing organizations that rely on IBM Maximo every day.

Our approach is simple: Start with the problem. Understand what a better outcome looks like. Then determine whether AI should be part of the solution.

With decades of Maximo experience, we saw opportunities to help maintenance and operations teams understand what needs attention, decide what should happen next, and move work forward while keeping people in control.

That thinking became interloc.ai.

Why Agentic AI?

Maximo has long served as the system of record for critical asset and maintenance information. We believe agentic AI creates an opportunity to build on that foundation and move toward a system of action—where information helps drive decisions and approved work.

interloc.ai is being built as a coordinated network of specialized AI agents. These agents are designed to analyze information, answer operational questions, identify risks and priorities, recommend next steps, and carry out approved tasks through Maximo, with governance and human oversight built into the process.

The value comes from matching those capabilities to a specific operational need. Each use case starts with the problem to be solved and the outcome that matters to the people doing the work.

Meet the Trailblazers

Getting there requires more than technology. It takes people willing to explore what’s possible, challenge assumptions, and translate ideas into practical applications.

At Interloc, Joe Mendoza, Khalid Sayyed Firoz, and Julio Perera have taken on that role as our AI Trailblazers.

They’ve been working directly with customers, IBM, and our own teams to understand operational challenges, test ideas, explore the architecture behind agentic AI, and identify where interloc.ai could make a meaningful difference.

Those conversations are already shaping the roadmap, highlighting opportunities in work-order analysis and planning, inventory optimization, proactive monitoring, data quality, governance, and industry-specific applications.

Our Trailblazers also continue to work closely with IBM to understand the direction of AI within the Maximo Application Suite (MAS) and how interloc.ai can complement that evolving ecosystem.

Where Do We Go From Here?

We’re still early in this journey.

Our vision is for interloc.ai to expand through Operational, Intelligence, and Industry Agents that combine Maximo expertise with specialized business and industry knowledge.

Utilities, transportation, manufacturing, and other asset-intensive industries each have distinct operating realities. The opportunity is to give AI the context it needs to interpret asset and maintenance data in light of those realities and recommend relevant next steps.

Our customers’ challenges, priorities, and feedback will continue to shape what comes next. We’ll start by understanding the problem, then evaluate whether AI can make a meaningful difference and what role it should play.

The question guiding us is simple:

“What problem are we trying to solve—and should AI be part of the solution?”

That is the approach we’re bringing to interloc.ai, and we’re just getting started.

Want to See Where We’re Headed Next?

Join Interloc this Thursday at 1:00 PM ET for Session 2 of our Agentic AI webinar series.

We’ll take a deeper look at interloc.ai, introduce additional agents, explore real-world enterprise asset management (EAM) use cases, and share what’s ahead on the roadmap. We’ll also discuss practical ways organizations can begin preparing for agentic AI, starting with the operational problems they want to solve.