In episode 170 of the Asset Champion podcast, host Mike Petrusky speaks with James Waddell, President and Chief Research & Innovation Officer at Cognitive Corp. They dive deep into a discussion on the current state of AI in asset and facility management, including its rapid evolution from experimental technology to a sophisticated tool capable of producing high-quality work results. They then look at practical applications of AI agents and agentic processes, the critical importance of strategic intent when deploying AI solutions, and the challenges of integrating enterprise data systems. They also address the importance of the human element in an increasingly automated industry, discussing how professionals can remain relevant and work alongside AI tools.
Agenda
- Evolution of AI from large language models to agentic processes in 2026
- Importance of strategic intent and measurable outcomes when deploying AI
- Challenges of enterprise data integration and the need for comprehensive data access
- Impact of AI on job roles and the future of work in facility management
What you need to know: Facility and maintenance takeaways
Takeaway 1: AI has evolved into a sophisticated tool capable of high-quality work through agentic processes
“If you give an agentic agent, an AI agent that’s following an agentic process, a job role in your company with job permissions just like you would a regular employee, so it has access to all the same data that a normal employee would, and you’ve done your homework and you’ve deployed this agent properly, it can produce high quality work results,” James explains.
Which raises interesting questions, he says.
“The question is how do we interface with them? How do we work with them? How do we make ourselves relevant in this new world?”
Takeaway 2: Strategic intent is essential—don’t deploy AI without clear goals and measurement
Organizations must have a well-defined strategic purpose and measurable outcomes before implementing AI solutions, or risk wasting resources on technology that delivers no real value, James warns.
“Without a clear strategic intent on what they are trying to do and how they measure it, there probably isn’t a reason that you would need to aggregate all this data…”
Instead of deploying AI just to say you have it, he stresses the importance of alignment with business strategy: “Every year there’s new strategic intents. If you can’t align what you’re trying to do with those strategic intents, you’re in trouble.”
When it comes to measuring success, James wants companies to look at more than just a single number.
“If you can show that you are increasing revenue and increasing human productivity, then you are moving in the right direction in the world now, right? Not just increasing revenue. You can take that all the way down to an individual employee.”
Takeaway 3: Humans must remain in the loop—AI should augment, not replace, human accountability
While AI can produce high-quality work, human oversight and accountability remain essential components of responsible AI deployment in facility management.
“We do not want the work results to be put into production without a human in the loop, right, so our approach is always, always, always make sure that there is a human that’s accountable for the work result,” James explains.
He also mentions the need for sophisticated validation processes: “The orchestrator then has a peer review process that goes, OK, I want this to be academically rigorous and go through peer review before I get a chance to see it. So, it does that.”
It’s the human-in-the-loop approach that ensures quality control while leveraging AI’s capabilities to enhance productivity and decision-making.
Maintenance management insights
- AI agents following agentic processes can now perform multi-step job roles with access to enterprise data, producing high-quality work results that rival or exceed human capabilities in certain tasks.
- Organizations must establish clear strategic intent and measurable outcomes before deploying AI, ensuring that technology investments align with business goals and demonstrate tangible returns through increased revenue and human productivity.
- Comprehensive enterprise data integration is essential for AI optimization, as siloed information prevents AI systems from delivering meaningful insights and recommendations across facility and asset management operations.
- Human accountability and oversight remain critical components of AI deployment, with organizations maintaining humans in the loop to review and validate AI-generated work results before implementation.
- The transformation timeline for AI impact on facility management jobs is accelerating beyond initial predictions, requiring professionals to actively learn how to work alongside AI tools and remain relevant in an evolving industry landscape.
Do a deep dive into more asset management insights by exploring all Asset Champion Podcast episodes.
Watch the full video here: https://www.youtube.com/watch?v=AWkIfXCltnA&list=PLSkmmkVFvM4H3pwnlU2AuqynuRDpvnh4J&index=1
