Dynamic, Intelligent & On-Demand: The Future of Maintenance Scheduling with the AI-Powered Co-Pilot

The facility management (FM) industry is on the cusp of a paradigm shift. For decades, FM professionals have relied on manual and reactive processes to run building operations and maintenance.?

Now, with the rapid rise of data and technology-driven FM, Artificial Intelligence (AI) has already started to automate repetitive and mundane maintenance tasks, freeing up time and effort that can be redirected toward more strategic, value-adding initiatives.

I believe this opens up the opportunity for scheduling and allocation of maintenance tasks to become automated, and possibly even unsupervised, to enhance team productivity, efficiency, and reliability. I’ve been thinking about the challenges this change could present and am working on pathways to help FMs make it a smooth transition.?

Let’s start by exploring why the prospect of AI in maintenance scheduling and resource allocation is promising and how FM teams can reclaim their time to focus on things that matter more.

The Burden of Manual Scheduling and Resource Allocation

Planned maintenance for many FM companies involves dedicated teams spending a lot of effort on repetitive, time-consuming tasks. Scheduling repairs, assigning technicians to maintenance requests, and optimizing resource utilization – these activities, while crucial, often leave them drained and with little time for more important work.?

On top of that, FM companies today are dealing with workforce shortages, skill gaps, and increasing customer expectations and cost pressures, which can make optimal scheduling, resource utilization, and adherence to SOPs even more challenging. Increasing backlogs, waning service quality, and reactive maintenance responses often become a consequence of this manual approach.

Maintenance Automation with AI: Co-Pilots for Scheduling Teams

As facilities and building systems evolve further, it adds another layer of complexity to modern-day maintenance needs. Asset utilization and building occupancy levels are no longer static, increased collaboration necessitates flexible workspace arrangements. and conventional spaces have transformed into ‘smart’ ones constantly generating large volumes of data.

With numerous variables and constraints affecting the use of data for operations and maintenance, the entire idea of scheduling has to become more intelligent, dynamic, and on-demand.?

The arrival of AI in FM offers a solution to this challenge. AI-powered assistants (I’m calling them Co-Pilots) can revolutionize scheduling and allocation processes by leveraging data and machine learning algorithms. Here's how:

• The Co-Pilot can ingest vast amounts of historical data, including maintenance schedules, technician availability, and asset utilization patterns that become the foundation for creating more dynamic and need-based schedules.
• The Co-Pilot can factor in unexpected equipment issues on the spot and predict potential failures by analyzing real-time data. It can optimize maintenance calendars and site visits, ensuring a balance between asset performance, operational efficiency, and costs.
• Managing work orders and maintenance tracking can also be automated with the Co-Pilot. Natural Language Processing (NLP) enables the interpretation of text-based work order submissions and autonomously creates work orders with proper categorization, location info, etc. It then tracks work order status and completion data to maintain digital maintenance records and history.

Important to note here that the Co-Pilot won't replace scheduling teams. Instead, it will augment their capabilities and reduce the time spent on scheduling by up to 75%, freeing them up to pursue higher-level functions.

Beyond Automation: How to Build a Strong Foundation for AI Success

The true power of AI in FM lies not just in automation, but in its ability to transform how teams utilize data. And its arrival doesn't mean the demise of the FM professional. Instead, it signals a fundamental shift in their role. While AI handles the routine and mundane, here are some areas where teams should spend more quality time:

• Data Quality: Tackling the Garbage In, Garbage Out Principle?

AI systems will only be as good as the data they're fed. Inaccurate, incomplete, or inconsistent data can lead to suboptimal scheduling recommendations and skewed insights. FMs must develop robust data quality control procedures, train personnel on proper data entry, and implement standardized forms? Regular data quality audits will also be crucial to identify and address any discrepancies.

• SOP Adherence: Ensuring AI Aligns with Established Protocols?

AI can automate tasks, but ensuring adherence to established SOPs requires human oversight. FMs need to develop training, capacity-building programs, and monitoring procedures to guarantee that AI-powered systems function within defined protocols.

• Knowledge Creation, Retention & Transfer: Your Competitive Advantage

Veteran FM personnel possess knowledge and experience that's often undocumented and at risk of disappearing when they leave the company. FMs can capture this knowledge in a digital repository by training the Co-Pilot on team conversations and interactions with SMEs. By making this knowledge readily accessible through the chatbot's conversational interface, you can proactively combat brain drain, foster continuous learning, onboard new talent, and preserve your competitiveness for the future.

Are You Embracing the AI Revolution in FM?

In the eventual move to data-led O&M, scheduling has to keep pace and evolve much beyond what it is now. And with the AI revolution in FM, companies will open up opportunities big and small by embracing it the right way.?

In a recent example, I started working on the first version of the Co-Pilot algorithm that helps schedulers and command center operators at FM companies fast-track allocations and scheduling of maintenance tasks.

I considered all factors in the business-as-usual scenario - a mix of tasks, SLAs, Ad-hoc Assessments, Escalations, Resource Constraints, etc.

I wanted to challenge myself, so I created an inventory of over 12000 tasks to be processed. It took under a minute to get the schedule out!

While it’s still a work in progress, I can't wait for it to find its way onto Xempla - Decision Support System for Enterprise Asset Management , and perhaps become accessible as an API for others to use.

I would love to share the thesis of my algo, understand your perspective, and whether it’s a problem you want to solve now.?

Let’s catch up here.

If you liked this article, join the 2200+ professionals reading the Forever Forward Newsletter and get notified of more such personalized insights from the industry. Break free from the status quo and help shape a future that's more inclusive and sustainable for everyone in facilities management, asset operations, and maintenance.