In our last chapter, we explored how structured playbooks bring clarity and control to the most common maintenance processes. By standardizing tasks like make-readies or in-unit repairs, teams start to build consistency—and with it, a foundation for smarter operations.
Once you’ve established that baseline, you’re ready to evolve from predictable execution to intelligent guidance. That next step is prescriptive operations: the highest stage of maintenance maturity where your systems and data actively recommend actions, not just record outcomes.
It’s important to note that not every workflow needs to—or should—reach this level. Applying the 80/20 rule, focus first on the 20% of maintenance problems that generate 80% of your cost, volume, or resident complaints. That’s where prescriptive strategies can deliver meaningful, fast wins.
The Maintenance Maturity Curve
Let’s revisit the four key stages:
- Reactive Maintenance
Problems are addressed only after they occur. This approach is chaotic, costly, and unsustainable at scale. - Preventive Maintenance
Tasks are scheduled based on time or usage intervals. It helps reduce failures, but can lead to unnecessary work and missed optimization. - Predictive Maintenance
Uses data and diagnostics to anticipate issues before they arise. A step forward, but still requires manual interpretation and planning. - Prescriptive Maintenance
Combines predictive data with contextual decision-making. It generates specific recommendations—what to do, when to do it, who should do it, and what resources are needed.
Prescriptive maintenance doesn’t just tell you that a failure might happen. It tells you exactly what to do about it—within your operational, financial, and labor constraints.
A Day in the Life of Prescriptive Maintenance
Imagine your system identifies three HVAC units nearing end-of-life in adjacent buildings. Rather than sending a generic alert, it offers a detailed action plan:
- Timing Recommendation: Replace all three units the first week of April, before summer demand spikes and contractor prices rise.
- Labor Assignment: Use Technician Miguel, who has the necessary certifications and open bandwidth that week.
- Parts Planning: Order three compressors in advance; two are in stock at the warehouse, and one will arrive in five days.
- Budget Insight: The replacements can be drawn from an already-approved capex line item, minimizing financial disruption.
In short, it doesn’t just detect a need—it prescribes a strategy.
Building Blocks for Prescriptive Operations
You don’t need a data science team or enterprise AI platform to get started. What you do need is thoughtful structure, consistent data, and a few high-leverage decision rules. Here’s how to get there:
1. Centralized and Standardized Data Collection
If your team is consistently executing playbooks, you’re already halfway there. Now, ensure that each work order is:
- Tagged by category, urgency, asset type, and resolution method.
- Logged with time to completion and technician notes.
- Closed out with structured, searchable information—not just freeform text.
This turns your past work into a decision-making asset.
2. Pattern Recognition and Prioritization
Start reviewing high-frequency issues. Look for:
- Asset classes with repeated failures (e.g., A/C units over 10 years old).
- Work orders with above-average time-to-close or resident dissatisfaction.
- Seasonal spikes in volume, such as plumbing issues during cold snaps or HVAC failures during heat waves.
Use these patterns to define your first prescriptive targets.
3. Simple, Actionable Logic
Begin with easy-to-implement rules that generate recommendations. For example:
- “If a water heater has more than 3 work orders in a year and is over 8 years old, trigger a replacement request.”
- “If multiple leases are ending in the same hallway, bundle unit turns and pre-stage materials two weeks prior.”
- “If a unit has had the same issue reported three times in a quarter, flag for supervisor review and possible asset upgrade.”
These aren’t complex algorithms. They’re just smarter ways to make decisions at scale.
4. Integration with Financial and Inventory Planning
The power of prescriptive maintenance lies in how well it aligns with other parts of the operation:
- Tie recommendations to approved capex budgets, so replacements and upgrades don’t get delayed due to unclear funding.
- Sync prescriptive triggers with inventory levels and procurement cycles to ensure parts are ready when needed.
- Align labor scheduling so that techs are assigned tasks based on skill, availability, and territory—reducing wasted trips and improving first-time fix rates.
Three Prescriptive Use Cases to Pilot
Prescriptive strategies don’t require a full-system overhaul to show value. Try piloting in one of these three high-impact areas:
Turnback Prevention
When a resident gives notice, use a prescriptive model to trigger a light inspection of the unit. Recommend common repairs or updates that can be done before move-out. This shrinks your average turn time and reduces the likelihood of rushed, reactive fixes.
High-Risk Asset Planning
Build prescriptive rules around aging HVAC units, appliances, or smart locks. Instead of reacting to breakdowns, group asset replacements by risk, location, and budget. This saves money and consolidates labor for higher efficiency.
Repeat Issue Escalation
If the same issue recurs in a unit or asset within a short period, trigger an automatic flag to inspect the root cause. Often, persistent issues point to a deeper problem—like the wrong part, improper install, or poor vendor performance.
Closing Thoughts
Prescriptive maintenance isn’t about more data. It’s about using the data you already have—cleanly, consistently, and strategically.
Once your team is executing consistently and your systems are capturing reliable information, you’ve got everything you need to move beyond reactive repair. This is where maintenance stops being just an operational function—and becomes a force multiplier for NOI, retention, and asset longevity.
Start with your most painful issue. Build a rule. Test the recommendation.
Then repeat.
That’s how you move from playbooks to precision—and from maintenance to momentum.
