The Future of AI in Maintenance: Podcast with Bo Lais of Lula and Joe Easton of Rent Manager

Maintenance has always been a core part of property management, but expectations around maintenance have changed faster than most teams and budgets can keep up with.

In Episode 143 of Rent Manager’s Beyond Rent podcast, Joe Easton sits down with Bo Lais, Co-founder and CEO of Lula, to break down what’s driving the biggest shift in maintenance today: an on-demand resident mindset, rising repair costs, and the rapid acceleration of AI in operations.

Watch the full episode

1. Residents now expect “on-demand” maintenance

One of the clearest takeaways from the episode is how dramatically resident expectations have changed. As Lais explains, “massive shifts in society” have reshaped how people think about service, and property maintenance is no exception.

In an on-demand world, residents increasingly expect speed and efficiency when something goes wrong. When a maintenance issue comes up, waiting days for a response or even basic scheduling updates quickly becomes frustrating, even if the issue isn’t an emergency.

That expectation puts real pressure on property managers. Delivering faster service is no longer a nice-to-have, it directly impacts resident satisfaction, retention, and a property’s overall reputation. At the same time, Lais emphasizes that speed has to be balanced with the financial realities of the industry. Rising costs mean property managers can’t simply move faster without smarter systems in place.

That’s why the conversation points back to leveraging technology and buying power as essential tools—ways to meet modern expectations without letting maintenance costs spiral out of control.

2. Repair costs are rising, making cost control more critical than ever

Higher resident expectations are only part of the challenge. As Lais explains in the episode, the cost side of maintenance has also changed significantly.

Repairs today are more expensive than they were just a few years ago. Labor shortages, inflation, rising material costs, and ongoing supply chain disruption have all contributed to higher maintenance expenses across the industry. Lais also notes that broader market conditions, including higher interest rates, have played a role in pushing costs upward.

This pressure does not stop at the maintenance budget. It often turns into difficult conversations with owners, especially when pricing is unpredictable or repairs escalate unexpectedly. Without clear visibility into costs, property managers are forced to slow down, gather multiple quotes, and manage uncertainty at every step.

That is why Lais emphasizes the importance of leveraging technology and buying power to help control maintenance costs. Predictable pricing and better cost management make it easier to move faster, protect margins, and maintain trust with both owners and residents.

3. Buying power and flat-rate pricing reduce uncertainty and speed up approvals

A more tactical part of the conversation focuses on how Lula approaches cost control through buying power. Lais explains that Lula completes roughly 1,000 work orders a day, which creates the volume needed to negotiate directly with manufacturers and standardize labor pricing across its network.

That scale allows Lula to offer a flat-rate service catalog for many of the most common and expensive maintenance categories, including HVAC replacements, water heaters, appliances, and roofing priced by the square. By going directly to manufacturers and pairing that pricing with negotiated labor rates, Lula is able to deliver more consistent and predictable costs.

For property managers, this predictability removes a significant source of friction. Instead of waiting on multiple quotes, teams can share pricing with owners almost immediately. Fewer unknowns mean fewer delays in approvals and a faster path to scheduling the work.

Lais also notes that speed matters on the resident side. When pricing is clear upfront, repairs can begin sooner, which shortens the overall repair cycle and improves the resident experience. This approach reflects Lula’s broader focus on predictable pricing and more standardized maintenance delivery.

4. Preventive maintenance isn’t optional if you want fewer emergencies

The conversation also highlights a clear shift away from the industry’s traditional reactive approach to maintenance and toward a more preventive model.

Lais points to data showing that preventive maintenance can reduce costs by 15 to 20 percent per year, per door. The reason is straightforward. Emergency repairs tend to be the most expensive, often happening after hours or under urgent conditions that limit options and drive costs higher.

During the episode, several practical examples of preventive maintenance are discussed. Routine HVAC servicing, including regular filter changes, can surface small issues before they turn into full system failures. Annual roof inspections can help identify minor leaks early, preventing long-term water damage. Gutter cleaning, often overlooked, can reduce the risk of water erosion around a property’s foundation.

Beyond cost savings, preventive maintenance also affects the resident experience. When residents see consistent, proactive upkeep, it signals that the property is being cared for. That perception can influence satisfaction and, over time, make residents more likely to renew.

5. AI is everywhere, but the use case matters more than the buzzword

The episode makes it clear that artificial intelligence is already deeply embedded in property management, but Lais is careful to separate real value from hype. While AI is powerful, he describes the industry as being in a “shiny object” phase, where teams feel pressure to adopt AI without always having a clear outcome in mind.

Throughout the conversation, Lais emphasizes the importance of starting with the problem, not the technology. Property managers should first identify the operational challenge they are trying to solve, then define what success looks like. That might mean faster response times, better cost control, or fewer unnecessary truck rolls. Only after that should AI be evaluated as a potential solution.

Lais also highlights several red flags to watch for when evaluating AI vendors. Overpromising is a major concern, particularly claims that everything can be fully automated. He also stresses the need for transparency around how AI models are trained, what data they use, and what guardrails are in place. Without those protections, AI can create risk by providing unsafe guidance or making commitments it should not.

6. Maintenance AI must be transparent, controlled, and trained for the job

As the conversation turns deeper into AI, both Lais and Joe are clear about the risks of getting it wrong. In property maintenance, an incorrect answer is not just inconvenient. It can be unsafe, costly, or create serious downstream issues.

They discuss how AI systems can “hallucinate,” confidently providing incorrect information when guardrails are not in place. In a maintenance context, that risk can show up in several ways. AI might guide residents through unsafe actions, particularly around electrical issues. It could approve or escalate the wrong scope of work, or misclassify urgency in ways that delay the right response.

Lais explains that Lula’s approach to AI is built around real operational data drawn from hundreds of thousands of work orders. That data is paired with clear guardrails and transparency so customers understand how decisions are being made. This foundation allows AI to support maintenance workflows without introducing unnecessary risk.

This philosophy is central to the direction Lula is taking with Foresight, its work order management and maintenance operations platform, which is designed specifically for the realities of property maintenance.

7. Clean data is the foundation for automation

One of the most practical points in the episode is that AI can only be as effective as the data it has access to. If key information lives outside the system of record, automation breaks down.

Joe summarizes this clearly during the discussion. When warranty details live in a PDF, a spreadsheet, or someone’s memory, systems cannot reliably act on them. 

Lais reinforces why getting data into the system of record matters. Accurate warranty information can prevent wasted spend. Work order history makes it easier to decide whether a repair still makes sense or if replacement is the better option. Asset-level context improves triage, scheduling, and coordination across teams.

This is also why maintenance dashboards and KPI visibility are becoming essential. They are no longer just reporting tools, but the foundation that enables automation, better decisions, and more consistent outcomes.

8. Predictive pricing and smart NTEs reduce guesswork

Beyond intake automation, the episode explores how historical maintenance data can be used to improve pricing decisions. Lais describes how predictive pricing allows teams to estimate what a diagnosis should cost based on past work orders.

From there, smarter NTEs can be set dynamically, adjusting based on the specifics of the issue instead of relying on static limits. 

Static thresholds frequently result in job stoppages, repeated approval requests, surprise invoices, and inconsistent vendor behavior. Predictive cost baselines help reduce these issues by aligning expectations earlier in the process.

This approach supports a broader effort to reduce uncertainty in maintenance operations and move closer to a more predictable, data-driven model.

9. The long-term future points to fewer humans touching routine work orders

One of the most forward-looking moments in the episode comes when Lais shares a prediction about automation. He believes that by the end of next year, roughly half of all work orders could move from intake to completion without being touched by a human, at least for repeatable and standardized issues.

In practice, that workflow would begin with a resident submitting a request. AI would gather the right details, verify urgency, and route the work to a qualified technician or vendor based on availability and skill set. Scheduling would happen automatically, documentation would be captured once the job is complete, and invoices would be reviewed and validated before updates are sent to all stakeholders.

Human involvement does not disappear. Exceptions, complex jobs, and edge cases still require hands-on attention. What changes is that routine work no longer consumes the majority of coordinator time, freeing teams to focus on higher-value tasks.

Bringing Modern Maintenance Together

The conversation highlights how quickly maintenance is evolving. Rising resident expectations, increasing repair costs, and rapid advances in AI are forcing property managers to rethink how maintenance is delivered. As Lais explains, meeting today’s demand for speed and efficiency requires balancing technology with financial realities, using buying power and preventive maintenance to stay in control.

The episode also makes clear that AI is becoming an increasingly important part of maintenance operations, but only when it is applied with clear use cases, transparency, and guardrails. 

While Rent Manager provides industry-leading tools for accounting, reporting, maintenance, and communication, partners like Lula extend that ecosystem with specialized solutions designed to help property managers navigate these changes more effectively.

You can listen to the full conversation here as well as on major platforms, including Apple Podcasts, Spotify, and Amazon Music. To discover more about the specific tools discussed, request info from Lula or explore Rent Manager’s full suite of features at RentManager.com.