Merrick Lackner is co-founder and CEO of Rently, a resident lifecycle management platform for single-family and multifamily operators. Since 2011, Rently has helped define self-guided touring and has expanded into leasing automation, smart access and resident experience technology. In this written Q&A with AI Time Journal, he explains why AI is shifting the advantage in SaaS from individual features to ownership of complete workflows, and what that means for pricing, competitive moats and enterprise transformation.
You argue that AI is shifting the competitive advantage in SaaS from individual features to ownership of complete workflows. What is driving that shift, and where are you already seeing it happen?
Two things are driving it. First, features have become cheap to build. With modern AI tools, a capable team can ship a chatbot, a scheduling widget or lead nurturing in weeks, so a feature alone rarely stays a differentiator for long. Second, AI is only as good as the context it has. An AI agent that sees one step of a process is guessing. One that sees the whole process can actually act.
Leasing is a clear example. A renter's journey runs from listing to inquiry, scheduling, identity verification, the tour itself and follow-up. For years, those steps lived in separate tools. A leasing AI bolted onto one of them starts every conversation cold. It doesn't know whether the renter booked a tour, showed up, stayed five minutes or forty.
When one platform owns the whole journey, the AI knows all of that and can respond to the specific moment. That is the shift we're seeing across our industry. Operators are consolidating vendors and asking which partner can run the full workflow, not which one has the longest feature list.
At Rently, how is AI changing the journey from initial leasing inquiry through scheduling, touring, and follow-up? Which improvements have been most meaningful in practice?
The biggest change is that the journey now keeps moving when the leasing office is closed. Renters today are searching and scheduling at all hours, and more than half of our self-guided tours happen outside office hours. Our AI leasing agent, Ria, answers inquiries in seconds by text and email around the clock and handles 95% of lead questions. It books tours and follows up based on what each prospect actually did.
The most meaningful improvement has come from connecting AI to touring and access. Because Ria runs on the same platform as our self-guided tours, she knows when a renter missed a tour or toured two homes and went quiet. She responds to that context instead of sending a generic drip. That connection is why clients have seen an 84% lift in completed tours.
We've also taken friction out of the front end. Renters can book a tour directly from a Zillow or Apartments.com listing in two clicks, and 88% of our leads tour the same day. For leasing teams, the payoff is up to 50 hours saved per agent each month.
What are the biggest operational challenges in moving from isolated AI features to end-to-end AI-driven workflows?
The first challenge is data. An end-to-end workflow only works if the AI can see clean, current information from every step. In leasing, that means unit availability and policies from the property management system, tour activity, access events and conversation history, all in sync. Most organizations have that data spread across vendors that were never designed to talk to each other.
The second is deciding where the AI acts and where a person takes over. A repeatable task like scheduling a tour or answering a pet policy question is a good fit for automation. A question about fees or concessions often isn't. Our research found 57% of renters still want a person available for those conversations. So you have to design the handoff, with full context, as carefully as you design the automation.
The third is people. When the AI handles the routine follow-up, the team's job changes. Leasing agents spend less time chasing leads and more time on conversations that close leases. That's a better job, but it takes training, new metrics and leadership that explains why the change is happening.
As software features become easier to replicate, where can SaaS companies build durable competitive advantages?
I'd point to four.
The first is proprietary context. We've facilitated more than 20 million self-guided tours. Every booking, no-show, door unlock and follow-up teaches us how renters actually behave. A generic model doesn't have that, and a new entrant can't recreate it quickly.
The second is the physical world. Rently designs and manufactures its own locks, hubs and access panels. When the AI and the hardware are built together, the AI can act on real events at a real door, like confirming a verified renter arrived or flagging access outside a scheduled tour. That's much harder to copy than software alone.
The third is integration depth. Our platform connects to the property management systems, listing sites and partners operators already use. Each connection takes years of trust and engineering to build.
The fourth is the customer relationship. More than 4,000 operators run on Rently, and the workflows we build come from years of discovering where their teams lose time. Domain expertise and customer trust compound in a way a feature never does.
How do you expect AI to change SaaS pricing and the way customers evaluate software value over the next three years?
Customers will stop buying capabilities and start buying outcomes. When AI does work a person used to do, a per-seat price makes less sense, because the value no longer scales with the number of logins. Over the next three years, I expect more pricing tied to the work completed and the results delivered: leads handled, tours booked, leases signed, hours given back to the team.
The evaluation will change too. Buyers are already asking sharper questions. What does this replace? What does it cost per outcome compared with how we do it today? How quickly will we see results? In our industry, operators look at cost per lease, time to lease and staff hours. A tool that can't show movement on those numbers will struggle to justify itself, no matter how impressive the demo is.
That raises the bar for vendors, and I think it's healthy. It rewards companies that own enough of the workflow to measure their impact end to end. It's hard to price on outcomes if you only touch one step of the process.
What advice would you give enterprise leaders trying to move beyond AI experimentation toward measurable business transformation?
Pick one workflow that matters to the business and own it end to end. Many AI pilots stall because they're scattered: a chatbot here, a summarization tool there, none tied to a number leadership cares about. Choose a process that is high-volume, repeatable and clearly measurable, and commit to transforming the whole thing rather than one step.
Set the scorecard before you start. Decide which outcome you're moving, measure the baseline and compare it against a period or a site without the change. For leasing, that might be response time, tour completion and lead-to-lease conversion. If you can't say what success looks like up front, the pilot will never graduate.
Finally, design for people. Be clear about which decisions stay human, build a clean handoff and show your team what they gain. In our experience, adoption follows when people see AI taking the repetitive work off their plate so they can focus on the conversations that move the business.
Performance figures and research findings cited in this interview are reported by Rently.
