The next advantage in real estate may come from something less visible than another lead source: having more capacity to take care of the people already in your business.
A past client needs a thoughtful check-in. A transaction needs attention. A document contains information that belongs in your CRM. None of these tasks is especially dramatic. Together, they consume the time and attention you need for the conversations that matter.
This is where AI agents become useful. They can help move defined pieces of work forward while you stay responsible for the relationship, the decisions, and the outcome.
It is also the idea behind Ellis, Realvolve’s AI operations partner: connect the history in your CRM to a useful next step, then help carry the work forward within the controls you set.
What the productivity research actually shows
There is a growing body of evidence that AI assistance can improve performance on specific kinds of work. The useful question is where it helps, how the work is organized, and what still needs human judgment.
Measured gains in customer support
In Generative AI at Work, researchers Erik Brynjolfsson, Danielle Li, and Lindsey Raymond studied the introduction of an AI assistant using data from 5,172 customer-support workers. Productivity, measured as issues resolved per hour, increased by 15% on average.
The gains varied across workers. Less experienced workers benefited more; the most experienced saw smaller speed gains and small declines in quality. The result supports thoughtful assistance in a defined workflow, rather than a blanket promise that every person and task improves equally.
Faster work when the task fits the technology
A field experiment involving 758 Boston Consulting Group consultants found that, on tasks within AI’s capabilities, participants using AI completed 12.2% more tasks and finished them 25.1% more quickly on average. Performance worsened on a complex task selected to fall outside those capabilities.
The practical lesson is to choose the work carefully. Preparing a first draft or organizing information is different from deciding whether a recommendation is appropriate for a particular client.
More capacity for higher-value work
Microsoft’s 2026 Work Trend Index surveyed 20,000 knowledge workers who use AI across 10 countries. Among the AI users surveyed, 66% said AI allowed them to spend more time on high-value work. And 86% said they treated AI output as a starting point, rather than a final answer, while remaining responsible for the thinking.
These are self-reported findings from a technology vendor’s research, rather than a controlled productivity experiment. They offer a useful picture of how people are using AI: delegating more execution while retaining responsibility.
None of these studies tested Ellis or established a productivity gain for real estate agents using it. They provide evidence for the broader approach: well-chosen AI assistance, connected to a workable process and human review.
From answering a question to carrying work forward
A chatbot can help when you bring it a question. An AI agent can also work with context, use available tools, and take permitted steps toward an outcome. The distinction is how much of the workflow it can help carry.
For a real estate business, that shift is meaningful. Asking for a generic follow-up email still leaves you to find the client history, decide who needs attention, adapt the message, and connect the next task to your process.
An operations partner inside your CRM has a different starting point: the contacts, conversations, notes, workflows, and transactions your business has already recorded.
That does not make the information automatically complete or correct. It makes the work more connected. The quality of the next step still depends on the available context and your standards.
The opportunity is to spend less attention assembling the work, and more attention deciding what matters.
How Ellis connects context to action
Ellis works inside Realvolve around a simple operating loop: notice, prepare, act, and keep you informed.
- Notice: identify relationships going quiet, transactions losing momentum, or closings that need preparation.
- Prepare: bring relevant history together with a drafted message, proposed task, or workflow recommendation.
- Act: carry out the action types you allow within your guardrails, keeping approval in place for the steps you want to own.
- Keep you informed: make executed actions visible and bring exceptions back when your judgment is needed.
That can support several familiar parts of an agent’s day. Ellis can prepare follow-up using available client context and voice preferences, recommend a workflow with an explanation of its fit, and propose CRM updates from document or email content.
You review extracted details and proposed record matches before committing document-driven changes. Team insights follow account permissions. Outcome and follow-up analysis depend on the history available in your CRM.
These details matter because productivity comes from a reliable working relationship with the system. You need to understand what it used, what it proposes, and what will happen next.
Why the future still needs your judgment
The research suggests that the advantage comes from combining human and AI strengths. It also shows why handing everything over without review is a poor operating model.
In real estate, your value includes understanding a client’s circumstances, recognizing when a conversation needs a personal call, negotiating through uncertainty, and knowing when to ask a better question.
Ellis is designed to support that role. You choose which action types require confirmation. Communication permissions stay separate from CRM updates. Selected routine work can proceed within your settings, while exceptions return for review.
A message can be well written and still be wrong for the moment. A date can be extracted cleanly and still need verification. Good delegation includes a clear place for those decisions.
More personal attention, with a system behind it
Consider a past client whose relationship has gone quiet. The useful result is a conversation that feels informed and appropriate.
Ellis can use the available history to help prepare that next step. You can review the draft, adjust the wording, or decide that a phone call would be better. The preparation supports your attention; it does not substitute for knowing the person.
The same principle applies to workflow recommendations and team handoffs. When the context is easier to find and the routine preparation is already underway, there is more room to focus on the client.
For an independent agent or a small team, this is a compelling direction: build more operating capacity around your relationships without making every additional task depend on your memory.
Start with one workflow and measure what improves
You do not need to redesign your entire business at once. Start with a repeatable activity where the intended result is clear.
- Choose the work. For example, preparing past-client check-ins or reviewing proposed CRM updates from a document.
- Check the context. Make sure the relevant records, preferences, and notes are reasonably current.
- Set the controls. Decide what needs approval and what routine actions you are comfortable delegating.
- Review the result. Look for accuracy, appropriate tone, useful next steps, and exceptions that need attention.
- Measure the whole process. Include review and correction time. Track preparation time, follow-up completed, or tasks that no longer fall through the cracks.
The goal is a better working process. More AI activity by itself is not evidence of a more productive business.
Our view is that the next generation of productive real estate businesses will be built around this kind of delegation: connected systems carrying more routine work, with people setting direction and protecting the relationships.
Explore Ellis and see it in a demo. Walk through a priority, the context behind it, the action Ellis prepares, and the controls that determine what happens next.
Build systems. Live life.
Research sources
- Brynjolfsson, Li, and Raymond, Generative AI at Work, Stanford Digital Economy Lab. The study reports productivity in customer support, measured as issues resolved per hour.
- Dell’Acqua and coauthors, Navigating the Jagged Technological Frontier, Organization Science, published 2026. The experiment studied consulting tasks and found that results depended on whether the work fit AI’s capabilities.
- Microsoft, 2026 Work Trend Index: Agents, Human Agency, and the Opportunity for Every Organization, May 5, 2026. Survey findings cited here concern knowledge workers already using AI.
