A commercial real estate agent closing three times more deals than a peer in another market sounds like hyperbole. It isn’t.
The gap is showing up clearly between agents who operate with AI embedded into their daily workflow and those still running deals through spreadsheets, inboxes, and manual follow-ups.
This is not a story about markets or cycles. Dallas versus San Francisco is incidental. The real difference is how agents spend their time. Industry data reinforces this shift. The National Association of REALTORS reports that 46% of agents now use AI-generated content, including listing descriptions, and one in five agents uses AI tools daily.
More telling, 66% say they adopt technology primarily to save time, while 64% cite improving client experience as the main driver. AI adoption is not about novelty; it is about reclaiming hours.
In one world, time disappears into updating Excel sheets, chasing internal approvals, rewriting marketing materials, and searching for past deal context. In the other, those tasks are largely automated. agents spend more time in live conversations, on deal strategy, and in negotiation, where judgment and experience directly translate into revenue.
Commercial real estate is at an inflection point. AI is no longer an experimental layer or a future investment. It is quietly reshaping agent productivity, deal velocity, and skill development inside the firms that have already committed to it. The firms that have not are not standing still. They are falling behind.
Who’s Leading the AI Charge in Commercial Real Estate
The firms pulling ahead are not treating AI as a standalone tool or an innovation lab experiment. They are embedding it directly into agent workflows, knowledge systems, and productivity infrastructure. The pattern is consistent across markets and firm sizes. AI is being used to reduce friction, standardize access to expertise, and scale agent effectiveness without diluting judgment.
NYC: Berkadia’s Berkie and the End of Administrative Drag
Berkadia's internal AI assistant, Berkie, was built to solve a familiar problem inside large CRE firms: agents spending too much time searching for information and recreating work that already exists somewhere in the organization.
Berkie acts as a centralized interface to Berkadia’s institutional knowledge. agents use it to:
- Retrieve internal data and market insights quickly
- Support preparation for offering memorandums and analyses
- Reduce back-and-forth with operations and research teams
The impact is not cosmetic. By shortening the time between intent and execution, Berkie allows agents to move faster without cutting corners. Administrative work does not disappear entirely, but it stops dominating the day.
San Francisco: Marcus & Millichap’s Explicit Bet on AI-Led Productivity
Some firms are not building brand-name assistants, but they are making their priorities clear. Marcus & Millichap has publicly stated that it is focused on productivity growth through technology and AI investments.
That statement matters because it reflects a strategic posture, not a feature launch. It signals that leadership views AI as a core lever for improving agent output and firm-wide efficiency. In practice, this means continued investment in systems that:
- Reduce manual deal tracking
- Improve access to market intelligence
- Support agents with better tools rather than more headcount
In a competitive market, incremental productivity gains compound. Firms that systematize those gains pull away from peers that rely on individual effort to overcome operational drag.
Austin: Keller Williams and KWIQ at Knowledge Scale
Keller Williams took a different but equally telling approach. With more than 170,000 agents globally, the challenge was not whether training content existed, but whether agents could access it when they needed it.
KWIQ, the firm’s AI-powered assistant, gives agents instant access to proprietary training resources, playbooks, and guidance. It is designed to:
- Answer questions in real time
- Help draft client-facing content such as listings and updates
- Reduce dependence on managers or scheduled training sessions
The result is a form of knowledge democratization. Newer agents are not forced to wait months to internalize best practices, and experienced agents are not slowed down by searching for information they already know exists.
The Pattern Behind the Leaders
Despite differences in execution, these firms share a common philosophy. AI is being used to:
- Remove low-value friction from agent workflows
- Centralize and surface institutional knowledge
- Allow agents to spend more time in high-stakes conversations
What they are not doing is trying to automate judgment, negotiation, or relationship building. The goal is amplification, not replacement. That distinction is what separates meaningful AI deployment from surface-level tooling.
Next, the question becomes more pointed: if AI is handling more of the work around the deal, what actually makes a great agent? And where does automation fall short?
What the Leaders Got Right About AI in CRE
The firms pulling ahead have avoided a common trap. They did not deploy AI to chase efficiency metrics in isolation or to reduce headcount. They used it to reshape how agent time is spent.
At a practical level, the shift is simple. AI takes on work that is repetitive, administrative, and structurally predictable. Agents stay focused on work that is contextual, judgment-heavy, and relationship driven. That division of labor matters more in commercial real estate than in most sales environments.
AI as a Time Reallocation Engine, Not a Cost-Cutting Tool
In CRE, the value of an agent is not measured by activity volume. It is measured by the quality of conversations they have and the decisions they help clients make. Leading firms understand this and deploy AI accordingly.
Across top firms, AI is being used to:
- Automate routine workflows such as internal data retrieval and content drafting
- Organize institutional knowledge so it is searchable and current
- Reduce the operational tax that accumulates around every active deal
When those tasks move off an agent's plate, the impact compounds. An agent spending 60 percent of their week on administrative work operates in a fundamentally different reality than one spending 20 percent. The second agent is not working harder. They are working on the right things.
Why Small Gains Create Outsized Advantages
CRE productivity does not scale linearly. Each additional hour spent with clients, prospects, or capital partners increases the likelihood of uncovering new opportunities and accelerating existing ones. Small efficiency gains, when applied across a CRE brokerage, create a widening performance gap.
This is why AI adoption at the firm level matters more than individual tool usage. When every agent has faster access to information, fewer internal blockers, and cleaner workflows, the firm’s collective output rises. Deal velocity improves. Client responsiveness tightens. Execution becomes more consistent.
Amplification Over Replacement
The most important insight these firms share is philosophical. AI is not there to replace agent judgment. It is there to protect it.
By removing distractions and low-value tasks, AI allows agents to show up sharper in the moments that matter. That clarity carries through discovery calls, negotiations, and final decision-making. The technology works precisely because it stays out of the way of human judgment.
And yet, even with better workflows and more available time, something critical is still missing. Time alone does not make great agents. Skill does.
What Automation Still Can’t Teach
For all the gains AI delivers in efficiency and organization, it does not produce great agents on its own. The firms leading on AI adoption understand this tension clearly. Automation changes how work gets done. It does not change how skill is built.
The gap shows up most clearly in live conversations.
Why Experience Still Separates Top agents
Top producers did not become effective by reading playbooks or sitting through onboarding sessions. They became effective through repetition. Cold calls that went nowhere. Follow-ups that stalled. Negotiations that forced them to think on their feet. Over time, those experiences shaped judgment, timing, and instinct.
In commercial real estate, that learning curve is unforgiving. Conversations are infrequent, high-stakes, and often irreversible. A misread objection or a poorly framed question in a $5 million negotiation does not come with a reset button.
This is why “learning on the job” has always been expensive in CRE. Every real call is a test with consequences.
The Limits of Traditional Training
Most CRE firms' training programs are still built around static content. Manuals, recorded sessions, shadowing, and occasional roleplay exercises. These methods convey information, but they struggle to build readiness.
They fall short because:
- They do not simulate the pressure of real conversations
- Feedback arrives too late to change behavior in the moment
- Practice opportunities are limited by manager availability
As a result, agents are often asked to perform before they are fully prepared. Even experienced agents can drift into habits that go unchallenged for years.
High Stakes Leave No Room for Trial and Error
Unlike lower-ticket sales environments, CRE offers little margin for experimentation. Every call is tied to reputation, trust, and capital. agents cannot afford to test new positioning, objection responses, or negotiation approaches live.
This creates a paradox. Firms want agents to improve and adapt, but the environment punishes visible mistakes. Automation may free up time, but it does not solve this problem.
That gap between available time and actual readiness is where the next layer of AI begins to matter.
AI Roleplay as the Missing Layer in agent Readiness
When automation removes friction from an agent's day, it creates space. What matters is how that space gets used. Leading firms are starting to recognize that time alone does not translate into better performance unless agents have a way to practice the moments that matter most.
This is where AI roleplay enters the picture.
A Safer Way to Practice High-Stakes Conversations
AI roleplay allows agents to rehearse real conversations without risking real deals. Instead of relying on scripted roleplays or infrequent coaching sessions, agents can simulate live interactions that reflect the ambiguity and pressure of actual CRE conversations.
Well-designed roleplay scenarios mirror reality:
- Prospects push back on pricing or timing
- Buyers test credibility early in the call
- Conversations shift based on tone, hesitation, or incomplete information
Agents can run these scenarios repeatedly, adjusting their approach and seeing how different choices play out. The value is not in memorizing responses. It is in building judgment under pressure.
Why Roleplay Works When Training Fails
Traditional training teaches what to say. Roleplay teaches when and how to say it.
By simulating live calls, AI roleplay exposes patterns that static content cannot. agents learn to:
- Read signals in real time
- Respond without overcorrecting
- Stay composed when conversations move off script
This kind of practice shortens the distance between knowledge and execution. New agents progress faster because they are not waiting for live opportunities to learn. Experienced agents stay sharp by testing refinements without reputational risk.
Where Outdoo Fits
Outdoo approaches roleplay as a readiness system rather than a one-off exercise. agents practice realistic scenarios aligned to specific skills, from discovery to objection handling. Each session produces structured feedback that highlights strengths, gaps, and patterns over time.
For managers, this creates visibility. Coaching is no longer based on anecdotes or isolated calls. It is grounded in observed behavior across repeated simulations.
The result is consistency. Agents enter live conversations having already navigated similar moments dozens of times. That confidence shows up not as polish, but as control.
From Practice to Performance
CRE firms using AI roleplay are seeing a shift in how agents ramp and how teams perform. Ramp time compresses. Call quality stabilizes. Conversations sound deliberate rather than improvised.
Automation made the workday lighter. Roleplay makes the agent sharper.
The final question is no longer whether AI belongs in commercial real estate. It is how intentionally firms choose to apply it.
Frequently Asked Questions
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It simulates real CRE conversations so agents can practice discovery, negotiation, and objection handling before live deals. Scenarios follow deal stages, with realistic stakeholder pushback.
Most agents know the market, but performance slips under pressure when clients challenge pricing, timing, or risk assumptions. Roleplay builds repeatable execution when stakes rise.
Leasing concessions and escalation pushback, investment sales valuation and debt objections, late stage retrades, and multi stakeholder negotiations. These are the moments that typically stall deals.
Yes. AI helps agents spend less time on admin work and more time on conversations, strategy, and negotiation. Over time, that shift leads to higher productivity and faster deal execution.
Primarily for repetitive and predictable tasks like data retrieval, content drafting, deal tracking, and knowledge management. The goal is to reduce friction, not replace agent judgment.
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