How Do You Build an AI Implementation Plan for a Brokerage?
By Emily Terrell — Top Coach and Speaker at Tom Ferry International. Licensed since 2016. Closing 70+ deals/year while coaching agents nationwide.
A brokerage AI implementation plan is a one-page operating document that names an owner, three scored use cases, an approved tool stack, data and compliance rules, a budget, and one adoption metric. Most offices skip the plan and buy tools instead. This guide walks through all six sections, with a scoring method you can run this week.
Key Takeaways
- A brokerage AI implementation plan fits on one page with six sections: owner, use cases, stack, rules, budget, and metric.
- Score every candidate use case on frequency, time, and risk, then launch only the top three.
- Split ownership so a compliance lead governs the plan and a respected producing agent drives adoption.
- Start with tools your agents already have, because two out of three say their brokerage already provides what they need.
- Put a measurement date on the calendar before launch, and measure weekly use rather than training attendance.
What is a brokerage AI implementation plan?
A brokerage AI implementation plan is a one-page document that decides which AI workflows your office adopts, who owns them, what they cost, and how you’ll measure them. It sits between your AI policy, which sets the rules, and your rollout timeline, which sets the sequence. The policy tells agents what they can’t do. The plan tells them what to do first.
Why this matters for real estate agents and brokerage leaders
Here’s the thing nobody wants to say in the leadership meeting: almost every brokerage is “doing AI,” and most of it isn’t producing anything.
Adoption isn’t the problem. According to Delta Media Group’s 2026 Real Estate AI & Leadership Survey, reported by HousingWire (August 2026), only 1.9% of brokerage leaders have no plans to adopt AI this year, down from 10.6% in 2024.
Results are the problem. According to NAR’s 2025 Technology Survey (September 2025), 17% of agents reported a significantly positive impact from AI on their business, while 46% saw no noticeable impact at all. The same survey found two out of three agents agree or strongly agree that their brokerage already provides all the tech tools they need.
Read those together. The tools are in the building. Agents are opening them. What’s missing is the decision layer that says which task changes, who owns it, and when you’ll check.
“Technology continues to be a powerful force in real estate, driving efficiency and marketing innovation.”
— Jessica Lautz, Deputy Chief Economist, National Association of REALTORS®
Regulators expect that decision layer too. The North Carolina Real Estate Commission told brokers in 2024 that anything AI produces still has to be reviewed and verified to stay within license law and commission rules. A plan is where that review gets a name attached to it.
The 6 sections of a brokerage AI implementation plan
One page. Six headings. Fill in every one before you buy a license, book a training, or announce anything at the sales meeting.
1. Who owns the plan?
Two people own it, because it’s two different jobs.
The governance owner is usually a managing broker or compliance lead. They approve tools, keep the policy current, and handle incidents. NAR’s March 2026 broker guidance recommends designating exactly this kind of oversight role.
The adoption owner is a producing agent your people already respect. They run a five-minute workflow demo at the weekly sales meeting and maintain the shared prompt library. When one person holds both jobs, governance wins and adoption quietly stalls. I’ve broken down why training without an internal owner decays inside about three weeks.
2. Which use cases go first?
Score every recurring task, then launch only the top three.
Rate each task 1 to 3 on three factors: frequency (how often agents do it each week), time (how long it takes each time), and risk (how much compliance or client exposure it carries if AI gets it wrong). Add frequency and time, then subtract risk.
| Task | Frequency | Time | Risk | Score |
| Lead follow-up drafts | 3 | 2 | 1 | 4 |
| Social captions | 3 | 2 | 1 | 4 |
| Listing descriptions | 3 | 3 | 2 | 4 |
| CMA narratives | 2 | 3 | 3 | 2 |
| Client-facing chatbot | 3 | 1 | 3 | 1 |
That table is an illustration from my coaching work, not survey data. Run it with your own agents’ real task lists and the order will shift.
Don’t forget brokerage-side use cases. Kate Hulbert, marketing director at Bozeman Real Estate Group, described in Inman (January 2026) how her brokerage built custom GPTs to check agent marketing against the brand book, answer company policy questions, and support new-agent onboarding. All three score well on this matrix: high repetition, low client exposure, and they free up staff hours immediately.
3. What’s in the approved stack?
Start with an inventory, not a shopping list.
List every tool the brokerage already licenses, then list the AI features that switched on inside those tools without anyone deciding: call summaries in your CRM, caption generators in your marketing platform, document readers in transaction management. Those features already touch client data, so they belong in the plan.
Then decide platform versus standalone. Brokerage leaders are consolidating: in the same Delta Media Group survey, leaders rated all-in-one marketing platforms with AI 7.3 out of 10, the highest score in three years of tracking. A standalone tool earns a spot only when it does one high-frequency task better than anything you already pay for.
Finish with two short lists: approved tools, and tools prohibited for client or transaction data.
4. What are the data and compliance rules?
Reference your AI policy here. Don’t rewrite it.
The plan needs four lines that connect each use case to the policy: which outputs require licensed human review before they publish, what client information never goes into an AI tool, which Fair Housing language scan runs on generated marketing, and which advertising rules apply. In Texas, that’s TREC Rule 535.155, which treats AI-written marketing the same as any other advertising. If any use case involves AI-generated voice, the FCC’s February 2024 ruling puts it under TCPA consent rules.
Your agents also need their own one-page version, because the brokerage’s document protects the brokerage, not the individual license. I covered that in why your brokerage’s AI policy isn’t enough.
This is general information, not legal advice. Have your broker and an attorney licensed in your state review your final plan and policy.
5. What does it cost?
Budget three lines: tool licenses, launch training, and the owners’ time.
The third line is the one most plans leave at zero, and it’s the one that decides whether anything sticks. If your adoption owner is giving up an hour a week of production time, put a number on it and decide how you’ll recognize it.
Consolidate before you add. According to NAR’s 2025 Technology Survey, 24% of agents already spend more than $500 a month on technology for their business. Your agents are paying for overlapping tools right now, and the plan is the place to stop it.
6. How will you know it’s working?
Pick one metric and put the check dates on the calendar before launch.
The metric is weekly active use: how many agents ran an approved workflow in the last seven days. Capture baseline hours on your three launch tasks in week one so you have a before number. Then check at day 45, day 90, and every quarter after.
For the phase-by-phase sequence that follows this plan, from the champion cohort to office-wide standards, see my guide to a realistic AI adoption timeline for a brokerage.
“If your AI plan doesn’t name an owner, a first workflow, and a check date, it isn’t a plan. It’s a software receipt. Write those three things down before you buy anything.”
— Emily Terrell, Tom Ferry Coach
How I use this in my own business
I run my own San Antonio business on this exact page. When I mapped AI onto my operation, I scored eleven recurring tasks with the matrix above. Lead follow-up drafts and listing descriptions came out on top. A client-facing chatbot scored last: high frequency, but every reply carries license exposure, so it stayed off the plan for the first six months.
Ownership is split the same way I teach it. My transaction coordinator owns the prompt library and updates it when a model changes and the output drifts. I own the review gate, so nothing client-facing goes out without a licensed name on it. That split is a big part of why I close 70+ transactions a year in roughly five hours a week of active management.
Common mistakes
Buying before scoring. A new platform without scored use cases becomes another login nobody opens. Score first, then decide whether you need anything new.
Giving one person both jobs. A broker who owns compliance and adoption will default to compliance every time, and the weekly demo slot disappears by week four.
Launching the riskiest use case first. Client-facing chatbots and pricing narratives look impressive in a demo and carry the most exposure. They belong in phase two, after the review process works.
Calling the policy the plan. A policy produces compliant agents. It doesn’t tell anyone which task to stop doing manually or by when.
Leaving owner time off the budget. If the adoption owner’s hours aren’t accounted for, the role gets dropped the first busy month.
Skipping the check date. A plan without a date on the calendar is a document you’ll rediscover next year.
Frequently Asked Questions
What should a brokerage AI implementation plan include?
A brokerage AI implementation plan needs six sections: a named owner, three scored use cases, an approved tool stack, data and compliance rules, a budget, and one adoption metric with a review date. Keep it to one page. NAR’s March 2026 broker guidance covers the policy elements in detail, but the implementation plan adds what the policy leaves out: which workflows come first, who runs them, and how you’ll know they’re working.
Is an AI policy the same as an AI implementation plan?
No. An AI policy sets the rules: approved tools, data limits, human review, Fair Housing checks, and enforcement. An implementation plan sets the work: which use cases launch, in what order, who owns them, what they cost, and how you’ll measure success. You need both, and the policy comes first. A policy without a plan produces compliant agents who never change how they work.
Who should own AI implementation at a brokerage?
Split ownership between two people. A managing broker or compliance lead owns governance: approving tools, updating the policy, and handling incidents. A producing agent the office respects owns adoption: running a five-minute workflow demo at the weekly sales meeting and maintaining the shared prompt library. When one person holds both jobs, governance usually wins and adoption stalls.
How do you choose which AI use cases to roll out first?
Score every candidate task on three factors: how often agents do it each week, how long it takes each time, and how much compliance risk it carries if AI gets it wrong. Launch the three tasks with the highest frequency and time scores and the lowest risk. Listing descriptions, lead follow-up drafts, and social captions usually win. Pricing and client-facing chatbots wait.
How much should a brokerage budget for AI implementation?
Budget per active user, not per office, and include three lines: tool licenses, launch training, and the owner’s time. Start with tools agents already have before buying new ones. According to NAR’s 2025 Technology Survey, 24% of agents already spend more than $500 a month on technology, so the first question is what you can consolidate, not what you can add.
Should a brokerage buy an all-in-one AI platform or separate tools?
Start with what your brokerage already licenses, then consolidate. Brokerage leaders are moving toward AI delivered inside unified platforms: in Delta Media Group’s 2026 survey, they rated all-in-one marketing platforms with AI 7.3 out of 10, the highest in three years. Standalone tools still earn a spot when they do one high-frequency task better than anything in your current stack.
Why do brokerage AI implementation plans fail?
Most fail because leaders write them as tool purchases instead of operating plans. Nobody names an owner, nobody scores the use cases, and nobody checks usage after launch. NAR’s 2025 Technology Survey found 46% of agents saw no noticeable business impact from AI, even though most were using it. The gap is rarely access. It’s the missing plan between the tool and the workflow.
Bring this to your team or event
Emily Terrell speaks at brokerage events, real estate conferences, and team trainings on AI, systems, and social media — the exact playbook in this post, delivered live to your audience. As a Top Coach and Speaker at Tom Ferry International and an active agent closing 70+ transactions a year, Emily speaks from the stage about what’s working right now, not theory. Recent stages include NAHREP and eXp Con.
Book Emily to speak at your next event:
Email: eterrell@yourcoach.com
Phone: (210) 400-9191
Web: coachemilyterrell.com
For real estate agents who want to implement this: Get the weekly real estate prompt library at weeklyrealestateprompts.com or follow @coachemilyterrell on Instagram for daily systems and AI breakdowns.