Smart-sounding isn't the same as right.
A model can top a benchmark chart and still be wrong about your situation. A benchmark is a signal. It isn't trust, judgment, or a workflow. Judge a model by how well it helps you finish the loop on real work.
Free class · Foundations
How to use AI to solve real estate business problems.
Ask a model about a stuck listing and it'll hand you a plan in seconds, usually before it understands the problem. This class gives you a repeatable loop that makes AI define the problem, find what's missing, and check its own work before it recommends anything.
Ryan Wanner, founder of AI AccelerationInnovation Lab North, Compass Lakeside, HendersonvilleEvery class on this page is free to read and watch. No account needed.
What you'll learn.
- 01Why the model jumps to advice before it understands your problem, and the one line that stops it.
- 02How OODA (observe, orient, decide, act) gives you a frame for any business problem you bring to AI.
- 03How to define the real problem in one sentence, with an owner, a win condition, and a constraint.
- 04How to make the model interview you and separate known facts from missing ones.
- 05How to audit an answer for hidden assumptions, weak evidence, and real risk before you act on it.
- 06How to turn the best option into the smallest useful action, then run the loop again from what happened.
Part 1
Use OODA as the frame. Don't rename it.
This isn't a new framework. It's the OODA loop: observe, orient, decide, act. The practical method sits inside it.
- 01
Observe
Look at what's actually happening before you type anything. The facts you bring in are yours to gather. AI can't observe your business for you.
- 02
Orient
Analyze the real problem, research what's missing, and audit the weak spots. This is where most of the work lives, and where most people skip ahead.
- 03
Decide
Create options with the tradeoffs in the open. Pick one on purpose instead of taking the first answer the model hands you.
- 04
Act
Implement the smallest useful move, then iterate. What happened becomes the next thing you observe.
Treat it like a consultant you verify.
The goal is to know your business well enough to say "that won't work here" to any expert, including the AI. Push back. Ask for sources and open every link. When a link is dead or doesn't say what the answer claims, say so and make it try again.
Part 2
Six moves inside the loop.
Analyze, research, and audit are how you orient. Create is how you decide. Implement and iterate are how you act. Most bad AI work starts at the first move, when someone asks for output before the problem is clear.
- 01
Analyze
State the problem in one plain sentence. Name the owner, the win condition, and the constraint: time, money, compliance, relationship, data, or operations. Don't ask for a marketing plan when the real problem is seller trust or pricing.
- 02
Research
List the facts you have, the facts you're missing, and any similar cases worth comparing. Make the model ask you its three most important questions before it recommends anything.
- 03
Audit
What is the model treating as true without proof? Which claims need a source or a human check? What would cost money, trust, or compliance if it's wrong? Does it sound smart while skipping the hard part?
- 04
Create
Ask for three moves, not one answer: the safest, the fastest, and the one with the most upside. Then make the model pick one and say what it's giving up.
- 05
Implement
Turn the choice into something outside the chat: an email, a script, a checklist, a CRM note, a calendar block. Give it an owner, a time, a definition of done, and a human review.
- 06
Iterate
What happened? What did the client, lead, or market tell you? What did the model miss or overstate? Change the context or the action, then run the loop again.
Vague
"Give me a marketing plan for my listing."
Analyzed
- Problem: showings are steady but nobody is writing offers
- Owner: me, with the seller's sign-off
- Win condition: a pricing decision the seller agrees to
- Constraint: updated comps are missing
The second version tells the model what's actually wrong. It might not be a marketing problem at all. That's the point of analyzing first.
Part 3
Run the loop on one real problem.
Open ChatGPT, Claude, or Gemini. Pick a problem that's actually sitting in your business, not a practice exercise. Paste this, fill in the brackets, and answer the questions it asks you.
The problem-solving loop
I want to solve a real business problem using OODA plus a structured AI process. PROBLEM: [describe the problem] BUSINESS CONTEXT: [who this affects, what is at stake, what constraints matter] Use OODA as the frame: Observe, Orient, Decide, Act. Inside that frame, run this process with me: 1. ANALYZE Restate the real problem in one sentence. Identify the owner, the win condition, and what might be getting confused. 2. RESEARCH List what information is needed before recommending anything. Separate known facts from missing facts. Ask me the 3 most important questions first. 3. AUDIT Check assumptions, weak evidence, risks, and places your answer could be wrong. Tell me what must be verified. 4. CREATE Give me 3 possible next moves: safest, fastest, and highest-upside. Include tradeoffs. 5. IMPLEMENT Turn the best move into a concrete action I can take today. 6. ITERATE Tell me what result to watch for, what to measure, and how we should adjust on the next pass. Do not jump to advice before completing Analyze, Research, and Audit.
The most important line is the last one. It stops the model from pretending it knows enough. The first pass doesn't have to be perfect. It has to produce a real signal you can learn from.
Paste the answer into a fresh chat and ask it to find every weakness. Bring the critique back and make the first chat respond.
"That's bad" teaches nothing. Too expensive, wrong audience, missing a step: name the actual miss.
If nothing changes outside the model app, you didn't implement. You had an interesting conversation.
Today, run it once. This week, use it before any important AI output, decision, or client-facing piece of work. After that, it becomes the way you use AI.
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- The full class slide deck
- The AI Business Problem-Solving Loop skill card for your AI
Questions agents ask.
How do I use AI when I'm stuck on a business problem?
Don't ask for the answer first. Give the model the problem and your business context, then make it work in order: define the real problem, list what's missing and ask you questions, audit its own assumptions, and only then offer options. The loop prompt on this page runs that sequence for you.
What is the OODA loop?
OODA stands for observe, orient, decide, act. It's John Boyd's decision cycle, and this class uses it as is rather than renaming it. Observe what's happening, orient by analyzing, researching, and auditing, decide by weighing options, and act on the smallest useful step. Then what happened becomes the next observation.
How do I stop ChatGPT or Claude from giving generic advice?
Tell it not to advise yet. End your ask with an instruction to finish analysis, research, and audit before recommending anything, and ask it to question you first. Generic advice usually means the model is guessing at facts you never gave it.
How do I check AI output before I act on it?
Audit it. Ask what it's assuming without proof, which claims need a named source or a local fact, and what would cost you money, trust, or compliance if it's wrong. For a second opinion, paste the answer into a fresh chat and ask it to find every weakness. You make the final call.
What kinds of problems does this work on?
Anything real that's sitting in your business: a listing that isn't moving, a confused client, a marketing bottleneck, an operations snag, a hiring decision, or a system you want to build. Bring one real problem, not a practice exercise.
Keep going.
Foundations
AI 101 for Real Estate Agents
Think about AI like a new hire, brief it properly, give it a reusable Context Card, and fact-check every claim.
Foundations
How to Use Claude in Real Estate
Hand a desktop AI agent one bounded job: set up a Claude Cowork project, check its permissions, have it interview you, then inspect, correct, and rerun.
Get found
How to Get Recommended by AI for Real Estate
Check what answer engines say about you, verify every source they cite, and publish one sourced answer worth citing.
Library
Every free class
The whole library of recorded classes, by topic.
Research sources
Check the current rules, capabilities, and source basis.
- 01NIST: AI Risk Management Framework Core ↗
The Govern, Map, Measure, and Manage framework, including training, intended-use, human-oversight, testing, and documentation outcomes.
- 02NIST: Generative AI Profile ↗
Model-neutral guidance for generative-AI risk, testing, documentation, transparency, and content provenance.
- 03OpenAI Academy: Prompting ↗
First-party prompting guidance for clear tasks, useful context, desired output, and iterative refinement.
Product access, limits, local listing rules, platform policies, and laws can change. Re-check the linked authority before important or public work.
Keep building
Put AI to work in your real estate business.
AI Acceleration: Real Estate is a private, self-directed curriculum for practical AI work across content, communication, lead follow-up, CRM, client research, operations, and relationship management. Build it. Review it. Keep what works.
Explore the Skool community ↗Follow Ryan + AI Acceleration
No ranking, revenue, closing, or time-saving promise. The classes teach the work and the review systems around it.
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