Who’s Accountable When AI Gets It Wrong?
Everyone loves to highlight AI’s speed, accuracy, and cost savings. What fewer people want to talk about is what happens when AI gets it wrong.
The reality is that it will. Wrong answers, outdated data, and misunderstood questions are part of the process. AI is powerful, but it is not magic. The real question is not if mistakes will happen, but who is accountable when they do.
Here’s the breakdown.
1. The Responsibility Stays with You
When your MLS or brokerage puts an AI assistant in front of members or clients, you are responsible for the experience. Vendors can provide technology, safeguards, and updates, but accountability for how AI is used rests with the organization that deploys it.
Practical step: Before launch, decide who in your organization has the authority to sign off on AI responses, workflows, and knowledge base updates.
2. Build a Review Process that Assumes Imperfection
AI works best when paired with a feedback loop. Mistakes should be expected, flagged, and corrected quickly. Without a process, wrong answers will spread and trust will erode.
Practical step: Establish a service level goal, such as correcting all flagged errors in the knowledge base within 24 hours. This builds confidence and keeps the system aligned with accurate, current information.
3. Require Transparency from Vendors
Not all AI tools operate the same way. If a vendor cannot explain how responses are generated, where data is stored, or how updates are made, that is a problem. You need to know how the system works in order to trust it.
Practical step: Ask three questions before committing to a vendor:
Does the model pull only from our data, or from the open internet?
Who owns the data once it is ingested?
How quickly can incorrect responses be fixed?
4. Train Staff on AI Etiquette
The most common failures are often human, not technical. Forwarding raw chatbot output, pasting unedited responses, and skipping fact-checks can instantly undermine credibility. Staff must know how to use AI responsibly.
Practical step: Create a one-page AI etiquette guide with simple rules: proof every response, check names and numbers, and rewrite outputs in your brand voice. Keep it visible where staff actually work.
5. Turn Accountability into a Strength
Members and clients do not expect perfection. They expect honesty and reliability. When an AI assistant cannot answer a question, the best response is: “I do not have that information yet, let me connect you with staff.” Acknowledging limits builds far more trust than presenting an inaccurate answer.
Practical step: Share your accountability approach openly. Let members know you have a review process, human oversight, and a commitment to accuracy. This shifts the narrative away from “robots replacing people” toward “better service with smarter tools.”
Final Takeaway
AI is not an excuse for poor service, and it is not a scapegoat. It is a tool, and tools require responsible use. Accountability does not disappear when you automate. If anything, it becomes even more important.
Organizations that set clear rules, train their people, and take ownership of outcomes will build trust and credibility even when AI makes mistakes.