What happens when artificial intelligence becomes smart enough to do more than answer questions, but still lacks the judgment to know when it is making a terrible decision?
That was one of the most memorable themes from Jeetu Patel, president and chief product officer at Cisco, during his Ai4 keynote conversation last week. Patel compared today’s AI agents to teenagers: highly intelligent, fearless about consequences and occasionally capable of remarkably poor judgment.
It was funny because it was instantly relatable, but it was also one of the clearest explanations I have heard of the risk facing companies as AI moves from assisting people to acting on their behalf.
For real estate leaders, that distinction matters. We already are moving beyond AI tools that simply generate listing descriptions, summarize documents or help draft an email. Agentic AI is the next wave: where AI agents take actions, access other software and complete multi-step tasks with far less human involvement. In fact, it takes control as if it were you.
That is where AI gets much more useful. It is also where things can get much more interesting – and the risk increases exponentially.
Smart enough to help. Smart enough to cause trouble.
Patel offered a terrific example. Imagine that an AI agent deletes 10,000 emails. Was the agent compromised by an attack, or did it actually believe deleting those emails was the best way to accomplish the task it had been assigned?
That sounds extreme until you consider the direction AI is heading – and this real-world example from a Meta tech leader. You can give your AI agent access to email, customer databases, transaction systems, contracts, financial information and internal company software. In real estate, that could mean an AI system touching some of the most sensitive information a brokerage, MLS or association manages: not only PII from buyers and sellers but their confidential financial information as well.
Patel’s point was that static safeguards will not be enough. As agents become more autonomous, businesses will need systems that monitor what AI is doing while it is doing it and can intervene when something starts drifting from what was intended. In other words, the smarter the agent becomes, the more sophisticated the guardrails need to become.
The best AI tools are also the safest AI tools
This is one of the central things I teach real estate agents about AI, and Patel reinforced it from a much larger enterprise perspective.
Most people still evaluate AI tools by what they can do. Is the output better? Is the model faster? Can it automate more tasks? Those questions matter, but they are incomplete. The more consequential question is whether you can trust the system with the access you are giving it.
Patel described the difference between delegating work to a trusted AI agent and an untrusted one as potentially becoming the difference between market leadership and bankruptcy. That is a dramatic way to put it, but the underlying point is hard to argue with. As companies hand AI more authority, security, oversight and control stop being IT issues and become core business issues.
For brokerages, that means the conversation about AI cannot simply be about adoption. It must include governance. What tools are agents using? What data are those tools seeing? What systems can they access? What happens when AI produces the wrong answer or takes the wrong action?
AI governance is not about slowing down innovation. It is about making sure innovation does not get ahead of judgment.
Your next competitive gap may be AI fluency
Patel also made another point that should get the attention of every real estate leader. The gap between people who understand AI and people who do not is going to become enormous.
He predicts that difference could eventually be closer to 50x than 10%. The exact number is less important than what is already becoming obvious: people who know how to use AI effectively are starting to operate very differently from those who do not.
That does not mean every real estate agent needs to become an AI expert. It does mean AI fluency is rapidly moving from an optional skill to a baseline business capability, much like email, internet search and smartphones did before it.
This is why training matters so much. Buying AI technology without teaching people how to use it safely and effectively is like handing someone a power saw without explaining what the buttons do. They may eventually figure it out, but you probably do not want your company learning that kind of lesson through trial and error.
AI changes the job. It does not erase the human.
The discussion about AI and employment was another place where Patel pushed back against today’s common narrative.
He believes some jobs will disappear and virtually every job will be reconfigured, but also argues that entirely new categories of work will emerge. His explanation was more interesting than the prediction itself: every time AI removes one bottleneck, another human bottleneck appears.
If coding becomes automated, code review becomes more important. If code review becomes automated, judgment and taste become more important.
Real estate offers an obvious parallel. AI can analyze information, summarize market data, generate marketing copy and automate routine communication. But it still cannot fully replace the judgment involved in handholding someone through what is likely the biggest financial and emotional decision of their life.
Should the seller reduce the price? Is this buyer truly ready to make an offer? How should an agent deliver difficult news without damaging the relationship? When should technology step aside and a human take over?
Those are not simply data problems. They are judgment problems.
Ironically, the more AI handles routine work, the more valuable human judgment may become.
We may be thinking too small about AI
Another comment from Patel stuck with me because it challenges how most businesses are measuring AI today.
We talk constantly about productivity. How much faster can AI complete a task? How much labor can it eliminate? How much time can it save?
Saving time is one of AI’s biggest benefits, but Patel argues that productivity may be the least interesting part of the long-term story. He believes the bigger opportunity will come from new insights and discoveries that humans could not have generated on their own, from medical breakthroughs to education and scientific research.
That does not mean ignoring the risks. Patel made the opposite argument. The stakes rise dramatically when AI operates at machine scale in hospitals, power systems or other critical infrastructure. The potential upside gets bigger at the same time the potential damage does.
That may be one of the most important things to understand about where AI is heading: two things can be true at once.
AI can create extraordinary opportunity and extraordinary risk.
The winners will not be the companies that blindly embrace AI or the ones that resist it. They will be the organizations that figure out how to use increasingly powerful AI while keeping humans, security and judgment firmly entrenched in the middle of the loop.
Which brings us back to Patel’s teenager analogy.
You can give a brilliant teenager more responsibility as they prove they can handle it. You probably would not hand them unrestricted access to your bank account, your business systems, and the keys to everything you own on day one.
AI should earn trust the same way.
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