The pressure for businesses to leverage generative AI (genAI) or agentic AI is massive, but when I hear executives complaining that there is no way to compete against businesses leveraging the fast-moving technology, I’m forced to chuckle. There is a powerful way to compete against genAI and it’s textbook simple: note all of the problems with the tech — the list is extensive — and build your value-add on those. Here’s a rundown of some of the competitive advantages companies can offer. Reliability GenAI systems have low reliability. That doesn’t mean most of the answers/recommendations it generates are necessarily wrong. It’s simply that errors occur frequently, with no particular pattern. That could be from a variety of causes, including: hallucinations; insufficient/outdated/poor-quality data that it was trained on; problems with the data that was used to fine-tune the model (the fine-tuning data might have also been accurate, but it somehow interfered with or contradicted the core training data); the system could have misinterpreted a query; the user could have mis-phrased a query; or a dozen other things. Another data accuracy/reliability issue involves language. GenAI models’ accuracy plunges when it is dealing with non-English information . For the typical multinational company, that could be a massive problem. And these systems often ignore guardrails , meaning they may choose to ignore any restrictions you try and impose. Add that all up and IT directors simply cannot rely on these systems. It’s like working with a brilliant employee who will periodically make stuff up in an official report. When confronted, the employee is apologetic but stresses that he or she will continue to make stuff up. Can you trust that employee with important work? I was recently talking with a cybersecurity vendor that decided to avoid genAI tools — and suffer all of its compliance, cybersecurity, accuracy and data leakage issues — and  instead simply rely on AI Machine Leaning. Alpha Level, which deals with event alert triage, uses an ML approach known as Time Series modeling. It also claims the cost is far lower, at least at the enterprise volume level. Real-world expertise Some executives talk about leveraging expertise as a way to compete against genAI. That is a decent point, but it has to be information that beats genAI. Consider a law firm. Even more narrowly, consider case law, where attorneys try to find precedent for an argument they want to make. At one level, genAI tools can win that battle. They literally can memorize every word of every court decision — globally, if need be. No lawyer can do that. But case law research is not merely about reading cases. The attorney needs to understand the intent, the nuances of a case and the relevant history. No genAI system can do that. Early in my reporting career, I was a full-time court reporter for a daily newspaper. One afternoon, I found myself in the courthouse basement in the law library. In the back, I saw the managing partner of one of the state’s largest law firms, flipping through books. I asked him why he was doing such work when he could easily assign it to a more junior lawyer. He smiled and said, “I’ve been doing this for 40 years. I routinely find obscure cases that these young hotshots would never find. I simply know where to look and how to interpret them.” That is precisely the kind of mastery that will elude genAI. Another example is in an area close to where I live: journalism. Some media outlets are trying to use genAI to write stories. There are some very basic stories where that might work , such as routine weather reports, maybe sports scores and perhaps even obituaries. But the ultimate story is what used to be known as “man bites dog” and today is simply “surprising the reader.” To do that, a reporter must find things that readers don’t know and that contradicts what they do know. That is exactly what genAI cannot do. Everything the technology churns out is simply a reworded version of what has already been said. If you look at fiction writers, such as those writing movie or television scripts, a similar discovery is made. GenAI could replace really bad writers. But the nature of genAI would almost certainly prevent it from writing hit shows where audiences are quoting lines the next day. Data Leakage Data leakage and the related “lack of data control” is connected to how these systems grow. Are they training on the queries made? Will the information shared in a query on Monday find its way into an answer given to a competitor on Friday? There are straight-forward ways to limit such leakage, whether through open source, on-prem closed systems, or even the extreme of using air-gapped systems. ( CapitalOne is a great example of an enterprise toying with such limitations to safely use genAI.) If a business created a closed-loop system to deliver the flexibility of genAI without the data risks, that could potentially do absurdly well . Agentic AI Agentic systems are simply begging for a company to devise a locked-down system for leveraging agents without the massive risks . In short, there are quite a few powerful ways to compete successfully with genAI and agentic systems. Just don’t ask ChatGPT to recommend any.