A few years ago, most companies picked one AI system and stuck with it. That phase is over. New research shows large companies are now nearly five times more likely than small ones to be running several AI models at once, and the pattern is spreading fast across every industry.
The problem nobody warned businesses about is what happens in between the models. When a company sends work to five or ten different AI systems depending on the task, someone has to decide which system handles what, who is responsible when it fails, and how to prove that later if a regulator or a client asks.
That is why a new piece of software, called an AI gateway, is quietly becoming standard equipment. Think of it as a switchboard operator sitting between your staff and the AI systems they use. Instead of an employee or an app calling ChatGPT directly, the request goes to the gateway first, and the gateway decides which AI model is best suited, cheapest, safest, or fastest for that specific job.
This is not a small corner of the software market. Analysts tracking this space expect it to grow past 11 billion dollars in yearly revenue by 2030, and some forecasts put the growth rate above a quarter more every single year. That kind of growth only happens when a real, widespread problem needs solving, not a hypothetical one.
The uncomfortable numbers come from a recent Grant Thornton survey of 950 senior executives. Only one in five companies has actually tested a response plan for when an AI model fails or gives a bad answer. Nearly four out of five leaders admitted they could not confidently pass an independent audit of their AI governance within three months. Companies are moving fast on AI and slow on the safety net underneath it.
The advice from people actually running these systems is consistent. Separate the big policy decision, which models are allowed and who pays for them, from the small daily decision of which model handles a specific piece of work. Put one person in charge of each AI tool who has the power to shut it down if something goes wrong. Do not assume the cheapest AI model saves money: a system that gives more wrong answers and needs more human correction often costs more in the end than a pricier, more reliable one.
For most businesses, this does not mean building a 50 agent system like the payments company at the center of this story. It means something simpler and more urgent: if your company already uses more than one AI tool across different departments, someone needs to own the decision of which tool does what, and a plan needs to exist for the day one of them gets something badly wrong. Right now, for most companies, that plan does not exist yet.
The businesses that get ahead here are not the ones with the fanciest AI. They are the ones who decided, in writing, who is accountable before something breaks.