Artificial intelligence has become the defining narrative across enterprise software. Nearly every briefing, product announcement, and marketing campaign now emphasizes AI capabilities, agents, automation, and the technological advances taking place behind the scenes. From an innovation standpoint, the excitement is warranted. The pace of progress has been remarkable, and capabilities that once seemed years away are quickly becoming reality.
Yet amid all of this innovation, many vendors appear to be making a fundamental mistake. They are speaking about AI in ways that matter deeply to technologists and industry insiders, but often fail to connect with the people they are actually trying to sell to.
As an example, at 3Sixty Insights, we have spent considerable time examining AI adoption among HR organizations. Across multiple studies, one finding has remained consistent. Only a relatively small percentage of HR leaders identify themselves as advanced users of artificial intelligence. Even that figure should be viewed cautiously, since self-identification does not necessarily translate into technical expertise. The broader takeaway is that most line-of-business leaders are still navigating AI from a practical standpoint. They are trying to understand how these technologies can help them improve outcomes, eliminate inefficiencies, and address persistent challenges. They are not trying to become experts in model architectures or agent orchestration frameworks.
Unfortunately, many vendors are acting as though they are.
During a recent briefing, one software provider spent a considerable amount of time discussing the large language model underlying its AI capabilities. The conversation eventually evolved into a detailed discussion around model selection, architectural choices, and the logic behind those decisions. These are legitimate topics and, in certain situations, important ones. However, it raised an interesting question. How many line-of-business executives truly care which large language model powers a solution?
The answer, at least early in the buying cycle, is probably very few.
That statement may sound controversial, but it reflects the reality of how enterprise software buying decisions are often made. Buyers are not initially evaluating vendors based on whether they use one foundational model or another. They are not asking detailed questions about agent frameworks or reasoning engines. Instead, they are focused on something much simpler. They want to understand whether a solution can address the challenges they face, how quickly it can deliver value, and why it is different from the alternatives available in the market.
Technical discussions absolutely matter, but timing matters as well.
As buyers progress through the purchasing process, security teams, legal departments, and IT stakeholders will naturally begin asking more detailed questions. At that stage, vendors need to be prepared to explain how their AI capabilities work, how customer data is isolated and protected, and why certain architectural decisions were made. Those conversations are essential. However, they typically occur after a vendor has already secured a seat at the table.
The problem is that many organizations are leading with those conversations rather than earning the opportunity to have them.
This issue extends beyond large language models. Across the software industry, vendors have become increasingly focused on feature functionality and AI announcements. The result is an overwhelming number of messages that sound remarkably similar. Every company is introducing agents. Every company is promoting automation. Every company is describing its AI capabilities as transformative. Consequently, many announcements blend together, making it increasingly difficult for vendors to distinguish themselves in the eyes of prospective buyers.
This growing sameness helps explain why so many software providers struggle to stand out despite making significant investments in innovation. From the buyer’s perspective, another AI feature announcement often looks very similar to the previous one. The market becomes saturated with capabilities, while differentiation becomes increasingly difficult to articulate.
The vendors that are likely to emerge from this environment most successfully will be those that shift the conversation away from technology and toward outcomes. Buyers want to understand the problems being solved. They want clarity around how solutions will help them improve productivity, enhance decision making, and achieve better business results. More importantly, they want to understand why one provider’s approach is fundamentally different from the next vendor that walks through the door.
Technology remains critically important, but buyers do not purchase technology for technology’s sake. They purchase solutions to business problems. The AI capabilities themselves are ultimately a means to an end, not the end itself.
For that reason, vendors should resist the temptation to lead with architecture diagrams, model names, and technical jargon. Those details have their place, but they are rarely the reason a buyer decides to engage in the first place. Winning attention requires something much more fundamental. It requires understanding the problems customers are trying to solve and communicating clearly how those outcomes will be achieved.
In many ways, the software industry finds itself facing the same challenge it has always faced. Technology evolves, capabilities expand, and buzzwords change. But the central question buyers ask remains remarkably consistent.
How are you going to help me?
The vendors that answer that question most effectively will ultimately separate themselves from those that simply join the growing chorus of AI announcements.