Generative AI's Branding Problem

The terminology around AI sounds incredibly technical. The actual skills required? Clear communication and good judgment.

The people who need AI most are convinced they cannot use it. Executives who spend their days synthesizing feedback into decisions think "generative AI" sounds too technical for them. HR professionals who communicate across every level of an organization assume it is for the tech team. Strategists who translate complex ideas for different audiences believe they lack the expertise. There is a gap between what generative AI sounds like and what it actually requires. The terminology is technical. The skill needed is communication. That gap is keeping some of your best people on the sidelines.

Consider these terms: "prompt engineering", "large language models", "context windows". They sound like they belong in a computer science lecture hall, not in a Monday morning strategy meeting. Yet these intimidating labels are related to tools that are fundamentally about communication. They help turn scattered thoughts into structured business cases, personalize client outreach, and transform meeting notes into clear action items. We have wrapped accessible technology in technical packaging.

How We Got Here

The AI community named these tools after their technical architecture: neural networks, generative models, transformers. It is the equivalent of calling email "asynchronous digital message protocol" or spreadsheets "tabular data computation software". Imagine if, in the 1990s, business leaders had to learn "Hypertext Transfer Protocol over Secure Socket Layer" before they could shop online. The technology would have remained in IT departments indefinitely.

That is what is happening now. Research confirms that the biggest barrier to AI adoption is not the technology itself but user confidence. Employees hesitate, wondering how it applies to their roles, whether they will use it correctly, or if they will appear less competent for relying on it. Before they can benefit from AI, they must first get past names that sound like a message to engineers only.

The Skills You Already Have (And How to Build on Them)

Here is the paradox: the most successful AI users are not the most technical. Ethan Mollick, a professor at Wharton who studies AI’s impact on work, observed: “The best users of AI I know are good managers, are good teachers. The skills that make you good at AI are not prompting skills, they’re people skills.”

If you are good at understanding what someone might be confused about, breaking down tasks into steps, or troubleshooting when something goes wrong, you already have the foundation. The person who writes clear project requirements, explains complex concepts to stakeholders, and delegates effectively possesses the core skills needed.

In practice, getting good at working with AI takes time and repetition. The good news is that this practice looks like the skill development you have done throughout your career. When you learned to delegate effectively, you did not take a course in Delegation Engineering. You tried, saw what worked, adjusted your approach, and improved.

Working with AI follows the same pattern. You learn to be specific about what you want. You offer better context. You recognize when a response is useful and when to refine your request. Even the technical-sounding term prompt engineering describes these same skills. According to IBM, essential capabilities for prompt engineers include strong communication, the ability to explain concepts to nontechnical stakeholders, and clear instructions for defining goals.

For example, a regional HR leader who had never used AI asked ChatGPT to turn a messy set of manager comments into a one-page performance summary. She then asked for three tone options and a checklist of next steps for each employee. The draft was not perfect, but it was good enough to spark faster decisions and a clearer conversation. That was not a technical feat. It was effective communication.

Research bears this out. In a landmark study involving 758 Boston Consulting Group consultants, roughly 90 percent improved their performance when using generative AI for creative tasks. These were strategy consultants doing the kind of problem-solving that characterizes knowledge work across industries. Consultants below the average performance threshold saw their work quality improve by 43 percent, and top performers gained 17 percent.

Reframing the Language

The terminology we use shapes who feels invited to participate. When we call it prompt engineering, we signal that this is a technical discipline requiring specialized training. When we emphasize large language models and neural networks, we suggest that understanding the underlying technology is a prerequisite for use.

Research on self-efficacy shows that skill alone does not determine behavior. What matters more is a person’s belief in their ability to use that skill effectively. Even professionals with access to powerful digital tools do not always feel confident using them. When they lack confidence, they avoid the technology or use it in limited ways. This is the real cost of technical branding. The language convinces capable professionals they should not try.

Other technologies succeeded by becoming invisible. We do not think about TCP/IP when we browse the web, or about relational databases when we search for a product. The technical complexity exists, but it is abstracted away behind intuitive interfaces and everyday language. AI deserves the same treatment.

The path forward requires a shift in how we talk about AI:

Use human analogies.
Treat AI like a competent teammate. Give it context, goals, and examples. That framing makes interaction intuitive for anyone who has led a project or collaborated with colleagues.

Celebrate communication skills.
The best AI practitioners are often the best communicators. Make this the headline when you talk about Generative AI.

Emphasize outcomes over architecture.
Skip the technical descriptors and explain what the tool does in plain language. For example, it can help you write annual performance reviews by synthesizing feedback from multiple sources, create job descriptions that attract qualified candidates, prepare for difficult conversations by exploring different approaches, or turn dense technical information into customer-friendly language.

The Bottom Line

Let’s stop calling it prompt engineering and start calling it what it is: good communication. Let’s stop emphasizing the generative technology and start emphasizing the practical capabilities. Create internal announcements. Prepare for presentations. Reorganize reports into different formats. Brainstorm alternatives when you are stuck.

The technology is ready. The skills are already there. The remaining task is to fix the language barrier we have erected between capable professionals and transformative tools. AI was not built for the technical elite. It was built for everyone who knows how to communicate clearly, think critically, and collaborate effectively.

That is a much larger audience than prompt engineering would suggest.