🧠 When Tech-Speak Becomes White Noise: AI Adoption Is Being Lost in Translation

If you’ve ever sat through an AI demo and left more confused than when you arrived, you’re not alone. One of the biggest obstacles to wider adoption of AI, especially in sectors like membership, charities, education, and even SMEs, isn’t the technology itself. It’s the way it’s being explained.

Too much of the conversation around AI is dominated by the voices of developers and technologists. These are brilliant people who understand functionality inside and out, but their ability to communicate the potential of tools to non-technical professionals often falls short. What many of us hear isn’t inspiration but a white noise of acronyms, jargon, and frameworks that feel completely disconnected from our day-to-day reality.

Worse still, that language often carries a subtle (and sometimes not so subtle) whiff of condescension. As if the inability to immediately grasp machine learning architecture is a character flaw. As if asking, “How would this actually help my team?” is a sign you’re not trying hard enough.

Let me be clear, this isn’t a swipe at developers or engineers. Their work is foundational; but if AI is to make a meaningful difference in how businesses operate, we need more translators, i.e., people who understand both the technical landscape and the lived experience of professionals who are trying to solve problems, serve members, or improve services. We need more conversations that start with challenges and outcomes, not tools and features.

Then there’s the issue of risk and cost

Tech culture has long embraced the mantra of fail fast, fail often, which makes sense when you’re in a startup environment backed by investors who expect a certain amount of burn for a potential unicorn, but most organisations can’t afford that luxury. They don’t have the appetite, or the budget to trial five different AI solutions just to see what sticks. They need solutions that are reliable, scalable, and practical now.

They’re not looking to experiment for the sake of experimentation. They’re looking to make better decisions, deliver better services, and get better value from limited resources. They want to know:

• Which tool is fit for purpose?

• What problem does it solve?

• What does it cost?

• How quickly can I implement it?

If we want AI to be genuinely adopted across the economy, and not just championed by those already fluent in the language of tech, we need to change how we talk about it, and that means:

• Fewer grand claims and more grounded use cases.

• Less jargon and more plain English.

• Less hype and more honesty about what works, what doesn’t, and what it costs to find out.

AI has enormous potential, but if we keep pitching it in ways that feel alien, expensive, and inaccessible, we shouldn’t be surprised when people stop listening.