Why Precision in Language Determines Whether We Build Trust or Confusion

The technology industry has a vocabulary problem. Not a complexity problem or even a communication problem. We have an honesty problem. And as artificial intelligence continues its rapid integration into business operations, customer experiences, and daily life, the gap between what we call things and what those things actually are has grown into a credibility chasm.

This isn’t semantic pedantry. The words we use to describe AI systems shape expectations, inform decisions, drive investment, and ultimately determine whether organizations deploy these technologies wisely or recklessly. When marketing departments co-opt terms with established meanings and stretch them beyond recognition, they don’t just create confusion; they set up their customers, their partners, and their own organizations for failure.

It’s time we had an honest conversation about what we’re actually building.

The Autonomy Illusion

Perhaps no term has been more egregiously misappropriated than “autonomous.” We hear about autonomous AI systems, autonomous agents, and autonomous decision-making. The word appears in pitch decks, product descriptions, and breathless analyst reports. There’s just one problem: autonomous AI doesn’t exist.

Autonomy, by its actual definition, means self-governing. It means operating independently, making decisions without external control, and determining one’s own rules and direction. An autonomous entity doesn’t require oversight, doesn’t need guardrails, and doesn’t depend on human intervention to correct its course.

The larger problem here is that there are people who might read that and say, “that’s exactly what my AI system is doing.”

No AI system meets this standard. Not one.

Every AI system in production today operates within parameters defined by humans. Every large language model requires prompting. Every machine learning system was trained on human-curated data, evaluated against human-defined metrics, and deployed with human-established boundaries. The most sophisticated AI systems still hallucinate, still require monitoring, still need humans to verify outputs before consequential actions are taken.

What we actually have are systems with varying degrees of automation, or the ability to execute predefined tasks with minimal human intervention during execution. That’s valuable. That’s genuinely useful. But it’s not autonomy, and pretending otherwise creates dangerous expectations about what these systems can and should be trusted to do.

When a procurement officer believes they’re purchasing an autonomous system, they make different decisions about oversight, staffing, and risk management than they would if they understood they were buying a highly automated tool that still requires human judgment at critical junctures. The vocabulary shapes the deployment, and the deployment determines whether the technology helps or harms.

Agentic AI: The Integration Layer Masquerade

The term “agentic AI” has become the darling of enterprise technology marketing, conjuring images of digital workers independently pursuing goals, making strategic decisions, and operating with something approaching intention. It sounds transformative. It sounds like science fiction made real.

What it actually describes, in most implementations, is an action-based intelligent integration layer. Systems that can orchestrate multiple tools, APIs, and data sources in response to complex queries. Systems that can break down multi-step tasks and execute them sequentially. This is genuinely impressive engineering and genuinely helpful capability.

But there’s a meaningful difference between an integration layer that can take actions across systems and an agent in any meaningful sense of that word. An agent implies agency, the capacity to act on one’s own behalf, to have interests, to make choices in pursuit of goals that the agent itself has adopted. Current AI systems don’t have interests. They don’t have goals they’ve chosen. They respond to prompts, follow instructions, and execute within the parameters of their training and constraints.

The conflation matters because it obscures both the genuine value and the genuine limitations of these systems. Agentic AI capabilities can dramatically increase productivity by automating complex workflows that previously required human coordination across multiple tools. That’s the fundamental value proposition, and it’s substantial. But positioning these as agents with something approaching autonomy or intention creates unrealistic expectations and, perhaps more dangerously, encourages deployments that assume levels of judgment and adaptability the systems don’t possess.

The Absurdity of “Agentic” Branding

The vocabulary problem becomes particularly acute when marketers attempt to brand entire categories with the agentic label. Consider phrases now appearing in enterprise technology marketing: “Agentic CX.” “The Agentic Enterprise.” These terms sound impressive until you apply the actual definitions of the words being used.

If “agentic” derives from agency, the capacity for autonomous action, then “Agentic CX” would translate to something like “Autonomous Customer Experience” or “Agency-Based Customer Experience.” What does that actually mean? Does your customer experience operate independently? Does it have its own interests and pursue its own goals? The phrase collapses under the weight of its own semantic confusion.

“The Agentic Enterprise” fares no better. If we’re being honest about vocabulary, this phrase implies that enterprises require some external system to provide agency to their operations, as though organizations haven’t been acting with agency since the invention of organizations. Enterprises are already agentic. They’re composed of people, and people have agency. They make decisions, pursue goals, adapt to circumstances, and act in the organization’s best interests.

What these brands are actually trying to convey, that AI tools can automate workflows and take actions across systems, is genuinely valuable. But the branding doesn’t communicate that value; it obscures it behind terminology that, when examined honestly, makes no coherent sense.

This isn’t an attempt to bend semantics or argue against marketing positions for sport. It’s simply correlating the actual definitions of words with what is being branded. “Agentic” has a meaning. When that meaning is applied to how it’s being used in these phrases, the phrases don’t hold together. The emperor has no clothes, and we’ve collectively agreed not to mention it.

The Redundancy of “AI Automation”

Consider the phrase “AI automation,” which has become nearly ubiquitous in enterprise technology marketing. If we’re being honest about vocabulary, this phrase reads as “automation automation.”

Artificial intelligence is, at its core, intelligent automation. It’s the application of computational systems to tasks that would otherwise require human cognitive effort. Machine learning automates pattern recognition. Natural language processing automates text interpretation. Computer vision automates image analysis. The entire field is fundamentally about automating cognitive tasks.

So what, exactly, does “AI automation” mean that “AI” alone doesn’t convey? The answer, in most cases, is nothing. It’s marketing amplification. It’s doubling down on a term to suggest something more substantial or differentiated than what’s actually being offered.

This might seem like harmless redundancy, but it contributes to a broader pattern of vocabulary inflation that makes it increasingly difficult for decision-makers to evaluate what they’re actually being sold. When every product description uses maximum-impact terminology regardless of accuracy, the signal-to-noise ratio collapses. Buyers can’t distinguish genuine capabilities from marketing enthusiasm, and the entire market becomes less efficient as a result.

The Intelligence Question

We should be honest that even the foundational term “intelligence” in artificial intelligence is generous. What we call AI demonstrates sophisticated pattern matching, impressive statistical prediction, and remarkable capability at specific cognitive tasks. Whether it constitutes intelligence in any meaningful sense remains an open philosophical question.

This isn’t to diminish what these systems can do. A system doesn’t need to be intelligent in the way humans are intelligent to be extraordinarily useful. But when we unreflectively apply terms like “intelligent,” “understanding,” “reasoning,” and “learning” to these systems, we import assumptions about their capabilities that may not be warranted.

When marketing materials describe a system as “understanding” customer needs, they’re likely referring to pattern-matching against training data that enables the system to respond appropriately to common queries. That’s useful. It’s also not understanding in the way humans understand, with context, intention, the ability to recognize when something doesn’t make sense, and the ability to push back. The vocabulary creates expectations that the system will behave with a comprehension it doesn’t actually possess.

Why This Matters Beyond Semantics

The argument for honest vocabulary isn’t about linguistic purity. It’s about consequences.

When we use inflated vocabulary, we set inflated expectations. Organizations deploy AI systems expecting autonomous operation and are unprepared for the oversight requirements. Teams implement agentic AI expecting digital workers, only to be surprised when the systems require careful prompt engineering and continuous refinement. Executives approve AI automation initiatives, expecting transformative efficiency, and are disappointed by the incremental improvements that constitute actual success.

Honest vocabulary leads to appropriate deployment. When you understand that you’re implementing a sophisticated automation tool rather than an autonomous system, you plan for human oversight. When you know that agentic capabilities are about workflow integration rather than independent agency, you invest in the prompting, monitoring, and refinement those systems require. When you understand that AI is already automation, you focus on evaluating specific capabilities rather than being impressed by redundant terminology.

Honest vocabulary also builds trust. As AI systems become more prevalent, public understanding and acceptance become increasingly important. Every time the technology fails to live up to inflated claims, trust erodes. Every time a system positioned as autonomous makes a mistake that human oversight would have caught, confidence declines. The vocabulary problem is a trust liability that affects the entire industry.

The Problem with Honest Vocabulary

Here’s the uncomfortable truth: honest vocabulary doesn’t sell.

The accurate descriptions of what AI systems actually do, “automated assistant,” “advanced planning system,” “contextual executor,” “AI-powered tooling,” “intelligent workflow coordinator,” are precise. They set appropriate expectations. They communicate real value without overpromising.

They’re also, by marketing standards, boring.

These phrases don’t sound futuristic. They don’t evoke visions of transformative change. They don’t make executives feel like they’re investing in the next technological revolution. They sound like… tools. Useful, practical, incremental tools.

And so the industry reaches for more evocative language. “Autonomous” sounds like breakthrough technology. “Agentic” sounds like the future of work. “AI automation” sounds like something different from the automation we’ve had for decades. The vocabulary is chosen not for accuracy but for impact, for the ability to cut through noise, capture attention, and position products as categorically different from what came before.

This is the fundamental tension: the words that accurately describe these systems don’t generate the excitement that drives sales cycles, media coverage, and investment rounds. The words that create excitement don’t accurately describe the systems. And so, collectively, the industry has chosen excitement over accuracy and hoped that no one would notice the gap.

People are starting to notice.

The Path Forward

None of this suggests that AI capabilities aren’t genuinely impressive or valuable. They are. Large language models represent a genuine breakthrough in natural language processing. Multi-modal systems that can work across text, images, and video open new possibilities for human-computer interaction. Workflow automation systems that can orchestrate complex multi-step processes offer real productivity gains.

The argument is simply that we should accurately describe these capabilities. We should use vocabulary that sets appropriate expectations rather than vocabulary designed primarily to generate excitement. We should prioritize precision over impact.

This is a choice. Marketing departments can choose accuracy over amplification. Product teams can insist on terminology that reflects actual capabilities. Executives can model honest vocabulary in their communications about AI initiatives. Industry analysts can push back on inflated claims rather than amplifying them.

The vendors and practitioners who choose honesty will build more trust, set more appropriate expectations, and ultimately deliver better outcomes than those who chase the latest buzzwords. In a market drowning in hype, clarity becomes a competitive advantage.

Artificial intelligence is powerful enough without exaggeration. The genuine capabilities of these systems are sufficiently impressive that we don’t need to stretch language to make them sound better than they are. What we need is vocabulary that helps us deploy them wisely, and that starts with saying clearly what we’re actually building.