Definition of Artificial: What It Means in Artificial Intelligence, and Why the Word Matters

Your organization is investing in AI. Your technology team is working to make it useful, leadership wants to see results, and someone suggests putting “AI-powered” at the center of your next product launch.

It sounds like an obvious selling point.

But what if calling attention to AI actually makes customers less likely to buy?

Research from Washington State University suggests that can happen. In a study of consumer purchase intentions, products described as using artificial intelligence generated less interest than otherwise identical products without the AI label.

Part of the challenge comes down to a single word: artificial.

Artificial means made by people rather than occurring naturally. But it can also mean fake, imitation, or insincere.

In artificial intelligence, the first meaning applies. Yet customers may associate the term with the second.

For business and technology leaders, this isn’t simply a branding question. It’s about how you communicate the value of AI, establish appropriate expectations, and make sure someone remains accountable for what the technology does.

This guide explains the definition of artificial, how the term became part of artificial intelligence, why it can influence customer trust, and what organizations should do differently when introducing AI.

The word artificial has two primary meanings.

The first is neutral: something made or produced by people rather than occurring naturally.

The second can be negative: something that imitates the real thing or appears insincere.

Major dictionaries recognize both meanings.

Dictionary	Made by people	Not genuine
Merriam-Webster	Made, produced, or done by humans, especially to resemble something natural	Not sincere or spontaneous, as in an artificial smile
Dictionary.com	Made through human skill rather than occurring naturally	Imitation, simulated, or sham
Vocabulary.com	Created by human skill or design rather than nature	May imply something inferior to the real thing
Webster’s 1828	Made through human skill and labor	Feigned, fictitious, or not genuine

That distinction shapes how people interpret the word.

Artificial flowers may be practical and attractive. An artificial heart can perform a life-sustaining function. But an artificial smile suggests something less trustworthy.

The word itself isn’t inherently negative.

Its meaning depends on context.

What is the opposite of artificial?

The most common opposite of artificial is natural: something existing in or produced by nature.

Depending on the context, other opposites include genuine, authentic, real, and sincere.

In technology, however, the opposite of artificial isn’t necessarily better.

A human-designed system can be highly effective, reliable, and valuable.

What matters is whether it performs the function it was created to serve.

The word artificial originally emphasized human skill.

Merriam-Webster traces its roots to the Latin artificium, meaning artistry or craftsmanship, from ars (skill) and facere (to make).

When the word entered English in the 15th century, it referred to things created through human effort rather than occurring naturally.

That meaning reflected design and craftsmanship.

Over time, the word also developed associations with imitation and insincerity.

Noah Webster’s 1828 dictionary captures both meanings. Its first definition concerns things made through human skill and labor. Its second describes something feigned, fictitious, or not genuine.

Both meanings survived.

That history matters because the negative interpretation was already established long before the term artificial intelligence existed.

When researchers named the field, they were describing intelligence created through human engineering.

But the word also carried associations that could influence how people perceived it.

The term artificial intelligence emerged from a research proposal in 1955.

John McCarthy, a mathematics professor at Dartmouth College, worked with Marvin Minsky, Nathaniel Rochester, and Claude Shannon to propose a summer research workshop focused on creating machines capable of intelligent behavior.

The proposal, dated September 2, 1955, introduced the term.

The workshop took place at Dartmouth in 1956 and became a defining event in the development of AI as a research field.

At the time, researchers used several different terms for related work, including cybernetics.

McCarthy wanted terminology that distinguished the emerging field from existing approaches.

In a National Academy of Sciences biographical memoir, his reasoning is summarized in practical terms: “I had to call it something.”

Not everyone preferred the name.

Researchers including Herbert Simon and Allen Newell used alternative terminology such as complex information processing.

But artificial intelligence became the established label.

And with it came the tension between something intelligently designed and something that might be perceived as an imitation.

Artificial intelligence refers to computer systems designed to perform tasks associated with intelligence, including recognizing patterns, interpreting information, making predictions, generating content, and supporting or making decisions.

There isn’t one universally accepted definition.

Different definitions emphasize the scientific goal, the system’s capabilities, or the way it operates.

Source	Definition or approach	Primary emphasis
John McCarthy, Stanford	“The science and engineering of making intelligent machines”	The goal of building intelligent systems
OECD	A machine-based system that infers from inputs how to generate outputs such as predictions, content, recommendations, or decisions	What the system does
EU AI Act	A machine-based system with varying levels of autonomy that infers how to generate outputs and may exhibit adaptiveness after deployment	How the system operates and influences its environment

What’s the most useful definition of artificial intelligence for businesses?

For business and technology leaders, operational definitions are often more useful than philosophical ones.

The OECD and EU AI Act focus on what a system does, how it generates outputs, and how those outputs influence decisions or environments.

That provides a more practical foundation for evaluating AI systems.

Instead of asking whether a system is truly intelligent, organizations can ask:

• What information does the system use?
• What outputs or recommendations does it produce?
• What decisions does it influence?
• What actions can it take independently?
• Who is responsible for reviewing its performance?

These questions help technology teams translate a broad concept into practical requirements for implementation, oversight, and governance.

The business question isn’t whether a system deserves to be called intelligent. It’s what the system does, what value it creates, and who is accountable for the result.

In artificial intelligence, artificial means designed and built by people.

It doesn’t mean fake or inherently inferior.

One of the clearest explanations comes from Herbert Simon’s 1969 book, The Sciences of the Artificial.

Simon described artificial systems as things shaped by human purposes and designed to perform particular functions.

As a review of his work explains, artificial systems aren’t defined by being superficial or dishonest. They’re defined by their design, intended function, and relationship to human goals.

That perspective is especially relevant to business technology.

An AI system is created for a purpose.

It may be designed to detect fraud, classify documents, recommend products, support customer service, or automate part of a workflow.

Its value depends on how well it fulfills that purpose.

More recent philosophical research has examined how growing AI autonomy changes our understanding of human-made systems.

In a 2021 paper, Francesco Bianchini argues that AI systems can become increasingly autonomous while remaining connected to the human decisions that created them.

For organizations, that distinction is critical.

Even when an AI system can act with limited human intervention, people still make decisions about its design, deployment, permissions, acceptable use, and oversight.

“The AI decided” doesn’t eliminate organizational accountability.

Technology leaders need to know who owns each system, what decisions it can make, and how the organization will respond when it produces an unexpected result.

Our guide to emergent artificial intelligence explores why AI behavior may not always be fully predictable and what organizations can do to manage that uncertainty.

The terminology organizations use to describe AI can influence how customers perceive their products and services.

Research suggests that emphasizing AI isn’t always an advantage.

In a study published in the Journal of Hospitality Marketing & Management, Washington State University researchers compared consumer responses to product descriptions that were otherwise identical except for references to artificial intelligence.

Across eight product and service categories, descriptions that mentioned AI produced lower purchase intentions.

The research involved more than 1,000 US adults.

The researchers found that emotional trust helped explain the effect.

When AI was emphasized, participants reported lower trust, which was associated with reduced willingness to purchase.

The effect was particularly pronounced for higher-risk products and services, where errors or failures could have more serious consequences.

That doesn’t mean every customer dislikes AI or that organizations should avoid mentioning it.

It means the AI label alone may not communicate the value customers care about.

Customers want outcomes, not technology labels

Consider two ways to describe the same capability.

Technology-focused message:

“Our AI-powered platform uses advanced machine learning to optimize invoice processing.”

Outcome-focused message:

“Identify invoice discrepancies before they delay payment or create costly rework.”

The first emphasizes the technology.

The second explains the problem it solves.

For customers evaluating business technology, that difference matters.

They’re not necessarily buying AI.

They’re buying greater accuracy, faster processes, lower costs, better decisions, or reduced operational risk.

The value of AI isn’t that it’s artificial intelligence. The value is what it helps people accomplish.

Why some organizations prefer “augmented intelligence”

The American Medical Association deliberately uses the term augmented intelligence.

The terminology emphasizes AI as a tool that supports physicians rather than replaces their judgment.

That distinction sets a clearer expectation about how technology and people work together.

The underlying technology may be similar, but the language communicates its intended role.

For business leaders, the lesson isn’t that every organization should rename AI.

It’s that terminology should accurately reflect what the technology does and how people remain involved.

Customer messaging is only one part of the problem.

Inside an organization, vague AI terminology can create misalignment between leadership, technology teams, operations, and customer-facing employees.

One executive may think an AI system will eliminate manual work.

The technology team may expect it to generate recommendations that employees still need to review.

Operations may assume the system will handle exceptions independently.

And customers may believe a person is making decisions that have actually been automated.

All four groups can be discussing the same technology while expecting different outcomes.

That disconnect creates practical problems:

• Unclear ownership: Nobody knows who is responsible when an AI-generated result is incorrect.
• Unrealistic expectations: Leadership expects cost savings before workflows are ready to support them.
• Operational friction: Employees are asked to use tools without understanding when to trust or override their outputs.
• Delivery risk: Technology teams are held accountable for outcomes that were never clearly defined.
• Customer trust issues: Marketing promises capabilities or levels of human involvement that don’t match the actual experience.

These aren’t necessarily failures of AI technology.

They’re failures to align the organization around how the technology should be used.

It’s not enough to agree that you’re adopting AI. You need agreement on what AI will do, what it won’t do, and what success looks like.

That clarity helps technology and operations leaders execute more effectively while giving executives a stronger basis for evaluating AI investments.

You don’t need to avoid the term artificial intelligence.

You need to use it precisely and make sure the organization is aligned on what the technology actually does.

Five actions can help.

1. Describe the business benefit, not just the technology

Lead with the problem being solved.

Instead of describing a product as “AI-powered,” explain how it improves accuracy, reduces delays, simplifies work, or helps people make better decisions.

AI can be part of the explanation without becoming the entire value proposition.

2. Be specific about the type of automation involved

A rules-based bot, an AI model that recommends actions, and an AI agent that executes tasks may require different levels of oversight.

Make sure teams understand those differences.

Our guide to automated intelligence versus intelligent automation explains how these technologies compare and when each may be appropriate.

3. Assign a human owner to every AI system

Every AI implementation should have a clearly identified business owner.

That person should understand the system’s purpose, expected performance, and escalation process.

Technical ownership matters too, but it doesn’t replace accountability for the business outcome.

4. Confirm which regulatory definitions apply

AI-related legal requirements may depend on how a system operates, what decisions it influences, and where it is used.

For organizations operating in Europe, the EU AI Act provides a relevant legal definition of an AI system.

Work with qualified counsel to determine which requirements apply to your specific use cases.

5. Communicate honestly about AI’s role

Customers and employees should understand what AI does, where its limitations are, and when people remain involved.

Avoid promising autonomy, accuracy, or human oversight that the system doesn’t actually provide.

Clear expectations build a stronger foundation for trust than broad technology claims.

How to frame the conversation with leadership

We shouldn’t measure the success of our AI initiatives by how much AI we’re using. We should measure them by the business outcomes they deliver, the confidence users have in the systems, and our ability to manage them responsibly.

This shifts the discussion from AI adoption as a goal to AI as a tool for improving business performance.

AI adoption creates opportunity, but it also introduces new decisions about technology, processes, accountability, and organizational readiness.

For technology and operations leaders, the challenge isn’t simply choosing AI tools.

It’s making sure those tools support business priorities, fit existing workflows, and deliver outcomes leadership can measure.

Without that alignment, even promising AI investments can create confusion, rework, unnecessary spending, and additional risk.

STG Consulting helps business and technology leaders connect AI initiatives to the outcomes that matter.

Using the STG Strategic Technology Framework®, STG helps organizations evaluate where AI creates value, where expectations or accountability may be unclear, and which decisions need to be made before adoption scales.

That includes helping leaders establish clearer priorities, understand operational impacts, and build a practical roadmap for responsible execution.

The goal isn’t to introduce more technology for its own sake.

It’s to help your team use technology with greater clarity, confidence, and measurable purpose.

Is your AI strategy creating value - or confusion?

STG can help you understand where AI fits into your business, identify gaps in alignment and accountability, and determine which initiatives deserve attention first.

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