Artificial intelligence for business costs from $50 to $50,000 per month. Under the term “AI” 5 different technologies are sold: from real LLMs to ordinary if-else logic with a sticker “AI-powered”. In order not to overpay, you need to ask the vendor 7 specific questions about the technology.
When a business owner says “we need artificial intelligence for business”, he can mean completely different things: from a large language model (LLM) for thousands of dollars per month to simple conditional logic that a programmer can write in an hour.
The problem is that the word “artificial intelligence” has long ceased to mean anything specific. It is a marketing name. It has been that way from the very beginning.
How did the term “artificial intelligence” come about?
Who coined the term “artificial intelligence”?
The term was coined by John McCarthy in August 1955, not to describe a technology, but to apply for a grant.
McCarthy was writing a proposal to the Rockefeller Foundation to fund a summer workshop at Dartmouth College. He was joined by Marvin Minsky, Nathaniel Rochester, and Claude Shannon.
They needed a name that would accomplish three things at once:
- It would sound ambitious for the foundation to allocate money for a “visionary” project
- Differentiates from competitors: from Norbert Wiener’s cybernetics
- Will be recognizable so that the scientific program crystallizes around it
McCarthy coined “artificial intelligence.” The foundation gave a grant. The workshop was held in the summer of 1956. The term stuck.
Did McCarthy really coin this term?
No, not a fact. In a 1974 interview with historian Pamela McCordack, McCarthy admitted: he is not one hundred percent sure that he invented the term himself. Perhaps he heard something similar earlier from Wiener.
Even with the authorship of the term, everything is not as clean as they say at conferences.
What happened before the term “AI”?
Before the Dartmouth workshop, the field had a working title: automata studies. That was the name of the collection that McCarthy and Shannon were preparing for publication in the same years.
“Automata studies” sounded too technical. Foundations were reluctant to give money for it. And “intelligence” sounded completely different. A big word with a hint of the future. It sold better.
Conclusion: the word “AI” was born not in a laboratory, but in a grant application. It is an advertising slogan, not a description of the technology.
What will be sold under the name “artificial intelligence” in 2026
How many different technologies are hidden behind the word “artificial intelligence for business”?
At least five completely different things:
- Real LLMs (large language models). Integration with APIs OpenAI, Anthropic, Google or self-hosted open-source models. Cost: from $50 to $50,000 per month depending on the volume.
- Classic machine learning. Algorithms from the 1990s: recommender systems, classifiers, predictive models. Formally also AI, but nothing new.
- Heuristics and expert systems. Rules written by a person that imitate “smart” behavior. Technology from the 1970s. Works, but no magic.
- Ordinary if-else. Conditional logic with a sticker “smart” or “AI-powered”. Zero intelligence. Just programming.
- Pure marketing. The product has not changed, but they added “AI” to the name, added neurons to the landing page in the illustration, and raised the price.
Why is this a problem for business?
Under one word, technologies coexist that are as different as a moped and a truck. Both are transport, but prices and capabilities differ by orders of magnitude.
When the average customer hears “service with AI,” he imagines something similar to ChatGPT. An intelligent system that understands language and makes decisions. Technically, this is called LLM (Large Language Models).
But “AI” in advertising can mean something completely different. And without specifics, it is impossible to understand what you are paying for.
How to distinguish real AI from marketing?
Comparison table: real AI vs marketing AI
| Criterion | Real AI (LLM/ML) | Marketing-AI |
| What’s under the hood? | Neural networks, transformers, trained models with billions of parameters | if-else rules, SQL queries, templates |
| Adaptation | Learns from new data | Works only according to preset scenarios |
| Documentation | Specific model (GPT-4, Claude), accuracy metrics | “Smart algorithm”, “AI-driven” without specifics |
| Result | 20-80% increase in metrics | 0-5% or not measurable |
| Cost | $3-5k (API) to $100+k (custom model) | Like regular software |
| Subscription | Depends on volume (tokens, requests) | Fixed, overpriced “because AI” |
Real examples from the Ukrainian market
Example 1: “AI copywriter” for $200/month
The Ukrainian service promised “text generation with artificial intelligence.” After checking, it turned out: a wrapper over the OpenAI API with a 300% markup.
The client could get the same for $20 (ChatGPT Plus) or $5-15 (direct API). Savings of $180/month.
Example 2: “Smart CRM with AI scoring”
“AI scoring” turned out to be a table of 6 parameters, each giving 1-10 points. Sum = “AI score”. This is not AI. This is an Excel formula.
Example 3: Real AI that paid off
The client asked for “something simple for FAQ, without AI.” We checked the task: 70% of requests are non-standard. We deployed an LLM bot on Claude with RAG. Cost: $150/month. We saved 20 operator hours every week.
Example 4: “AI Recommender” = sorting by category
WooCommerce plugin for $80/month showed “similar products” by category and price. Algorithm from 2005. No ML.
Example 5: SEO texts “with AI” without results
SaaS generated texts via OpenAI API with the template “write about [keyword]”. Without strategy, semantics, competitive analysis. The texts were not ranked. The client paid $120/month for a wrapper worth $8-10.
Regularity: the more the word “AI” and the less specifics, the more likely you are to pay for a sticker.
7 questions before buying artificial intelligence for business
Ask these questions to any vendor selling an “AI service”:
If the vendor gets offended by these questions, it’s a red flag.
1. What exactly is the technology under the hood?
Need specifics: “LLM from OpenAI,” “ML model on scikit-learn,” “rule-based engine.” Not “smart algorithm.”
2. What are the system quality metrics?
Accuracy, precision, recall, BLEU, user satisfaction score. If there are no metrics, the system is not measured, it does not work predictably.
3. How much better than the alternative without AI?
Comparison with baseline. If the difference is 3%, there is no point in paying for “AI”.
4. How does it behave in non-standard scenarios?
Real AI will give a reasonable attempt. A rule-based system will fail or produce a pattern.
5. Does it use our data for further training?
Critical for privacy. Especially for Ukrainian jurisdiction with potential GDPR conflict.
6. How much does a provider’s API cost?
If a vendor charges $200 and the OpenAI API costs $15 for your volume, you know the markup. Sometimes it’s justified (integration, support), sometimes it’s not.
7. Can we see a demo on our data?
Real AI solutions are easy to demonstrate. Marketing AI is demonstrated with cherry-picked examples.
Why don’t AI detectors (Copyleaks) work?
Can Copyleaks and other AI detectors be trusted?
No. The detectors are very unreliable.
OpenAI shut down its own AI detector in 2023 due to low accuracy. Stanford and University of Maryland research shows:
- 50-70% false positives on structured journalistic content
- Often labeled as “AI” texts by non-native English speakers
- Academic texts regardless of authorship are labeled as “AI”
- Different detectors give different results on the same text
What exactly works as “AI text”?
Detectors look for statistical markers:
- Uniform parallel lists
- Formulas “first / second / third”
- Balanced sentences with dashes
- Repetitive rhetorical constructions
- Lack of authorial voice
Irony: These are signs of good editorial style, not AI.
Is it worth fighting the detectors?
Depends on the context.
Not worth it if:
- Publish on your own blog under your own name
- Content has value for readers
- Google does not penalize AI content (only spam without value)
It is worth it if:
- Internal editorial policy requires
- Publish on a third-party platform with a detector
- Write for an academic journal
What is more important than AI detectors?
E-E-A-T for Google
Google rates content according to E-E-A-T:
- Experience: The author has practical experience in the topic
- Expertise: The author is knowledgeable about the topic
- Authoritativeness: The author is recognized in the field
- Trustworthiness: The information is verified, sources are cited
For an article about AI marketing, this means:
- Author: Agency CEO + AI lecturer
- Specific cases from practice (5 examples)
- Numbers and metrics (cost, savings)
- Verified historical facts (Dartmouth 1956, McCarthy)
For someone implementing artificial intelligence for business, this is more important than any AI detector.
Artificial Intelligence for Business in 2026: How to Deal with AI Hype
Three main rules for working with AI in 2026
Rule 1: AI does not replace expertise, but enhances it
LLM can generate SEO text for business, but without a strategy it’s just words. But without a strategy it’s just words. An ML model can evaluate a lead. But without business logic it’s random noise. For more information on what tasks AI can really take on, see the article “What professions will AI replace?”
Rule 2: Ask for specifics, not labels
The word “AI” in the brief means nothing. It’s as vague as “transport”: it’s unclear whether you’re talking about a bicycle or a rocket.
Rule 3: Technology doesn’t solve problems, process does.
AI can be part of the process, sometimes a key one. But the question always starts with the problem, not the technology.
When should you use AI?
Before choosing artificial intelligence for business, it’s worth answering a few questions honestly.
- When there is a clear metric of improvement (20%+ increase)
- When the non-AI alternative is truly worse
- When the budget allows for a 3-6 month payback
- When there is data for training/context
When should you NOT use AI?
- When “everyone just says we need AI”
- When the vendor can’t name a specific technology
- When they promise “magic without your effort”
What you need to know about AI before buying
What is artificial intelligence in simple words?
Artificial intelligence (AI) is an umbrella term for various technologies that mimic human intelligence. It is not a single technology, but an entire field: from simple if-else rules to complex neural networks with billions of parameters.
Who coined the term “artificial intelligence”?
The term was coined by John McCarthy in August 1955 for a grant application to the Rockefeller Foundation. It was the name of a project, not a description of the technology.
Are all LLMs artificial intelligence?
Yes, LLM (large language models) is a subset of AI. But not all AI is LLM. Classical ML, expert systems, heuristics, and even the usual if-else are also sold under the umbrella of “AI”.
How much does real AI cost for business?
Depends on the task:
- API integration (OpenAI, Claude): $50-500/month
- Custom ML on existing data: $3-10k one-time
- Custom LLM model: $50-100+k one-time + infrastructure
How do I check if it’s real AI?
Ask the 7 questions from the checklist above. If the vendor cannot name a specific technology, quality metrics, and baseline comparison, you have marketing AI.
Why do AI detectors get it wrong?
Detectors look for statistical patterns (uniformity, structure) that are inherent in both AI texts and high-quality editorial texts. False positive rate: up to 70%.
Does Google penalize AI content?
No. Google penalizes low-quality spammy content with no value, regardless of who wrote it, something like ChatGPT. AI content that has value for users and meets E-E-A-T ranks normally.
What is E-E-A-T in SEO?
E-E-A-T are Google’s criteria for evaluating content quality:
- Experience
- Expertise
- Authoritativeness
- Trustworthiness
Why do we need a checklist of questions for vendors?
The checklist helps distinguish real technology from marketing hype. A vendor selling real AI will easily answer all 7 questions specifically.
Can AI replace human expertise?
No. AI is an amplifier of expertise, not a replacement. LLM generates text, but without SEO strategy it’s just words. ML evaluates leads, but without business logic it’s noise.
The main rule: artificial intelligence for business is a tool, not magic.
Need a consultation? If you doubt whether your business needs AI (and what kind), write to SELECTOR.SPACE. Let’s analyze your specific task and be honest: here you really need LLM, here classic ML will suffice, and here you are overpaying for a word on the label.