Everything you need to know. No marketing, no hype.
This guide answers the questions that business owners, managers, and professionals ask us every day. No buzzwords or unrealistic promises: just practical information to help you understand if and how AI can help your business.
Generative AI works in a radically different way from traditional software. It doesn't follow rules written by a programmer: it has "learned" to respond by reading billions of texts, documents, and conversations.
Imagine completing the sentence: "The king sat on his..."
Your mind, having read fairy tales and stories, immediately suggests "throne." Not because someone programmed you to say it, but because you've built a mental map where "king" is close to "throne," "crown," "kingdom" — and far from "refrigerator" or "skateboard."
AI works the same way, but on a massive scale. It has built a map of relationships between concepts: king→queen has a strong connection (0.9), king→man is linked (1.0), king→cat is weak (0.5). When you ask it something, it "navigates" this map to construct the most sensible response.
This is why AI can write, reason, translate, analyze data — without anyone explaining how. It extracted these "patterns" from the enormous amount of material it was trained on.
The fundamental limitation: AI doesn't "know" things in the human sense. It has no experiences, doesn't verify facts, has no common sense. It can produce wrong answers with great confidence. That's why human supervision is always necessary.
Fire was the first great invention for converting energy: it transforms wood into heat, light, protection. It changed everything: cooking food, metalworking, civilization.
Generative AI is an equally powerful form of conversion: it transforms data into usable knowledge. But with a fundamental difference:
You can't ask fire how to light it.
You can ask AI. It's the first transformative technology that can explain itself, adapt to your questions, help you use it better.
AI capabilities are divided into three progressive levels. Each level includes the previous one and adds new possibilities.
The assistant that works with the information you give it
This is the AI you use when you open ChatGPT, Claude, or Gemini in your browser. It works with what you write or upload to the chat.
Ideal for: individual professionals, spot tasks, exploring possibilities.
The assistant that accesses your business systems
AI is connected to company data sources: ERP, CRM, databases, document folders, sensors. It can query up-to-date information in real time.
Ideal for: operational teams, data-driven decisions, daily efficiency.
The assistant that executes tasks autonomously
AI doesn't just respond: it can act. It writes code and executes it, creates files, interacts with software, completes complex workflows with minimal supervision.
Ideal for: process digitization, advanced automation, scaling expertise.
There's no "best" platform overall. There's the right one for your case. Here's an honest comparison based on user type, not technical features.
| Platform | Base plan | Ideal for | Key strength |
|---|---|---|---|
| ChatGPT (OpenAI) | Plus: $20/mo | Those seeking versatility, already familiar with AI, want many integrations | Broadest ecosystem, GPT Store, advanced multimodality |
| Claude (Anthropic) | Pro: $20/mo | Professionals who write a lot, work with long documents, code | Text quality, reasoning, long context (200k tokens) |
| Gemini (Google) | Advanced: $20/mo | Those already using Google Workspace, want native Gmail/Drive/Docs integration | Google integration, 2TB storage included, multilingual |
| Copilot (Microsoft) | Pro: $20/mo* | Those working in Microsoft 365, want AI inside Word/Excel/Outlook | Native Office integration, existing business workflows |
| Perplexity | Pro: $20/mo | Those doing research, fact-checking, needing cited sources | Web search with citations, integrated fact-checking |
* Note on Copilot: The standalone "Pro" plan has been discontinued. To use Copilot, you need Microsoft 365 ($10-13/mo for personal use). For businesses, Copilot costs $30/user/month on top of the M365 license.
Our practical advice: Start with the free version of 2-3 platforms for a week. Try your real use cases. The "best" one is the one you feel most comfortable with — not the one with more features on paper.
We chose Anthropic and Claude. Not for benchmarks or features of the moment, but for deeper reasons:
It's a choice, not an obligation. But when we build AI solutions for our clients, we do it on foundations we trust.
Free versions: Your data may be used to train future models. Not suitable for sensitive business information.
Paid versions: Generally, data is not used for training. Enterprise plans offer contractual guarantees, advanced encryption, EU data residency.
Rule of thumb: If you wouldn't write it in an email to a stranger, don't put it in the free version.
Free versions: Tight message limits (e.g., 10-15 messages/3 hours). Less powerful models. Queues during peak times.
Paid versions: 5-20x more capacity. Access to most advanced models. Priority during high traffic.
Rule of thumb: If you use AI more than 30 minutes a day, the paid plan pays for itself in productivity.
Paid plans only:
Team plans (~$25-30/user/month): Basic collaboration, admin controls, simple SSO. Min. 5 users.
Enterprise plans (custom): Advanced governance, audit logs, compliance, guaranteed SLAs, dedicated support. Starting from ~$50k/year.
Rule of thumb: Under 20 users, Team plans are sufficient. Beyond that, consider Enterprise for control and security.
For an individual professional, AI costs $20/month. Less than a business lunch per week.
For a business team, costs scale:
If you want to build custom solutions with APIs, costs depend on usage. Roughly: from a few dollars/month for light use to hundreds of dollars for intensive applications.
In some cases, ROI is easy to calculate:
Example: A salesperson saves 1 hour per day thanks to AI for emails and reports. 20 hours/month × hourly cost $50 = $1,000/month recovered. AI cost: $20. ROI: 50x.
But often the most important value can't be measured in dollars:
That's why we also use the concept of VOI (Value of Investment): the overall value that the investment generates, even when it can't be directly quantified in money.
Pros: No hardware, always updated, unlimited power, ready in minutes.
Cons: Recurring costs, data travels to provider, vendor dependency.
Ideal for: most businesses, those who want to start quickly.
Pros: Data stays in-house, predictable costs, total independence.
Cons: Requires expensive hardware (GPUs), technical expertise, less powerful models, maintenance.
Ideal for: regulated industries, ultra-sensitive data, high volumes.
Training an AI model requires processing trillions of words on thousands of GPUs for weeks. The global data center infrastructure now consumes about 1.5% of the world's electricity — and could double by 2028. A single AI response consumes 10-100 times more energy than a traditional Google search.
The good news: efficiency is improving rapidly. Google claimed to have reduced energy per request by 33x in one year. But it remains an open issue for the future.
It depends on how you look at it. AI doesn't replace people: it replaces tasks. If your job is 90% repetitive tasks that AI can do, yes, it's at risk. If you use your brain to decide, create, relate — AI becomes a tool that makes you more effective.
The real risk isn't AI: it's standing still while others learn to use it.
You can start on your own: take a task you do often — an email, a document, data to analyze — and try doing it with AI. Experiment, make mistakes, redo. Curiosity is the engine.
But a course accelerates everything. In a few hours, you see real use cases that would take you months to discover on your own. You learn to choose the right tool for each situation. And most importantly, you understand where AI has limits — and when you need help from those who use it every day.
Yes, and it does. AI can invent facts (it's called "hallucination"), reason incorrectly, lose context in long conversations. It's not an oracle: it's a powerful but imperfect tool.
The golden rule: don't entrust critical decisions to AI without human verification. Use AI to accelerate, not to replace your judgment.
No. Generative AI only exploded in 2023. We're still at the beginning. Those who start today have time to learn, experiment, build skills before it becomes essential.
In five years, using AI will be as normal as using Excel. The question isn't whether to learn it, but when.
We don't sell products: we guide you step by step. From team training to implementing AI systems connected to your processes.