« What does the term artificial intelligence mean? » The question comes up everywhere. Good news: the answer is simpler than it seems, and it helps sort the buzz from truly profitable business use cases.
« What does the term artificial intelligence mean? » The question comes up everywhere, from executive committees to training rooms. Good news: the answer is simpler than it seems, and it helps sort the buzz from truly profitable business use cases.
Artificial intelligence refers to computer systems capable, for objectives defined by humans, of transforming inputs into useful outputs, such as predictions, recommendations, decisions, or content. These systems learn from data or rules, then execute autonomously in real or digital environments.
Two important clarifications:
AI does not mean consciousness. An AI model does not « understand » like a human. It calculates probabilities based on learned data and parameters.
AI is not a single technology. It is a set of approaches, from symbolic rules to deep neural networks and Large Language Models (LLMs).
To go further, the OECD and the European Union propose recognized definitions and frameworks to describe an « AI system » and its risk categories. See the OECD classification framework and the page dedicated to the AI Act in Europe.
1956, Dartmouth conference, birth of the term. The first approaches are symbolic, based on rules written by experts.
1990s to 2010s, statistical machine learning and data explosion. Machines learn models from examples.
2012, breakthrough of deep learning with computer vision. Then, in 2017, the Transformer architecture paves the way for foundation models and LLMs.
2022 to present, democratization of generative AI and emergence of AI agents capable of chaining actions via software tools.
These stages do not erase previous ones. In companies, rules, classic machine learning, and deep learning often coexist for reasons of cost, data, and explainability.
Symbolic AI: rules, decision trees, inference engines. Useful when business rules are stable and explicit.
Supervised learning: learning from labeled examples. Typical use cases: customer scoring, demand forecasting, fraud detection.
Unsupervised learning: revealing structures in raw data. Examples: customer segmentation, anomaly detection.
Reinforcement learning: learning by trial and error by maximizing a reward. Applicable to dynamic pricing optimization or robotics.
Generative AI: production of text, image, code, or audio. LLMs are language models capable of synthesis, writing, conversational agents, and prompt-guided automation.
An AI project follows a predictable lifecycle, with vocabulary useful to know.
Data: raw or structured, they carry business reality. Their quality and representativeness are decisive.
Annotation and preparation: cleaning, labeling, balancing, anonymization if necessary.
Model: parameterized function that learns patterns. We speak of millions or even billions of parameters for large models.
Training: adjustment of parameters on training data. A computationally expensive step.
Evaluation: measuring performance on test data, checking for bias and robustness.
Inference: using the model in production to respond to new inputs.
Step | Goal | Key Resources | Watchpoints |
|---|---|---|---|
Training | Learn from data | GPU/TPU, datasets, MLOps pipeline | Cost, data drift, bias |
Inference | Produce outputs on demand | Latency, costs per request, integrations | Security, confidentiality, quality |
What AI does very well:
Detecting subtle patterns in large volumes of data.
Automating repetitive tasks and improving execution speed.
Assisting in writing, information retrieval, and code generation.
What AI does less well or with precautions:
Hallucinations of generative models if context or data are inadequate.
Bias and drift over time if the environment changes or if training data is unrepresentative.
Reliable multi-step reasoning without external structure, hence the interest in tooled agents, enterprise context retrieval, and guardrails.
The NIST proposes a risk management framework specific to AI, useful for framing quality, security, and governance. See the NIST AI RMF.
Gains come less from the « magic » of the model than from its integration into existing processes and tools.
Support and customer relations: 24/7 assistants, responses guided by internal knowledge base, reduction of first response time.
Sales and marketing: augmented prospecting, lead scoring, message personalization, high-volume content generation with brand control.
Operations and finance: document classification, data extraction, automatic reconciliations, anomaly detection.
IT and product: code generation and review, testing, documentation, incident diagnosis aid.
Industry and logistics: computer vision for quality control, demand forecasting and inventory optimization, predictive maintenance.
Several independent reports, like the Stanford AI Index and McKinsey's State of AI 2024, converge on the idea that economic impact is tangible when use cases are clear, measured, and industrialized.
Talking about AI also means talking about integration and governance.
Data: mapping where it lives, who accesses it, what data leaves the organization and under what legal bases.
Security: API key management, network segmentation, prompt filtering, protection against exfiltration, interaction logging.
Quality: precision metrics adapted to the business, sandbox testing, human control where impact is critical.
Compliance: GDPR, intellectual property, and risk framework by categories as provided by the European AI Act.
For API integrations and production deployment, you can consult our dedicated guide, AI API, clean and secure integration patterns.
Algorithm: set of instructions to solve a problem or train a model.
Model: mathematical representation learned from data to predict or generate outputs.
Parameters: internal model values adjusted during training.
Training set: data used to learn model parameters.
Validation and test: separate sets to evaluate performance without overfitting.
Embedding: numerical vector representing the meaning of a text, image, or object.
Prompt: instruction provided to a generative model to guide its response.
Hallucination: plausible but factually false response produced by a generative model.
RAG (Retrieval Augmented Generation): technique that injects your organization's knowledge at the moment of the query to make the response more reliable.
Fine-tuning: light retraining of the model on your examples to adapt it to your tone, domain, or formats.
AI Agent: system that chains multiple steps and calls tools or APIs to achieve an objective.
Do we have enough data to start? Often yes, because many uses rely on pre-trained models. The critical work is the selection, preparation, and governance of your key data.
Do we need a custom large model? Rarely at the start. We prioritize a foundation model + RAG + guardrails, then only invest in fine-tuning if the impact justifies the cost.
How to measure ROI? Define metrics linked to the business process, for example processing time reduction, automation rate, customer satisfaction, savings per transaction. See our article Transforming AI into ROI, proven methods.
Formulate a clear business objective (fewer tickets, more qualified MQLs, fewer product returns).
Audit your processes to spot textual, documentary, or repetitive tasks suitable for automation.
Secure data (sources, access, anonymization if needed) and decide what can go to an external provider.
Prototype a use case in 2 to 4 weeks with a limited scope and simple success metrics.
Integrate into existing IS (CRM, ITSM, ERP, Data Warehouse) with guardrails and a human supervision mode.
Train teams on good practices, prompt writing, and risks.
Industrialize what works, monitor, iterate.
For a synthetic adoption framework for C-levels, you can consult our quick guide for executives 2025 and our 2025 AI Report.
AI is a set of techniques that transform inputs into predictions, decisions, or content, serving human objectives.
Value arises from alignment with a business process, integration into existing tools, and solid governance of data and risks.
Starting small, measuring, securing, and iterating remains the best strategy for 2025.
Impulse Lab in a nutshell: we help organizations transform AI into measurable value, with opportunity audits, custom development, integrations with your tools, adoption training, and weekly delivery managed via a dedicated client portal. Do you have a use case in mind or wish to prioritize the right projects in 2025?
Launch an AI audit adapted to your context.
Prototype an AI agent connected to your data and business tools.
Upskill your teams with practical training.
Tell us about your objective, we frame a first iteration in a few days. Visit impulselab.ai or discover how to choose an AI agency with our dedicated guide, How to Choose an AI Agency in 2025.
Got questions? We've got answers.
Leonard
Co-founder
Our team of experts will respond promptly to understand your needs and recommend the best solution.
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