In this guide
- What is artificial intelligence?
- AI, machine learning and generative AI: what is the difference?
- Which AI use cases are useful in business?
- What artificial intelligence can — and cannot — guarantee
- What governance should be in place before deployment?
- How can a business adopt AI responsibly?
- A 90-day AI roadmap
- How can value be measured without inventing ROI?
- What should you explore next?
- Artificial intelligence FAQ
- Official sources
The 30-second summary
Artificial intelligence covers systems that can produce predictions, content, recommendations or decisions from input data. In business, its value does not come from an impressive demo but from improving a process in a measurable way. Start with a bounded, reversible task, define permitted data and human oversight, test representative cases, and compare results with an observed baseline before expanding the use case.
Key takeaways
- 01AI is a family of systems; machine learning and generative AI are related approaches or subsets, not synonyms.
- 02A plausible output can be wrong, so verification should match the consequences of an error.
- 03The best first use case is frequent, bounded, measurable and easy to resume manually.
- 04Governance starts before procurement: purpose, data, owner, access, controls and a stop rule should be defined.
- 05A pilot demonstrates value only against an observed baseline and criteria agreed in advance.
What is artificial intelligence?
Under the OECD’s updated definition, an AI system is a machine-based system that, for explicit or implicit objectives, infers from its inputs how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments. Systems vary in autonomy and in whether they adapt after deployment.
The definition covers tools as different as a fraud filter, product recommender, demand forecast, writing assistant or agent connected to business software. It does not mean that the system understands the world as a person does, can operate autonomously in every context or produces a correct answer.
OECD: Explanatory memorandum on the updated OECD definition of an AI system
AI, machine learning and generative AI: what is the difference?
These terms describe different levels. Artificial intelligence is the broad category. Machine learning refers to methods that learn patterns from data. Generative AI creates new content from instructions and examples. A single product may combine several approaches.
| Concept | Typical function | Business example | Essential control |
|---|---|---|---|
| Artificial intelligence | Produce a prediction, content, recommendation or decision | Prioritise alerts or recommend a next action | Check the purpose, impact and human accountability |
| Machine learning | Learn statistical relationships from data | Forecast demand or detect an anomaly | Monitor data quality, errors and drift |
| Generative AI | Create or transform text, images, audio, video or code | Draft a reply, summarise a file or prepare a first version | Check facts, sources, rights and exposed data |
OECD: Explanatory memorandum on the updated OECD definition of an AI system · CNIL: Using generative AI in small and medium-sized businesses
Which AI use cases are useful in business?
A strong use case starts with an observed process problem, not a product looking for a home. Describe the trigger, inputs, expected output, user and following decision. The most controllable first pilots generally assist a professional instead of acting alone on a person or a critical system.
| Function | Use case | Outcome to measure | Human control |
|---|---|---|---|
| Customer service | Draft a reply from an approved knowledge base | Accuracy, handling time and escalation rate | An adviser verifies and sends the reply |
| Sales | Structure call notes and prepare next steps | CRM completeness and preparation time | The salesperson confirms commitments and customer data |
| Marketing | Create variants from an approved brief | Brief compliance, editorial quality and corrections | The brand owner approves claims and the published version |
| Operations | Extract document fields or classify requests | Error rate, exceptions and cost per approved case | Uncertain cases enter a review queue |
| Internal knowledge | Search and summarise authorised documents | Source fidelity and traceability to the original passage | The user checks references before acting |
| Software development | Explain code, propose tests or draft documentation | Defects found, useful coverage and review time | Code review, testing and security checks precede production |
CNIL: Using generative AI in small and medium-sized businesses · FPS Economy — Belgium: Artificial intelligence — SME digitalisation
What artificial intelligence can — and cannot — guarantee
- It can accelerate a defined task — It can classify, extract, summarise, forecast or draft when inputs, output format and criteria are explicit.
- It does not guarantee truth — A fluent output may contain an incorrect fact, link, citation or line of reasoning. Material claims need reliable verification.
- It depends on context — A model that performs well in a demo may fail on your vocabulary, exceptions, documents or working language.
- It does not remove bias — Data, objectives and instructions may produce systematic errors or unfair treatment. Effects on people require stronger scrutiny.
- It does not carry accountability — The organisation remains responsible for system selection, use, access, controls and decisions based on outputs.
- It does not replace a fallback — Vendor dependency, outages or insufficient quality should be covered by a documented manual or alternative process.
OECD.AI: OECD AI Principles · CNIL: Using generative AI in small and medium-sized businesses · EUR-Lex: Regulation (EU) 2024/1689 on artificial intelligence
What governance should be in place before deployment?
Governance is not a final approval added at the end. It connects the system’s purpose, data, affected people, responsibilities, controls and evidence of performance. The OECD principles emphasise areas including human rights, transparency, robustness, safety and accountability.
In the European Union, the AI Act takes a risk-based approach and assigns different obligations depending on the system and the organisation’s role. The GDPR and other applicable rules continue to apply where personal data or regulated sectors are involved. This guide provides a general operational framework, not legal advice.
| Question | Evidence to retain |
|---|---|
| Why are we using the system? | Purpose, user, expected output and prohibited uses |
| Which data is processed? | Inventory, origin, permission, minimisation and retention |
| Who is accountable for the output? | Business owner, technical owner and escalation path |
| How is quality tested? | Representative cases, stable rubric, errors and acceptance thresholds |
| What can the system do? | Access, connectors, least privilege and approval-gated actions |
| How do we stop or roll back? | Stop rule, manual process, export and incident procedure |
| When is the system reviewed? | Date, owner, model changes and drift signals |
OECD.AI: OECD AI Principles · EUR-Lex: Regulation (EU) 2024/1689 on artificial intelligence · CNIL: Using generative AI in small and medium-sized businesses · FPS Economy — Belgium: Artificial intelligence — SME digitalisation
How can a business adopt AI responsibly?
- 01
1. Start with the process — Observe current work, volumes, errors, delays and exceptions before selecting a tool.
- 02
2. Limit the first scope — Choose a reversible task whose output can be checked quickly by a qualified person.
- 03
3. Classify the data — Identify personal, confidential, strategic and sector-regulated data before any test.
- 04
4. Test difficult cases — Include exceptions, incomplete documents, ambiguous inputs and likely failure modes—not only favourable examples.
- 05
5. Design oversight — Name the person who accepts, corrects or rejects the output and give them the sources, time and authority to intervene.
- 06
6. Train users — Explain permitted uses, limitations, data protection, expected verification and incident reporting.
- 07
7. Reassess over time — Track changes to the provider, model, data, process and applicable framework.
OECD.AI: OECD AI Principles · CNIL: Using generative AI in small and medium-sized businesses · EUR-Lex: Regulation (EU) 2024/1689 on artificial intelligence · FPS Economy — Belgium: Artificial intelligence — SME digitalisation
A 90-day AI roadmap
- 01
Days 1–15 — Frame — Select a process, observe the current baseline, name the owner, and document the objective, users, data and prohibited uses.
- 02
Days 16–30 — Design — Build a representative test set, evaluation rubric, minimum access, human oversight, manual fallback and incident procedure.
- 03
Days 31–45 — Compare — Test multiple configurations on the same cases. Retain failures, calculate full cost and choose against criteria defined before testing.
- 04
Days 46–65 — Pilot — Open the process to a small trained group. Limit permissions, log corrections and keep approval before sensitive or irreversible action.
- 05
Days 66–80 — Measure — Compare quality, time, cost, incidents and adoption with the baseline. Segment errors instead of hiding them in an overall average.
- 06
Days 81–90 — Decide — Expand, modify or stop under the agreed rule. Record the decision, remaining limits, owner, budget and next review date.
CNIL: Using generative AI in small and medium-sized businesses · OECD.AI: OECD AI Principles · EUR-Lex: Regulation (EU) 2024/1689 on artificial intelligence
How can value be measured without inventing ROI?
Measure the current process first on a comparable sample. Then use the same unit of work, definitions and dated period for the pilot. Generation time alone is not a gain: include preparation, correction, escalation, operation and errors.
| Dimension | Indicator | Method |
|---|---|---|
| Quality | Outputs accepted without major correction | Score a defined sample with a stable rubric |
| Accuracy | Confirmed facts and errors by type | Compare with approved sources and count omissions and inventions separately |
| Time | Median time per completed and approved unit | Include preparation, review, rework and escalation |
| Cost | Full cost per approved unit | Include licences, integration, operation, human review and error handling |
| Risk | Incidents and near misses | Log data exposure, incorrect action and policy violations |
| Adoption | Trained users completing the process correctly | Observe real use without equating a login with delivered value |
OECD.AI: OECD AI Principles · CNIL: Using generative AI in small and medium-sized businesses
What should you explore next?
- Structure a task — Use the ChatGPT prompt library to define context, action, data, result and verification.
- Compare products — Use the best AI comparison to test tools on the same needs with a transparent method.
- Deploy generative AI — Read the generative AI guide for content-generation use cases, their specific risks and controls.
Artificial intelligence FAQ
What is a simple definition of artificial intelligence?
Artificial intelligence refers to machine-based systems that infer from inputs how to produce predictions, content, recommendations or decisions. Their levels of autonomy and adaptiveness vary.
What is the difference between AI and machine learning?
AI is the broad category. Machine learning covers methods that learn relationships from data. Not every AI system relies only on machine learning.
What is the difference between AI and generative AI?
Generative AI is the part of AI focused on creating or transforming content. Other AI systems predict, classify, recommend or detect anomalies without generating text or images.
Which first business use case should we choose?
Choose a frequent, bounded and reversible task with permitted data, an output that is easy to verify and a measured baseline. The best choice depends on the actual process, not a product’s popularity.
Can artificial intelligence operate without human control?
Some systems execute steps automatically, but oversight should match the impact of an error. Sensitive, personal, financial or hard-to-reverse actions need stronger controls and an appropriate assessment.
Does the AI Act apply to every business in the same way?
No. Duties depend on factors including the system, use, risk level and the organisation’s role. This guide is not legal advice; sensitive cases should be reviewed by qualified specialists.
How should an AI project’s ROI be calculated?
Measure the process without AI first, then compare quality, time, full cost, incidents and adoption on an equivalent sample. A theoretical estimate or generation time alone does not demonstrate return on investment.
Official sources
- 01Explanatory memorandum on the updated OECD definition of an AI system — OECD
- 02OECD AI Principles — OECD.AI
- 03Regulation (EU) 2024/1689 on artificial intelligence — EUR-Lex
- 04Using generative AI in small and medium-sized businesses — CNIL
- 05Artificial intelligence — SME digitalisation — FPS Economy — Belgium
Read next
Turn an AI use case into a controlled process
Devauras helps you frame the need, connect useful data, embed human controls and measure AI automation in your operations.
Explore AI automation