Practical guide
Artificial intelligence: what it is, how it works and how to use it wisely
Artificial intelligence is often discussed as if it were one clever tool. In reality, it is a family of methods and systems: one recognises objects in images, another forecasts demand, while generative AI produces text, images, audio or code.
This guide is for readers who want more than a definition. It explains what AI can usefully do, where it fails, how to test a real workflow and what organisations in Latvia and the EU should prepare for in 2026.
Vitalij Kolotikov 20-minute read + practical tools 
What is artificial intelligence? The short answer
Artificial intelligence is the capability of a machine-based system to infer, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions. This practical wording follows the OECD definition of an AI system.
The important word is “infer”. Traditional software follows rules written in advance. An AI model uses patterns learned from data to calculate a likely result even when no developer wrote a separate rule for that exact situation.
AI, generative AI and automation are not the same thing
ChatGPT is a generative-AI product, not a synonym for the entire field. Many reliable automations use no AI at all. If a fixed rule solves a task, the rule is often cheaper, easier to audit and more predictable.
| Type | What it does | Simple example |
|---|---|---|
| Rule-based automation | Runs a defined if–then action | Sends an invoice when an order becomes complete |
| Machine learning | Finds patterns to classify or predict | Flags transactions that may be fraudulent |
| Generative AI | Creates new text, images, audio or code | Drafts an email from supplied notes |
| AI agent | Plans several steps and uses permitted tools | Reads a request, retrieves data and pauses with a reply for approval |
How artificial intelligence works without the technical jargon
Models differ, but a five-stage mental model helps a practical user see both the value and the failure points.
1. Objective and data
Developers define the desired capability and prepare examples or other training data.
2. Training
The model adjusts internal parameters to find relationships between inputs and desired outputs; it does not simply store a catalogue of finished answers.
3. New input
A user supplies a question, document, image or dataset together with context and constraints.
4. Inference
The model calculates a possible output. A language model, simplified, predicts suitable text elements in sequence.
5. Human verification
A responsible person compares the output with original sources, rules and the task objective before it is used.
Where AI can help in everyday work
The strongest first use case is rarely “do everything for me”. It is one bounded stage with a clear input and an obvious review point.
| Task | Give AI | Human verifies |
|---|---|---|
| Meeting summary | Permitted notes and a required structure | Decisions, owners and deadlines |
| Email draft | Recipient, objective, verified facts and tone | Facts, promises and confidentiality |
| Document comparison | Two approved versions and criteria | Legally or financially material differences |
| Data organisation | An anonymised table and clear categories | Row counts, exceptions and calculations |
| Idea variants | Audience, problem and strict constraints | Originality, relevance and feasibility |
What changed in artificial intelligence in 2026
AI is moving beyond a chat window. Current systems can process text, images and audio in one workflow, call permitted tools and complete several steps. A useful instruction is increasingly: retrieve from this approved source, draft the result and stop before sending.
Capability is still uneven. Stanford’s 2026 AI Index shows large gains in agent performance while also documenting simple-looking tasks where systems remain far below human performance. A difficult success therefore does not guarantee reliability on the next ordinary task.
88%
AI use is widespread; agents are not yet routine
In Stanford AI Index surveys, 88% of organisations reported using AI, while agent deployment remained in single digits across most business functions.
Stanford AI Index 2026 ↗66,3%
Agents can act, but failures remain common
On the OSWorld computer-use benchmark, agent performance rose from about 12% to 66.3%. That is major progress, but still roughly one failed attempt in three.
Technical performance chapter ↗02.08.
EU transparency duties become operational
From 2 August 2026, Article 50 of the AI Act sets duties for certain AI interactions, deepfakes and public-interest AI-generated text published without human editorial control.
European Commission guidance ↗Six practical AI use cases with quality measures
“AI for marketing” is too broad to operate safely. A real use case names the input, output, review point and metric. Explore the six complete workflows below.
Interactive use-case library
Choose a work area
Every example identifies the input, human control point and measurement that turn a demo into a real workflow.
From a customer question to a checked reply draft
- Safe input
- A question stripped of unnecessary personal data, an approved price list, FAQs and tone.
- Workflow
- AI searches the supplied sources, drafts a reply and flags what it cannot establish.
- Output
- A draft that an employee approves or completes.
- Human checks
- Price, timing, promises and every claim absent from the source.
- What to measure
- Average preparation time and material edits across 20 replies.
From notes to actions without losing accountability
- Safe input
- Permitted meeting notes and a standard task format.
- Workflow
- AI separates decisions, open questions and actions, then looks for owners and deadlines.
- Output
- Structured minutes with a missing-information section.
- Human checks
- No invented owner or date; every decision retained.
- What to measure
- Missed decisions and minutes until distribution.
One approved source, several channels
- Safe input
- Product fact sheet, audience, brand voice and channel limits.
- Workflow
- AI adapts one approved message for email, social media and a landing-page introduction.
- Output
- Editable variants, not automatically published content.
- Human checks
- Facts, superiority claims, copyright, tone and consistency.
- What to measure
- Time to the first editable draft and rejection rate.
Explore a table without trusting calculations blindly
- Safe input
- An anonymised CSV, column definitions and a specific question.
- Workflow
- AI proposes checks, spots possible exceptions and drafts formulas or analysis code.
- Output
- A list of hypotheses and reproducible calculations.
- Human checks
- Row count, missing values, formulas and results in a trusted tool.
- What to measure
- Issues found and subsequently confirmed.
Compare two documents with traceable evidence
- Safe input
- Two permitted versions and comparison criteria.
- Workflow
- AI groups changes and points to the relevant source location.
- Output
- A change register with traceable references.
- Human checks
- Legal, financial and deadline clauses in the originals.
- What to measure
- Material changes missed against a human benchmark.
Turn an approved procedure into practical training
- Safe input
- The current procedure, employee role and typical situations.
- Workflow
- AI drafts scenarios, explanations and discussion questions.
- Output
- A role-specific training draft.
- Human checks
- Alignment with the current procedure and safe practice.
- What to measure
- Change in task errors before and after training.
Interactive AI task risk assessment
Before a pilot, consider data sensitivity, the consequences of an error, verifiability and human control—not only whether the model can perform the task.
The 30-minute test: is AI worthwhile for this task?
Test one real task before buying another tool or automating an entire process. The question is whether total work becomes faster or more accurate while risk stays controlled.
Choose one repeatable task
It should happen often enough to matter and be safe enough that an error can be caught before publication.
Keep a good human example
Use it as the benchmark; “looks good” is not a measurable quality standard.
Supply context and a required format
Define the audience, objective, approved source, constraints and structure.
Measure the whole cycle
Include input preparation, edits and fact-checking—not only generation seconds.
Make a decision
Adopt only if total time falls or quality rises without creating uncontrolled risk.
| Measure | Without AI | With AI |
|---|---|---|
| Total time | ___ min | ___ min |
| Material edits | ___ | ___ |
| Unverifiable claims | ___ | ___ |
| Did the result meet the objective? | Yes / No | Yes / No |
How to write an AI brief that can be checked
A long prompt is not automatically a good prompt. A reusable six-part brief gives every sentence a job.
Reusable AI brief
GOAL: what must be achieved? AUDIENCE: who is the output for? CONTEXT: what does the model need to know? SOURCE: use only the supplied material and identify missing information. CONSTRAINTS: what must not be assumed, disclosed or promised? FORMAT AND CHECKS: how should the answer be structured and what must be flagged for review?
Build your own brief in the interactive prompt builder
Complete only the fields your task needs. The tool runs entirely in this page; entries are not sent to Hexa Academy or retained after you leave.
Private browser tool
Build a verifiable AI brief
Nothing entered here is sent to a server or stored. Do not enter personal or confidential information.
Complete the fields — your brief will appear here.
Five checks before using an AI output
- Facts: find the original source rather than trusting a citation produced by AI.
- Numbers: recalculate totals, percentages and date ranges in a trusted tool.
- Completeness: compare the output with the brief and look for silently omitted conditions.
- Privacy: confirm that the data was permitted in that service and account.
- Accountability: name the person who approves the final result. “AI wrote it” is not quality control.
When artificial intelligence is the wrong tool
Technical possibility is not a business case. The more expensive an error and the harder the result is to verify, the stronger human control must be—or the task should use another method.
| Situation | Why it is risky | Safer option |
|---|---|---|
| Unapproved personal, customer or trade-secret data | Information may leave the intended control boundary | Anonymise or use an organisation-approved environment |
| Final legal, medical or financial decision | Consequences are serious while the answer may sound convincing | Use AI only for preparation and refer to a qualified professional |
| No way to verify the output | There is no quality threshold | Build sources, a benchmark and review process first |
| The task is simple and deterministic | AI adds cost and variability without value | Use a formula, template or rule-based automation |
EU AI Act in 2026: what an organisation should do
The EU AI Act does not require every AI-edited email to carry a label. Duties depend on the system, role and use. From 2 August 2026, Article 50 particularly addresses direct AI interaction, deepfakes, certain synthetic content and public-interest text published without human review or editorial control.
The AI-literacy duty has applied since 2 February 2025. The European Commission says training should reflect employees’ knowledge, the systems used, the context and the risk. There is no single compulsory certificate; a practical record of role-specific training and guidance is useful evidence.
| Action | Document | Purpose |
|---|---|---|
| AI inventory | Tool, owner, users, data and intended purpose | Know where AI is used |
| Role-based training | Permitted data, verification and escalation | Match literacy to real use |
| Human control | Named approver and quality criteria | Make oversight operational |
| Transparency | When people must be told about AI or synthetic content | Apply Article 50 consistently |
| Incident log | Error, impact, correction and process change | Prevent silent repetition |
How to start today
Choose one task you know well, remove sensitive information, define a good human example and run the 30-minute test. A useful goal is specific: reduce weekly meeting-summary preparation from 25 to 10 minutes without losing any decision or deadline.
From idea to controlled pilot
Seven-day implementation plan
Progress is stored only in this browser.
Frequently asked questions
What is the difference between AI and ChatGPT?
Artificial intelligence is a broad field. ChatGPT is one generative-AI product. Not every AI system is conversational, and not every automation uses AI.
Does artificial intelligence think like a person?
No. Current systems calculate outputs from learned patterns. Convincing language and difficult problem-solving are not the same as human understanding or responsibility.
Can AI answers always be trusted?
No. Generative AI can invent facts, references and conclusions. Check material claims in original sources, recalculate numbers and keep a responsible human decision-maker.
What is the best first AI task for a business?
Choose a frequent, low-risk, easily verified task with a clear quality benchmark, such as structuring your own notes or drafting an email from approved facts.
Does the EU AI Act require all AI content to be labelled?
No. The requirement depends on the type of system and use. Article 50 focuses on specified interactions and synthetic content; obtain specialist advice for your exact case.
Sources and further reading
- OECD — updated definition of an AI system
- NIST — AI Risk Management Framework
- Stanford HAI — 2026 AI Index: technical performance
- Stanford HAI — 2026 AI Index: economy
- European Commission — transparency of AI-generated content
- European Commission — AI literacy Q&A
- Latvian Data State Inspectorate — chatbot privacy
Test AI on one real work task
Bring a real, non-confidential task. Together we will define the input, quality criteria and review process so you leave with a repeatable working method.