Operational reasons
Why introduce AI into a business.
Artificial intelligence can change how a business gathers information, prepares documents, supports people, compares alternatives and coordinates a process. It becomes useful when a system capability enters a specific activity and leaves a result that someone can check.
Use dispersed information
Documents, email, procedures and data become more accessible when sources, permissions and updates are organised around the activity.
Prepare a first result
Summaries, drafts, classifications and comparisons can reach the person who must review them sooner.
Make a process legible
Sources, handovers, exceptions and approvals reveal friction and responsibility that were previously implicit.
Coordinate work and tools
The system can preserve context across requests, data, preparation, review and state updates.
Develop a new capability
Contextual support, analysis across many sources or continuity over recurring requests can become hypotheses to test in real work.
From experiment to organisation
When individual AI use becomes a business project.
AI may already be used inside a company without there being a company project. Someone may use it to find information, prepare text or compare documents, while the result still depends on that person’s account, instructions and knowledge.
It becomes an organisational project when the work no longer depends only on individual initiative and five elements become explicit:
Activity and result
The business can state which work is being examined and what it should produce or improve.
Context and sources
Documents, data, rules and permissions do not remain implicit in one person’s memory.
Responsibility
Someone is assigned to review the result, approve its use and stop the process when necessary.
Shared test
The team has examples and criteria for comparing the system with the current work.
Organisational learning
Corrections and decisions improve the method instead of disappearing into one person’s chat history.
This transition does not require a broad integration at the start. It requires a first experiment that other people can understand, verify and improve.
In 2025, Istat reported that the share of Italian businesses with at least ten employees using one or more AI technologies rose from 8.2% to 16.4%. Among businesses that had considered but not made AI investments, almost 60% identified a lack of suitable skills as a barrier. These figures do not describe every company, but they show why access to a model is different from the capability to build a project.
Observe the work
Where to find a first AI use case.
A first use case is easier to understand when it concerns work that people already know. Useful signals include:
- a recurring question that requires the same research each time;
- similar documents prepared from changing information;
- handovers where context is lost;
- many sources that must be compared before a decision;
- repetitive checks with recognisable criteria;
- important knowledge concentrated in a few people;
- a useful service that currently requires too much work to provide consistently.
Which result should change, and what comparison would allow us to recognise that change?
Selection criteria
How to choose a first AI project.
A useful first project produces knowledge about the case while keeping consequences and responsibility visible.
Recognisable activity
People can describe when it starts, which information it uses and what it produces.
Real friction
Research time, repeated work, delays, handover errors or a capability that is difficult to sustain.
Checkable result
Sources, criteria or human judgement make it possible to test completeness, consistency and required corrections.
Assigned responsibility
A person knows what can be accepted, what requires approval and when the process should stop.
Bounded first test
Documents, users or requests are limited enough to compare the result and change direction.
Form of the system
Assistant, automation or agentic system?
A person conducts the work.
The AI searches, explains, compares or prepares an object. The person reviews each response and decides how to use it.
The route is defined.
Inputs, steps and destination are established; the model classifies, extracts or generates one part of the result.
The route depends on context.
The system maintains an objective and state, uses several tools and prepares actions within permissions and stop conditions.
The choice depends on variation in the work, possible effects and how often human judgement is required.
Practical object
First AI Project Canvas.
Complete the nine fields for one real activity. Answers may be provisional: missing information shows what needs to be clarified before choosing a solution.
- 01
Activity
Which work do you want to observe or change? Where does it begin, and what does it produce today?
- 02
Friction or possibility
What makes the work slow, fragile or difficult to provide consistently?
- 03
Frequency and variation
How often does it happen? Are the cases similar, or do they vary substantially?
- 04
Information and sources
Which documents, data, applications or knowledge are required? Who maintains them?
- 05
People and roles
Who performs the work, uses the result and has the knowledge required to assess it?
- 06
Responsibility
Which steps may the system assist, and which decisions require a person?
- 07
Desired result
What should become clearer, faster, more accessible or more consistent?
- 08
Test and verification
Which comparison will show usefulness, errors and required corrections?
- 09
Risk and boundary
Which data, actions or decisions remain outside the first test?
We want to assist [people or role] with [activity], using [sources] to prepare [result]. [accountable person] checks [criteria] before [action or use]. The first test excludes [risk or boundary].
Illustrative scenario
Preparing a first sales proposal draft.
A company receives varied requests and prepares proposals from client email, price lists, technical sheets and project documents. A first AI hypothesis could identify available information, show what is missing, retrieve relevant material from authorised sources and prepare a structure for the responsible person.
The test could compare the time required to reach a first draft, missing information found and corrections required. Prices, commercial terms and delivery to the client remain subject to approval by the responsible person.
This scenario illustrates a possible structure and does not describe a client project.
Project conditions
Data, responsibility and continuity.
Data and confidentiality
Clarify which information may be used, where it is processed, which providers may receive it and who can access the result.
Source quality
Assign responsibility for updates and preserve a way to trace important information to its origin.
Actions and permissions
Preparing a draft, updating a business system and sending a message require different permissions and controls.
People and organisation
People using the system need to know what it can do, where it can fail and how to handle exceptions and problems.
Total cost
Consider models, software, data, integrations, human review, security, training and maintenance.
Dependency and maintenance
Identify which configurations and knowledge remain transferable when models, prices, interfaces or providers change.
Frequently asked questions
Questions about artificial intelligence in business.
Which business process should we start with?
Start with a recurring, recognisable activity that has accessible sources, a real friction, an accountable person and a result that can be checked.
When does individual AI use become a business project?
It becomes a business project when the activity and result are defined, sources and permissions are shared, a person remains accountable, and the team can test and improve the system without depending on one person’s chat history.
Do we need to choose an AI tool first?
No. Describing the activity, sources, people, expected result and boundaries first makes it easier to compare tools and architectures.
How large should the first AI project be?
Its scope should support a credible test while keeping responsibility and the causes of the result visible. It can cover one stage, document type, team or controlled set of requests.
How can we tell whether the project creates value?
Before the test, define the change to observe: time, completeness, corrections, continuity, number of steps or quality accepted by the people who use the result.
Must company data be sent to an external provider?
It depends on the architecture. Local, external and hybrid options should be compared against data sensitivity, performance, cost, integrations and the requirements of the use case.
When is a consultation useful?
Human assessment is useful when the case involves sensitive decisions, cross-functional processes, confidential data, important integrations, investment or unclear responsibility.