Applied AI can be useful without replacing the company's knowledge. Before implementation, confirm the problem, data quality, permissions, human review, and what happens when the result is wrong.
Look for a clear work problem
An AI idea becomes concrete when it is tied to a task, a user, and an outcome. Describe what the person is trying to do today and where time or visibility is lost.
- Find answers in approved internal documents.
- Classify requests and route them to the right team.
- Extract information from documents for human validation.
- Summarise long information to prepare a decision.
- Support drafts that a person reviews before publication.
AI, automation, or custom application?
These solutions can work together, but they are not the same. AI helps interpret or generate information; automation executes predictable steps; an application manages users, data, permissions, and business rules.
- Use automation when the rules are clear and repeatable.
- Use AI when text, classification, search, or synthesis benefits from probabilistic support.
- Use an application when data, statuses, users, and history need to be managed.
- Combine all three only when each part has a clear purpose.
Check the data before choosing a solution
An intelligent solution depends on the information it receives. Incomplete, outdated, or overly accessible data can create risks and results that are difficult to explain.
- Which data will be used and who can access it?
- Is there an authorised and current source for each answer?
- Does the information include personal, confidential, or restricted data?
- How will an incorrect or outdated answer be corrected?
- How long should requests, outputs, and logs be kept?
Keep human review where it is needed
A generated answer can sound convincing and still be wrong. The company should define when a person can accept, edit, reject, or request a source before using the result.
- Start with drafts, suggestions, or assisted search.
- Show the source information when possible.
- Record decisions and corrections during the initial period.
- Prevent unreviewed output from automatically changing critical data.
How to choose a first project
A first project should be small, have an owner, and allow comparison with the current process. Avoid starting with a vague promise to transform the entire business.
- Define the task, users, and expected outcome.
- Choose a controlled and authorised data set.
- Define acceptable answers and error situations.
- Decide how usefulness, quality, and review needs will be assessed.
- Document what is outside the first phase.
What a responsible solution must maintain
After launch, the solution needs attention. Models, data, permissions, costs, and team needs can change. Maintenance should include review of content, access, errors, use, and security rules.