1. Understand
Describe current work before imagining the solution.
- Define the problem and expected outcome
- Size frequency, time and cost of error
- Identify people, sources and systems
Practical guide
How to implement AI in an SMB: from problem to pilot.
Responsible implementation starts by understanding the work, not by buying a tool.
2. Design
Choose a small scope that supports learning without putting the whole process at risk.
- Map exceptions and risk
- Define permissions and human review
- Agree on success indicators
3. Test and decide
A pilot provides evidence for deciding the next step.
- Test real scenarios and boundaries
- Prepare the people who will use it
- Measure, refine and decide whether to scale
Frequently asked questions
What to clarify before moving forward
What should the first project be?
A frequent, focused and measurable process with an accountable person who can review the result.
Must all data be perfectly organized?
Not all of it, but the process needs minimum sources and a plan for quality, permissions and versions.
When should we scale?
After proving quality, usage, exception control and enough value relative to effort and risk.
First step