AI automation can improve a process, but choosing the wrong first project often creates more work than it removes. A Malta SME does not need to automate its most complex workflow first. It needs a contained pilot that solves a real operational problem, can be checked by a person and produces evidence for the next decision.
This guide explains how to select an AI automation pilot in Malta using a practical scoring method. It complements our AI automation readiness checklist, which helps businesses assess their wider processes, data and governance before implementation.
What makes a good first AI automation pilot?
A strong first pilot usually has five characteristics: the task happens frequently, the current method is measurable, the inputs are reasonably consistent, errors can be detected, and a human can intervene before harm occurs. The pilot should be valuable enough to matter but limited enough to stop, adjust or reverse.
Examples could include classifying incoming enquiries for human review, extracting standard fields from supplier documents, preparing a first draft of routine content, or summarising internal notes. The best option depends on the data, consequences and controls in your own business. A task involving employment, credit, health, essential services or consequential decisions requires much more careful legal and risk assessment than a low-impact administrative draft.
Step 1: List problems, not fashionable tools
Begin with a short workshop involving the people who perform the work. Ask where time is lost, where information is copied between systems, where backlogs form, and which routine decisions follow repeatable rules. Record the process before discussing products.
For each candidate, write one sentence in this format: “When this trigger occurs, the team performs these steps to produce this outcome.” Then add the systems used, the data involved, the average volume, the person accountable and the main failure modes. This prevents a vague objective such as “use AI in customer service” from becoming an uncontrolled technology project.
Step 2: Separate automation from AI
Not every workflow needs AI. A fixed rule, form validation, scheduled reminder or system integration may be more predictable and easier to maintain. AI becomes more relevant when the task involves language, images, variable documents, classification or patterns that cannot be handled well through simple rules alone.
For every proposed use case, ask whether a conventional workflow can solve it. If the answer is yes, compare both approaches. A simpler automation may offer better reliability, clearer testing and lower operating risk.
Step 3: Score each candidate use case
Use a consistent score from 1 to 5 for each factor. A score of 5 should always mean “more suitable for an initial pilot.” This makes competing ideas easier to compare without pretending that the result is a perfect financial forecast.
| Factor | Question | A higher score means |
|---|---|---|
| Frequency | How often does the task occur? | The workflow occurs regularly enough to test. |
| Effort | How much staff time does the current process consume? | There is meaningful repetitive work to reduce. |
| Consistency | How standard are the inputs and expected outputs? | Examples and acceptance criteria are clear. |
| Measurability | Can current and pilot performance be compared? | Baseline measures already exist or are easy to collect. |
| Reversibility | Can a person correct or reject the output? | Mistakes can be caught before affecting customers or rights. |
| Data readiness | Are suitable, lawful and accessible data available? | The team understands the data and can use it appropriately. |
| Integration simplicity | How many systems and owners are involved? | The pilot has few dependencies. |
| Risk | What happens if the output is wrong? | The consequences are limited and controllable. |
Add the scores, but do not let a total conceal a critical concern. A high-risk use case should not become the first pilot merely because it is frequent. Treat privacy, security, legal restrictions, safety and effects on individuals as separate gates that can stop or redesign the project.
Step 4: Define the human role before building
Specify who checks the system, which outputs require approval, how exceptions are escalated and who can pause the automation. Human oversight should be a real operating control, not a label added after implementation. The reviewer needs sufficient context, authority and time to challenge an output.
NIST’s AI Risk Management Framework recommends defining roles and responsibilities for human and AI configurations. The EU AI Act also uses a risk-based approach and includes transparency and human-oversight requirements in relevant contexts. Malta businesses should assess their exact role and use case rather than assuming every AI tool has the same obligations.
Step 5: Check the data path
Map what enters the workflow, where it is sent, where outputs are stored and who can access them. Identify personal data, confidential business information, customer material and third-party intellectual property. Review supplier terms, retention, security, international transfers and whether information may be used to improve a provider’s models.
Use the minimum data needed for the pilot. Where possible, begin with synthetic, redacted or non-sensitive examples. If personal data is involved, obtain appropriate data-protection advice and document the lawful purpose, controls and responsibilities. Automated decisions affecting people may require specialist assessment.
Step 6: Write measurable acceptance criteria
A pilot needs a pass or fail decision. Establish a baseline before implementation and select measures relevant to the process. These might include turnaround time, percentage requiring correction, number of exceptions, completion rate, staff effort, customer response time or cost per completed item.
Quality must be measured as well as speed. For a document-extraction pilot, for example, define which fields must be captured, the acceptable accuracy for each field, the sample size, how discrepancies are reviewed and when the system must send an item to a person instead of guessing.
Step 7: Limit the pilot’s scope
Choose one process, one team and a controlled data set. Set a start date, review points and an end date. Decide the maximum transaction volume and which cases remain outside the pilot. Keep the current process available until the new approach has been tested properly.
A useful pilot plan names the process owner, technical owner, reviewer, data source, vendor, success measures, risks, controls, budget boundary and exit method. It should also explain what must happen before the pilot can expand.
Step 8: Test normal cases, edge cases and failure modes
Do not test only the clean examples that make a demonstration look good. Include incomplete records, unusual language, duplicates, contradictory instructions and inputs that should be rejected. Confirm what happens when a connected system is unavailable or the AI produces an uncertain result.
Keep a simple issue log showing the input type, observed output, expected outcome, severity, correction and preventive action. This evidence is more useful than a general impression that the tool “worked well.”
Step 9: Prepare the people using it
Staff need to understand the tool’s intended use, limitations, data rules, review duties and escalation path. The European Commission’s current AI literacy guidance says organisations should consider their role, the system’s risks, staff knowledge and the context in which the AI is used. Training should therefore match the actual pilot rather than consist only of a generic presentation.
Step 10: Decide whether to stop, improve or scale
At the end of the pilot, compare results with the baseline. Review quality, workload, exceptions, user feedback, costs and risks. There are three valid outcomes: stop because the case is unsuitable, improve and retest, or scale under a controlled change plan.
Scaling should trigger another review of access, security, training, supplier dependency, monitoring and support. A solution that is manageable for one team may need stronger controls when it becomes part of everyday operations.
A practical first-pilot checklist
- The business problem and current process are documented.
- A non-AI solution has been considered.
- The use case scores well for value, measurability and reversibility.
- Privacy, security, legal and safety concerns have been reviewed.
- A named person can approve, reject and escalate outputs.
- The data path and supplier responsibilities are understood.
- Baseline measures and acceptance criteria are recorded.
- The scope, duration, volume and exit method are limited.
- Normal cases, edge cases and failures will be tested.
- Relevant staff receive practical training.
- A documented decision will be made before scaling.
Digital Consulting Pros helps businesses identify, design and implement AI automation solutions in Malta. A discovery session can be used to map candidate workflows and select a pilot with clear controls and measurable objectives. Contact DCP to discuss an appropriate starting point.
Authoritative guidance
- NIST AI Risk Management Framework
- NIST AI RMF Playbook
- European Commission AI Act overview
- European Commission AI literacy questions and answers
- EDPB data-protection guidance for small businesses
This article provides general operational information and is not legal advice. Featured photo by Patrick Perkins on Unsplash.
