An AI automation pilot should answer a decision, not simply demonstrate that a tool can produce an output. Before a Malta business invests in a wider rollout, it needs evidence that the automation improves a real process, performs reliably, can be supervised and does not introduce unacceptable operational, privacy or compliance risks.
This guide explains the AI automation pilot metrics Malta businesses can use to compare a pilot with the current way of working. It builds on DCP’s AI automation readiness checklist and pilot-selection guide. It is practical business guidance, not legal advice or a substitute for a case-specific regulatory assessment.
Start with the decision the pilot must support
Define what management will decide when the pilot ends. Typical outcomes are to scale, revise, extend, pause or stop. Without that decision, teams often collect impressive-looking activity data that does not show whether the workflow is better.
- What business problem is the pilot intended to solve?
- Which users, customers or employees are affected?
- What process and system boundaries are included?
- What outcome must improve, and what must not deteriorate?
- Who owns the decision and who can stop the pilot?
- What evidence is required before wider deployment?
A useful pilot statement is specific. For example: “Test whether assisted classification can reduce the time needed to route incoming service requests while maintaining the agreed review quality and preserving human control over exceptions.”
1. Establish a baseline before automation
Measure the current process using real operational data before the pilot starts. If no baseline exists, the team cannot distinguish improvement from normal variation, seasonality or a change in workload.
- Number and type of cases handled
- Elapsed time from request to completion
- Hands-on staff time
- Rework, correction and escalation rates
- Backlog and waiting time
- Customer or employee complaints
- Process costs that can be evidenced
- Existing control failures, bottlenecks and exceptions
Use a representative period and document unusual events. If the process has different languages, customer types, products or risk levels, keep those segments visible rather than hiding them inside one average.
2. Choose a balanced metric set
A pilot should not be judged by one headline number. Faster processing is not a success if quality falls, staff must repair outputs manually or sensitive data is exposed. Use a small set of linked measures across business value, quality, control, adoption and risk.
Business outcome metrics
- Cycle time or response time
- Work completed per period
- Backlog reduction
- Staff time redirected from repetitive work
- Conversion, resolution or completion rate where relevant
- Cost per completed case using documented inputs
Measure the outcome that matters to the process, not merely how many prompts, messages or automated actions were produced.
Quality and reliability metrics
- Correct output rate against an approved reference
- False positive and false negative rates where applicable
- Incomplete or unusable outputs
- Rework required after automation
- System availability and failed runs
- Performance by important case type or user group
Define what “correct” means before testing. For content or classification tasks, use a clear review rubric and, where practical, more than one qualified reviewer for a sample. Record disagreements instead of forcing ambiguous cases into a simple pass or fail result.
Human oversight and exception metrics
- Percentage of outputs reviewed by a person
- Override rate and reason for each override
- Number and type of escalations
- Time needed to investigate exceptions
- Cases where staff relied on an output without appropriate review
- Ability to pause, correct or reverse the automated action
Human oversight must be operational, not decorative. The reviewer needs enough information, authority and time to challenge the output. The Malta Digital Innovation Authority highlights human oversight, logs, risk management, accuracy, robustness and post-market monitoring among the controls relevant to higher-risk AI systems. Its AI regulatory overview also explains that classification and obligations depend on the system and use case.
Adoption and operability metrics
- Percentage of eligible users who use the workflow correctly
- Training completion and support requests
- Workarounds or shadow processes created by staff
- Time needed to learn and operate the system
- User feedback on clarity, workload and confidence
- Dependency on one supplier, administrator or specialist
Low adoption may indicate poor design, inadequate training or a mismatch between the automation and the real process. It should not automatically be treated as employee resistance.
Privacy, security and compliance measures
- Personal data fields used and their purpose
- Access, retention, deletion and audit-log behaviour
- Data sent to external providers or other jurisdictions
- Security incidents, access failures or unauthorised disclosures
- Transparency notices and consent behaviour where applicable
- Completion of required legal or regulatory reviews
The European Data Protection Board explains that a data protection impact assessment is required before processing likely to create a high risk to people’s rights and freedoms. Use its DPIA guidance and obtain qualified advice when the pilot involves consequential decisions or sensitive personal data.
3. Link each metric to evidence
For every metric, define the data source, calculation, owner, frequency and acceptance threshold. This makes the result repeatable and reduces the temptation to change the success criteria after seeing the data.
- Metric name and business purpose
- Baseline value and measurement period
- Pilot target or tolerance
- Source system or review record
- Person responsible for collection
- Measurement frequency
- Segmentation required
- Known limitations or missing data
The NIST AI Risk Management Framework recommends selecting measurement methods for the most significant risks and documenting risks that cannot be measured. Its Measure guidance also emphasises production monitoring, audit logs, overrides, complaints and go or no-go decisions.
4. Test normal, difficult and failure conditions
A pilot that only sees easy examples does not provide reliable evidence. Build a controlled test set covering routine work, boundary cases, incomplete information, unexpected formats and conditions where the system should refuse, escalate or fail safely.
- Define representative normal cases.
- Add rare but important cases and known historical problems.
- Include invalid, missing and contradictory inputs.
- Test user permissions and unauthorised access attempts.
- Interrupt integrations or dependencies where safe to do so.
- Verify that people can identify, correct and escalate failures.
- Retest after material configuration, model or workflow changes.
Keep test data lawful and proportionate. Do not copy live personal data into an uncontrolled test environment simply because it is convenient.
5. Monitor results by segment, not only by average
An average can hide poor performance for a specific language, document type, customer group or exception category. Review the slices that matter to the use case and the people affected by it. Where the sample is small, state that limitation rather than claiming certainty.
The NIST framework treats AI as a socio-technical system whose performance depends on data, technology, people and context. That is why technical accuracy alone is not enough to determine whether a pilot is ready to scale.
6. Avoid common pilot vanity metrics
- Number of outputs generated without measuring usefulness
- User registrations without correct adoption
- Total time “saved” based only on vendor estimates
- Accuracy reported without a defined reference or sample
- A demo success rate based on hand-picked examples
- Return on investment that ignores setup, review, rework and support
- Positive feedback collected only from the project team
Vendor dashboards can be useful, but the business should retain its own evidence and definitions. If the supplier controls the input, calculation and interpretation, independent review becomes difficult.
7. Set go, revise and stop criteria
Define the decision rules before the final review. A pilot can show business value and still be unsuitable to scale because a control, privacy or reliability threshold was missed.
- Scale: required outcome, quality and control thresholds are met with manageable residual risks.
- Revise: the use case remains valuable but the workflow, data, training or controls need improvement.
- Extend: evidence is promising but the sample or operating period is too limited.
- Stop: the pilot does not achieve its purpose, creates unacceptable risk or costs more to control than the benefit supports.
NIST’s Manage guidance recommends a formal determination of whether an AI system achieves its intended purpose and whether deployment should proceed. Stopping a weak pilot is a useful result because it prevents a larger commitment to the wrong solution.
8. Check readiness before scaling
- Accountable owner and human-review roles are confirmed
- Monitoring, incident response and escalation are documented
- Data protection and security reviews are complete
- Supplier responsibilities and exit arrangements are clear
- Logs, evidence and configuration records are retained
- Training and support can cover the wider user group
- Integration capacity and failure recovery have been tested
- Budget includes ongoing review, licences, maintenance and change control
If a provider is involved, use the AI automation vendor due-diligence checklist before committing to a broader rollout.
Copyable AI automation pilot scorecard
- Decision to be supported
- Process owner and accountable approver
- Baseline period and source data
- Business outcome measures
- Quality and reliability measures
- Human review, override and escalation measures
- Adoption and workload measures
- Privacy, security and compliance checks
- Test cases and evidence location
- Success thresholds and risk tolerances
- Scale, revise, extend or stop decision
- Post-pilot monitoring owner and review date
Measure the pilot, not the promise
A useful AI automation pilot creates traceable evidence about performance, people, controls and risk. The aim is not to prove that AI is impressive. It is to determine whether a specific workflow is better, safer and sustainable in the business’s real operating environment.
For help mapping a process, selecting a practical pilot and building the measurement plan, explore DCP’s AI automation services in Malta.
Featured image: Photo by Stephen Dawson on Unsplash.
