An AI automation proposal can sound impressive while leaving essential questions unanswered. Before a Malta business gives a provider access to customer data, inboxes, internal documents or operational systems, it should understand exactly what will be automated, how outputs will be checked and who remains accountable.
This AI automation vendor checklist for Malta businesses is designed for early supplier discussions and proposal reviews. It is not a substitute for legal, data-protection, information-security or sector-specific advice. Its purpose is to make the commercial and operational decision more disciplined.
1. Define the outcome before discussing tools
Start with the workflow and business problem rather than a preferred AI product. Describe the current process, where delays or errors occur, who owns each step and what a useful improvement would look like.
- Which task or decision is in scope?
- Who performs it today?
- Which systems and data are involved?
- What must never happen automatically?
- How will the business recognise a successful result?
A provider should be able to explain why automation is suitable for the workflow and where a simpler rule, integration or process change may be more appropriate. A clear problem statement also prevents the scope from expanding before risks and responsibilities are understood.
2. Ask the provider to map the complete workflow
Request a plain-language map showing inputs, processing steps, external services, outputs, storage locations and human review points. Include exceptions, failures and the path for stopping or reversing an action.
For example, an enquiry-handling automation might receive a form submission, classify the request, draft a response and create a record in a customer relationship system. The map should show whether the response is sent automatically, which data reaches the model, where logs are stored and how a staff member corrects an error.
3. Identify the provider, platforms and subcontractors
The business contracting with you may rely on several other services, including model providers, automation platforms, hosting companies, messaging services and analytics tools. Ask for a current list of material subprocessors and the role each one performs.
- Which legal entity is your contracting party?
- Which third-party AI models and software platforms are used?
- Where is data processed and stored?
- How are changes to subprocessors communicated?
- Can a required third party be replaced without redesigning the entire workflow?
The European Data Protection Board explains that a processor acts on a controller’s instructions and that authorised subprocessors require contractual protections. Its controller and processor guidance for SMEs is a useful starting point when personal data is involved.
4. Examine data use, retention and deletion
Do not rely on a general statement that data is secure. Ask what categories of data will enter the workflow, which fields are necessary, whether prompts or outputs are retained and whether customer data is used to train or improve any model.
Record the retention period for operational data, logs, backups and support records. Confirm how deletion is requested, how long it takes and what evidence can be provided. If sensitive or confidential material is not necessary for the stated purpose, remove or mask it before it reaches the automation.
The EDPB’s SME guidance stresses data protection by design and default, including limiting personal data to what is necessary for the purpose. Review its data-protection compliance guidance with the appropriate adviser for your use case.
5. Establish access and security controls
List every account, mailbox, database, API and document repository the automation will access. Give the minimum permissions needed for the task and avoid using shared administrator credentials when a restricted service account is available.
- How are credentials stored and rotated?
- Is multi-factor authentication supported for administrative access?
- Can access be limited by role and environment?
- Are actions and configuration changes logged?
- How quickly can access be revoked?
- What happens after a suspected security incident?
Security answers should be specific to the proposed architecture. A policy document is useful, but it should connect to the actual systems and data included in your project.
6. Define human oversight and escalation
Decide which outputs require human approval, which actions can be automated within limits and which decisions must remain entirely manual. Name the people responsible for review and give them enough information and authority to intervene.
The European Commission’s AI Act guidance describes human oversight, monitoring and action on identified risks as important responsibilities for deployers of high-risk AI systems. Even where a proposed workflow is not high-risk, the underlying discipline is useful: someone must understand the system well enough to recognise and stop harmful behaviour. See the Commission’s AI Act questions and answers.
Ask the provider to demonstrate the manual fallback. Staff should know how to pause the workflow, complete the task without it and report an incident. A human approval step is not meaningful if reviewers are overloaded, lack context or routinely approve outputs without checking them.
7. Agree how quality will be tested
A convincing demonstration is not the same as a controlled test. Agree a representative set of normal, unusual and unacceptable scenarios before launch. Define what counts as a correct result, a recoverable error and a stop condition.
- Who prepares and approves the test cases?
- Does the test include poor, incomplete and conflicting input?
- How are false or unsupported outputs identified?
- Are permissions, logging and manual override tested?
- What evidence will be retained?
NIST’s voluntary AI Risk Management Framework Playbook organises risk work around Govern, Map, Measure and Manage. That structure can help a business ask whether governance, context, testing and ongoing controls have all been considered rather than treating implementation as a one-off technical task. Review the NIST AI RMF Playbook.
8. Check monitoring after launch
AI services, connected applications, prompts and business rules can change. Ask what is monitored, who reviews exceptions and how the business will know if output quality declines.
Useful operational records may include failed runs, manual corrections, approval rates, repeated exceptions, model or configuration changes and incidents. Select measures that expose risk and business value rather than producing a large dashboard with no decision attached.
9. Clarify change control
Specify which changes the provider may make without approval and which require documented review and testing. This can include new model versions, revised prompts, different subprocessors, additional data fields, new integrations and broader automated permissions.
Ask how urgent fixes are handled and how the previous configuration can be restored. The agreement should also address notification when a third-party service changes a relevant feature, price, data policy or availability condition.
10. Review transparency and user communications
Determine whether customers, employees or other people interact directly with the AI system or receive AI-generated content. Decide what information should be provided, where it appears and who approves it.
Article 50 transparency obligations under the EU AI Act have applied since 2 August 2026 to specified AI interactions and content. The exact duties depend on the system and use case, so review the European Commission’s current AI transparency guidance and obtain qualified advice where necessary.
11. Protect ownership and portability
Confirm who owns the workflow design, prompts, custom code, documentation, test evidence and configuration. Identify which elements depend on the provider’s proprietary platform and what can be exported.
- Will the business receive administrator access?
- Can configurations, logs and documentation be exported?
- Who owns custom integrations and code?
- What licences are required after the engagement ends?
- How will data be returned or deleted?
- What assistance is provided during transition?
A workflow that cannot be understood or operated without one supplier creates commercial risk. Portability does not always mean that every component can be moved unchanged, but the dependencies and exit process should be clear before implementation.
12. Start with a controlled pilot
Use a limited scope, named owner, representative test set and explicit stop conditions. Avoid connecting a new automation to every system before the team understands its behaviour. A pilot should generate evidence for a decision: stop, improve, expand or redesign.
Our AI automation readiness checklist for Malta SMEs can help you assess internal preparation. When the business is ready to test a use case, follow the framework in How to Choose Your First AI Automation Pilot in Malta.
Questions to include in the proposal review
- What exact workflow and outcome are included?
- Which AI models, platforms and subprocessors are used?
- What data is processed, retained and used for model improvement?
- Which actions require human approval?
- How will quality, exceptions and security controls be tested?
- How are changes approved and documented?
- What monitoring and incident support are included?
- What can the business export when the contract ends?
Choose evidence over promises
A suitable AI automation provider should be able to explain the workflow, limitations, data path, controls and exit arrangements in language your operational team can understand. The decision should rest on a defined business case and testable safeguards, not a polished demonstration alone.
If you need help identifying and implementing an appropriate workflow, review DCP’s AI automation services or contact the team.
