An automation proposal can sound convincing while leaving the practical questions unanswered: what will change, who will check the output, and what happens when the system gets something wrong? For a Malta SME comparing providers, the useful starting point is a reviewable project brief. This guide focuses on the evidence to request before commissioning a first build and the decisions to agree before it goes live.
Bring a real workflow to the first conversation
Choose a task your team can demonstrate from start to finish. Bring representative examples of the input, the system where the result belongs, and the checks staff perform along the way. Include awkward cases as well as straightforward ones: an enquiry with missing details, a duplicate attachment or a record that cannot be matched. Remove unnecessary personal information from samples shared during early discussions.
Explain what currently goes wrong and who owns the process. A provider needs to understand whether the problem is copying information, finding the right record, applying a consistent rule or making a judgement. That distinction helps decide whether fixed rules, an AI component or a simpler change to the process is appropriate. A tool demonstration alone does not establish that fit.
Ask what the proposed system will actually do
Request a plain-language description of the inputs, processing steps and outputs. For inbox triage, this might mean identifying a request type, proposing a category and sending uncertain messages to a review queue. It should also state what stays outside the project, such as sending customer replies or changing account details.
Ask which connected systems need access and what permissions are necessary. A plan to read messages differs from a plan to send them; preparing an accounting entry differs from posting it. Give each action an explicit boundary. If a provider cannot explain where the automation stops, narrow the scope before discussing implementation.
Make evaluation part of the proposal
Agree on a set of sample cases and expected outcomes before building. The set should include ordinary work, known exceptions and cases where the correct response is to ask for help. Judge the result against the business task: correct fields, useful categories, clear explanations and an appropriate handoff when information is incomplete.
Define how the pilot will be compared with the current process. Record manual handling time, corrections, cases awaiting review and any additional checking introduced by the automation. Assess the whole workflow rather than the speed of an isolated AI response. Agree who will review the evidence and what would justify continuing, changing direction or stopping.
Check support and ownership before launch
Identify the accounts, credentials and services the implementation depends on. Ask who owns them, who can change access and how staff can obtain the configuration and documentation. A project handover should explain how to inspect a failed run, retry safely and return to the manual process.
Clarify responsibility for updates to connected software, recurring service costs and changes to the workflow. Agree how an incident is reported and who can pause the automation. Discuss the handling of business data and request written details of the systems involved; obtain specialist advice where your obligations need interpretation. Do not treat a provider’s location as evidence that every service meets your requirements.
Commission a bounded pilot
Use a proposal that names the process owner, intended users, allowed actions, test cases and acceptance criteria. Start in a mode where outputs can be reviewed before they affect live records. Move towards unattended steps only when the evidence supports that change and staff know how to intervene.
A pilot should end with a concrete decision. If the approach works, document the operating procedure before extending it. If the review workload remains high, inspect the input quality and scope before adding more AI. A provider should be able to discuss those tradeoffs openly and show what was learned from the test.
When comparing AI automation providers, ask for a clear scope, representative tests and an accountable operating plan. Those deliverables let your team judge a proposal on its practical fit. Bring a workflow and its difficult cases to a MindStack scoping conversation through the automations page to explore a suitable first pilot.