Choose evidence before promises
A good AI automation partner should be able to explain a real workflow from input to controlled output. Ask how information enters the process, how it is validated, what happens when the system is uncertain and who approves high-impact actions.
A polished demo matters less than whether the agency can define measurable acceptance criteria using representative business data.
1. Can you map our current workflow before proposing technology?
The provider should identify triggers, owners, systems, repeated steps, exceptions and required outputs before recommending tools or models.
2. Can you show a tested result rather than a generic AI demo?
In a tested Micro AI document workflow, one 40-page multi-record PDF generated 40 Excel files, 40 XML files, 40 CSV files and 40 JSON files: 160 structured files in total. The tested batch completed in under 10 minutes with 99.5% extraction accuracy under the tested document conditions.
Ask the provider to separate tested evidence from illustrative examples. Larger workload claims should have their own benchmark rather than being extrapolated from a smaller test.
3. How is accuracy defined?
A single percentage can hide important mistakes. Ask whether accuracy is measured by field, record and business-critical value, and how corrections are recorded.
4. What happens when the AI is uncertain?
The answer should include confidence thresholds, validation failures, an exception queue and a clear human-review path with the original source visible.
5. Which decisions remain human-controlled?
Ask the agency to identify approvals or decisions that should not be automated by default, particularly commercial, financial, quality, safety, technical or regulatory decisions.
6. Can the automation work with our existing systems?
A credible provider should assess the APIs, approved connectors, files, folders, email triggers and permissions available in the systems you already use before recommending replacement.
7. How do you protect the source and audit trail?
Look for non-overwriting outputs, unique processing references, source preservation, reviewer history and traceable delivery to the final destination.
8. What documents and edge cases will you test?
A pilot should include normal files as well as unclear scans, missing fields, unusual layouts and examples that trigger the business's exception rules.
9. What are the acceptance criteria?
Agree success measures before implementation. Useful measures include field accuracy, exception rate, staff handling time, processing time, output parity and audit completeness.
10. Who owns the workflow after launch?
Confirm who monitors failures, updates business rules, manages system permissions and decides when a workflow change requires new testing.
11. How will we measure business value?
The provider should compare the pilot against a baseline of current workload, staff handling, errors, delays and exceptions instead of relying on generic productivity claims.
12. Can we start with one measurable process?
A focused first workflow is usually easier to test, govern and improve than a broad programme that tries to automate several departments at once.
A simple evaluation scorecard
The best agency for a process-heavy business is not necessarily the one with the longest list of AI features. It is the one that can make the workflow measurable, controlled and maintainable.
- Workflow understanding and relevant evidence
- Clear human-control boundaries
- Representative testing and measurable acceptance criteria
- Integration approach that respects existing systems
- Traceability, monitoring and post-launch ownership
- Ability to explain limitations without overpromising