Choosing a provider
AI automation services
AI automation is a crowded field, with providers ranging from careful engineers to sellers of ready-made 'AI agents' with impressive demos. The difference shows when the automation meets your real data: unusual documents, ambiguous emails, missing information and edge cases nobody mentioned.
This page explains how to choose an AI automation provider, what a responsible engagement looks like and what you should own at the end.
Illustrative diagram
- Map the process
- Choose steps
- Prototype on real data
- Pilot alongside people
- Roll out
- Monthly review
A responsible engagement
Discovery
The current process mapped, with volumes, exceptions and who does what.
Output: Process map and candidate steps
Prototype
The AI step tested on a sample of your real, anonymised examples.
Output: Test results with failures listed
Pilot
Run alongside the existing process, with people reviewing results.
Output: Pilot report
Rollout
Switched on with monitoring, logs and a review routine.
Output: Live workflow and documentation
Look past the demo
Demos use clean, typical examples. Your process includes scanned documents, emails that cover three topics at once, missing fields and customers who write in unexpected ways. Ask any provider to test on a sample of your own examples, including the awkward ones, and to show you where the automation failed as well as where it succeeded.
A provider who only reports successes has either not tested properly or is not telling you everything.
Questions to ask a provider
Testing
- Will you test on our real examples?
- How will you report errors and uncertain cases?
- What happens to items the AI cannot handle?
Data
- Which AI provider will process our data, and under what terms?
- What data will be sent, and can it be minimised?
- Where are logs stored and for how long?
Ownership
- Whose accounts will the tools run under?
- Will we receive the prompts and workflow configuration?
- Can another provider maintain it later?
Warning signs
Guaranteed accuracy or savings
Results depend on your data and process; they can be measured, not promised.
No human review step
Errors will reach customers or records unnoticed.
Tools in the provider's own accounts
Your workflows and data become dependent on them.
Vague answers about data
You need to know where personal data goes and why.
What you should own
At the end of the engagement, the automation platform accounts, AI provider accounts, prompts, workflow configurations and documentation should belong to your business. The provider should work through access you grant. This keeps costs transparent, because you see the usage bills directly, and makes it possible for someone else to maintain the workflow if needed.
Monitoring after launch
AI workflows can drift: inputs change, providers update models and new edge cases appear. Agree how the workflow will be monitored, who reviews samples of its output and what happens when accuracy falls. A short monthly review of logs and corrections is usually enough to catch problems early.
Why discovery matters
The most valuable part of an AI automation project is often the first week: understanding exactly how the work is done today, which exceptions occur and which steps genuinely need judgement. Discovery frequently reveals that part of the process can be simplified or handled by ordinary automation, leaving a smaller, better-defined task for AI. A provider who skips this step tends to automate the wrong thing.
Discovery also identifies who will review results and how corrections feed back into the workflow.
Involving the people who do the work
The staff who currently handle the task know its exceptions better than anyone. Involving them in discovery and the pilot produces a better workflow and helps them trust it. Automation introduced without them often fails on cases they could have predicted.
Personal data and AI providers
If the workflow processes personal data, such as customer emails or applicant documents, check the AI provider's terms on data retention and use, whether data is used to train models, and where it is processed. Minimise what is sent, remove details the AI step does not need and record the processing in your privacy documentation. A responsible provider raises these points without being asked.
Questions about AI automation services
Can you guarantee the AI will be accurate?
No. We test on your real examples and report accuracy honestly, including failures.
Whose accounts do the tools use?
Your business's, with access granted to us.
Will we get the prompts and configuration?
Yes, documented, so the workflow can be maintained by anyone.
Which AI provider do you use?
It depends on the task, data and terms. We explain the choice and alternatives.
Do you run a pilot first?
Yes, alongside your existing process, before the automation takes over.
Who checks the AI's work?
A person reviews uncertain or important results, and samples are reviewed regularly.
Can you work with our existing tools?
Usually, if they offer integrations or APIs. We check during discovery.
How long is discovery?
It depends on the process. We agree its scope and length before starting.
Will our data be used to train AI models?
That depends on the AI provider's terms and settings. We check and explain them before any data is sent.
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