Choose a task worth testing
Possible starting points include finding information, extracting fields from documents or drafting material for review. We first consider whether a simpler rule or existing tool would solve the problem.
Understand the data and boundaries
Before a prototype uses business information, define what it may access, which providers may process it and what users are allowed to do. The scope should include retention, permissions and actions that require approval.
Evaluate before expanding
A promising demonstration is a starting point. Test representative examples, difficult inputs and cases where the system should decline or ask for help. Review accuracy, response time and cost against the task you set out to improve.
Integrate with a way to recover
Plan human review for consequential actions, a fallback when the model or service is unavailable and a way to observe errors. Wider rollout should follow evidence from the pilot, with clear operating responsibilities.
What we can include
We agree the deliverables, responsibilities and acceptance criteria with you before work begins.
- A defined use case and feasibility assessment
- A focused prototype and evaluation examples
- An integration plan with data boundaries
- A staged rollout and human review approach
Questions before you start
Do we need to train a model from scratch?
Not necessarily. An existing model, a connection to your information or a conventional automation may be a better starting point. The decision should follow the task, data and evaluation results.
Can AI run a process without a person checking it?
That depends on the consequences of an error and the evidence from testing. Start with appropriate review and limits, especially where a system can change records or send messages.
Explorehow to plan and evaluate a useful AI pilot before expanding it to more users.