Start with the work, not the model
List where people spend time transferring information, searching records or checking routine documents. Choose a bottleneck with enough volume to matter and a team that can review the output. Invoice processing, internal knowledge search and request triage are useful candidates—not promises that AI is the right answer in every case.
Establish a baseline
Record current processing time, exception frequency and the cost of correcting mistakes. A faster demonstration is not enough: the proposed workflow must be reliable on the actual documents, language and edge cases your team encounters.
Decide where people stay in control
Specify which actions an assistant may take and which require approval. Provide a way to inspect sources, handle uncertainty and recover from a failed integration. Higher-impact decisions need more deliberate controls than drafting a response.
Check the information boundary
Identify confidential material, personal data, user permissions and provider arrangements. Consider retention, access and overseas processing when choosing hosted, private-cloud or on-premises deployment. These choices require a project-specific review.
Agree the production gate
Set acceptance criteria for accuracy, exceptions, response time and operating cost. Decide who owns monitoring, model changes and maintenance. A pilot becomes a service only when someone can operate it responsibly.
Prepare for the conversation
- One accountable process owner
- Representative evaluation data
- A measurable baseline
- Defined human approval points
- A documented operating and recovery plan
Further reading: PDPC: Data protection obligations.
This is a planning guide. The right requirements and delivery approach depend on your organisation and the agreed engagement.
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