AI and workflow automation

Start with the operating evidence.

These are common triggers for discovery, not eligibility criteria or promises that a particular implementation is appropriate.

• Teams spend significant time classifying, summarising or routing repetitive information
• Operational exceptions are difficult to prioritise across fragmented sources
• Internal knowledge exists but is slow to locate or apply consistently
• A business process may benefit from machine assistance but material decisions must remain reviewable

Typical work.

• Human approval for material decisions
• Defined input and output boundaries
• Confidentiality and data-location review
• Model and provider assessment
• Testing, monitoring and fallback handling
• Controlled use of customer data

Typical outputs.

• Use-case assessment
• Risk and data review
• Prototype or controlled integration
• Human-review workflow
• Test evidence
• Operating guidance

Controls that shape the engagement.

• Human-in-the-loop controls where decisions carry material consequence
• Explicit data permissions, boundaries and retention assumptions
• Provider and model choices reviewed against the operating context
• Evaluation, fallback and monitoring defined before wider use