Recognise strong candidates
A process is promising when it occurs frequently, has a clear objective, offers enough examples and produces outcomes that experts can review. High variability may justify AI; unambiguous rules often do not need it.
- relevant work volume
- clear input and output
- available information
- named owners
- manageable risk
Begin with a real, bounded pilot
The pilot should include representative normal and exceptional cases. Quality, time or process criteria are agreed beforehand so value is assessed rather than assumed.
Rules, workflow and AI serve different purposes
Stable decisions, deadlines and mandatory checks can often be represented deterministically. AI is more useful where language, documents or context vary and experts can assess the outcome.
A robust solution combines the approaches: rules set boundaries, workflow controls handovers and AI prepares variable information work within that frame.
Prerequisites for a meaningful pilot
The current workflow, its exceptions and owners are documented before solution design. Data access, necessary systems, sensitivity and a safe fallback to people also need to be clear.
- documented current process
- business owner
- representative cases
- expected outcomes and tolerances
- escalation and fallback path
Decision criteria instead of demonstration effect
A pilot is assessed against the baseline and criteria agreed in advance. These cover not just outcome quality and processability but also the consequences of error, human review effort, integration risk and ongoing maintenance.
The decision may be to scale, continue within bounds, return to conventional automation or stop deliberately.
Warning signs of an unsuitable process
A use case is weak where the objective and accountability are unclear, dependable examples are missing, errors surface too late or the necessary data access would be disproportionate. Rare exceptional cases with major effects are not automatically suitable for autonomous handling either.
Process clarification, rules-based automation or decision support may be a better next step in these situations.
Source note
The selection and evaluation principles follow the voluntary NIST AI Risk Management Framework and BSI's use-case-specific risk approach. Primary sources reviewed on 11 August 2026.
Primary sources
Official sources, editorially checked on 11 August 2026.
- AI Risk Management FrameworkNational Institute of Standards and Technology (NIST)
- Generative AI Models: Opportunities and Risks for Industry and AuthoritiesBundesamt für Sicherheit in der Informationstechnik (BSI)