AI Hiring
Look beyond the demonstration.
A convincing demonstration is useful evidence of exploration. It does not, on its own, establish the experience needed to maintain a capability in use.
In this article
Establish personal ownership
Ask which decisions and deliverables the specialist owned. Distinguish contribution from overall project outcomes, and invite them to describe how they worked with other disciplines.
Explore evaluation choices
Ask what was tested, which examples were difficult and how quality was judged. Look for a reasoned account of limitations and trade-offs rather than a single headline metric.
Discuss real constraints
Explore data access, integration, user behaviour, cost and operating requirements. Ask what changed between the prototype and later use, and how the team responded.
Understand failure and maintenance
Ask about an error or underperforming approach: how it was detected, what the person changed and what they learned. Clarify monitoring and handover responsibilities without expecting every specialist to have owned every part of the system.
Match the evidence to the role
Research, discovery and operational delivery are all valuable. Choose the experience that fits the requirement rather than treating one career path as universally stronger. Use a relevant discussion or proportionate assessment, respecting confidential project information.
A useful contract discussion might explore an example of joining an unfamiliar system: what did the specialist need to understand first, which assumptions proved wrong and how did they leave the work maintainable? Ask what they personally contributed and how they communicated risks to the client. Use a fictional scenario or an appropriate past example; specialists should not disclose another organisation’s confidential information.
Put the requirement into practice
Build the capability you need.
Discuss your priorities, the experience you need and the team around the role with Theseus.
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