What Questions Should I Ask Before Hiring an AI Automation Consultant?

The Short Answer

Ask: What problem would you validate first? Which assumptions affect scope? What data and permissions are required? How will exceptions be handled? What will be logged? Who owns accounts and assets? How are changes tested? What support is included?

What Shapes the Decision

Request examples involving similar workflow complexity rather than only the same industry. Ask the consultant to explain architecture in plain language, identify where human approval belongs, and compare a simpler rules-based option with an AI-assisted approach.

A Practical Way to Start

Share representative inputs, volumes, systems, users, current metrics, and known failure cases. Request a written proposal with deliverables, acceptance criteria, security responsibilities, dependencies, exclusions, timeline, pricing, maintenance, and handover documentation.

Controls and Common Pitfalls

Clarify confidentiality, data retention, subprocessors, credential storage, intellectual property, incident response, termination, and export. Be cautious if the consultant needs production access before discovery or cannot describe a manual fallback.

How to Measure the Outcome

Agree on baseline and outcome measures before implementation. Also measure reliability, exception rate, correction effort, adoption, and documentation quality so success is not reduced to the number of workflow executions.

How this guide was prepared

This guide was prepared and reviewed by the Slarivo Editorial Team. It combines practical automation experience with established guidance for responsible AI implementation.

• NIST AI Risk Management Framework

  A practical reference for identifying and managing risks across the AI system lifecycle.

• OECD AI Principles

  International principles for trustworthy, human-centred AI systems.

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