AI Analyst

Role Overview

Data Edge is looking for an AI Analyst – Enablement & Literacy to support a large-scale AI adoption programme within a Finance function of approximately 850 FTEs.

This role is focused on helping Finance teams use AI tools in a practical, confident, and responsible way. It is not primarily a model-building role, but rather an enablement role focused on adoption, training, playbooks, prompt libraries, and stakeholder support.

The AI Analyst will work closely with the AI Programme Manager, Data & Analytics teams, Data Engineering teams, and Finance stakeholders to help embed AI capabilities such as Microsoft Copilot, AskV3, ChatGPT, or similar enterprise AI tools into daily Finance workflows.

Key Responsibilities

• Develop and maintain AI literacy materials, practical guides, and playbooks tailored to Finance roles and use cases, including month-end reporting, accounts payable processing, variance analysis, and similar workflows.
• Design and deliver hands-on training sessions, drop-in clinics, workshops, and self-service learning resources across the Finance function.
• Support the adoption of AI tools such as Microsoft Copilot, AskV3, ChatGPT, and other approved AI productivity solutions.
• Monitor and report on adoption metrics, including active usage, time saved, playbook downloads, user engagement, and other relevant programme KPIs.
• Build and curate a validated library of Finance-specific AI prompts, workflow examples, and automation use cases.
• Identify potential AI Champions within Finance teams and support their development as peer advocates and first-line AI adoption supporters.
• Liaise with Data & Analytics and Data Engineering teams to understand new AI capabilities and translate them into accessible enablement content for Finance users.
• Support the AI Programme Manager with governance activities, including readiness tracking, adoption blocker identification, stakeholder communication, and programme reporting.
• Promote responsible AI usage by ensuring that guidance, prompts, and enablement materials are aligned with internal governance and compliance requirements.
  
  
  

Required Skills And Experience

• Solid working knowledge of Microsoft Copilot, ChatGPT, AskV3, or similar AI productivity tools in an enterprise environment.
• Strong ability to translate AI concepts into clear, practical, plain-language guidance for non-technical users.
• Experience designing, preparing, or delivering training, enablement, onboarding, or change adoption programmes in a corporate environment.
• Excellent written communication skills, with experience creating playbooks, user guides, slide decks, training materials, and internal communications.
• Analytical mindset, with the ability to work with adoption data, usage metrics, and reporting dashboards to track progress and identify improvement areas.
• Good stakeholder management skills and the ability to work with both technical and non-technical teams.
• Finance background or previous exposure to Finance processes is considered a strong advantage.
• Formal AI, data, or technical certifications are not mandatory, but curiosity, adaptability, and a strong interest in practical AI adoption are essential.
  
  
  

Nice to Have

• Previous experience supporting AI, automation, digital transformation, or productivity tool adoption programmes.
• Familiarity with Finance operations such as month-end close, AP/AR, reporting, planning, forecasting, or variance analysis.
• Experience working with Microsoft 365, Power Platform, Power BI, or enterprise collaboration tools.
• Previous exposure to governance, compliance, or responsible AI frameworks.
  
  
  

Profile

The ideal candidate is a strong communicator and practical AI enthusiast who can bridge the gap between technology and business users. They should be comfortable creating simple, useful content, running enablement sessions, and helping Finance teams understand how AI can support their daily work.

This role is best suited for someone who enjoys adoption, training, stakeholder engagement, and practical implementation rather than deep technical AI development.