AI Specialist

Responsibilities

1. Operate the AI Use-Case Intake: log, triage to the correct lane, and prepare requests for prioritization (20%)

2. Requirements Gathering, Process Mapping and Use-Case Documentation (20%)

3. Backlog Management, Delivery Cadence and Sprint Coordination with the engineering team (20%)

4. Portfolio, Adoption and Benefits Reporting for the AI Council, prepared with the Head of Data & AI (15%)

5. Release Coordination and Go-Live Readiness, including user documentation and handover (10%)

6. Stakeholder Liaison with business functions and spoke AI Leads, at working level (10%)

7. Community of Practice and Enablement Support, with the AI literacy owner in HR (5%)

Competencies & skills requirements

Analytical Thinking and Structured Problem-Solving

• Competency: Strong analytical ability and structured thinking; the core requirement for this seat, in place of a long delivery track record.
• Skill: Ability to break an ambiguous business request into a defined problem, a scope, and a measurable outcome, and to show the reasoning.
• Application: Ability to turn the requests arriving at the AI Centre of Excellence into documented, comparable use cases that can be scored on value, feasibility and risk.
 

Requirements and Process Analysis

• Competency: Understanding of business analysis fundamentals: requirements elicitation, process mapping and acceptance criteria.
• Skill: Ability to interview business users, document a current-state process, and specify what a solution must do without pre-supposing how it will be built.
• Application: Ability to produce the requirement and process documentation that fusion squads and partner engineers build from, so that scope is understood before work starts.

Delivery Coordination and Agile Ways of Working

• Competency: Understanding of Agile delivery, backlog management and iterative release.
• Skill: Ability to maintain a backlog, run a sprint cadence, track dependencies and keep a delivery plan honest and current.
• Application: Ability to keep the AI delivery cadence predictable and visible, and to escalate slippage early rather than reporting it late.
 

Communication and Working-Level Stakeholder Management

• Competency: Clear written and verbal communication, and the confidence to work across functions as a credible partner.
• Skill: Ability to explain AI capability and its limits in plain language, run a working session, and write documentation people actually read.
• Application: Ability to hold the day-to-day relationship with business functions and spoke AI Leads, escalating executive-level negotiation to the Head of Data & AI rather than attempting it alone.

Value Measurement and Reporting

• Competency: Understanding of how benefit is defined, baselined and evidenced, and why adoption is the leading indicator.
• Skill: Comfort with data: able to build and maintain a reporting pack, work with adoption and usage figures, and present findings including negative ones.
• Application: Ability to prepare the monthly AI Council pack and to ensure every use case carries an agreed value metric before build begins.

AI and Data Literacy

• Competency: Working understanding of generative AI, its capabilities, its failure modes, and the difference between a demonstration and a production service.
• Skill: Practical familiarity with AI tools, and an active interest in developing depth in retrieval, evaluation and AI product patterns.
• Application: Ability to hold a technically credible conversation with the Architect and the Senior AI Engineer, and to develop that depth on the job with structured support.

Governance and Data Protection Awareness

• Competency: Awareness that data protection and AI regulation are design inputs, and knowledge of when to stop and ask.
• Skill: Working understanding of GDPR and of RHG's AI intake and guardrails; the judgement to route a use case to Legal, the Data Protection Officer or Security rather than proceeding.
• Application: Ability to triage requests to the correct lane and risk tier, and to flag governance questions early enough that they do not become late-stage blockers.

Learning Agility, Ownership and Resilience

• Competency: High learning agility and genuine ownership; the attributes that make a development seat succeed or fail.
• Skill: Demonstrated ability to take on unfamiliar work, ask for help early, act on feedback, and follow through without being chased.
• Application: Ability to grow into the full AI Product Manager accountabilities over an agreed development period, with performance against those accountabilities reviewed at defined points.

Function: Information Technology
Experience Level: Not Applicable