Product Analyst, AI Adoption & Use-Case Development

Job DescriptionThis role sits at the intersection of business needs and AI product capability. The Product Analyst is the person who takes a business team from 'we've heard about our AI products' to 'we can't imagine working without them.' Working closely with the Product Manager/Builder and directly with business stakeholders, you own the full arc: discovering use-cases, running PoCs, enabling adoption, and feeding real-world evidence back into the product roadmap. Your work is what turns a product into a platform. Did you feel a hit of dopamine? Yes? Then keep reading. You combine strong analytical instincts with real stakeholder credibility. You know that adoption doesn't happen by accident — it takes structured discovery, honest PoC design, and sustained enablement long after launch day.Key ResponsibilitiesUse-Case Discovery & Business Analysis: Engage directly with business teams to understand their workflows, pain points, and unmet needs — going beyond what they ask for to find the underlying problem worth solving. Document use-cases with rigour: goals, success metrics, constraints, assumptions, and what 'good' looks like before work begins. Prioritise use-cases in partnership with the Product Manager — balancing business value, product fit, and adoption feasibilityRequirements & Feature Definition: Translate business use-cases into clear, structured requirements and user stories that engineering and design can act on. Work with the Product Manager to define and refine AI features — including what the model should and should not do in each use-case context. Ensure requirements reflect the realities of working with LLM-based systems: manage stakeholder expectations about AI capabilities, variability, and appropriate confidence thresholdsOnboarding & PoC Management: Plan and run onboarding of new business use-cases onto the core AI platform — from first briefing through to live usage. Coordinate PoCs with clear scope, success criteria, and timelines. Structure each PoC as a learning exercise, not just a demo — what hypothesis are we testing and how will we know? Track PoC progress, surface risks early, and communicate status transparently to stakeholders and the Product ManagerAI Product Adoption & Change Enablement: Own the adoption arc for each use-case: training, enablement materials, live support during rollout, and proactive check-ins in the first 60 days. Collect structured feedback from users and business teams — tracking where the product is delivering value and where friction or distrust is limiting use. Work with Product and Change/Communication teams to design adoption interventions — because a well-built product that nobody uses is still a failureProductisation of Use-Cases: Evaluate PoC outcomes against pre-defined success criteria and make a clear recommendation: productise, iterate, or stop. Define what's needed to move from a successful PoC to a robust, reusable capability in GPTeal: scalability, governance, support model, documentation. Collaborate with Product, Engineering, and Operations to integrate the use-case into the standard product offering — so the next team to onboard benefits from everything the first team learnedContinuous Improvement & Product Feedback Loop: Monitor usage, engagement, and business impact of live use-cases — distinguishing between 'adopted' and 'used once and abandoned.' Feed structured evidence from use-cases — user friction, missing capabilities, recurring patterns — back to the Product Manager to inform the product roadmap. Identify patterns across use-cases that point to reusable components, shared prompts, or configuration templates that reduce onboarding effort for future teamsReports to: Product Manager/BuilderWorks closely with: Product Manager/Builder, Engineering, UX/Design, Business Stakeholders, Change & Communication, Data & AnalyticsRequirements2+ years of experience in a product, business analysis, change management, or consulting role — with direct exposure to technology products and end-user adoption challengesDirect experience with AI-powered tools — practical understanding of LLM capabilities, limitations, and the human factors that determine whether adoption succeeds or failsDeep understanding of product and business analysis craft: use-case framing, requirements writing, outcome-based success metrics, and structured stakeholder engagementHands-on experience with AI-powered tools (as user, analyst, or enabler) — practical understanding of where LLMs add value and where they need guardrailsAbility to manage stakeholder expectations for AI-based systems: variability, confidence thresholds, and what the model should and should not doStrong ability to synthesise qualitative feedback and usage data into clear product insights — using AI-assisted tooling where it accelerates, not replaces, judgmentExceptional communication skills — translates between business language and product/technical language without losing either audienceProficiency in product and collaboration tools (Jira, Confluence, Mural) and comfort adopting AI-native discovery and synthesis toolsOrganised and structured — can manage multiple use-cases in parallel, track adoption health, and surface risks before they become blockersBachelor's degree in any field. Practical experience and demonstrated learning in product, data, or AI disciplines valued over credentialsDemonstrated continuous learning in AI/ML product space — courses, published work, shipped products, or equivalent practical experience This role is for someone who wants to be at the front line of AI adoption — not just watching it happen, but making it happen. If you care about whether the products you work on actually get used, this is your seat.What We OfferExciting work in a great team, global projects, international environmentOpportunity to learn and grow professionally within the company globallyHybrid working model, flexible role patternPension and health (Canadian Medical) contributionsInt