Role Overview
We are seeking an AI Product and Portfolio Lead to own the end-to-end lifecycle and portfolio of the agency's AI product offerings and internal AI applications. This role will oversee AI-enabled tools, agents, workflows, platforms, and service offerings that improve how our teams work and how we deliver value to clients.
This person runs the intake process, prioritizes use cases, manages the roadmap, and ties work to KPIs and ROI. The role is integral to a successful AI Centers of Excellence that do not just support experimentation - it evaluates requests, maintains a backlog, leads pilots, and reports business outcomes.
The AI Product and Portfolio Lead will partner with agency leadership, client-service teams, technical teams, vendors, and internal users to identify high-value AI use cases, define product requirements, execute pilots, scale successful solutions, and drive measurable impact through adoption and performance management.
This role is ideal for a product manager with 3-5 years of product management experience, including 2-3 years working with AI, machine learning, generative AI, automation, data products, or AI-enabled workflows. The candidate does not need to build models personally, but should understand how AI products differ from traditional software, including the importance of data quality, model behaviour, performance evaluation, user feedback, responsible AI, and continuous improvement.
Key Responsibilities
Portfolio Intake, Prioritization, and Roadmap Management
- Run a structured intake process for AI requests across the agency, including discovery, scoping, feasibility checks, and prioritization recommendations.
- Evaluate incoming requests, maintain a centralized backlog, and manage transparent prioritization across internal needs and client-facing opportunities
- Own and maintain the AI product portfolio roadmap, ensuring alignment to strategy, dependencies, capacity, and delivery sequencing.
- Tie portfolio and roadmap decisions to KPIs and ROI, define measurement plans, and report business outcomes to leadership on a regular cadence.
- Lead pilots end-to-end (from hypothesis and MVP definition to rollout, adoption, and measurement), and scale successful pilots into repeatable products or offerings.
Own the AI Product Lifecycle
- Own AI products and internal AI applications from discovery through development, testing, rollout, adoption, measurement, and continuous improvement.
- Translate business needs and user pain points into clear product requirements, user stories, acceptance criteria, and prioritised backlogs.
- Define success metrics and performance dashboards that connect adoption, quality, model/product performance, and business impact.
- Manage release plans, change enablement, stakeholder communications, and post-launch optimisation cycles.
Develop Agency AI Product Offerings
- Partner with client-service and specialist teams to identify opportunities for AI-powered client offerings and advisory.
- Support the packaging, positioning, and evolution of AI-enabled service offerings into repeatable, productised solutions.
- Develop MVPs and pilots with clear hypotheses, success measures, and go-to-market readiness criteria.
- Conduct competitive and trend scanning to inform portfolio direction and commercial relevance
Assess and Prioritise Internal AI Applications
- Assess internal AI application opportunities across functions and markets, and prioritize based on user value, feasibility, data readiness, risk, scalability, and cost-to-serve.
- Determine whether a business problem requires AI or whether simpler workflow redesign, automation, templates, or rules-based approaches are more appropriate.
- Build reusable patterns and templates to reduce duplication and accelerate safe, scalable delivery.
Bridge Business, AI, Data, and Technical Teams
- Act as the translation layer between business stakeholders, end users, developers, AI builders, vendors, and leadership.
- Collaborate with technical teams to define requirements for AI agents, LLM workflows, data integrations, knowledge bases, evaluation flows, guardrails, and user interfaces.
- Communicate complex AI concepts and trade-offs in clear, practical language to drive alignment and decision-making.
Drive Responsible AI, Governance, and Quality
- Embed responsible AI principles into product requirements, including privacy, security, transparency, human oversight, auditability, and brand safety.
- Define testing, evaluation, human-in-the-loop review, escalation paths, and quality assurance processes for AI outputs.
- Monitor AI product performance after launch, including user feedback, failure patterns, adoption barriers, and improvement opportunities.
Lead Rollout, Adoption, and Enablement
- Develop rollout plans, documentation, and enablement materials (guides, training content, demos, FAQs) to drive successful implementation and sustained usage.
- Work with champions and function owners to drive adoption, gather feedback, and refine products based on real-world usage.
- Track performance and provide regular updates on adoption, outcomes, risks, learnings, roadmap progress, and next-step recommendations.
Required Qualifications
- 3-5 years of product management experience in digital products, SaaS, enterprise platforms, automation, or internal business applications.
- 2-3 years of experience working on AI, machine learning, generative AI, automation, data-driven products, or AI-enabled workflows.
- Experience in Microsoft 365, Copilot, Power Platform, SharePoint, Teams-based workflows, and low-code/no-code tools.
- Demonstrated experience running intake, backlog management, prioritization, roadmap planning, and outcome reporting tied to KPIs and ROI.
- Experience owning discovery, requirements, roadmaps, MVP development, launch planning, and post-launch optimisation.
- Strong stakeholder management skills with the ability to influence without direct authority.
- Strong analytical skills with the ability to interpret product usage data, adoption metrics, and qualitative feedback.
- Excellent written and verbal communication skills ability to explain complex concepts to technical and non-technical audiences.
- Comfort operating in ambiguity and fast-moving environments.
