Lead AI Engagement Specialist

About QAD:

Qualifications

• 7+ years in product management, business or digital transformation, operations strategy, or top-tier consulting — with a track record of change that shipped and stuck, not recommendations that were accepted.
• Demonstrable ownership of product specifications or PRDs that engineering teams built from. We will ask to discuss one in detail.
• Strong process redesign capability. Lean, Six Sigma, service design or similar training is welcome; evidence of redesigned work in production matters more than certification.
• Business case and value modelling — comfort with baselines, unit economics, cost to serve and payback, and the intellectual honesty to model the downside.
• Data literacy: able to interrogate data, work with BI tooling, and define and track metrics that hold up to scrutiny.
• Working fluency in what current AI systems can and cannot do — retrieval, agents, hallucination and grounding, evaluation, human-in-the-loop, and cost. Enough to scope realistically with engineers and to push back on hype and on pessimism with equal confidence. Coding is not required.
• Change management and adoption experience with real end users, including users who did not ask for the change.
• Strong influence at senior levels without formal authority, and the standing to tell a function leader that their favourite idea is not the right first build.
• Excellent written communication — clear specifications, clear reasoning, clear status.
• Deep functional expertise in the business function this opening covers — the specific requirements are set out under “Functional depth” below.
• Enterprise B2B SaaS experience.
• Manufacturing, supply chain or ERP domain knowledge.
• Having deployed AI or automation into production and lived with the results — including a deployment that underperformed and what you did about it.
• Experience operating across multiple regions and time zones.
• Experience working alongside system integrators or external delivery partners.
• Not a process documentation role. Mapping is an input; redesigned, adopted, measured work is the output.
• Not a change communications role. You own the product, not the announcement.
• Not a requirements-gathering role. You will not hand a wish list to engineering and wait; you will specify, decide and stay accountable through production.
• Not a role that ends at go-live. Managing agent performance afterwards is a permanent part of the job.
• A credible, ranked workflow pipeline for your functions, grounded in real process data and endorsed by function leadership.
• The first one or two PRDs written and accepted into build by the architect and engineering without a translation gap.
• Baselines established for the metrics your first workflows will move.
• First workflows live in production, with adoption instrumented and quality actively monitored.
• A functioning feedback loop between live agent performance and the engineering backlog.
• A 12-month roadmap for your function that leadership has signed up to.
• Measured, defensible business value delivered against pre-established baselines — reported honestly, including where the result fell short.
• Adoption at a level where the redesigned workflow is the normal way the work is done, not an optional tool.
• At least one reusable pattern contributed to the CoE and adopted by another function.
• A function that is materially more capable of absorbing the next wave than it was when you joined.
• Portfolio conversation — a transformation or product initiative you owned end to end, including the value actually realised versus the value forecast.
• Written exercise — a short PRD or workflow redesign on a realistic problem from your function.
• Working session with the AI CoE on scoping and quality definition for a live QAD workflow.
• Stakeholder interview with leadership from the function you would cover.