QAD | Redzone is building an enterprise AI platform to support AI-enabled and agentic workflows across the organisation. The AI Centre of Excellence (CoE) is accountable for turning that ambition into deployed, measured, production workflows across priority business functions.
The CoE is small, senior and high-leverage. It combines architecture and engineering with a set of functional AI Engagement Specialists who own the demand side: what gets built, why it is worth building, whether anyone uses it, and whether it actually works once live.
The Lead AI Engagement Specialist owns a portfolio of AI workflows within one or more business functions, end to end. You will identify where AI genuinely changes how the work is done, redesign that work, write the PRD, drive the build with the architect and engineers, land the adoption, and then manage the performance of the agents in production as a product you are accountable for.
This is a product and transformation role, not a business analysis role. The distinction matters and we mean it: you will own a roadmap, write specifications precise enough to build from, make scope calls, and be measured on realised value rather than on delivered documentation. If a workflow does not clear its quality bar or nobody uses it, that is your problem to solve, not to report.
Where this role sits
Reports to: Head of the AI Centre of Excellence
Alignment: Dotted line to the leadership of the business function you cover
Works with: AI Engagement specialists, Lead AI Architect and Senior AI Engineer daily; external implementation partners; Security, GRC, Legal and Data teams as required
Owns: The prioritised workflow pipeline, PRDs, roadmap, adoption and live agent performance for one function. Lead & manage other AI Engagement specialists.
Accountable for: Realised, measured business value — not deployment count
What you will own
Opportunity identification and prioritisation
Own the AI workflow pipeline for your function — a ranked, evidence-based view of where AI creates value, refreshed as the business and the technology move.
Distinguish rigorously between work that AI genuinely changes and work that merely looks automatable in a demo — and be willing to argue that a well-sponsored idea is not worth building.
Prioritise on value, feasibility, data readiness, risk and time to impact, in partnership with the architect.
Process discovery and workflow redesign
Map the current state properly: volumes, cycle times, cost to serve, exception paths, decision points, systems and data touched, and where the work actually breaks — grounded in observation and data, not in what the process document claims.
Redesign the work, not just the tool. Decide what the agent does, what the human does, where the handoff sits, what the escalation path is, and what changes in roles, policies, SOPs and controls as a consequence.
Design the human-in-the-loop model with the architect: where review is mandatory, where an agent may act autonomously, and how a wrong action is caught and reversed.
Business case and value ownership
Build the business case: baseline metrics, benefit hypothesis, quantified target, build and run cost, payback and sensitivity — and the honest version of it, including the case against building.
Establish the baseline before launch. A benefit that cannot be measured against a pre-existing baseline will not be believed, and should not be.
Own benefits realisation after go-live — report the real number, including when it is below target, and recommend iterate, rebuild or retire accordingly.
Product specification and roadmap
Write the PRD: problem, users, scope and non-scope, target workflow, acceptance criteria, quality bar, data and system dependencies, non-functional expectations, risks, and success metrics — detailed enough that the architect and engineers can design and build from it without a translation layer.
Own the roadmap for your function: sequencing, dependencies, release planning, and a defensible view of what comes next and why.
Partner with delivery daily — make scope calls, resolve ambiguity, unblock, test, and accept. Be available to engineers at the pace they work.
Agent performance management
Define the quality bar in measurable terms with engineering — what a correct output is, what an acceptable error rate is, and what failure mode is unacceptable at any rate.
Build and curate the evaluation datasets for your workflows, drawing on real cases from your function.
Review live agent output on an ongoing basis and run the feedback loop back into engineering.
Monitor production performance — accuracy and quality, containment or deflection, escalation rate, latency, cost per transaction, and user satisfaction — and drive the iteration cycle.
Treat a live agent as a product under active management, not a project that was completed.
Adoption and change
Own adoption as an outcome you are measured on: enablement, training, communications, champions, policy and SOP updates, and management routines.
Instrument usage and act on what it shows — including the uncomfortable finding that a well-built workflow is being avoided.
Engage with resistance seriously. Where people are working around the agent, understand why before attempting to change the behaviour.
Work with function leadership on role definition, capacity and performance expectations as work is redesigned.
Governance and CoE contribution
Ensure workflows meet responsible AI, data handling, privacy and approval requirements, working with Security, GRC and Legal.
Feed reusable patterns back into the CoE so that a solved problem in one function does not get re-solved from scratch in another.
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.
Functional depth
This role requires credibility with practitioners and leaders inside the function from the first week. We are looking for someone who understands how the work is genuinely done — its operating rhythm, its systems, its economics and its performance measures — well enough to challenge how it is done today rather than to describe it.
Strongly preferred
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.
What this role is not
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.
What success looks like
First 3 months
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 6 months
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.
First 12 months
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.
How we assess
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.
About QAD:
QAD | Redzone is redefining manufacturing and supply chains through its intelligent, adaptive platform that connects people, processes, and data into a single System of Action. With three core pillars — Redzone (frontline empowerment), Adaptive Applications (the intelligent backbone), and Champion AI (Agentic AI for manufacturing) — QAD | Redzone helps manufacturers operate with Champion Pace, achieving measurable productivity, resilience, and growth in just 90 days.
QAD is committed to ensuring that every employee feels they work in an environment that values their contributions, respects their unique perspectives and provides opportunities for growth regardless of background. QAD’s DEI program is driving higher levels of diversity, equity and inclusion so that employees can bring their whole self to work.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.
United Kingdom
Ireland