Lead AI Engagement Specialist

QAD

About The Position

The Lead AI Engagement Specialist owns a portfolio of AI workflows within one or more business functions from opportunity identification through production performance. This product and transformation role identifies where AI can genuinely change work, redesigns workflows, writes build-ready PRDs, partners with architects and engineers to deliver solutions, drives adoption, and manages agents as live products. The role is accountable for measured business value rather than deployment count or documentation. The specialist owns the function’s prioritized workflow pipeline and roadmap, establishes business cases and pre-launch baselines, defines measurable quality standards, monitors production performance, and leads iteration or retirement decisions. They also own adoption and change activities, including training, communications, process and policy updates, and work with function leadership on changes to roles, capacity, and performance expectations. The role ensures workflows meet responsible AI, privacy, data handling, and approval requirements, and contributes reusable patterns to the AI Centre of Excellence. The role reports to the Head of the AI Centre of Excellence, with a dotted-line relationship to the leadership of the covered business function. It works daily with AI Engagement Specialists, the Lead AI Architect, and Senior AI Engineer, and collaborates with external implementation partners and Security, GRC, Legal, and Data teams as needed. It also leads and manages other AI Engagement Specialists. Success includes establishing a credible workflow pipeline and baselines, getting initial PRDs accepted into build, launching workflows with monitored quality and instrumented adoption, delivering measurable business value against established baselines, and sharing reusable practices across functions. The assessment process includes a portfolio conversation, a written PRD or workflow redesign exercise, a working session with the AI CoE, and a stakeholder interview. QAD | Redzone describes a collaborative culture focused on growth, opportunity, and idea-sharing, and offers programs intended to support work-life balance. Compensation is based on experience and desired skill set. The company states that it is an Equal Opportunity Employer and describes its commitment to diversity, equity, and inclusion.

Requirements

At least 7 years of experience in product management, business or digital transformation, operations strategy, or top-tier consulting.
A track record of change that shipped and remained in use, rather than recommendations alone.
Demonstrable ownership of product specifications or PRDs that engineering teams built from.
Strong process redesign capability.
Experience building business cases and value models, including baselines, unit economics, cost to serve, and payback, with the ability to model downside scenarios honestly.
Data literacy, including the ability to interrogate data, work with BI tooling, and define and track defensible metrics.
Working fluency in current AI capabilities and limitations, including retrieval, agents, hallucination and grounding, evaluation, human-in-the-loop design, and cost.
Ability to scope realistically with engineers and challenge both hype and pessimism; coding is not required.
Change management and adoption experience with real end users, including users who did not request the change.
Strong influence at senior levels without formal authority, including the ability to challenge function leaders’ preferred ideas.
Excellent written communication, including clear specifications, reasoning, and status updates.
Deep functional expertise in the business function covered by the opening, sufficient to establish credibility with practitioners and leaders from the first week.

Nice To Haves

Enterprise B2B SaaS experience.
Manufacturing, supply chain, or ERP domain knowledge.
Experience deploying AI or automation into production, including learning from an underperforming deployment.
Experience operating across multiple regions and time zones.
Experience working with system integrators or external delivery partners.
Lean, Six Sigma, service design, or similar training; production evidence of redesigned work is valued more than certification.

Responsibilities

Own and maintain a ranked, evidence-based AI workflow pipeline for the covered function, evaluating value, feasibility, data readiness, risk, and time to impact.
Identify workflows where AI can materially change work and challenge ideas that are not worth building.
Map current-state processes using observed work and data, including volumes, cycle times, costs, exceptions, decision points, systems, and data.
Redesign workflows, defining agent and human responsibilities, handoffs, escalation paths, human review, autonomous actions, and controls.
Build business cases with baselines, quantified benefit hypotheses, targets, build and run costs, payback, sensitivity, and the case against building.
Establish pre-launch baselines and own benefits realization after go-live, reporting actual results and recommending iteration, rebuilding, or retirement.
Write detailed PRDs covering problems, users, scope, workflows, acceptance criteria, quality bars, dependencies, non-functional expectations, risks, and success metrics.
Own the function roadmap, including sequencing, dependencies, release planning, and decisions about scope; partner with delivery teams to resolve ambiguity, unblock work, test, and accept releases.
Define measurable agent quality standards with engineering and build and curate evaluation datasets using real functional cases.
Review live agent output, monitor production quality and operational metrics, and feed findings into engineering iteration.
Own adoption through enablement, training, communications, champions, policy and SOP updates, and management routines; instrument usage and investigate avoidance or resistance.
Work with function leadership on role definitions, capacity, and performance expectations as work is redesigned.
Ensure workflows meet responsible AI, data handling, privacy, and approval requirements in partnership with Security, GRC, and Legal.
Contribute reusable patterns and lessons to the AI Centre of Excellence.
Lead and manage other AI Engagement Specialists.