AI Transformation Lead

AI Transformation Lead

Overview

OPEX is seeking an AI Transformation Lead to translate the company’s AI strategy into measurable business outcomes. Owns the AI opportunity portfolio from discovery and prioritization through experimentation, adoption, value realization, and operational handoff. They will work across business functions, Information Systems, Data, Information Security, Legal, Privacy, Finance, HR, AI Operations, and process owners to identify high-value opportunities, shape solutions, lead pilots, and scale AI capabilities that improve how work gets done. This position will be in our Moorestown, NJ office.

This is a senior cross-functional role for someone who can combine business-process analysis, applied AI fluency, product and program discipline, change leadership, and enterprise judgment. The successful candidate will move AI beyond experimentation by ensuring initiatives address business problems, are governed responsibly, adopted by users, measured against outcomes, and transitioned into sustainable operational use when justified.

The role does not require the candidate to be an AI researcher or machine-learning engineer. It requires practical understanding of how AI-enabled capabilities, including copilots, agents, automation, analytics, embedded application intelligence, and human-in-the-loop workflows, can be evaluated, piloted, governed, adopted, and scaled in an enterprise environment. The purpose is to ensure OPEX invests in AI opportunities that create durable business value rather than isolated experiments or disconnected tools.

Key Responsibilities

• Partner with business leaders, process owners, and employees to identify AI opportunities in high-friction, repetitive, information-heavy, delayed, or error-prone work.
• Analyze business processes, handoffs, systems, data dependencies, decisions, exceptions, and user workflows to determine where AI, automation, embedded platform capabilities, or process redesign can create value.
• Manage the enterprise AI opportunity backlog, including intake, prioritization, stage-gate progression, status, risks, dependencies, decisions, and actions.
• Identify opportunities to consolidate overlapping AI tools, redundant pilots, vendors, and use cases to reduce duplication, cost, technical debt, and unmanaged AI proliferation.
• Shape AI pilots and experiments with hypotheses, target users, success measures, business and technical owners, governance requirements, adoption plans, and decision criteria.
• Develop business cases and value hypotheses for prioritized opportunities, including expected benefits, implementation and operating costs, adoption assumptions, baselines, risks, and success criteria.
• Coordinate prototypes and pilots that test AI capabilities against business needs, actual users, available data, system constraints, and operational realities.
• Evaluate solution options across business value, user experience, accuracy, security, privacy, cost, integration complexity, supportability, scalability, vendor maturity, and risk.
• Lead or support build vs. buy vs. configure vs. automate evaluations, especially across Microsoft 365 Copilot, Power Platform, Microsoft Fabric, Dynamics 365, Azure AI, embedded platform capabilities, and third-party vendor solutions.
• Work with technical teams, data owners, application owners, Information Security, Legal, Privacy, and AI Operations to ensure AI solutions are governed appropriately.
• Drive adoption through demonstrations, guidance, office hours, departmental champions, communication, training, and feedback loops.
• Translate AI activity into measurable outcomes such as cycle-time reduction, productivity, quality, throughput, backlog reduction, cost avoidance, margin improvement, customer experience, or employee experience.
• Track realized value after implementation to determine whether expected benefits are achieved and whether additional adoption, process, or solution changes are needed.
• Recommend whether AI initiatives should scale, continue, change direction, defer, or stop based on evidence, value, readiness, risk, and organizational capacity.
• Support operational handoff of successful AI capabilities with ownership, documentation, monitoring, support model, access controls, cost controls, and recurring value reviews. Prepare portfolio updates, recommendations, decision packages, risks, blockers, and value-realization summaries for leadership and the AI Strategy Team.

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