Applied AI and Business Enablement Director (E6116)

IEEE•Piscataway, NJ

About The Position

The Director, Applied AI & Business Enablement is a newly created, high-visibility leadership role within the Data & Analytics organization. Reporting to the Chief Data Officer, this leader will serve as the primary bridge between the enterprise's data and AI capabilities and its business partners, driving AI adoption, literacy, and strategic value realization across the organization. This is not a model-building or tool-development role. The ideal candidate is a practitioner and evangelist—someone with deep, hands-on fluency in modern AI tools (large language models, copilots, agentic frameworks, and no-code/low-code AI platforms) who can translate that expertise into business impact. They will partner directly with senior business leaders to identify high-value AI opportunities, upskill teams, and shape the organization's applied AI roadmap. This role sits as a peer to the Directors of Enterprise Data Science, Commercial Data Science, Data Governance, and Data Engineering, serving as the connective tissue between what those teams build and what the business needs.

Requirements

10+ years of progressive experience in data, analytics, AI, or technology roles, with at least 3–5 years in a business-facing advisory, enablement, or strategic consulting capacity.
Deep, hands-on expertise with modern AI tools and platforms (e.g., ChatGPT/OpenAI, Claude/Anthropic, Gemini/Google, Microsoft Copilot, and equivalent enterprise tools)—as a power user, not a developer.
Demonstrated ability to teach, coach, and upskill non-technical audiences on AI concepts and tools. Exceptional communication and storytelling skills.
Strong understanding of data science, machine learning, and analytics concepts—enough to serve as a credible thought partner to technical teams without being a hands-on builder.
Experience conducting use-case discovery, value-stream analysis, or business process assessment in a data/AI context.
Familiarity with AI governance, responsible AI principles, and data privacy fundamentals.
Proven ability to influence senior stakeholders, build cross-functional relationships, and drive change management in large organizations.
AI Acumen: Stays current on the rapidly evolving AI landscape and can translate emerging capabilities into practical business applications. Equally comfortable in a prompt engineering session and a boardroom strategy discussion.
Business Orientation: Thinks in terms of business outcomes—revenue, efficiency, risk reduction, customer experience—not technology for its own sake. Leads with “what problem are we solving?”
Communication & Influence: Exceptional presenter and facilitator. Can make complex AI concepts accessible and compelling to any audience, from frontline teams to the C-suite.
Strategic Partnership: Operates as a trusted advisor to business leaders, building durable relationships grounded in credibility, responsiveness, and shared accountability for outcomes.
Connector Mindset: Naturally bridges silos. Ensures business demand is channeled to the right internal teams (Data Science, Data Engineering, Governance) rather than creating parallel tracks
Change Management: Experienced in driving organizational adoption of new tools and ways of working. Understands that technology enablement is fundamentally a people challenge.
Broad network in data and analytics community
Effective leader across direct and indirect report structures
High level of personal maturity and natural authority
Ability to deal with decision making processes
Excellent teamwork and ability to galvanize and inspire team
Strong communication and persuasion skills
Charismatic people leader, embracing a culture of trust
Entrepreneurial and proactive "can do attitude”
Good measure of creativity and social competence
High energy levels, stress resistance and cultural openness
Bachelor's degree or equivalent required

Nice To Haves

Master's or other advanced degree
Experience with agentic AI frameworks, autonomous workflow tools, or intelligent automation platforms.
Background in management consulting, internal strategy, or enterprise transformation programs.
Experience building AI champion networks, centers of excellence, or similar distributed enablement models.
Familiarity with no-code/low-code AI platforms and their application to business workflows.

Responsibilities

Design and deliver enterprise-wide AI education programs tailored to business audiences—executives, functional leaders, and individual contributors
Teach business partners how to effectively interact with data and AI tools, including prompt engineering, evaluating AI outputs, and understanding tool capabilities and limitations.
Develop reusable enablement assets: playbooks, quick-reference guides, training curricula, and use-case libraries.
Run workshops, office hours, and hands-on demonstrations that build confidence and competence across the organization.
Build and manage an AI Champion Network—a distributed group of power users embedded in business units who accelerate peer-to-peer adoption.
Work with business units to deeply understand workflows, pain points, and strategic objectives.
Surface high-value AI use cases that may not be immediately obvious to business partners, contributing an applied AI lens to the broader discovery process
Contribute to AI opportunity evaluation within the prioritization frameworks being developed within IEEE to ensure AI POCs are assessed using consistent criteria
Continuously evaluate the evolving landscape of agentic AI—autonomous workflows, multi-step AI agents, and intelligent automation—and translate trends into enterprise-relevant opportunities.
Identify processes and workflows across the organization that would benefit from agentic AI, distinguishing between where human-in-the-loop augmentation vs. autonomous execution is appropriate.
Develop the organization’s applied agentic AI roadmap in partnership with the CDO and peer Directors, including recommended pilots and phased deployment strategies.
Serve as the organization’s subject matter expert on agentic tools and frameworks, advising on vendor capabilities, integration patterns, and readiness requirements.
Partner closely with the Director of Data Governance to ensure all enablement activities, POCs, and deployed use cases operate within established data governance and responsible AI guardrails.
Advocate for governance as an enabler (not a blocker) of AI adoption, helping business partners understand policies around data privacy, bias mitigation, model transparency, and acceptable use.
Contribute to the development and evolution of AI governance policies by bringing front-line feedback from business adoption efforts.
Ensure that all AI enablement programs incorporate responsible AI principles, including fairness, explainability, and human oversight.