Sr Mgr - AI Engagement & Transformation

Iberdrola•Orange, CT

3d•$163,360 - $204,200

About The Position

The Senior Manager – AI Engagement & Transformation leads business engagement and process transformation efforts that deliver measurable outcomes. This role partners with business stakeholders, IT, and Data & AI teams to identify high-value use cases, translate business needs into scalable AI-enabled process improvements, and drive adoption of AI solutions that deliver measurable outcomes. The role engages business areas to shape AI use cases, support AI literacy and enablement, facilitate process redesign, and ensure solutions are implemented with appropriate governance, value tracking, and sustainable operating practices. It requires strong leadership, stakeholder management, transformation execution, and the ability to explain complex AI concepts in practical, business-focused terms. This position reports to the Sr. Director – Data, AI Strategy & Adoption.

Requirements

Bachelor’s degree in Computer Science, Data Science, or a related field, with at least ten (10) years of relevant experience; an equivalent combination of education and experience may be considered.
Experience in power and utilities or adjacent heavy-asset industries.
Proven success delivering cross-functional digital or AI initiatives, including productized data solutions, in enterprise environments.
Background in business analysis, business architecture, operations, or engineering, with practical familiarity in data and AI project delivery.
Strong business and domain knowledge, including utility capabilities such as asset management, grid operations, customer experience, and DERs, as well as operational constraints related to safety and regulation.
Demonstrated ability to facilitate cross-functional workshops, co-design processes with operations, OT, HR, and Legal teams, and drive adoption through human-centered change plans.
Ability to embed governance, ethics, and compliance practices, including explainable AI, fairness reviews, privacy by design, and vendor requirements for transparency and lifecycle accountability.
Strong measurement and value realization skills, including defining outcome-based metrics, establishing baselines, and tracking ROI and business impact.
Practical experience supporting AI or automation deployments from pilot to production, including quality gates, testing, monitoring, and sustainment.
Ability to communicate effectively across executive, business, IT, and quantitative stakeholder groups.

Nice To Haves

Master’s degree in Computer Science, Data Science or a related field or a related field

Responsibilities

Lead AI engagement activities, including business-unit workshops, to identify, assess, and prioritize transformation opportunities aligned to strategy, operational needs, measurable value, success metrics, and ROI.
Transform business processes, pain points, and performance objectives into clear AI-enabled use cases, adoption roadmaps, and actionable transformation plans.
Develop and support AI engagement plans, including stakeholder alignment, business readiness, AI literacy, and practical enablement activities that drive effective adoption.
Support the AI initiative lifecycle from engagement and discovery through prioritization, pilot readiness, deployment support, adoption tracking, and performance monitoring.
Partner with business leaders to map current- and future-state processes, identify automation and augmentation opportunities, and embed AI capabilities into redesigned ways of working.
Map technology and AI opportunities to business capabilities, prioritizing high-impact areas such as asset lifecycle optimization and customer experience management for digital enablement and value realization.
Coordinate with data scientists, domain stewards, and D&A architects to ensure AI-ready data, observability, lineage, and model orchestration across IT, OT, and ET systems; validate model robustness and integration with SCADA, ADMS, and GIS where relevant.
Embed AI governance and risk controls into project lifecycles, including explainability, fairness, privacy, and security; require vendor documentation and lifecycle support; and support enterprise AI governance boards.
Define and track KPIs linked to business outcomes, operational diagnostics, AI agent effectiveness, and adoption metrics; report progress and value realization to executives.
Evaluate vendor pricing, licensing, SLAs, and exit terms; collaborate with IT, platform providers, and procurement to ensure transparency, data return provisions, and lifecycle support.
Support pilot-to-production transitions, ensure testing and production monitoring, and coordinate ongoing model validation, drift detection, and lifecycle maintenance.