Senior AI Product Manager

UL Solutions•Northbrook, IL

1d•Hybrid

About The Position

This role is Hybrid, 3 days a week on-site at the Northbrook or Chicago, IL Office. As a Sr. AI Product Manager for UL's Testing, Inspection and Certification team you will own the discovery, strategy, and lifecycle of high-impact AI products, identifying and prioritizing use cases that deliver measurable business value while ensuring AI is the right solution. Lead initiatives end to end from problem definition and data readiness through prototyping, human-in-the-loop deployment, and scaled adoption, with clear success metrics and benefits realization. Act as a steward of Responsible AI, partnering across business, technology, and governance teams to deliver trusted, compliant, and outcome-driven AI solutions at scale.

Requirements

Bachelor’s degree in Engineering, Computer Science, Data Science, Business, or equivalent experience required.
8–12+ years of experience in product management, business transformation, process optimization, digital, or technology enabled operations, with increasing scope and leadership responsibility.
Demonstrated experience leading complex, cross functional initiatives delivering measurable business outcomes.
Strong working knowledge of AI concepts (e.g., generative AI, ML, LLMs) sufficient to engage deeply with technical teams on data pipelines, APIs, model performance, and limitations—without needing to be a data scientist.
Proven ability to move beyond “interesting technology” to focus relentlessly on ROI, productivity gains, cost reduction, and operational effectiveness.
Experience in process mapping, streamlining, and redesign, using technology as an enabler of broader operational transformation.
Exceptional stakeholder management skills, with the ability to influence senior business leaders, IT, and technical teams without direct authority.

Nice To Haves

BA or executive education in AI strategy, digital transformation, or product leadership strongly preferred.

Responsibilities

Proactively identify and prioritize high‑impact AI use cases that address material business friction across UL Solutions service lines, labs, and COUs.
Lead structured problem definition and intake, validating that AI is the appropriate solution versus simpler automation, process redesign, or system changes.
Define clear success metrics upfront (e.g., cycle time reduction, throughput, cost savings, quality improvement), ensuring each initiative has a quantified value hypothesis.
Own the AI product roadmap for TIC, guiding initiatives from discovery and proof‑of‑concept through production deployment and scale.
Partner with IT and technology teams to translate business needs into clear product and technical requirements, while remaining accountable for outcomes rather than outputs.
Partner and support to conduct Build vs. Buy analysis to determine the most effective path to value, balancing speed, cost, risk, and strategic differentiation.
Ensure data feasibility before solution build by validating data availability, quality, accessibility, labeling readiness, and privacy considerations.
Work with domain experts and data teams to establish data standards and labeling approaches needed for supervised and generative AI solutions.
Identify and escalate data, privacy, or compliance risks early to avoid downstream delays.
Drive rapid prototyping approaches (e.g., PoCs, prompt‑based pilots, etc..) to validate user experience and value before heavy investment.
Ensure output quality, accuracy, and trust are validated early, not just at deployment.
Design and implement Human‑in‑the‑Loop (HITL) workflows that balance automation with human oversight, particularly in regulated or safety‑critical environments.
Champion the change management for rapid adoption, Benefits Realization & Adoption.
Own benefits realization, tracking whether AI solutions actually deliver expected productivity, cost, quality, and customer‑experience improvements.
Partner with business leaders to drive process redesign, SOP updates, role changes, training needs, and KPI evolution required to fully capture AI value.
Establish feedback loops so user insights and performance data continuously improve both the AI solution and the underlying process.
Act as a business steward of Responsible AI, ensuring transparency, explainability, bias monitoring, and compliance with data privacy and industry regulations.
Ensure users understand when and how AI is used, and that AI outputs are appropriate for their decision‑making context.
Partner with enterprise governance bodies to embed AI solutions safely and responsibly at scale.