Staff AI Product Solutions Manager

Kohl's

. Partner with business and technology leaders to identify, assess, and prioritize AI opportunities based on business value, feasibility, risk, and organizational readiness .

Posted 10/14/2026full-timeRemote • Wisconsin • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI opportunity assessment, product strategy, and implementation, with a strong focus on value metrics and cross-functional collaboration. Proficient in leading AI initiatives, managing complex technology projects, and communicating insights to stakeholders.

Highest-signal resume keywords

AI Opportunity AssessmentProduct StrategyLarge Language Models (LLMs)AI Vendor EvaluationDigital Transformation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Use Case DefinitionModel EvaluationRisk AssessmentCost Metrics AnalysisImplementation Guidance

Soft Skills

Cross-Functional CommunicationThought LeadershipStakeholder Engagement

Tools & Technologies

Vector DatabasesAgent OrchestrationModel Context Protocol (MCP)

Certifications & Qualifications

Advanced Degree in Business or Technology

Industry Keywords

Digital TransformationMachine LearningNon-Deterministic SystemsCentralized AI Model

About the role

Key responsibilities & impact

Partner with business and technology leaders to identify, assess, and prioritize AI opportunities based on business value, feasibility, risk, and organizational readiness
Define AI use cases, target outcomes, success measures, user needs, and adoption considerations for enterprise AI capabilities
Lead discovery and rapid experimentation using synthetic user personas and simulation environments to validate user value, model performance, technical feasibility, and cost-to-serve
Drive build-versus-buy assessments for AI capabilities, platforms, open-weights models, commercial foundation models, in-house Small Language Models, and vendor solutions
Establish product direction for probabilistic and agentic experiences, including guardrails, human-in-the-loop workflows, escalation paths, and acceptable performance thresholds
Author natural-language context specifications and agent playbooks; manage probabilistic tolerances for multi-agent systems and workflows
Translate validated AI solutions into capability briefs, reusable patterns, and implementation guidance
Partner with delivery, change management, and business teams to develop adoption plans and embed AI capabilities into workflows
Define and monitor value, quality, risk, and cost metrics, including compute unit economics, token costs, model accuracy, and latency
Recommend whether to scale, refine, pause, or retire AI solutions based on metrics
Communicate complex AI concepts, tradeoffs, recommendations, and outcomes to executive and cross-functional stakeholders
Provide thought leadership on emerging AI capabilities, practices, and responsible enterprise adoption
Perform additional assigned tasks

Requirements

What you’ll need

Bachelor’s degree in Business, Technology, Data/Analytics, Computer Science, or related field; equivalent experience considered
7+ years of experience in product strategy, managing concurrent product portfolios, digital transformation, technology, or related work
3+ years leading AI, machine learning, data platform, or other complex technology initiatives
Experience taking AI or technology opportunities from discovery through implementation and measurement
Knowledge of modern AI concepts, including Large Language Models (LLMs), retrieval-augmented generation (RAG), model evaluation, and non-deterministic systems
Experience assessing value, feasibility, risk, and investment tradeoffs across cross-functional partners
Experience using business, product, operational, or financial metrics to guide decisions
Advanced degree, certification, or training in a relevant business or technical discipline (preferred)
Experience with AI vendor evaluation and build-versus-buy decisions (preferred)
Familiarity with AI architecture and tooling, such as vector databases, agent orchestration, Model Context Protocol (MCP), and tool-calling interfaces (preferred)
Experience in a centralized AI, hub-and-spoke, or center-of-excellence model (preferred)
Experience managing AI platform, compute, inference, or vendor costs (preferred)