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About This Role
Description
Summary:
The AI Product Manager drives the planning, prioritization, and delivery of AI and data-enabled products that advance Commercial Banking’s sales enablement and service transformation agenda. This role owns the product plan for one or more priority AI initiatives end-to-end – from problem definition and business case through build, governance, launch, and measured adoption – partnering closely with engineering, data science, segment risk, compliance, and the central Enterprise Data & AI Strategy (EDSA) and Technology Innovation functions. The AI Product Manager is accountable for translating Commercial Banking’s highest-value opportunities into a clear, resourced, and executable roadmap that delivers measurable value to bankers and clients.
Duties and Responsibilities:
Product Ownership & Investment
- Own end-to-end accountability for the broader product plan, stakeholder messaging, and investment case for assigned AI initiatives – not just the backlog or delivery schedule.
- Build and defend the business case (cost, benefit, PPNR/revenue impact, and risk) required to secure and sustain funding through Huntington’s AI governance and prioritization process.
Serve as the credible, single point of accountability that segment leaders, technology partners, and executive sponsors can turn to for status, trade-offs, and outcomes. *
User-Value-Led Product Definition
- Lead with user value: define the colleague or client benefit and the problem to be solved before selecting a capability, model, or vendor.
- Use design thinking and direct engagement with relationship managers, service teams, and clients to validate needs, pain points, and workflow context before scoping solutions.
Translate validated needs into clear product requirements and acceptance criteria for cross-functional delivery teams. *
Integrated Portfolio View
- Maintain one integrated view across initiatives, segments, data readiness, cross-functional dependencies, and adoption.
- Partner across Commercial, Consumer & Regional Banking, Payments, Risk, and Technology Innovation to align priorities, sequencing, and shared data foundations (including ongoing CRM/Salesforce migration work).
Maintain a current, transparent roadmap and dependency map that leadership can use to make sequencing and resourcing decisions *
Success Definition & Measurement
- Define success before launch: establish baselines, target metrics, business outcomes, and attribution methodology in partnership with Finance and Analytics before development begins.
- Ensure measurement goes beyond usage counts to tie adoption to real business outcomes – revenue, banker productivity, client experience, and risk/quality.
Report progress against the defined baseline and value case throughout the product lifecycle, adjusting scope when tracking falls short of plan. *
Workflow-Embedded Adoption
- Build within workflows: design and deliver capabilities that meet colleagues where they already work (e.g., Salesforce, Copilot, core banking systems) rather than requiring new tools or habits.
- Partner with HR, segment leaders, and change management to build role-specific training, manager reinforcement, and adoption reporting for each launch.
Continuously gather adoption and quality feedback post-launch and iterate on the product to close usage and value gaps. *
Execution & Delivery
- Move from roadmap to execution: convert prioritized concepts into resourced, sequenced delivery plans with clear owners, milestones, and go-live dates.
- Serve as product owner for one or more cross-functional pods (engineers, data scientists, analysts), prioritizing the backlog and ensuring iterative, quality delivery each sprint/quarter.
Navigate Huntington’s AI governance lifecycle (business value definition, solution assessment, governance/risk review, contracting, and implementation) to keep initiatives on track and in compliance. *
Basic Qualifications:
- Bachelor's degree in Business, Finance, Computer Science, Data/Analytics, or a related field.
- 7+ years of Product Management or Digital project delivery experience in Consumer or Business and Commercial Banking
Previous experience building and leading high-performing teams *
Preferred Qualifications:
- Experience in commercial banking, corporate banking, or another regulated financial services environment, including familiarity with credit, treasury, or relationship-management workflows.
- Experience with generative AI/agentic AI products, CRM platforms (e.g., Salesforce, including Financial Services Cloud), and enterprise data platforms.
- Familiarity with model risk management, AI governance, and regulatory considerations (e.g., fair lending, data privacy) within banking.
- Track record partnering with Risk, Compliance, Legal, and Finance to move AI initiatives through formal governance and funding approval processes.
- Strong executive communication skills, with experience presenting business cases, roadmaps, and value narratives to senior leadership and steering committees.
- Experience working in an Agile/Scrum delivery model as a product owner or product manager for a cross-functional team of engineers, data scientists, and analysts.
- Demonstrated experience building or managing a product roadmap, writing business requirements, and defining measurable success criteria (KPIs, OKRs, or equivalent).
5+ years of product management experience, including direct ownership of AI, machine learning, or data-driven products from concept through launch. *
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Exempt Status: (Yes = not eligible for overtime pay) ( No = eligible for overtime pay)
Yes Workplace Type:
Office Our Approach to Office Workplace Type
Certain positions outside our branch network may be eligible for a flexible work arrangement. We’re combining the best of both worlds: in-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter. Specific work arrangements will be provided by the hiring team.
Huntington is an Equal Opportunity Employer.
Note to Agency Recruiters: Huntington Bank will not pay a fee for any placement resulting from the receipt of an unsolicited resume. All unsolicited resumes sent to any Huntington Bank colleagues, directly or indirectly, will be considered Huntington Bank property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Huntington Bank, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Huntington Bank AI Hiring
Huntington Bank has 1 open AI role right now. They're hiring across AI Product Manager. Based in Charlotte, NC, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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