AI Product Manager Job Description

Nov 29, 2024

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I have defined the Data and AI Product Management function for over 30 clients since 2016. Getting buy-in for the role used to be a lot harder, but that's changed in the last 3 years. Most businesses understand the need. However, they struggle to get capable people into the role because they don’t know how to define it.

My job descriptions are more comprehensive than most. My framework is:

Workflow: What workflows does the person manage, and what’s their ownership of specific lifecycles?

Capabilities: What capabilities do people who are successful in the role use to manage those workflows and lifecycles?

Outcomes: What outcomes are they accountable for, and how do we measure success?

The Workflow-Capabilities-Outcomes framework makes it easier to select candidates based on their ability to deliver results rather than low-quality indicators like years of experience, job titles, or degrees. Candidates self-screen and self-select accurately, so we get more qualified applicants. Auto-apply spammers are obvious, making them easy to weed out. However, external talent pipelines are the wrong approach.

I help clients build internal training and upskilling programs because Data and AI Product Managers are VERY DIFFICULT to source externally. My Data & AI Product Management curriculum design and content are built to deliver the frameworks and transfer the experience required to do this job.

I run a customized instructor-led cohort for clients (I also offer it publicly). We can meet the business’s talent needs in 1 quarter (3 months) for what recruiters charge to source a single candidate. The best approach is always to train and promote internally.

Digital Product Managers and Technical Data ICs need new capabilities to be successful in the role. Businesses that support internal promotions fall into a trap by pressing them into service without training and mentorship. It’s a setup for failure.

Other companies lay off Digital/Technical PMs and then try to hire Data and AI PMs. They lose the institutional knowledge, relationships, product expertise, and customer/market intelligence. It takes over a year to hire, onboard, reteach, and rebuild what’s lost. This approach is the most common, expensive, and time-consuming. It’s also the least effective.

You can use my AI Product Manager’s job description to understand the role better, get buy-in from the C-suite to create the function, and develop an internal training and upskilling program. The job description helps deliver a holistic view of the problems solved and the value delivered.

Role Overview

The AI Product Manager plays a critical role in connecting the business's strategic goals with the data team's ability to deliver AI products that generate revenue and create operational efficiencies. This role requires a deep understanding of both AI technology and business strategy, with a proven track record of turning data and AI opportunities into initiatives with tangible business outcomes. AI Product Managers are expected to connect strategy with execution.

Responsibilities

Opportunity Discovery and Use Case Selection: Support front-line (bottom-up) and C-level leaders (top-down) to identify and evaluate potential applications for data and AI that align with the business's strategic goals and challenges. Opportunities must create new revenue streams, improve margins, and accelerate business growth.
Opportunity Estimation: Develop accurate cost and ROI estimates for data and AI initiatives, enabling business leaders to prioritize high-value projects.
Initiative Breakdown and Roadmap Development: Break down complex data and AI initiatives into manageable phases, creating a product roadmap that delivers value incrementally (every 8-12 weeks) and supports the business's data and AI maturity journeys.
AI Product Design: Map the problem space to the workflow and outcome levels, design user-friendly and interpretable AI products, and ensure seamless integration into user and customer workflows.
AI Product Go-to-Market (GTM): Develop and execute GTM strategies for AI products, including pricing, marketing, release management, feature pruning, cost optimization, ecosystem development, and ongoing product improvement.
Stakeholder Management: Effectively communicate with and manage stakeholders' expectations across the business, including C-level executives, product teams, and data science teams.
AI Strategy Implementation: Translate the business's AI strategy into actionable product plans, ensuring alignment between business goals and data team execution.
Product Performance Monitoring and Improvement: Continuously monitor AI product performance, identify areas for improvement, and iterate on product design and functionality based on user feedback, product usage monitoring, competitive analysis, and market trends.

Required Skills and Experience

Proven Track Record of Delivering Business Outcomes with Data and AI: Candidates must discuss specific examples of how their work resulted in revenue growth, cost savings, or other significant business impacts. Quantifiable results are essential, showcasing the candidate's ability to translate AI initiatives into tangible business value. The AI Product Manager is expected to be a multiplier. The candidate should understand why they were successful and articulate the frameworks they use to repeat that success at a new company.
Understanding of Data and AI Technology and their Applications: A strong foundation in AI concepts, including machine learning, deep learning, natural language processing, and computer vision, is necessary to guide product development effectively.
Strategic Thinking and Business Acumen: The ability to connect data and AI capabilities with business needs and identify high-value customer opportunities is crucial. Candidates should demonstrate a strong understanding of business strategy and operations.
Product Management Expertise: Experience defining a product vision, developing roadmaps, managing product lifecycles, implementing monetization frameworks, and determining pricing models for AI products and platforms. Comprehension of AI platform strategy and aligning initiatives by Feature -> Product -> Platform is required.
Communication and Collaboration Skills: The ability to effectively communicate how complex technologies address business challenges and high-value use cases to non-technical audiences. A first principles approach is critical. The candidate should employ communication frameworks for impact that build strong stakeholder relationships and enable effective collaboration between cross-functional teams.
Experience with Data and AI Product Design Frameworks: Familiarity with design patterns and best practices specific to AI products, including usability, interpretability, reliability, human-machine teaming and collaboration, agent-app and agent-agent teaming, and ethical considerations.

Preferred Qualifications (How To Get On The Shortlist)

Experience in a Relevant Industry: Experience working in an industry where AI is actively applied, such as retail, finance, healthcare, or tech, is highly desirable.
Technical Background: While not mandatory, a technical background in analytics, data science, data engineering, applied research, or a related field is advantageous.
AI Strategy Experience: Candidates with experience introducing, developing, or implementing AI strategies will also be prioritized.

Key Outcomes Delivered

Candidates should be prepared to discuss specific past projects that demonstrate their ability to deliver results, quantifying the impact of their work in business terms. Examples may include:

Led the development and launch of an AI-enabled product/feature that generated $[amount] in new annual recurring revenue.
Implemented an AI solution that reduced operational costs by $[amount] annually.
Increased customer conversion rates by [percentage] by implementing an AI-driven personalization engine. (Specific use case, outcome metrics, and solution)
Improved customer satisfaction scores by [percentage] by leveraging AI to enhance customer service interactions.
Developed and executed an AI strategy that resulted in a [percentage] increase in employee productivity.

Compensation

Compensation for AI Product Managers is highly competitive. AI Product Managers earn higher salaries than Data Engineers, Data Analysts, and Data Scientists. Salary ranges of $200K-$300K+ for experienced AI Product Managers are common.

Salary information is based on LinkedIn, Indeed, Glassdoor, and our internal research. It will vary depending on location, company size, candidate capability, and other factors. Researching salary benchmarks and gathering reliable data are challenging. Expect ranges to be accurate, but also expect significant variance.

Businesses often mistitle lower-skill roles as Data and AI Product Managers and offer lower salaries. They also often over-title the role as a Director or VP with a much higher salary. Anomalies at both ends of the range should be expected.

Time is running out to enroll in my Data & AI Product Management: From 0 To ROI instructor-led cohort.

There are only a few spots left, so act fast to reserve your place in the only course that covers all these topics and more in 8 weeks. It’s an accelerated boot camp designed to kickstart capabilities development.

You’ll leave with more than just knowledge of these topics. You’ll be prepared to do the work that delivers high-value data and AI features, products, and platforms.

I cover data, traditional analytics, machine learning, generative AI, agentic, simulations, robotics, and information products. The curriculum is based on my 30 years of experience and over a dozen case studies from multiple domains.

Exercises reinforce capabilities and create a no-risk environment to practice the frameworks against real-world scenarios.

You’ll get the self-paced companion course, a 1-hour 1:1 meeting, and access to the office hours for a year. Your learning outcomes are my highest priority, so my support doesn’t end when the classes are finished.