In various industries, the usage of artificial intelligence in digital products is growing, including fraud prevention applications, recommendation systems, and generative AI applications. People working in this profession should possess adequate knowledge about the technologies and users’ expectations. One example of such a job is the position of an AI Product Manager (AI PM).
AI PM relates financial objectives with user needs and data and engineering’s and machine learning teams’ work. Unlike a usual product manager, AI PM is required to understand how models function and how they are evaluated.
Why AI Product Management is Becoming Important?
Companies engaged in investing and finance, SaaS, health, and e-commerce are increasing their implementation of AI technology and solutions. But the fact of creating an AI system is only the first step toward developing a successful product.
Companies should work out whether they have enough data at their disposal, what value is added by their invention, how users operate with it, and how to control its performance. AI product managers coordinate this activity.
What Does an AI Product Manager Do?
AI Product Manager is responsible for the entire lifecycle of AI products, starting with discovering use cases and ending with their implementation and development.
Their responsibilities commonly include: Product Strategy: Identify tangible applications of AI for solving business or consumer problems. Feasibility Study: Analyze data available, technical challenges, development costs, and expected outcomes. Requirements Planning: Translate the business requirements into specific goals for engineering teams and data scientists. Cross-functional Coordination: Collaborate with designers, data scientists, ML engineers, business stakeholders, and development teams. Performance Measurement: Monitor using metrics such as reliability, precision, recall, popularity, adoption, etc.
Testing and Implementation: Assist in experiments, A/B-testing, limited releases, and production rollouts.
Post-launch Follow-up: Monitor changes in model performance, gather feedback from users, and record the occurrence of unexpected results.
Key Competencies for AI Product Manager
It is not necessary for AI PMs to be knowledgeable in developing machine-learning solutions. However, they still have to be sufficiently knowledgeable in technical matters to assist them in making decisions concerning the project.
Technical Competencies
Successful AI PMs should be familiar with the basics of statistics, data quality, the model-building process, APIs, and metrics such as precision, recall, ROC-AUC, inference, training, etc.
Business and Product Competencies
Competencies such as product discovery, prioritization, roadmapping, market analyses, experimentation, budgeting, and setting KPIs are still essential.
Responsible AI and Communication
Privacy issues, bias concerns, security risks, and violations of laws are some challenges associated with AI products. Thus, the product managers must know the principles of responsible AI, transparency, explainability, and applicable regulations.
Communication is important because AI PMs often need to help non-technical stakeholders understand technical ideas.
The differences between AI product management and traditional product management are as follows:
Area AI Product Management Traditional Product Management
Focus AI product experience (models and data) Product features and user requirements
Development Method of experiments and iterations Method of features and releases
Measuring success Business metrics and indicators Product performance metrics
Maintenance Monitoring and improving models is needed Product performance
Risk More to do with data biases and aspects of AI credibility Concerning products, industry, and credits
Average salary of an AI Product Manager in India
With wide variance margins, salary expectations are roughly as follows:
Career level Roughly annual salary
Junior: 8,000,000-15,000,000 rupees
Mid-level: 15,000,000-28,000,000 rupees
Senior/Lead: 30,000,000 rupees+
This data can be considered as average salary statistics, and actual salary may vary depending on knowledge, experience, company, region, industry, and skills.
Where can AI product managers work?
AI product managers are required in all sectors, such as technology corporations, AI startups, and fintech companies.
As per the job description, organizations are looking for applicants with experience in product, analytical minds, and good communication skills.
How to Become an AI Product Manager
Here’s a practical approach to pursue your career as an AI product manager:
Get acquainted with product management concepts such as surveying, creating roadmaps, weighing significance, and determining efficacy.
Gain knowledge about tools and analytics, including basic SQL and statistics.
Acquire knowledge about machine learning concepts related to products.
Participate in AI-based projects and initiatives.
Understand different aspects of experimentation, as well as responsible machine learning approaches in principle.
Know how to prepare for job interviews for product roles by studying various cases.
Build a portfolio showing practical applications of AI technologies in product development.
Artificial Intelligence Product Managers Toolbox
AI PMs utilize various tools — SQL, Tableau, Looker, Jira, Confluence, Miro, Figma, MLflow, and monitoring dashboards — depending on what technologies are available.
Difficulties of the Job
AI product managers frequently have to cope with incomplete data, changing model performance, unpredictable outputs, privacy constraints, infrastructure expenses, and business expectations not aligning with engineers’ timelines.
Generative AI complicates the situation due to the manifestations of hallucinations, difficulty in evaluating the outcome, prompt conception, safety of content generation, and costs of implementing inference. Thus, AI PMs should engage in experiments while managing risk.
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