Artificial intelligence, once predominantly the domain of well-funded tech companies, is now being explored by—and bringing real value to—organizations around the world. While the level of adoption varies across industries, we are starting to see interesting applications and investments beyond FAAMG, with 61% of high-performing organizations increasing their AI adoption amid the COVID-19 pandemic.
With this progress also comes a new level of complexity. Companies are juggling experimentation, proofs of concept, AI productization, infrastructure evolution, and strong competition, among other efforts. And integral to all of this is the challenge of building the perfect team structure for their AI initiatives.
I have held many roles in data and AI and can confirm that opportunities abound for experienced product managers. In 2021 alone, demand for AI product managers nearly doubled as companies sought expertise to assist them in developing the next generation of AI-enabled solutions. This demand will only continue to grow.
If you are an experienced product manager, here’s what you need to know to chart a path and thrive in the world of AI.
The Scope of an AI Product Manager
While the beginnings of AI generated a surge in demand for data scientists, the variety and granularity of data tasks required for AI projects has since increased dramatically. These tasks now include data pipelining, exploratory analysis, MLOps, and more, and have necessitated new roles, such as data engineer, data analyst, and machine learning (ML) engineer. Artificial intelligence projects are now considered a team sport, with the AI product manager serving as head coach.
In order to garner more value from in-house AI developments, organizations are moving implementations to production, creating new product lines or integrating AI capabilities into existing lines. This requires people to conceptualize solutions and turn them into reality. Enter the AI product manager.
Successful AI product management merges three strands:
- Strategic. Before dedicating a budget to an initiative, a company will want assurance of the return on investment. Most companies are focused on increasing revenue for current product lines, but some want to enable new sources of revenue with new products. A product manager with a clear understanding of the potential of AI or ML technologies, the effort required for implementation, and how that relates to business objectives—including available infrastructure and staff resources—is an invaluable strategic asset.
- Tactical. The product manager versus product owner split that we typically see can differ for AI teams at smaller companies. These AI product managers might work at the day-to-day team level: planning, iterating, conducting user research, leading sprint retrospectives, and presenting demos. Even where this isn’t the case, an AI product manager needs to be able to communicate with both executives and their team.
- Technical. AI product managers need a solid foundation of technical knowledge. The ability to discuss model trade-offs, experimentation approaches, infrastructure choices, and technology stacks, for example, is integral to the role. There are several ways to augment your technical knowledge: upskilling through online courses; continuous learning via blogs, articles, videos, and forums; and gaining project-based experience.
The scope of an AI product manager’s efforts will depend on the organizational context and the assembled team, as well as their individual skills and expertise. For example, an AI product manager may need to perform some data analysis if the team lacks a data analyst, or they may play a role in developing architecture if they have a background in a technical field such as software engineering.
Recommendations for Making the Leap Into AI Product Management
As more companies invest in AI, product managers should ensure they are ready to capitalize on the growing opportunity. The following recommendations will help you prepare for your next AI product management role, analyze opportunities during the negotiation process, and make a positive impact once you join a team.
Prioritize Continuous Learning to Keep Pace With Developments
The AI industry is always evolving, so you should be too. Earning diplomas and certifications, such as those offered by Udacity or MIT, validates your knowledge and can make you more attractive to employers. Cultivate a mindset of continuous learning and keep up with the latest AI developments by participating in the discourse, whether reading articles and books, attending conferences, or taking courses online. These resources are more up to date and granular, whereas traditional university curriculums and certification programs tend to evolve more slowly than trend cycles. Industry leaders that offer AI services for businesses, such as GitHub, Salesforce, and Accenture, have vibrant communities and message boards filled with professionals eager to share their expertise.
Build a Strong AI Portfolio
Project experience is highly valuable and will form a key aspect of your portfolio. However, it’s the chicken-and-egg conundrum: AI roles require AI experience, but to gain AI experience you need to land an AI role. To bridge that gap, leverage academic projects (such as small AI implementations in online classes or hackathons), and use cloud platforms to develop and test AI functionalities. If you have industry-specific experience, seek AI opportunities related to your existing background.
Leverage Multi-industry Experience
If you have worked across industries, think about how you could apply those diverse experiences to AI project use cases. My experience handling myriad technical processes in telecommunications, for example, can help me lead robotic processing automation initiatives in an AI role. The quantitative methods I used in fintech might support implementing AI or ML security solutions. My past work in the supply chain industry required that I master stakeholder communication while managing many parts of a complex system. These are skills that could prove useful in adopting AI-enabled supply chain optimization software for a product team.
Prepare for a Variety of Interview Styles
There is no industry standard for interviewing and validating AI skills. You may encounter hypothetical questions, hypertechnical interviews with data scientists and engineers, product management-oriented discussions, and HR interviews with AI buzzword checklists. Prepare for a range of potential scenarios to boost your confidence and improve your chances for success.
Seek Well-balanced Multidisciplinary Teams
Use caution when choosing a new project—an unbalanced team could make your job as AI product manager difficult. If an organization is in the early stages of its AI journey, it may have only hired one or two data scientists—AI architects and ML engineers may come later. When assessing an opportunity, pay attention to these details and ask about the choices that have been made around talent acquisition and division of labor. A more fledgling team may change the parameters of your role.
Hire Experienced Talent to Support You
If you are working with a company that has low AI maturity and you have the opportunity to hire team members, source people with the exact experience needed. Hiring best-in-class talent may seem expensive at the outset but can help you avoid mistakes and ultimately lower the overall project cost. Consider giving less-experienced professionals a chance once the initiative is more mature and you are better positioned to develop talent. You should also utilize both internal and external talent. Don’t discount sourcing contractors—the total cost can be lower than hiring permanent employees, particularly when seeking hyper-specialized AI professionals. Helping the executive team hire the right talent is critical to the role of an AI product manager.
Don’t Forget the Value of Soft Skills
Companies tend to magnify the need for technical skills, but being able to deliver AI-enabled solutions requires a good deal of managing, explaining, facilitating, and motivating. AI product managers must collaborate with and guide a team, coordinate with executives, and deal with high levels of uncertainty. Developing and demonstrating skills such as creativity, critical thinking, communication, and emotional intelligence is as important in AI roles as it is in other areas.
Connect the Dots
If you have a strong team structure, technical resources, and executive support, you are ready to connect the dots. Your AI initiatives should align with data management strategies (to improve your current data collection and integration activities), infrastructure evolution (to develop good on-prem versus cloud approaches), and other product management activities (to feed other products with your AI developments). As a highly skilled AI product manager, you are the person to bring these elements together.
General AI knowledge will soon become a commodity and product managers must keep up. Focus on continuous learning, building experience, and identifying the combination of skills that will increase your value in this rapidly expanding field.
Understanding the basics
An AI product manager leads teams on product initiatives related to artificial intelligence or machine learning.
The role of an AI product manager is to develop and launch products using AI technologies. It combines strategic, tactical, and technical aspects.
Companies increasingly recognize that AI and ML technologies are no longer a trend but can enhance the value of their product offerings.