Have you ever heard of this concept before? This is the role(s) I’ve had at Google & Meta ever since I can remember. This ‘scope structure’ or ‘role’ is a great way to provide a wide & important enough scope for your senior team members (or yourself!) and are looking for cross-org impact and want to grow to leadership positions.
𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗕𝗮𝗿 (𝗗𝗲𝗲𝗽 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝗻 𝗮 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗿𝗲𝗮):
The projects that fall within this category require expertise. For AI this could be: deep technical knowledge in ML, data science, natural language processing, computer vision, or another specific subfield of artificial intelligence. As a Product Manager, this involves expertise in the AI product development lifecycle, the day to day, the challenges, risks, the experimental nature of having to pivot quickly and often as well as finding AI Product Market Fit (market needs, product strategy, roadmapping, and prioritizing the right features).
𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗕𝗮𝗿 (𝗕𝗿𝗼𝗮𝗱, 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀):
These projects may or not be related to the vertical projects but they refer to XFN (crossfunctional skills) like effective & transparent communication, getting buy-in across multiple stakeholders, understanding the broader business context, deep and consistent empathy for the users and the user experience, and the ability to work with multiple different teams.
𝗔 𝗧-𝘀𝗵𝗮𝗽𝗲𝗱 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿
In practice, a T-shaped professional in AI Product Management would be someone who:
✨ Has knowledge in a specific technical area of AI (like developing complex machine learning models for i.e. vision) or a specific aspect of product management (like growth).
✨ Possesses a broad set of skills enabling them to collaborate effectively with various other teams and stakeholders, understand and align with the broader business goals, and communicate technical concepts to non-technical team members.
Example: The scope of being an AI PM at Tesla
Now, let's consider a (totally hypothetical just for the purposes for this example) T-shaped AI Product Manager role at say, Tesla - focusing on a feature like self-driving technology. This role would combine deep expertise in a specific area with broad, collaborative skills. Here's how it might look:
Vertical Bar: Deep Expertise in Self-Driving Technology
Technical Understanding: In-depth knowledge of the technologies and algorithms behind launching and improving certain metrics within self-driving technology, like machine learning models for computer vision, sensor fusion, and decision-making algorithms.
AI Product Development Lifecycle: Experience in the unique challenges and risks associated with developing AI-based products like autonomous vehicles. This includes understanding regulatory requirements, safety considerations, and the experimental nature of AI development.
Feature Prioritization: Identifying key features for the self-driving system that align with Tesla's strategic goals and customer needs. This could involve balancing innovation with practicality, ensuring features are both cutting-edge and reliable.
Horizontal Bar: Broad, Collaborative Skills
Cross-Functional Collaboration: Working closely with various teams such as engineering (software and hardware), design, legal (for compliance issues), marketing (for positioning the feature), and customer support (to understand user feedback).
Stakeholder Communication: Effectively communicating the vision, progress, and challenges of the self-driving feature to different stakeholders, including non-technical team members and external partners.
Market Awareness: Keeping abreast of the broader automotive industry trends, competitor advancements in autonomous vehicles, and changing consumer expectations.
Overall Ownership
Integration with Vehicle Roadmap:
Scope: Ensuring the self-driving feature is aligned with the upcoming vehicle models in Tesla's roadmap. This might involve adapting the technology to different car models and ensuring that hardware requirements are met.
Example: Integrating advanced self-driving capabilities in the upcoming Tesla Model X refresh, requiring coordination with hardware teams for sensor placement and software teams for feature integration.
Regulatory Compliance and Safety:
Scope: Working with legal and engineering teams to ensure the self-driving feature complies with global regulatory standards and safety requirements.
Example: Leading a project to adapt the self-driving software for different regulatory environments in Europe and Asia, which may have different requirements than the U.S.
User Experience and Market Fit:
Scope: Collaborating with design and customer support teams to understand user feedback and enhance the self-driving experience.
Example: Developing a user-friendly interface for the self-driving mode in Tesla vehicles, based on customer feedback about ease of use and comfort.
Innovation and R&D:
Scope: Staying at the forefront of AI and self-driving technology to keep Tesla's offerings ahead of the curve.
Example: Leading a research team to explore new machine learning techniques for improving the accuracy and reliability of autonomous driving in urban environments.
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