AI-Powered Product Management: A Maturity Framework

Product management is undergoing a massive AI disruption. This transformation affects not just products and features but the profession itself. Every product manager should educate themselves on these changes and adopt AI proactively to stay relevant.

Let's explore a maturity framework for the evolution of AI-powered product managers.

The 5 Levels of AI Product Management Maturity

Level 1: AI Beginner 🌱

At this level, product managers have minimal AI knowledge and AI use is limited to basic productivity tools. They function as traditional PMs with AI as an efficiency booster.

AI Usage: Automating routine tasks like drafting documents or summarizing notes. Most PM work is done manually with limited AI adoption.

PM Activities:

• Conducting manual market research and customer interviews

• Managing backlogs in spreadsheets

• Using AI for first drafts of PRDs or to summarize meeting notes

• Generating initial competitor research with basic AI tools like LLMs

Challenges: Being overwhelmed by data, inefficiency, and lack of awareness of AI's potential.

💡 Skills Required: Basic understanding of AI concepts, awareness of industry trends, and familiarity with simple AI tools.

Level 2: AI-Enabled Supporter 🔍

Here, product managers adopt AI tools to support decision-making. They recognize AI's potential to enhance their work and it serves as an advisor for insights and productivity. They still own most activities but use AI to accelerate tasks.

AI Usage: Providing data-driven recommendations and automating basic analysis. AI becomes an enabler for faster insight, but it's not yet deeply integrated.

PM Activities:

• Using AI to draft emails and communications across the company and customers

• Analyzing basic metrics with AI assistance, spotting trends

• Leveraging AI to suggest prioritization frameworks

• Using AI chatbots to summarize customer feedback across multiple sources

• Analyzing user data to recommend roadmap priorities

Challenges: Learning to trust AI outputs, understanding limitations, and keeping up with tool advancements.

💡 Skills Required: Familiarity with AI tool options beyond LLMs, prompting techniques, and ability to interpret AI outputs.

Level 3: AI-Driven Practitioner 💪

Product managers at this point actively integrate AI into core PM tasks. They introduce AI agents, automate defined workstreams, and leverage advanced analytics. These PMs serve as orchestrators who define processes for AI to execute.

AI Usage: Automating workflows and providing predictive insights for market trends or user behavior. Routine PM work is significantly automated, freeing the PM for deeper strategic thinking.

PM Activities:

• Using AI for comprehensive market research and competitive analysis

• Implementing AI-powered customer segmentation models

• Designing A/B tests with AI predictive capabilities

• Having AI analyze social media and feedback channels to identify pain points

• Setting up autonomous monitoring of metrics with AI-suggested corrective actions

• Prioritizing features based on AI analysis of engagement metrics

Challenges: Ensuring AI accuracy and unbiasedness, collaborating with data teams, and balancing AI with human intuition.

Skills Required: Knowledge of AI applications in product management, ability to interpret complex data, sophisticated prompting techniques, and basic coding skills.

Level 4: AI-Integrated Leader 🌟

Product managers at level four lead AI integration into products and processes. They use advanced techniques for strategic decisions, and AI agents manage significant portions of product areas. Their focus also shifts to oversight, big-picture strategy, and ethical concerns.

AI Usage: Managing entire product workflows, roadmaps, and stakeholder coordination with human oversight. Multiple AI agents act on behalf of product managers, with approvals for more important decisions.

PM Activities:

• Leading AI-driven features like recommendation engines and predictive analytics

• Using AI to simulate complex product scenarios and forecasts

• Having AI assist with roadmap management and prioritization

• Setting up AI systems to run routine product rituals and maintain documentation

• Leveraging AI to coordinate cross-functional teams and resources

Challenges: Balancing technical and business needs, managing cross-functional teams and their cooperation with AI agents, and aligning AI with organizational goals.

💡 Skills Required: Understanding of machine learning and data science, leadership in cross-functional teams augmented with AI, and communication skills that balance human-AI dynamics.

Level 5: AI-Transformed Strategist 🧠

AI agents autonomously handle most traditional PM tasks. These range from backlog grooming to product launches to strategy. The PM transforms into a "Strategic Visionary & AI Guardian." They ensure AI-driven decisions align with business values and user welfare and oversee system-level orchestration.

AI Usage: The majority of traditional PM tasks are handled by autonomous or near-autonomous AI agents, including complex decisions across the full product lifecycle.

PM Activities:

• Reviewing and refining AI-generated strategies and roadmaps

• Defining high-level goals for AI systems to execute

• Ensuring ethical AI use across products and operations

• Approving AI-crafted product strategies while innovating new AI applications

• Focusing on stakeholder relationships while AI manages existing product areas

• Exploring visionary changes beyond AI's current capabilities

• Driving innovation at the organizational level with AI as a strategic partner

• Ensuring the innovation results in commercial success

Challenges: Staying ahead of AI advancements, monetizing innovation, managing organizational transitions, and ensuring ethical AI deployment.

Skills Required: Deep knowledge of AI ethics and governance, strategic planning, go-to-market execution, leadership skills, and financial oversight.

Transformation at the Highest Level

At Level 5, the PM's role evolves into an AI Product Strategist, possibly evolving into an AI General Manager type of role, with AI handling most operational tasks. PMs become stewards of the product ecosystem, focusing on:

• Strategic Oversight: Defining goals and ensuring AI aligns with them

• Human-Centric Decisions: Acting as the moral and ethical guardrail, ensuring AI-driven decisions stay aligned with user wellbeing and brand values

• Stakeholder Engagement: Maintaining a narrative that unites stakeholders around the product's purpose and direction

• Innovation Leadership: Leveraging AI for optimization while driving visionary, outside-the-box changes

Commercial Success: Ensuring the go-to-market, sales, and profitability are maximized

What's Your Level? 🤔

What steps are you taking to level up?

I welcome your input to further refine this model as we challenge ourselves to become AI-powered product managers. After all, if we don't adapt, we might find ourselves being product-managed by AI instead! 😉

Let's navigate this AI transformation together—one level at a time.

About the Author

Filip Szymanski has over 20 years of experience as a product leader and is passionate about leveraging AI to power a new generation of product managers. Follow my journey at productpath.ai or add me on LinkedIn.

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