12 ways to build better products.

The ability to define requirements, functionality, and context in a clear, actionable form that enables humans and agents to deliver the right product.

Problem definition. Build a clear understanding of the customer problem, business goals, and constraints before deciding what to build.

Product scope. Make clear what the product should do, why it matters, what is out of scope, and what success looks like.

Decision clarity. Make clear which decisions are agreed, which questions remain open, and which details are AI-generated assumptions or included for illustration purposes.

Product specification. Use specs, PRDs, and prototypes to give people and AI agents the context they need to build the right product.

Level of detail. Include enough detail for the team to act, without adding detail they do not need.

Work sequencing. Break connected work into parts that can be built in the right order.

Living specs. Keep specs, PRDs, and prototypes consistent and up to date as decisions change.

Product review. Review others' specs, PRDs, and prototypes and help them improve.

Spec coaching. Coach PMs and product leaders to improve their teams’ specs, PRDs, and prototypes.

Spec standards. Set shared standards for clear specs, PRDs, and prototypes, including context for AI agents.

Review process. Improve the processes and rituals teams use to create, review, and communicate specs, PRDs, and prototypes.

Review systems. Use lessons from product reviews to improve how teams define future work.

Shared context. Make sure teams and AI agents understand the current strategy, goals, requirements, and decisions.

The ability to work with a cross-functional team to quickly and iteratively deliver product improvements that achieve established goals.

Delivering together. Work well with the people needed to deliver the product.

Making tradeoffs. Make practical choices about scope, speed, and quality.

Cross-team coordination. Coordinate work across teams, including sequencing, how the pieces fit together, and what must be ready for launch.

Iterative development. Use feedback during development to improve the product so it meets the requirements and delivers the intended customer experience.

Risk escalation. Ask for help or escalate when product delivery is at risk.

Agent oversight. Agree which decisions AI agents can make, when they must ask for help, and who reviews their work.

Momentum. Help the team stay motivated and committed when the work gets difficult.

Team alignment. Keep the team aligned on progress, priorities, and blockers through standups or other regular check-ins.

Clarifying responsibilities. Establish clear ownership and handoffs for work that spans across teams.

Team resourcing. Make sure teams have the people, skills, and support needed to deliver the roadmap.

Removing bottlenecks. Fix recurring operational problems that slow delivery.

Prioritizing across teams. Decide which work gets priority and how to divide shared resources when teams’ needs conflict.

The ability to set the quality bar, continuously evaluate against it, and maintain technical, functional, and business quality throughout a product’s life across all relevant platforms, markets, and use cases.

Set the quality bar. Define what good enough means for customers and the business.

Expert review. Bring in the right partners to assess complex or risky work.

Test coverage. Test the product across relevant use cases and conditions for both people and agents.

AI evals. Use AI evals to assess quality across different inputs, tasks, and customer segments before and after release.

Issue severity. Judge how serious a problem is, quantify potential risks, and prioritize accordingly.

Protecting quality. Reduce scope or change launch plans rather than ship below the agreed quality bar.

Product resilience. Give customers ways to recover from errors and limitations when the product fails to meet their needs.

Quality monitoring. Use ongoing monitoring of product data, customer feedback, and AI eval results to catch declining quality after launch, even when no code has changed.

Issue resolution. Follow quality problems through to a complete fix and reduce the chance of the issue recurring.

Quality checkpoints. Build quality checks into planning, development, and release.

Quality ownership. Make clear who monitors quality and who takes responsibility when something goes wrong.

Team quality standard. Establish shared expectations for product quality, including AI performance. Align teams to work toward the same standard and build shared tools, systems, and practices to support it.

Quality investment. Fund the people, tools, and infrastructure teams need for testing and AI evals.

Quality culture. Build a culture where teams take pride in product quality, raise problems early, and make time to fix them.

The ability to turn data into actionable insights, use those insights to achieve product goals, and connect those goals to meaningful business outcomes.

Decision framing. Start with the business decision, then define the questions and evidence needed to make it.

Measurement needs. Define the data needed to understand how people and agents use the product and whether they complete the customer’s task.

Reporting. Use clear reports and dashboards to communicate results, progress toward goals, and emerging problems.

Data insights. Use data and AI tools to query, classify, and analyze evidence to understand customer behavior and identify ways to improve the product.

Evidence reliability. Check conclusions against the underlying data and judge whether the evidence is reliable enough to support a decision.

Data exploration. Explore data to find problems or opportunities beyond the current project.

Causal levers. Explain the product’s causal levers: what customers do, why they do it, what the team can change, and the expected outcome. Make clear which links are supported by evidence and which remain assumptions.

Lever discovery. When the product’s causal levers are unclear, use data, customer research, and experiments to discover them.

Testing causality. Test whether a product change causes the expected change in customer behavior and outcomes. Use the results to update your understanding of the product’s levers.

Evidence-based goals. Use evidence about the product’s causal levers to set goals and choose which product improvements to invest in.

Business drivers. Connect evidence across product areas to explain what drives business results.

Data coaching. Help others get better at using data to make decisions.

Data democratization. Make sure teams can access trustworthy data and the tools to use it.

Measurement standards. Set shared standards for product metrics, experiments, and AI evals.

Data-centric culture. Build a culture where teams use data to understand what drives outcomes and change course when evidence challenges their assumptions.

The ability to use customer feedback in all its forms — from casual conversations to formal studies — to understand customer needs and behavior, make better product decisions, and drive meaningful business outcomes.

Customer relationships. Maintain direct relationships with customers to understand their needs, motivations, and how they solve problems today.

Problem understanding. Establish who has the problem, how important it is, and how they solve it today before investing in a solution.

Research planning. Decide when customer research is needed and which methods can answer the question. Choose participants who reflect the customers you need to understand.

Research skills. Improve your ability to conduct customer research: ask questions that uncover needs without leading customers, and give them space to challenge assumptions.

Solution testing. Use realistic prototypes to observe whether a proposed solution meets customer needs before committing to it.

Research synthesis. Use AI and other methods to synthesize feedback from multiple sources into customer insights. Check important conclusions against the original evidence.

Informed conviction. Judge whether to act on customer evidence or keep researching, based on what remains uncertain and the consequences of being wrong.

Customer-driven decisions. Combine customer research with product data to set priorities and make recommendations that balance customer and business needs.

Research coaching. Coach others to build research skills and customer empathy.

Research access. Make sure teams can reach relevant customers and find and use existing customer research.

Research standards. Establish shared standards and practices that help teams conduct reliable customer research.

Customer-centric culture. Build a culture where teams stay close to customers, understand their needs, and challenge assumptions through direct customer contact.

Customer advocacy. Represent customer needs in executive decisions and company strategy.

Customer-informed investment. Make customer research and product data a consistent part of major investment decisions.

Balancing business and customer needs. Resolve trade-offs between short-term business results and long-term customer needs.

The ability to apply usability principles, design patterns, and interaction models — independently and with design partners — to make it easy for people and agents to access a product’s full value.

Design partnership. Work with design partners to turn customer needs into effective product experiences.

Design exploration. Explore meaningfully different design options before choosing a direction, rather than settling for the first idea or AI-generated output.

Visual communication. Use sketches, wireframes, or prototypes to make product ideas and direction clear.

Interaction design. Design interactions and experiences for both people and agents that reflect how customers behave.

Usability. Make interactions, interfaces, and responses clear and easy to use.

Information architecture. Organize product capabilities and information so people and agents can find what they need and understand how things fit together.

Product copy. Create clear, useful labels, instructions, explanations, and other product copy that help people and agents understand and use the product.

Accessibility. Identify and remove barriers that prevent people with disabilities from using the product effectively.

Design critique. Give specific, actionable design feedback and explain how it would improve the experience.

Brand expression. Make sure the product experience reflects the company’s brand and positioning.

Design outcomes. Judge design by how well it helps customers achieve their goals and supports business results.

Design coaching. Help PMs develop design judgment and improve how they work with design partners.

Design systems. Establish and evolve design systems and shared standards that make experiences consistent across products and interfaces.

Design culture. Build a culture where teams value good design and make time to improve the customer experience.

The ability to drive meaningful business outcomes by connecting product improvements to the strategic objectives of the team and the company.

Value creation. Explain how customer value will create value for the business.

Outcome definition. Define the business outcome before investing in the work.

Opportunity cost. Compare an investment with other things the team could work on.

Reliable commitments. Make realistic commitments and flag when they are at risk.

Outcome review. Evaluate business outcomes after launch and use the results to decide what to do next.

Investment reallocation. Watch for diminishing returns and move effort to better opportunities.

Team effectiveness. Change how the team works when it is holding back results.

Outcome ownership. Set clear outcome ownership and measures across product teams.

Executive alignment. Build executive agreement on the outcomes product teams will deliver.

Return on investment. Make sure teams evaluate whether product investments deliver enough business value to justify their full costs, including AI and other operational costs.

Impact-driven priorities. Use product results to change company priorities and investment.

The ability to translate company and product strategy into a compelling vision, then bring that vision to life through a roadmap of highly prioritized features and initiatives.

Roadmap context. Explain how current work connects to the roadmap and company goals.

Product vision. Define a clear vision for your product area that guides what teams build next. Communicate it clearly through specs, wireframes, storyboards, and prototypes.

Managing dependencies. Identify dependencies and use them to coordinate the order of planned work.

Roadmap alignment. Flag work that does not clearly support the roadmap.

Investment sequencing. Sequence investments so each step delivers value, tests assumptions, or makes future progress possible.

Living plans. Update roadmap priorities and sequencing as new evidence or conditions call for it.

Opportunity discovery. Identify opportunities to advance the vision, including those made possible by AI and other technology changes.

Roadmap coaching. Help other PMs make better roadmap choices.

Planning horizon. Define how teams balance near-term priorities with longer-term investments.

Market shifts. Continuously adapt the vision to new customer needs, changing market conditions, and technology shifts.

Cross-team roadmapping. Connect roadmaps across teams into a clear product direction.

Planning process. Set a planning process that connects team roadmaps to company priorities.

The ability to understand and contribute to the strategy for the team and the company, and to bring that strategy to fruition through the consistent delivery of business outcomes over time.

Strategic understanding. Explain how product goals and roadmap priorities connect to the company’s strategy.

Strategic clarity. Ask how a proposed decision supports the strategy and seek clarification when the connection is unclear.

Strategic assumptions. Identify the assumptions behind a strategic direction and what evidence would support or challenge them.

Competitive advantages. Assess how market and technology changes strengthen or weaken the advantages that set the company apart.

Strategic choices. Use strategy to decide which opportunities to pursue and which to leave behind, even when they are easy to build.

Strategic trade-offs. Explain the rationale behind product decisions, what that decision trades off, and why that trade-off supports the strategy.

Compounding investments. Recommend investments that build or protect lasting advantages and make future investments more valuable.

Strategic outcomes. Evaluate whether product outcomes strengthen the company’s competitive position and business economics, beyond improving individual product metrics.

Team priorities. Help leaders translate company strategy into clear team priorities and resolve conflicting interpretations.

Company strategy. Help shape how the company will compete and grow, and define the product’s role in that strategy.

Strategic alignment. Align executives on what the strategy should achieve and how progress will be assessed across teams.

Course correction. Review progress across product areas and change strategic priorities or investment when results fall short.

The ability to build relationships with the right stakeholders, understand their requirements, and create the alignment needed to deliver the best possible product.

Including the right people. Identify and involve the people affected by the work and the experts needed to shape it before key decisions are made.

Using stakeholder input. Understand stakeholders’ goals, needs, and constraints, and use their input to improve product decisions.

Cross-team awareness. Stay informed about other teams’ priorities and plans so you can anticipate how changes will affect your work.

Anticipating concerns. Anticipate stakeholder concerns and prepare evidence or options to address them before asking for a decision.

Decision ownership. Agree who owns each decision, whose input is needed, and who needs to be informed as responsibilities cross team boundaries.

Resolving competing needs. Recommend the direction that best serves customers and the business when stakeholder needs conflict, escalating decisions that remain unresolved.

Building commitment. Build commitment to carry out a decision, including among stakeholders who preferred another option, while making room to revisit it when evidence changes.

Closing the loop. Explain what was decided, why, how stakeholder input shaped the decision, and what it means for the people affected.

Trusted partnerships. Build ongoing relationships across teams so partners share concerns early, contribute openly, and trust that their perspectives will be considered.

Stakeholder coaching. Coach PMs to involve the right stakeholders, work through disagreements, and build support without relying on authority.

Define clear roles. Establish clear rules across teams for who decides, when others must be consulted, and how unresolved disagreements are escalated.

Improving collaboration. Identify recurring failures in stakeholder involvement and change how teams work together to prevent them.

Inclusive culture. Build a culture where teams seek diverse expertise, welcome constructive disagreement, and treat stakeholders as partners in product decisions.

The ability to manage and mentor direct reports with the goal of enabling them to successfully deliver in their areas of ownership, continuously improve against these competencies, deliver meaningful business outcomes, and achieve their career objectives.

Teamwork. Follow through on commitments, support teammates, and learn from others to help the team succeed.

Peer feedback. Give timely, specific feedback that helps teammates understand what is working and what they can improve.

Mentorship. Provide ongoing guidance and opportunities to practice that help others deepen their strengths, build skills, and apply what they learn.

Empowerment. Help the PMs you mentor weigh options and make sound decisions without depending on you for the answer.

Leadership opportunities. Create opportunities for the PMs you mentor to lead meaningful work and ensure they receive credit for their contributions.

Developing others. Work with each direct report on a practical plan to deepen strengths, address growth needs, and use AI effectively, then review progress together.

Delegation. Give direct reports clear ownership, decision authority, and support, with agreed expectations for when to involve you.

Career growth. Understand each direct report’s career goals and help them take on the responsibilities and opportunities needed to progress.

Performance management. Address performance gaps with clear expectations, timely feedback, and support, and make difficult personnel decisions when improvement falls short.

Hiring. Assess candidates against the capabilities the team needs and hire people whose strengths complement the existing team.

Career mobility. Help people find roles or assignments beyond your team when those opportunities better support their growth.

Manager development. Coach managers to develop their people, delegate effectively, and address performance problems without taking over their teams.

Talent roadmapping. Anticipate the capabilities the organization will need and plan how to build them through development, hiring, and internal moves.

Org design. Organize teams and responsibilities around the product strategy, bringing together people whose strengths complement one another.

Growth culture. Build a culture where leaders make time for coaching, people seek feedback, and learning leads to greater capability and impact.

The ability to leverage senior managers and executives in the organization to help achieve goals, deliver meaningful business outcomes, and positively influence the strategic direction of the team and the company.

Leadership alignment. Understand what your manager and senior leaders need to achieve, and align how your work contributes to those priorities.

Managing expectations. Set realistic expectations with leaders and keep them informed about progress, impact, risks, and changes that affect the work.

Timely escalation. Recognize when a problem needs leadership help and raise it early, explaining what is blocked and what you need.

Framing decisions. Present leaders with clear options and a recommendation that explains the expected value, feasibility, costs, and remaining uncertainty.

Executive support. Secure senior leaders’ backing, resources, and help for important work by making clear asks and following through on commitments.

Executive influence. Influence company priorities and resources with evidence, challenging leadership assumptions when needed and putting company goals ahead of your team’s preferences.

Learning from feedback. Seek specific feedback from your manager, peers, and senior leaders, and use it to improve your judgment and effectiveness.

Owning your development. Take responsibility for your development by agreeing on growth priorities with your manager, seeking opportunities to practice, and following through.

Managing up coaching. Coach PMs to make clear asks, communicate their work effectively, and build their own relationships with senior leaders.

Translating decisions. Explain the reasoning behind leadership decisions and help teams understand what should change in their priorities or work.

Executive access. Create reliable ways for product leaders to raise risks and decisions with executives and receive timely guidance or support.