In 2025, AI crossed the chasm. It’s no longer experimental or peripheral—it’s becoming a structural force in how digital products are conceived, built, and improved. For product leaders, it’s a moment to reassess priorities and position their teams to thrive in a changing landscape.
What was once considered AI experimentation is now becoming a core business imperative. Competitive advantage increasingly hinges on a team’s ability to embed AI meaningfully into user experiences, decision systems, and operational workflows. Here are five changes to make in the product function to enable the successful adoption of AI at scale.
Conventional product delivery is often based on features and milestones. While this approach has been at the core of how most product organisations operate, it begins breaks down in the context of AI. Model development doesn’t follow the same patterns as feature development, instead it requires parrelised capability development and capability application.
To be effective, product teams need to adopt a capability driven approach. Developing capability maps that define what the organisation wants to be good at can help provide a clear view of how data, models, and user experience intersect over time. These maps can also be used as strategic planning tools to make prioritisation and investment decisions.
In contrast to product features, the value of capabilities compound over time, enabling teams to iterate faster, learn quicker, and be more adaptable in a rapidly changing environment.
Delivering AI-native products requires more than new tools. It demands new team constructs. Most organisations are seeing the limits of operating in functional silos. Cross-functional collaboration is no longer optional—it’s foundational.
Effective AI product teams integrate PMs, data engineers, data scientists, designers, and software engineers into persistent pods. These teams can then work closely together on shared objectives, sharing knowledge and learning as they go.
Meanwhile, product managers don’t need to become machine learning experts—but they do need to understand the basics. Knowing how models are trained, how prompts work, or how feedback loops are structured enables better decisions and faster iteration. Upskilling here builds credibility, not just capability.
Discovery work changes when the solution space includes generative models and probabilistic systems. Traditional techniques like interviews and usability tests remain valuable—but they’re no longer sufficient.
AI product discovery now involves:
Teams also need to address model explainability and ethical considerations from day one. Decisions made early in development—about data sources, output controls, or feedback visibility—can shape long-term trust and performance. Discovery becomes more multidisciplinary, more technical, and more iterative.
KPIs for AI products must evolve beyond adoption rates and task completion. Many standard metrics fail to reflect the dynamic, learning-based nature of AI systems. What’s needed is a layered approach to measurement that captures both user outcomes and model performance.
Core success signals now include:
This broader measurement approach ensures that teams optimise not just for launches—but for sustained performance and responsible iteration.
AI maturity depends on tight collaboration across product, engineering, and data disciplines. The most effective organisations align early—on feasibility, tooling decisions, and ownership of models and infrastructure.
This shift also raises new governance questions. Who is responsible for model safety? How are trade-offs made between performance and interpretability? How do teams respond to failure scenarios? The answers lie in building shared accountability between product and technical leaders.
When handled well, this partnership becomes a flywheel. Product insights guide data investment. Technical feasibility shapes scope. Together, teams move faster and with greater confidence.
The rise of AI is changing what it means to lead in product. It calls for a broader understanding of systems, a deeper integration of strategy and technology, and a renewed focus on organisational learning.
Product leaders must move now. Start by auditing current strategy through an AI lens. Identify one workflow that could benefit from LLM-driven prototyping. Establish an internal forum to share learnings across functions.
The organisations that succeed with AI won’t just add it to their products. They’ll adapt how they think, how they plan, and how they lead too.