Rebuilding the PM Stack with AI | BILL Product Lead

Transforming B2B Mobile Apps with AI

• 🚀 This session explores transforming traditional B2B mobile applications into AI-native product experiences.
• 🎯 The core challenge is designing AI for strategic business decisions while ensuring accuracy and trust for executive users.
• 💡 The goal is to move beyond feature addition to fundamentally rethink how B2B mobile software creates value with AI.

Shifting from Reactive to Predictive

• ⚠️ Traditional B2B platforms typically offer reactive dashboards, manual forecasting, and static reports.
• 📈 Customers increasingly demand proactive and predictive intelligence for business outcomes and strategic actions.
• ✅ AI should provide real-time optimization suggestions and automated scenario planning, anticipating user needs.

Three Phases of AI-Native PM Stack

• 🧠 Phase 1: Intelligent Discovery focuses on predictive intelligence, behavioral pattern recognition, and proactive opportunity/risk notifications.
• 🛠️ Phase 2: Dynamic Interface Architecture transforms static UIs into adaptive experiences, prioritizing information and automating routine tasks.
• 🔗 Phase 3: Seamless Intelligence Integration ensures AI feels natural and trustworthy through confidence scoring, human-in-the-loop design, and learning feedback loops.

Core Principles for AI-First Design

• 🔑 The breakthrough insight is that AI should function as the primary interface, not merely an added feature.
• 🌱 Product managers must design intelligent workflows and build interfaces that support AI decision-making, rather than layering AI onto existing screens.
• 🤝 The ultimate aim is to create an intelligent business companion that empowers users by running on their devices and guiding strategic decisions.

Building Trust and User Agency

• 🔍 Explainable AI is crucial for transparency, showing users the rationale and confidence levels behind AI recommendations.
• 🧑‍💻 Design for human-in-the-loop collaboration, allowing users to easily review, modify, or override AI decisions.
• ✅ The most effective implementations balance automation for routine tasks with clear manual controls, preserving user agency and building trust.