"The best way to predict the future is to observe the present carefully and extrapolate the trends you see. The mistakes people make are in direction, not magnitude."
Practice Lead Who Has Made This Prediction Before
AI Product Landscape Trajectory
The AI product landscape will evolve significantly through 2028. Understanding these trends helps you position products and organizations for the changes ahead.
The trends in this chapter build on the commoditization and positioning themes from earlier sections. The overall direction is clear: AI capability will continue to commoditize, integration and domain depth will differentiate, and the products that win will be those that make AI useful in specific contexts.
2026 Predictions
Model Layer
The AI product landscape will evolve significantly through 2028, building on the commoditization and positioning themes from earlier sections. For 2026 predictions, the model layer shows prices approaching marginal cost as foundation model prices fall 80-90% from 2025 levels with margins compressing for model providers. Open source matches proprietary as open source models match proprietary performance for most use cases, with the primary remaining proprietary advantage being frontier capability for bleeding-edge applications. Specialization emerges as domain-specific models begin to show meaningful advantages over general models for specific tasks.
Product Layer
At the product layer, AI-native products mature as the first generation shows clear winners and losers where distribution and integration matter more than raw capability. Vertical AI consolidation occurs as vertical AI products that own the full stack from data to interface capture more value than horizontal plays. Enterprise AI standardizes as enterprise AI procurement shifts from exploration to standardization and buyers know what they want.
2026 Strategic Imperatives
Move up the stack: If you are competing primarily on model capability, you are in a race to the bottom. Position around application and integration.
Own the interface: Interface control points become primary defensibility. Invest in interfaces that create lock-in and collect valuable data.
Build data moats: Proprietary data becomes more valuable as generic AI commoditizes. Accelerate data collection.
2027 Predictions
Agentic AI Becomes Real
Agentic AI becomes real as AI that takes multi-step actions, using tools and executing plans, becomes reliable enough for production applications. Reliable tool use means AI can consistently use APIs, browse web, and execute code with high success rates. Long-horizon tasks mean AI can maintain context and execute plans over hours or days rather than minutes. Error recovery means AI can recognize and recover from errors without human intervention.
Product Implications
New UX patterns emerge as products must design for AI that acts, not just responds. New failure modes require new safeguards since agentic AI creates new categories of errors. New trust requirements mean users must trust AI to take actions, requiring new explanation and oversight mechanisms.
HealthMetrics Agentic Planning Assistant
2026 state: AI suggests care plan components based on patient data
2027 trajectory: AI coordinates with multiple hospital systems, schedules appointments across providers, ensures care plan completion, and alerts care coordinators when intervention is needed. Human role shifts to oversight and exception handling.
Lesson: Agentic AI does not replace humans; it changes the human role from operator to overseer.
2028 Predictions
AI Infrastructure Maturation
By 2028, AI infrastructure maturation occurs as standard deployment patterns become best practices for AI deployment standardized and automated. Evaluation infrastructure means production eval becomes table stakes with industry-standard frameworks. Monitoring becomes commodity as AI monitoring and observability become built into platforms rather than custom-built.
Human-AI Collaboration Patterns
Products settle into patterns for human-AI collaboration. AI as coworker means AI agents that colleagues collaborate with, not just tools that people use. Appropriate automation establishes clear norms for what AI should automate and what humans should decide. Seamless handoffs enable smooth transitions between AI and human handling based on task characteristics.
The Integration Imperative
By 2028, AI capability will be widely available and relatively undifferentiated. The products that win will be those that integrate AI into useful workflows, maintain proprietary data advantages, and create user lock-in through interface and workflow investment. The window for pure capability differentiation is closing; the window for integration and domain depth is open.
Organizational Implications
Team Structure Evolution
AI product teams evolve as AI capability matures:
AI product teams evolve as AI capability matures. Less prompt engineering and more system design occur as AI becomes more capable, with prompt engineering becoming less critical while system design and integration become more important. Eval becomes universal skill as evaluation literacy spreads from specialists to all product team members. New AI roles stabilize as roles like AI PM and AI UX Designer become established career paths.
Skill Investment Priorities
Skills that remain valuable through 2028 include domain expertise as deep knowledge of the domain where AI is applied, systems thinking as ability to design complex AI integrations, evaluation literacy as ability to define and measure AI quality, and human-AI interaction as skills in designing effective human-AI collaboration.