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Original article
AI-Native Applications: A Framework for Evaluating the Future of Enterprise Software
Key takeaway
- For entrepreneurs: AI-native applications must not only incorporate AI technologies but also offer continuous improvement through user feedback loops and proprietary AI enhancements to stay competitive.
- For investors: The surge in funding for AI-native applications in 2024 signals a market shift towards applications that are inherently AI-driven, with significant revenue traction, suggesting lucrative opportunities in this sector.
Summary
The article from Sapphire Ventures explores the emergence and characteristics of AI-native applications in the enterprise software market. It discusses the importance of AI not just as a feature, but as the core of the application experience, which includes learning from data, understanding context, generating novel outputs, and improving over time. With investment in AI-native applications reaching $8.5B in 2024 and companies like Perplexity, Poolside, and Magic raising substantial funds, the market is witnessing rapid growth and innovation. The article outlines a framework for evaluating AI-native applications across five dimensions: Design, Data, Domain Expertise, Dynamism, and Distribution, providing insights into how these applications will redefine enterprise software.
Insights
- Design: AI-native applications are expected to revolutionize user interaction by introducing new interfaces like chat, voice, and multi-modal models, enhancing user experience.
- Data: Effective data management and the ability to leverage latent data are crucial. AI-native applications can turn unwritten knowledge and unused data into structured information, creating business value.
- Domain Expertise: Applications with deep vertical knowledge can significantly accelerate workflow and decision-making, offering superhuman capabilities in speed and accuracy.
- Dynamism: Enterprises will seek more adaptive and personalized experiences, shifting from static to dynamic software interfaces.
- Distribution: There's a shift toward more flexible pricing models beyond traditional SaaS, opening up new business models like outcome-based pricing.
Implications
- Transformation of Enterprise Software: AI-native applications will lead to a complete overhaul of traditional enterprise software, focusing on speed, scalability, and continuous improvement.
- Business Model Evolution: New pricing strategies and business models will emerge, reflecting the value AI delivers through personalized experiences and outcomes.
- Market Opportunities: For entrepreneurs, understanding and leveraging AI-native characteristics can provide a competitive edge. For investors, the sector offers significant growth potential as AI-native companies are not just building on existing platforms but are redefining software creation and value delivery.
- Customer Expectation Shift: Companies will need to evolve how they interact with customers, adapting in real-time to user behavior and preferences, aligning with the new, dynamic nature of AI applications.