Why AI Will Push Us Toward Platform Products
The rise of AI is fundamentally changing how we build software for business and enterprise. Microsoft CEO Satya Nadella recently predicted that AI agents will transform SaaS as we know it, suggesting that traditional business applications might "collapse" as AI takes over business logic. This shift isn't just about adding AI features - it's about rethinking how we architect software products entirely.
This transformation is likely to accelerate the need for platform products.
What Makes a Platform Product? The development of user-focused platforms falls along a continuum, from point solutions to full use-case-agnostic platforms like AWS. Somewhere in the middle fall platform products - multi-use-case SaaS products that enable multiple use cases within a domain, in a highly interconnected way with considerable customizability.
Three key characteristics define platform products:
Multiple connected use cases within a domain
Strong data flows between these use cases
Modular, swappable components that preserve business workflows and provide flexibility
The Historical Challenge Building platform products has traditionally been hard. It requires significant upfront investment in architecture and often means slower time to market. Most startups can't afford to spend years building a platform backend before launching their first product. This has led many companies to start with point solutions and try to evolve into platforms later, often with mixed results. Regardless of the difficulty of transitioning towards platform architecture over time, it has typically not made sense to focus too much on platform capabilities early in a product’s life because of the cost.
The AI Shift
AI, and especially AI agents, will make this investment make more sense.
Why? Because AI is changing how we think about software architecture. Instead of hardcoding business logic into individual applications, AI can handle complex workflows across multiple systems. Take Microsoft's example of Python in Excel - it's not just about adding AI to spreadsheets. It's about Excel becoming part of a larger system where AI agents can plan, analyze, and execute across different tools.
This shift has three major implications:
Business logic becomes fluid. Instead of being locked into specific applications, rules and workflows can be managed by AI across systems. Also, user expectations are already moving towards fluidity. Adding new capabilities seems easier than ever from a user-perspective, so the expectation that all software should work that way is increasing.
Data needs to flow. AI needs access to data across different use cases to be effective. The old model of data silos in individual applications doesn't work.
Tools become interchangeable. When the intelligence lives in the AI layer, individual tools can be swapped out more easily while maintaining the overall business workflow. Also, the rapid pace of technology change requires businesses to stay nimble with respect to the tools and approaches they use.
Why This Leads to Platform Products
This AI-driven shift naturally pushes software products toward platform architectures. You now need to chain together different AI systems, layer in traditional machine learning and workflow automation, and manage quality to avoid embarrassing hallucinations and errors.
Consider what happens when AI starts managing more of your business processes:
First, you need connected data. If you're using AI to help with property management, for example, it needs to understand maintenance requests, accounting data, and tenant communications together - not as separate silos, but all integrated and available everywhere. This forces you to think about data flows and interconnectivity upfront.
Second, workflows become more fluid. AI can handle complex, multi-step processes that cross traditional software boundaries. A single user request might touch your accounting system, maintenance tracking, and communication tools. Your architecture needs to support this flexibility.
Third, tools need to be modular. As AI gets better at handling business logic, you'll want to be able to upgrade or swap out individual components without disrupting the overall workflow. Maybe you want to try a new machine learning model for maintenance predictions, or upgrade your communication tools. A platform architecture makes this possible.
The end result? Products that wouldn’t have originally been on the platform spectrum at all start to look a lot like the platform products we defined earlier - multiple connected use cases, strong data flows, and modular components. But instead of this being a nice-to-have, AI makes it almost a requirement.
What This Means for Practitioners
If you're building software products today, this shift has important implications for how you approach development. The standard advice of "start focused and add platform capabilities later" will need to be updated.
Start Platform Thinking Early You don't need to build a full platform from day one, but you do need to think like a platform product from the start. This means:
Design your data architecture thoughtfully. How will different types of data connect? What schemas will support multiple use cases?
Build good API hygiene early. Use APIs internally even if you're not exposing them yet.
Think modular. Design components that can be upgraded or swapped and connected to each other in different ways without breaking the whole system.
Balance Speed and Architecture Most startups still need to get to market quickly. The trick is knowing what to invest in early versus what can wait:
Do Early:
Data modeling that supports multiple use cases
Basic API structure
Clear component boundaries
Can Wait:
Full platform infrastructure
Multiple use case support
Advanced customization capabilities
Learn from Others' Journeys Companies that have tried to retrofit platform capabilities later often struggle. It's much harder to restructure data and workflows once they're embedded in your architecture. At the same time, companies that spend years building perfect platforms often never make it to market.
The sweet spot? Build for your initial use case, but architect with platform principles in mind. This approach costs a bit more up front but saves massive headaches later - especially as you start adding AI capabilities.
The Future is Platform-Shaped As AI continues to reshape how we build software, the line between applications will blur. Success will depend less on individual features and more on how well your product supports AI-driven workflows across use cases. Starting with platform thinking isn't just good architecture - it's becoming a strategic necessity.