The SaaS landscape is evolving quickly, and AI is the catalyst. As a business owner juggling multiple SaaS solutions, you need to be smart about your build vs. buy decisions. Here's how to leverage AI and maintain control at scale:
- Switching Costs: You're likely familiar with the challenges of vendor lock-in. In this era of AI disruption, minimizing switching costs becomes even more critical. Prioritize flexible architectures and modular components that allow you to seamlessly transition between providers or even bring solutions in-house. Ingress speed is often a primary concern, but during the considerations you should also think of egress of your integrations.
- API-First Approach: APIs are your gateway to flexibility. Integrating features via APIs allows you to avoid being held hostage by a single vendor. This approach enables you to replace individual components without overhauling your entire system. Own your front-end, proxy Saas services when possible. This will allow you to fully control the customer experience which has less chance of changing when switching services. Time and experience will allow you to model agnostically.
- Negotiation Power: The faster you can switch between providers or build in-house, the greater your leverage in negotiations. Shoot for short-term contracts. We have more reason than ever to acknowledge that new solutions are imminent.
- Implementation Layer: An API-driven implementation layer acts as a buffer between your core systems and third-party vendors. This not only minimizes lock-in but also gives you more control over your "buy vs. build" decisions. You may even convert a previous solution "buy" into a "build" with more ease than ever before.
- Open Source Expansion: AI is fueling the growth of open-source solutions. Expect AI-powered tools that simplify development, automate testing, and enhance collaboration. This could open up more viable open-source alternatives to proprietary SaaS, giving you more options in the build vs. buy debate. However, carefully evaluate the long-term viability and support of open-source projects. They can be unpredictable and if support drops, you'll need to migrate to another solution.
- Data Structure Simplification: AI's data processing capabilities demand simplified data structures. This improves efficiency, interoperability, and scalability, whether you're building or buying. AI can help identify and resolve data inconsistencies, but maintain strong data governance throughout the process.
- AI-Driven Development: The future of software development will shift more so toward a declarative process. It's very possible that we shift into a "solution on demand" environment where we simply state the feature we want to create or problem we want to solve. We will ideate, AI create, and customers validate.
- Security Considerations: AI introduces new security challenges. Implement robust security measures to protect your systems, ensure the integrity of any AI-generated code, and prevent unauthorized data access. Whether you're building or buying, security must be a top priority.
Summary
AI is transforming software rapidly, creating opportunities to optimize speed, value, and cost. By embracing AI strategically, you can accelerate development and make informed build vs. buy decisions that could maybe give you the best of both worlds.