In the rush to build and deploy enterprise AI systems, every leader and builder faces a familiar fork in the road:
- Do we solve this problem with a quick point solution?
- Or do we invest in a flexible, multipurpose platform that can power multiple use cases over time?
At first glance, the appeal of the point solution is undeniable. Need an AI chatbot for your website? Choose one of the hundreds out there. You can have a virtual agent answering FAQs by tomorrow. Problem solved.
But here’s the catch: what happens when your ambitions grow? What if tomorrow you want that same AI to escalate complex tickets, personalize customer experiences, generate real-time insights for your product teams, or automate back-office workflows? Suddenly, that easy-to-deploy point solution becomes a dead end. It’s a single-use tool in a world that demands flexibility.
The hidden tradeoff
Research and industry data consistently show this tradeoff. For example:
- According to McKinsey, 70% of digital transformations fail — often because they rely on disconnected tools that don’t scale together.
- A Forrester study found that companies using integrated platforms for automation achieve 3–5x faster time-to-value for new use cases compared to those relying on point solutions.
- Gartner notes that by 2026, 75% of large enterprises will shift from building siloed AI applications to adopting AI platforms to enable composable and reusable capabilities.
The lesson?
- Point solutions win on speed — once.
- Platforms win on speed — again and again.
Why DevRev takes the platform road
At DevRev, we believe in building AI systems on a foundation that is extensible by design. A good platform should give you two critical superpowers:
- Enough functionality out of the box so you can go live quickly.
- Enough flexibility and multi-purpose architecture so you can reuse what you’ve built — your data pipelines, your workflows, your knowledge graph — to power the next use case, and the next, and the next.
In a world where every department wants AI and automation, choosing not to platform is choosing to limit your options.
So, to platform or not to platform?
It’s always tempting to take the shortcut. But for organizations serious about transforming how they build, support, and grow digital products, the real question isn’t “How fast can I solve this problem?” It’s “How fast can I solve this problem — and the next one?”
Sources:
1. McKinsey – “70 Percent of Transformations Fail”
- Common pitfalls in transformations: A conversation with Jon Garcia
- Why do most transformations fail? A conversation with Harry Robinson
2. Gartner – AI Platform Adoption
3. Academic research
- Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption
