by Jag Gattu, CEO at UptimeAI
This article originally appeared on LinkedIn.
If you’re a CIO, CDO, of VP of Operations and you aren’t asking which of your SaaS tools could be retired in the age of generative AI, you’re missing a big part of your job right now.
Here’s a small example from our own experience. Last week, one of our team members used Claude Code to rebuild a sales relationship-mapping tool we’d been paying for as a SaaS subscription. A few days of work, and we no longer need to renew that contract. Genuinely great use of GenAI — a well-scoped, single-purpose application, recreated in-house faster and cheaper than re-negotiating the contract with the vendor.
Here’s what we didn’t try to rebuild: our CRM. Nobody on our team seriously proposed it, and not because it wasn’t technically possible. It’s because scope, scale, complexity, and the sheer depth of integrations a CRM touches make it a terrible build candidate — even in a world where a single engineer with the right AI tools can do more than an entire team could five years ago. Add in how fast the economics of the big model providers are shifting, and “build it ourselves” gets shakier by the month, not sturdier.
That contrast is the whole build-versus-buy question in miniature. GenAI has genuinely moved the line on what’s worth building in-house, but it hasn’t erased the line.
And the data backs this up. MIT’s widely-cited 2025 study (enter “95% of AI pilots fail to make it to production”) on enterprise AI found that internally built AI deployments succeed at roughly half the rate of externally partnered ones — 33% versus 67%. Gartner has separately warned that at least half of generative AI projects will blow through budget due to poor architectural choices, and that most organizations attempting to build custom models will eventually abandon those efforts due to cost, complexity, and technical debt. That’s not a knock on any one company’s engineering team. It’s what happens structurally when you take on a mission-critical or business-critical system that has to keep working, keep learning, and keep being right, indefinitely.
So where does something like UptimeAI fall on that line? For the majority of industrial organizations, we see a strong case for not building it yourself — but it’s worth being precise about why, because it’s no one reason between scale, complexity, risk, and upside.
Most organizations that try to BIY this space end up building a knowledge graph, wiring up retrieval over their operational data, and putting a chatbot on top. That’s a real project, and a legitimate one — but it’s retrieval. It answers, “what does the data say.”
Bridging from retrieval to reasoning is a big leap. Ours come with the data access, the knowledge graph, the contextual relationships between assets and failure modes, the domain skills of experienced engineers, and the orchestration across sub-agents already built in — tuned to respond to specific high-impact business problems like “what’s actually wrong, and what should you do about it” the way your best expert would. That’s not a chatbot with good retrieval. That’s domain expertise and engineering judgment, encoded, and scaled. It’s a much harder thing to stand up yourself, and it’s exactly the layer where buying beats building.
Retire the SaaS tools GenAI can genuinely replace. Just don’t confuse that win with being able to BIY it all. Reasoning and retrieval are fundamentally different jobs — and at UptimeAI we take pride in doing the hard jobs in a way that generates big returns for your business.
Schedule a demo to see our off-the-shelf reasoning agents for yourself.
