Most enterprises pour money into generative AI and see almost nothing come back. The new Opkey’s report, “Build vs. Buy: The Enterprise Cloud Application AI Decision,” pulls together three major studies to show why that happens; and what the highest performing teams do differently when they bring AI into Oracle, Workday, and other cloud apps.
To explore key insights, failure patterns, and a framework for smarter AI decisions.
The GenAI Divide: Who actually gets value?
The report opens with a simple split: a small set of organizations turn AI into real outcomes, while almost everyone else stays stuck in pilots and proofs of concept. You’ll see how often integrated AI projects reach production, and how that compares to the narrative you hear in boardrooms and conferences.
It also contrasts executive optimism with what quality engineering leaders report from the front lines. The gap between “we have an AI strategy” and “we have AI in daily production use” turns out to be much wider than most teams expect.
The blind spot inside ERP and cloud apps
The research digs into where enterprises do try AI; and where they don’t. You’ll get a taste of how rarely teams apply GenAI to ERP testing and cloud application operations, even though those systems run finance, HR, supply chain, and procurement.
At the same time, the report hints at how little of the overall test portfolio most organizations automate today, and what that means when Oracle and Workday ship quarterly changes on a fixed schedule. The numbers around this gap are sharp enough that most readers end up rethinking where they point their AI budget.
Build vs. buy: a 2x difference
Without giving away every chart, the research makes one pattern hard to ignore: organizations that buy or partner for cloud AI capabilities move faster and reach production far more often than those that build everything themselves. The ratio isn’t subtle, and it stays consistent across industries.
The report doesn’t just say “buy”; it shows how deployment timelines, success rates, and long‑term adaptability change when teams rely on purpose-built platforms instead of one‑off internal projects. You’ll also see why speed doesn’t just matter for convenience; it influences risk, shadow AI use, and competitiveness.
Why internal builds keep stalling
The paper sketches three recurring failure modes in internal AI builds; around integration, learning, and workflow coverage; without turning into a how‑to manual in the blog. Think of this as a preview: you’ll recognize some of these patterns from your own projects, but the detailed breakdown, examples, and comparison table live in the full report.
How Opkey CALM platform changes the question you should ask
Under the covers, the research points toward a different way to frame the whole discussion. Instead of asking, “How do we add AI to testing?” or “How do we add AI to configuration?”, the top performers ask, “How do we bring AI into the entire cloud application lifecycle?”
That’s where Cloud Application Lifecycle Management (CALM) comes in. Opkey’s CALM platform uses domain‑specific AI across configuration analysis, impact assessment, testing, and training for Oracle, Workday, and other ERP systems. So, when you read the report’s build‑vs‑buy data, you can see how a lifecycle platform like CALM fits into the “buy/partner” side of the story; without this blog turning into a product datasheet.
Most AI decks look great in theory. This report shows what actually happens in practice; where projects stall, where they break through, and how cloud application leaders decide when to build and when to buy.
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