Enterprises have embraced generative AI with high expectations – new business insights, automated agents, real-time decision-making. What many got instead are failed proofs of concept, fragile prototypes, ballooning infrastructure costs, and a sobering realization: AI, at scale, is harder than anyone wants to admit.
A recent study by S&P Global found that nearly 46% of executives are abandoning AI initiatives before they even reach proof-of-concept. The “pilot paralysis” problem is widespread and widely reported with staggering and consistent findings. In August 2025, an MIT report made headlines, finding 95% of generative AI pilots at companies are failing.
Most of today’s AI failures aren’t because models don’t work. They fail because teams are trying to force AI into infrastructures that were never built to support it. These AI pilots are misaligned from day one. They have vague goals driven by experimental enthusiasm, “let’s see what we can do” rather than being tied to measurable business outcomes. Teams obsess over models instead of workflows, pipelines, and deterministic code.
Too often pilots demo well but fail to integrate well with other systems. The infrastructure to operationalize these AI-driven initiatives simply doesn’t exist. Traditional data stacks were optimized for tabular data and batch analytics. They weren’t designed to handle context windows, semantic routing, retries, or LLM inference in the loop. So, engineering teams do what they’ve always done: duct-tape a fragile set of scripts and services together.
The result? Inference pipelines that break at scale, can’t be debugged, and never reach production. And when nothing ships, fatigue sets in. Engineers burn out. Executives cut budgets. Another AI initiative goes back on the shelf.
The Way Out: Infrastructure Built for Inference
What separates the 5% of AI pilots that succeed from the overwhelming majority that don’t?
Successful projects are anchored to KPI’s and real business metrics such as reduced post-call QA time or faster document processing. They don’t boil the ocean but instead begin with one narrow use case, iterating on it to make it as bulletproof and deterministic as possible – only then expanding to other business scenarios. They’re designed with production and operationalizing AI in mind – evals, tracing, observability, and human-in-the-loop where needed.
To achieve this winning strategy, teams need to stop bolting AI onto legacy systems and start treating AI as a first-class workload and inference as the new transform. New AI data infrastructures and data engines like Typedef are purpose-built for scalable, deterministic AI pipelines that integrate directing with your existing stack. Teams can build, deploy, and scale production-ready AI workflows – deterministic workloads on top of non-deterministic models – without the complexity and strain of managing infrastructure.
- No fragile glue code – Instead, clean APIs and semantic operators
- No operational mystery – Built-in observability, retries, and batch orchestration and durable context artifacts
- No pilot purgatory – Move from idea to production in days, not quarters
From Fatigue to Flow
With the structure and reliability provided by these nw AI-native data engines to power inference-first workloads, engineering teams can do what they do best: ship things that work, that scale, and that deliver business value.
If your team is feeling stuck, burned out, or disillusioned with the state of their AI initiatives, the time is now to rethink your supporting infrastructure to ensure it’s built from the ground-up to handle modern AI-native workloads at scale.
It’s time to stop building prototypes—and start building for production (where ROI lives).
