Failure to Launch – 11 Red Flags We See in Struggling Search & AI Projects

Across industries, organizations are pouring time and money into modernizing search, experimenting with vector databases, and building Retrieval-Augmented Generation (RAG) pilots. The early demos look amazing. Prototypes impress. Leadership gets excited.

And then, reality hits.

When real content, real users, and real queries enter the picture, many search and AI initiatives fail to launch. They stall on the pad. They sputter. They never become the production systems stakeholders were promised.

At MC+A, we've spent two decades diagnosing search problems and helping enterprises modernize their architectures. In the past 18 months—fueled by the hype around AI—we've been called into more “stuck” RAG and search projects than ever before. Across all of them, a familiar set of red flags emerges again and again—patterns that quietly sabotage teams before they ever leave the ground.

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Below are some of the most common issues we see.

Unclear Definitions of Relevancy

Relevancy is the foundation of search. Yet many organizations can’t clearly define what a “good result” looks like for their users or their scenarios. Teams often assume the engine—or worse, Google-like magic—will figure it out. It won’t.

Without explicit relevance guidelines and a shared understanding of evaluation criteria, tuning becomes random, and failures compound down the line. This is one of the earliest—and most preventable—red flags.

Overestimating AI Expertise (Internally and Externally)

Everyone sounds like an AI expert right now. But real outcomes depend on fundamentals: retrieval quality, content readiness, indexing strategy, and evaluation frameworks. Without these, teams build impressive demos that crumble instantly when exposed to real-world complexity.

When organizations confuse prompt engineering with production engineering, projects drift off course quickly.

Treating LLMs as Knowledge Bases

One of the fastest ways to derail a RAG program is to expect the LLM to “just know” things. LLMs are not truth engines. They’re not databases. They’re not knowledge repositories.

The moment a team relies on model memory instead of grounding answers in actual content, hallucinations appear, accuracy drops, and stakeholder trust evaporates. This is not an AI failure—it’s an architectural one.

Ignoring the Retrieval Layer

Most RAG demos work because they use carefully selected examples with ideal context. But once deployed against real enterprise data, teams discover a painful truth:

Search isn’t good enough to support RAG.

The retrieval layer—not the LLM—accounts for the majority of answer quality. When retrieval is weak, misconfigured, or built on messy content, the entire AI experience breaks down. RAG is not a shortcut aroun

Want a checklist?

We've compiled a list of red flags from our recent tech talk.