What are the 2 categories of AI use and why do they matter?

"Generative AI is evolving along two distinct tracks: on one side, savvy users are building their own retrieval-augmented generation (RAG) pipelines, personal agents, or even small language models (SLMs) tailored to their contexts and data. On the other, the majority are content with "LLMs out of the box": Open a page, type a query, copy the output, paste it elsewhere. That divide - between builders and consumers - is shaping not only how AI is used but also whether it delivers value at all."

"The difference is not just individual skill. It's also organizational. Companies are discovering that there are two categories of AI use: the administrative (summarize a report, draft a memo, produce boilerplate code) and the strategic (deploy agentic systems to automate functions, replace SaaS applications, and transform workflows). The first is incremental. The second is disruptive. But right now, the second is mostly failing."

"The Massachusetts Institute of Technology recently found that 95% of corporate GenAI pilots fail. The reason? Most organizations are avoiding "friction": They want drop-in replacements that work seamlessly, without confronting the hard questions of data governance, integration, and control. This pattern is consistent with the Gartner Hype Cycle: an initial frenzy of expectations followed by disillusionment as the technology proves more complex, messy, and political than promised."

"Why are so many projects failing? Because large language models from the big platforms are black boxes. Their training data is opaque, their biases unexplained, their outputs increasingly influenced by hidden incentives. Already, there are companies advertising " SEO for GenAI algorithms" or even " Answer Engine Optimization," or AEO: optimizing content not for truth, but to game the invisible criteria of a model's output. The natural endpoint is hallucinations and sponsored answers disguised as objectivity."

Generative AI usage divides between technically adept users who build RAG pipelines, personal agents, or small language models tailored to specific contexts and the majority who use out-of-the-box LLMs for simple query-and-copy tasks. Organizations face two AI categories: administrative tasks that provide incremental gains and strategic initiatives intended to automate functions, replace SaaS, and transform workflows. Most strategic pilots fail because organizations seek frictionless drop-in solutions and avoid confronting data governance, integration, and control challenges. Black-box models with opaque training data, unexplained biases, and hidden incentives enable gaming through Answer Engine Optimization, producing hallucinations and sponsored outputs.

Read at Fast Company