Rob Hanna: corporate AI lacks not breakthroughs but content management

Rob Hanna, co-founder and CEO of Precision Content, a consulting company specializing in technical communication, told The Next Web in June 2026 that many corporate initiatives to implement artificial intelligence lose momentum not because of a lack of new breakthrough models, but because of the absence of content management systems that make corporate knowledge reliable.

Why corporate AI projects stall

According to Hanna's observation, organizations continue to treat language as if it were structured data — like rows in a database table — while ignoring systems that should ensure the accuracy, relevance, and consistency of knowledge that corporate AI tools train on or draw context from. As a result, even the most powerful model connected to a company's internal knowledge base inherits all the problems of that base: outdated document versions, conflicting instructions, and scattered sources of truth across different departments. From the outside, this looks like a "model problem" — the assistant gets confused or gives an inaccurate answer — when in reality the root of the problem lies at the data level, which nobody systematically brought into order long before the AI tool even appeared.

"Technical communication teams already have many of the competencies needed to build..." —

Rob Hanna, CEO of Precision Content.

What companies need for reliable corporate AI

Hanna points out that technical documentation teams have been doing for years exactly what is now critical for the quality of AI systems: a single source of truth for content, taxonomies, metadata, stylistic standards, and fact-checking processes before publication. Without this discipline, any model that relies on corporate documents to answer employees or clients — whether through search with retrieval-augmented generation (RAG) from an internal knowledge base or through fine-tuning on corporate texts — risks providing contradictory or outdated information regardless of how powerful the underlying model is.

Why this is overlooked when implementing AI

Leaders more often invest in choosing the model, paying for subscription licenses, and training employees on prompting than in reconsidering how the company creates, versions, and approves documents. Technical writers and content specialists are traditionally viewed as a support function rather than as a key component that determines how accurate corporate AI assistant answers are. Hanna calls for reconsidering this hierarchy of priorities and including content teams among the first who are brought into corporate AI initiatives.

Precision Content as a consulting company has spent years helping organizations build exactly these kinds of processes — unified documentation repositories, version control, and consistent terminology — and Hanna's position essentially appeals to his own company's professional experience as a ready answer to the new problem that corporate AI has created.

What this means

Hanna's conclusion shifts the focus of conversation about corporate AI from model selection to investments in content operations: organizations that already have mature technical documentation teams may not need a new model but rather a restructuring of content management processes so that AI tools rely on reliable data.