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Most organizations are focused on choosing the right AI tools. The more important question is whether their content is ready to support them.
A quality manager at a global pharmaceutical company recently described a situation that will feel familiar to anyone who has worked inside a large organization.
An operator on the manufacturing floor needed to follow a standard procedure. The document existed. It had been approved, version-controlled, and stored in the right system. It was technically compliant. It took twenty minutes to find the right section and another ten to interpret what it meant. When something still felt unclear, the operator asked a colleague. The procedure was followed, the batch was completed, and a deviation was logged the next day.
The failure had nothing to do with the technology used to store or manage that document. It had everything to do with how the document itself was written. And that same failure is playing out every day across life sciences, manufacturing, energy, government, and global enterprises.
Most organizations assume their documentation is doing its job. Standard operating procedures, policies, and manuals are often written by subject matter experts under time pressure, and over time they become dense, inconsistent, and difficult to navigate. Terminology shifts. Formats vary. Critical steps are implied rather than stated. The result is what many teams quietly call a "wall of words." Teams are not reading SOPs like a book. They are searching them, scanning them, and trying to interpret them under pressure, often in high-stakes environments. When they cannot find what they need quickly, they improvise.
Can AI solve this?
If information is hard to find, the assumption is that copilots, search assistants, or chat interfaces will fix the problem. AI fed poor inputs does not return better outputs — it returns more of the same problems at greater speed and scale. AI systems still produce incorrect or fabricated answers when the underlying documentation is inconsistent or unclear.
Recently, multiple legal cases highlighted AI-generated hallucinations where systems cited non-existent legal precedents. The source material, not the model, drove those failures. Gartner has also noted that poor data quality is one of the primary barriers to successful AI outcomes, estimating it costs organizations an average of $12.9 million per year. When documentation is fragmented, duplicated, or ambiguous, AI systems cannot reliably interpret it.
The consequences are already embedded in daily operations across industries. In pharmaceutical manufacturing, unclear procedures contribute directly to deviations, rework, and audit findings.
Regulatory agencies including the FDA consistently cite inadequate or unclear procedures as a common compliance issue, and a single deviation can delay production, trigger investigations, and increase cost.
On the factory floor, operators rely on work instructions to execute tasks safely and consistently, and the National Institute of Standards and Technology has linked poor documentation and process clarity to increased production errors and inefficiencies. In clinical settings, the stakes are higher still.
Research published in the Journal of Patient Safety has highlighted the scale of medical errors linked to communication failures. Government agencies face a different version of the same problem, where citizens struggle to understand policies, forms, and processes, driving up operational costs and delaying service delivery.
The U.S. Plain Writing Act was introduced specifically because unclear communication was creating systemic inefficiency and public frustration.
Organizations often try to solve this with better writing. Shorter sentences, simpler language, editing guidelines. These help, but they do not address the root cause, which is structural. Content is written in different formats, with inconsistent labels, mixed structures, and varying levels of detail. Without a consistent way to organize information, even well-written content becomes difficult to use.
Documentation problems scale with the organization. More people create more content, and without standards, inconsistency grows. Over time, knowledge becomes fragmented across documents, emails, and individuals.
Three real-world scenarios illustrate what this looks like in practice
A quality reviewer preparing for an audit finds that the SOP exists, but key steps are described differently across sections, one in narrative text, another as a list, a third referencing a different document entirely. Hours are spent reconciling differences, and the risk is not just inefficiency but inconsistency in execution.
A new operator joining a production line encounters training materials that are dense and inconsistent, and relies on colleagues for clarification. Training takes longer, knowledge transfer depends on individuals, and when experienced workers leave, knowledge leaves with them. A global organization launches an AI assistant to help employees find information, connecting the system to thousands of documents. Early results are disappointing. Answers are inconsistent, confidence is low, and adoption stalls. The underlying content was never structured to support that kind of use.
AI is accelerating a shift that has been building for years. For decades, documentation was written for compliance and record-keeping. Today it is becoming operational infrastructure, supporting decision-making, training, automation, and AI. Most organizations are still working with content that was never designed for these uses, and that gap is what causes so many AI initiatives to stall well before they deliver value. The models are capable. The information they depend on often is not.
How to Start Writing for AI
The principles below come from the Information Mapping methodology, a research-based framework developed at Columbia and Harvard universities to help organizations structure content so that both people and AI systems can find, process, and use it reliably.
Write in blocks, not paragraphs.
Paragraphs often contain multiple topics, which makes it difficult for readers and AI systems to isolate the specific information they need. Blocks of information focus on a single topic and are separated visually, making each unit readable and understandable on its own. A collection of related blocks forms a map, and a map carries a clear title that signals what the section covers.
Label everything clearly and consistently.
Block labels function as metadata. They help readers scan a document quickly and give AI systems a reliable way to locate relevant content when responding to a prompt. Labels should be short, specific, and consistent across documents. A label like "FDA Samples Collection" and a parallel label like "Non-FDA Samples Collection" are immediately distinguishable. Labels like "Receipt of FDA Samples" and "Other (Non-FDA Samples)" are not, and that inconsistency creates problems for both human readers and AI retrieval.
Distinguish between process and procedure.
Process information answers the question of how something works. Procedure information answers the question of how to do something. These are different, and presenting them the same way creates confusion. In an SOP context, process content is best presented using stage-description tables that show what happens and when. Procedure content is best presented as step-action tables that walk the reader through each action in sequence. When these formats are applied consistently, AI systems can recognize what type of information they are encountering and interpret it accordingly.
Put information on the same topic in the same place.
When content on a single subject is spread across multiple sections or documents, readers miss pieces of it and AI systems work with incomplete inputs. Incomplete inputs produce unreliable outputs. Grouping all information related to a topic into a single map eliminates gaps, reduces the risk of hallucination, and makes documentation easier to maintain and update.
Remove irrelevant content.
Subject matter experts write from a position of deep knowledge, and that expertise often results in documentation that includes more information than a specific reader needs in a specific moment. Applying the relevance principle means including only what belongs to the topic at hand and nothing more. Leaner, more focused content is faster to read, easier to act on, and more accurately interpreted by AI.
Standardize presentation modes.
Inconsistency in how information is presented, whether a list, a wall of sentences, or a table, forces readers to reorient themselves constantly and introduces ambiguity for AI systems. When the same type of information always appears in the same format, both people and machines can process it with less effort and greater accuracy.
Organizations that have applied this approach have seen measurable results
That is time that structured documentation gives back, and it is time that compounds across every team, every site, and every AI initiative an organization is running.
The methodology behind these results has been applied across life sciences, pharmaceutical manufacturing, healthcare, and global enterprise for over 40 years.
The core insight has not changed: when information is structured consistently, people can use it. When people can use it, AI can too. Across the industry, the same pattern is emerging. AI performs in proportion to the quality of the content it can understand. In most organizations, that is where the real work begins.