Introduction
AI systems learn from data and replicate patterns. When the patterns they observe are incomplete, ambiguous or undocumented, the results are unpredictable. While many organisations invest in cuttingedge models, they often overlook the human and procedural knowledge that makes an AI deployment successful.
Surveys reveal that 65% of business leaders do not know when or where to apply AI, and 52% lack a foundational understanding of how it works (datasociety.com). This lack of shared understanding stems partly from undocumented processes and “tribal knowledge” kept in the heads of experts.
Why documentation matters for AI?
Hidden decision logic
Operational workflows often contain undocumented steps, exceptions and heuristics. AI cannot replicate these hidden rules unless they are codified. Kyndryl’s readiness report indicates that although leaders believe they have the tools to innovate, more than half of projects stall because employees are not truly ready for AI (kyndryl.com). This readiness gap reflects a lack of explicit process knowledge.
Data provenance and context
Riverbed’s study shows that organisations deploy an average of 13 observability tools and that 95% see standardising data across applications and infrastructure as critical (riverbed.com). Yet documentation of how data is collected, transformed and used is often scattered or absent. Without clear provenance, AI models risk propagating errors and bias.
Reproducibility and compliance
Regulations demand that organisations explain how AI decisions are made. Lucid Software’s AI survey found that 61% of leaders say AI strategy is misaligned, and only 45% have formal AI ethics guidelines (lucid.co). Poor documentation makes it difficult to trace decisions and meet regulatory requirements.
Consequences of poor documentation
Failed automation
When business logic is undocumented, automation fails to capture key edge cases. For example, AgileEngine’s analysis of agentic AI experiments notes that some systems made poor commercial decisions, such as selling products at a loss, because important business rules were missing (agileengine.com).
Bottlenecks and handoffs
Informal processes rely on specific individuals to carry out tasks. If those people are unavailable or leave the organisation, projects stall. McKinsey’s survey shows that only 23% of companies have scaled agentic AI, suggesting that undocumented processes contribute to difficulties scaling from pilots (mckinsey.com).
Erosion of trust
Stakeholders will not accept AIdriven recommendations if they cannot trace how the system reached its conclusions. The IBM CDO study found that only 29% of organisations have measures to assess datadriven outcomes (aidataanalytics.network), indicating that many lack the documentation and metrics needed to build trust.
Building a knowledge management strategy
Codify processes and policies
Start by documenting workflows, business rules and decision criteria. Use process mapping tools to capture steps, exceptions and key decision points. Store this documentation in a repository accessible to AI teams and subjectmatter experts.
Integrate documentation into data pipelines
Implement metadata management and data lineage tools that automatically record how data moves through the system. Platforms like Cribl advocate an “agentic telemetry” layer that ingests humangenerated artefacts such as runbooks, tickets and code commits alongside machine data (cribl.io). Such context helps AI agents understand operational processes.
Encourage a documentation culture
Build incentives for teams to write and maintain documentation. Make documentation part of performance metrics and recognise contributors who keep knowledge up to date. Provide simple templates and automation to reduce the burden.
Establish governance and review cycles
Assign owners for each critical process and schedule regular reviews to ensure documentation remains accurate. Align documentation efforts with compliance requirements and ethical guidelines to maintain transparency and accountability.
Balanced perspective
Some argue that exhaustive documentation slows innovation. It is true that excessive bureaucracy can hinder agility. However, the goal is not to produce encyclopedic manuals but to capture the tacit knowledge that AI systems need to operate safely and effectively. By integrating documentation into existing tools and workflows, organisations can maintain agility while enabling AI to act with confidence. As AI moves from pilot to production, robust knowledge management becomes a prerequisite, not a nicety.
Conclusion
AI success relies as much on clear process knowledge as on sophisticated algorithms. Poor documentation creates blind spots that sabotage automation, slow scaling and erode trust. By codifying hidden rules, integrating documentation into data pipelines and fostering a documentation culture, organisations can ensure that AI systems replicate not just outputs, but the reasoning behind them, laying a foundation for sustainable innovation.
