Feature
Enterprises are racing to deploy AI-driven tools, from conversational assistants to cognitive search engines. But amid this AI surge, one challenge looms large: making corporate knowledge accessible, understandable and useful to machines. While humans can piece together meaning from scattered PDFs, slide decks, wikis, email messages and decades-old file systems, ambiguity, inconsistent formats and unstructured contexts can stump the most advanced AI system.
Organizations that treat knowledge preparation as a strategic priority, so information is structured, accurate and governed for both people and machines, are the ones that will thrive.
Table of Contents
- The State of AI Readiness in the Enterprise
- Balancing AI Accessibility With Governance and Security
- Building a Semantic Foundation for AI
- Training, Culture and Ongoing Knowledge Maintenance
The State of AI Readiness in the Enterprise
Protiviti's recent AI Pulse Survey highlights this link clearly:
- 97% of organizations that report high AI ROI say they are confident in their ability to retrieve, organize and understand the data needed to achieve their goals.
- At the same time, more than half of early adopters (51%) report challenges stemming from limited resources and training gaps.
These findings highlight a central tension: While organizations may have the data, workers in the organization may not have the proper skills, governance and structured knowledge to use it. Consequently, AI tools cannot reach their full potential, the report noted.
The survey also points to the challenges enterprises face in balancing AI accessibility with information security, maintaining fresh and accurate knowledge and aligning knowledge management practices with long-term AI strategy.
Organizations struggle to reconcile the need for open AI access with protecting sensitive information. Protiviti's findings suggest that implementing tiered access controls, automated content audits and governance frameworks mitigate these risks while supporting effective AI use.
Moreover, the survey reveals a gap between training availability and effectiveness. Even when resources exist, employees must know how to interpret, structure and apply knowledge efficiently. This challenge becomes particularly acute when organizations attempt to broaden AI initiatives beyond pilot programs, where inconsistent knowledge preparation often becomes a bottleneck to enterprise-wide deployment.
"Information must be divided into structured forms on the basis of regular templates, titles and clear sentences,” said Yad Senapathy of the Project Management Training Institute. The Protiviti survey shows this structured approach is the difference between AI ROI and AI tools that produce inconsistent or unreliable outcomes.
Balancing AI Accessibility With Governance and Security
Corporate knowledge must be stored in layered formats to serve both human employees and AI agents, said Jonathan Garini, CEO and enterprise AI strategist at Fifthelement.
"A Wikipedia-style approach works best — brief abstracts, tagged metadata and links to deeper documents," Garini said. “This approach ensures surface accessibility without overwhelming either humans or AI, allowing each to extract the right level of detail.”
Metadata and taxonomies play a role in machine readability. Clear labeling of project names, departments and customer segments acts as a GPS for enterprise content. Companies that consistently apply metadata achieve 30%–40% faster retrieval times, improving team-wide efficiency, Garini said. This structured, labeled approach helps AI tools deliver accurate and relevant results rather than sifting through noise.
Messy or duplicate data hurts AI performance, so Garini recommends regular "knowledge audits" to merge or archive outdated content into one coherent repository.
Similarly, organizations need to clean legacy files, remove duplicates and standardize formats, said Adam Ilowite, CEO of Axero Solutions. "AI is only as good as the information it can access,” he said. “Standard formats, clear taxonomies and consistent tagging help systems understand the meaning behind content rather than just the words."
Governance is key to maintaining both usability and security. Sensitive information must be tiered with access control, so AI systems see only what they are allowed to, Senapathy said. This balance of clarity, structure and governance makes corporate knowledge readable and actionable for AI while safeguarding assets.
Building a Semantic Foundation for AI
Preparing knowledge for AI requires more than cleaning data—it requires a semantic foundation, said Dave Mariani, CTO and founder of AtScale. Consistent business definitions are essential, because AI cannot interpret terms like "customer churn" differently across departments, he explained.
Alongside this, Git-based version control, audit trails and consistent security policies create an environment where AI systems reliably gain access to institutional knowledge without compromising security. "Knowledge preparation for AI isn't a one-time project,” Mariani said. “It requires clear definitions, consistent standards and continuous governance." This structured approach changes unstructured content into usable intelligence while maintaining enterprise guardrails.
AI handles routine classification and flag inconsistencies, but human oversight remains important for nuanced decision-making. AI might identify duplicate policies, but compliance officers decide which version is legally binding, Garini points out. Humans must validate AI-suggested knowledge to prevent the system from being fed noise, agreed Lonnie Johnston, CEO of WizeCamel. He recommends a structured process: documenting common topics, letting AI find potential answers and then involving humans to select the correct content. This workflow supports growth through automation while maintaining accuracy and intent.
Maintaining accurate, up-to-date information is vital. People lose trust in AI tools if the underlying knowledge is outdated. Organizations benefit from regular content inspections, automated reminders to content owners and linking updates to compliance checklists. Both Garini and Ilowite stress governance and routine maintenance to prevent AI from delivering misleading or obsolete insights.
Training, Culture and Ongoing Knowledge Maintenance
The evolution of AI tools will shift knowledge management from static repositories to living knowledge systems. Employees will interact with AI agents that aggregate answers across silos, providing context-aware responses without manual searching. Organizations investing today in structured knowledge, governance and human oversight will set the stage for AI-native knowledge ecosystems, Garini said. Future knowledge management will focus on creating ecosystems where humans and technology navigate information confidently, rather than building new silos, Ilowite added.
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As AI becomes increasingly central to enterprise operations, preparing corporate knowledge is no longer optional. It requires structured content, consistent metadata, robust governance, human oversight and ongoing training. From Protiviti's survey insights to the practical experiences shared by industry experts, the message is clear: enterprises must treat knowledge preparation as a strategic priority to unlock the full potential of AI.
Investing in structured, accurate and governed knowledge means AI tools deliver value while helping employees work smarter. Organizations that succeed in AI will be those that combine technology with disciplined knowledge practices, continuous training and thoughtful oversight, changing scattered information into a coherent, accessible and future-ready corporate knowledge ecosystem.
Editor's Note: Read more about the intersection of knowledge management and AI:
- How AI and Knowledge Management Can Power Each Other Forward — Knowledge management has a role in helping define what a future workplace culture should look like in an AI-enhanced world.
- Knowledge Graphs: The Secret Sauce Behind AI Development — Pairing knowledge graphs with LLMs boosts AI’s reasoning, accuracy and explainability.
- Knowledge Management Means More Than Just Mining Digital Exhaust — If we want machine intelligence to play a role in knowledge creation, we need conscious design, not lazy accidents.