How to Audit Your Knowledge Management System for AI Data Readiness

You’re pouring resources into your artificial intelligence (AI) strategy, but are you overlooking a crucial ingredient? It’s not just about the algorithms and processing power–it’s about the fuel that drives those engines: your data. And if that data is trapped in silos, outdated, or poorly organized in your knowledge management system (KMS), your AI initiatives could be headed for a disappointing stall. 

For this reason, knowledge audits become an indispensable part of your strategy. When conducting an audit, evaluate the information within your knowledge base, identify gaps, and amend outdated content. Plus, you can extend the audit to your KM platform to assess AI readiness down to the technical nit and grit. Read on to learn more about the value and key steps of auditing your KMS for AI strategies.

What Is AI Data Readiness?

The principle of quality over quantity is not lost in AI data and knowledge management. AI needs high-quality, relevant, and reliable data to function effectively. That said, AI readiness ensures your data is clean, consistent, and properly formatted. It’s about organizing company information or data, so your AI can easily access and process what it needs to produce valuable insights.

Similarly, high-quality data can be sidelined if your knowledge management software lacks the essential AI capabilities to understand your data landscape. It can’t identify gaps and inconsistencies and establish processes for data quality control. This is why you need to take a good, hard look at your existing knowledge base through knowledge audits.

An example of an AI-data-ready company was Siemens. In early 2024, the company shared how its predictive maintenance solution was equipped with generative AI to optimize industrial asset performance further. 
This development highlights the growing importance of AI-powered knowledge management, where AI can analyze vast amounts of sensor data and operational history to predict failures and recommend maintenance actions. However, the success of such AI applications hinges on your data’s AI readiness.

Why Audit Your Knowledge Management System for AI Data Readiness

An APQC report highlights the growing importance of AI in knowledge management. Their findings show that 38% of knowledge management teams prioritize AI tools, particularly for enhancing content recommendation and knowledge asset delivery. This suggests a shift towards using AI to personalize and streamline knowledge sharing within organizations.

Naturally, your knowledge management system houses the foundation upon which your AI initiatives will be built. However, simply having data isn’t enough. To truly unlock the potential of AI, that data needs to be accurate, relevant, and accessible. 

Auditing your KMS for AI data readiness is essential to ensure your AI strategy isn’t built on shaky ground. This process helps identify potential roadblocks, uncover hidden opportunities, and ultimately sets the stage for AI success. Specifically, knowledge audits pave the way for the following advantages:

1. Identify and eliminate ROT

Knowledge audits can spot and remove or amend redundant, outdated, and trivial data (ROT), ensuring that only relevant and high-quality information remains. This process streamlines datasets, improves accuracy, and prepares your data for effective AI implementation. 

2. Close knowledge gaps

With your overall data’s AI readiness assessment, a KMS audit can uncover hidden knowledge by identifying frequently asked questions, documenting critical processes, and capturing essential expertise. In doing so, organizations can improve efficiency and decision-making while enabling AI systems to learn from a complete and reliable knowledge base.

3. Implement procedural content structure

An organized approach to collecting, storing, and presenting information makes it easier to process and use for automation. With knowledge audits, identifying and addressing procedural information that requires a proper form. Plus, structured knowledge–whether implicit, tacit, or explicit–empowers AI systems to learn and perform more effectively as they operate in logic and order.

4. Enhance data accessibility

A knowledge audit helps you identify structural or organizational barriers that prevent easy access to information. It can optimize data accessibility and ensure your AI systems have a clear path to the needed knowledge. This creates a solid foundation for accurate, data-driven decisions and empowers your AI to reach its full potential.

5. Ensure data compliance

Audits can identify and address gaps in your data and knowledge management practices, such as unauthorized access and inadequate security controls. Proactively identifying and mitigating these issues safeguard your data from unauthorized access and ensure your KMS complies with all applicable regulations.

6. Optimize for AI-specific needs

AI often requires data to be formatted in specific ways. A knowledge audit can pinpoint any deficiencies between your current KMS and AI requirements and then take steps to align your KMS accordingly. This will ensure that your data is in the right format and structure for AI to use, which will ultimately improve the accuracy and effectiveness of your AI models. 

For example, through natural language processing (NLP), AI can cluster topics based on keywords. In this process, content has to be substantial for AI to understand the context of the data. In this case, you can implement a specific structure or keyword groups for articles or documents that make it easier for AI to decipher the purpose of the content.  

Overall, auditing your KMS for AI data readiness is a critical step toward maximizing your KM return on investment (ROI). An in-depth audit reduces the costs associated with storing and processing unnecessary data while simultaneously enhancing the accuracy and effectiveness of your AI applications.

Key Steps to Audit Your Knowledge Management System’s AI Data Readiness

A thorough audit will reveal if your system is ready to harness the potential of AI or if there are critical gaps hindering your progress. This evaluation systematically examines data quality, system architecture, and integration capabilities to ensure your knowledge base can effectively support AI-driven insights and automation. Here are the core steps to consider:

1. Assess your data quality

You need to evaluate your KMS data across various dimensions. Start by identifying and documenting your data sources, then analyze the data for completeness, accuracy, consistency, timeliness, and relevancy. A checklist for these criteria can help you qualify data and knowledge.   

Check for duplicates, outdated information, and irrelevant content. Then, assess data accessibility and understandability, ensuring it’s easily retrievable and in a format suitable for AI applications.  

Finally, measure the data’s overall effectiveness in supporting your AI goals, considering factors like its ability to generate insights, support decision-making, and drive desired outcomes.

2. Evaluate your KM platform’s capabilities

At the core of auditing your KMS for AI data readiness, the most valuable attribute to consider is your KM platform’s ability to effectively capture tacit knowledge from experts and transform it into explicit, machine-readable formats. To check for this capability, you must ask the following questions:

• Is your KM software capable of data ingestion, cleansing, and transformation?
• Can it manage large volumes of data and ensure data quality?
• Does it have integration capabilities with AI tools and technologies?
• Can it support data governance and security through automation? 
• Does it have features that facilitate knowledge discovery and retrieval, such as semantic search and natural language processing? 

Power Move: It’s no secret that KM platforms are now equipped with basic AI properties–from internal search capabilities to automated organization. With Bloomfire, however, you can experience AI power on a much broader scale. As an AI-powered knowledge management solution, you get to enjoy the following features: 

• Semantic search
• Deep indexing
• Automated tagging
• Video and audio intelligence
• AI author assist
• Generative AI for content creation
• AI chat
• Personalized recommendations
• Analytics and reporting
• Integration with other systems

Bloomfire’s AI capabilities aim to make knowledge sharing, discovery, and utilization more efficient and effective within organizations. These features empower employees to quickly find the information they need, fostering a culture of collaboration and informed decision-making.  This, in turn, leads to increased productivity and innovation across the organization.

3. Check your organization’s readiness for AI data processing

Assessing your organization’s readiness for AI data processing requires evaluating its current data infrastructure, resources, and capabilities. Determine whether your data is well-organized, standardized, and error-free. Also, evaluate its data governance policies and procedures, particularly whether it has the tools to ensure data security, privacy, and compliance with relevant regulations. 

Beyond technical capabilities, consider your organization’s culture and mindset. Ask the following questions:

• Is there a willingness to embrace AI and data-driven decision-making?  
• Are employees open to adopting new tools and workflows?  
• Evaluate the level of executive support and the availability of resources for AI initiatives.  

Finally, identify clear objectives and use cases for AI data processing. Understanding how AI can address specific business challenges and contribute to your overall goals is essential for successful implementation.

4. Document your findings

Much like any other company’s knowledge, keeping track of your auditing efforts is imperative to have a reference for decision-makers and future use. Documenting your findings comprehensively records your organization’s knowledge assets, including their location, format, and quality. This documentation serves as a valuable roadmap for understanding your data landscape and identifying areas for improvement.

Moreover, documenting your findings facilitates communication and collaboration among stakeholders. It provides a clear picture of the current state of your knowledge base, allowing everyone to understand the challenges and opportunities associated with AI for acknowledgment management implementation.

Bonus: Auditing Data Trustworthiness

Assessing data trustworthiness is a knowledge audit example that requires a meticulous evaluation of key attributes. This includes verifying accuracy and consistency with real-world entities, assessing completeness to ensure no crucial information is missing, and evaluating timeliness for relevance in the current context. 

To audit data worthiness, you can follow these steps:

1. Trace data lineage to its origin, revealing potential biases and establishing credibility.
2. Look for inconsistencies across all systems and databases. 
3. Validate against predefined standards and confirm adherence to expected formats.
4. Evaluate security measures protecting against unauthorized access and data corruption.
5. Assess accessibility for authorized users, guaranteeing they can readily utilize the data for their needs.

Data trustworthiness is a significant factor in your KM platform’s ability to process accurate and updated information. It is the foundation of AI data readiness and should be established at the forefront of your knowledge management implementation. Otherwise, you could be feeding your KM system with unreliable knowledge that goes to waste.

Knowledge Audits for AI Success

The adage “garbage in, garbage out” holds true for AI-driven knowledge management systems.  Feeding an AI system with flawed, inaccurate, or irrelevant data will inevitably lead to unreliable outputs and misguided decisions. But with a thorough knowledge audit, you can identify and rectify potential data quality issues, ensuring that your AI-powered KM system only processes trustworthy information. Remember, your AI strategy is only as strong as the data it’s built on.

An AI-powered KMS Can Help

Struggling to keep your data AI-ready? Let Bloomfire simplify the process.

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About the Author

Betsy Anderson

Betsy leads the customer success and implementation teams at Bloomfire. Passionate about the people side of knowledge engagement and knowledge sharing, Betsy shares real-world experience with the challenges faced by companies with a knowledge management problem.