Interest in generative AI has skyrocketed since the release of tools like ChatGPT, Google Gemini, Microsoft Copilot and others. Along with the hype comes concerns about privacy, personal identifiable information (PII), security and accuracy.

Organizations are treading cautiously with generative AI tools despite seeing them as a game changer. Many seek the “sweet spot” – enabling benefits right now while identifying more strategic future uses, all without compromising security.

One area in which gains can be immediate: Knowledge management, which has traditionally been challenging for many organizations. However, AI-based knowledge management can deliver outstanding benefits – especially for IT teams mired in manually maintaining knowledge bases.

How generative AI and knowledge management intersect

Generative AI refers to a type of artificial intelligence that can create new content, such as images, video, text or music, based on existing data. It uses machine learning algorithms to analyze and learn from large datasets, then uses that to generate new content.

Knowledge management is the process of capturing, organizing and sharing knowledge within an organization. It involves collecting information from various sources, storing it in a centralized database and making it easily accessible to employees as needed. 

Many organizations carry out knowledge management manually, resulting in out-of-date or poorly written content. By automating knowledge management processes, generative AI can improve their efficiency and effectiveness. 

Generative AI knowledge management benefits

Here are some of the specific ways generative AI can achieve these ends:

Automate creation of knowledge articles

Generative AI can automatically draft knowledge articles from existing data sources like product documentation, customer support tickets and employee training materials. This frees IT professionals for more strategic tasks, such as developing new knowledge management initiatives and improving existing articles.

With 56% of IT workers saying that helpdesk ticket volume is up, and 78% blaming hybrid/remote work for the jump, enhancing a knowledge base can enable quicker and more effective issue resolution and free teams to address more strategic tasks.

Create more personalized and engaging content 

Generative AI can personalize content for each user to improve his or her experience. Knowledge articles, particularly for HR, can be personalized by region or language. Being able to generate content unique to an employee persona will enhance that employee’s usage and experience.

Improve the quality of knowledge

Generative AI can identify and correct errors, add context and additional information to knowledge articles and archive outdated information. So, employees will only access accurate and up-to-date information.

Generate new ideas and insights

Generative AI can combine existing knowledge in new ways. For example, HR, Facilities and IT might all have articles about onboarding and offboarding employees. Generative AI can use these to produce a merged knowledge article that covers all three departments, so an employee won’t have to search through multiple articles.

Solve problems quicker

Generative AI can rapidly solve problems by identifying data patterns to help improve decision-making and performance. For instance, it might examine IT incidents over a given period and identify a common resolution for similar issues. It can then generate a knowledge article for service desk agents on how to expedite resolutions, or for employees on how to do so via self-service. 

Improve search accuracy

Generative AI can improve search accuracy by personalizing knowledge delivery based on each employee’s needs and preferences. Since an average worker spends 3.6 hours a day searching for information, any time savings here is a win that enhances their digital experience.

Enhance automation

Generative AI can help automate routine tasks, even those not directly related to creating knowledge management articles. Spiraling workloads are why 92% of IT workers see automation as “necessary” or “very necessary,” so identifying new ways of streamlining processes can free them from routine tasks to tackle other work while cutting costs.

Generative AI knowledge management drawbacks

Generative AI for knowledge management has the potential to revolutionize many industries and fields – but it’s not without issues that are important to address in implementation.

Security and privacy

AI knowledge management systems might contain sensitive or confidential information, so it's crucial to ensure they’re secured against cyberthreats. Also, there may be privacy concerns if the AI is generating content using personal or identifying information.

Quality and accuracy

While generative AI models can produce impressive outputs, quality and accuracy can vary widely depending on the quality of input data and task complexity.

Risk of misinformation

Generative AI can potentially produce incorrect or misleading information, leading to serious consequences for IT – for example, by introducing malware or incorrectly recommending turning off functionality used to secure the IT environment against malicious actors. Say a user is trying to install a printer driver and asks AI for help. Among its instructions, AI might tell the user to disable antivirus software or a firewall, providing a window for malware to be installed.

Dependence on AI-generated content

Companies that are too reliant on this may not prioritize human-generated content or critical thinking skills, potentially leading to lost expertise. Human oversight is still essential in validating and approving AI-generated outputs.

Data bias

Generative AI models can inadvertently reflect biases present in their training data, leading to skewed or inaccurate results. This can be an issue in knowledge management, where accuracy is critical. If an AI knowledge management model is trained on data predominantly from the United States, for instance, it may generate outputs less relevant to people in other countries.

Ethical concerns

These include potential bias in AI knowledge management training data, perpetuating existing inequalities.

Get the Interactive Report: AITSM: How AI is Redefining IT Service Desk Automation

Making AI knowledge management work

Despite the concerns, generative AI knowledge management can be a powerful tool. By carefully considering potential drawbacks and taking steps to mitigate them, organizations can make good use of generative AI in knowledge management.

Here are five things to consider when deploying AI knowledge management:

Identify data types used to train the generative AI model: Identification of data types will help to ensure that the data used is accurate and reliable. Are you going to use existing knowledge articles, incident data, problem data or a combination of types?

Ensure identified data is accurate, complete and up to date: Generative AI is only as good as the data it's trained on; “garbage in, garbage out” still applies.

Monitor the output of your generative AI knowledge management model: Check it for bias, misinformation, completeness and accuracy to ensure the information being generated is reliable. 

Develop policies and procedures to manage risks: This is crucial to address issues such as data security, privacy, and ethical considerations in using generative AI for knowledge management.

Put an approval process in place: Review and authorization must happen before any generated outputs are shared publicly. 

Implementation tips for generative AI knowledge management

There’s no doubt that generative AI knowledge management can be a valuable tool – or that there’s still much to be learned about both its benefits and possible pitfalls.

An organization must evaluate the potential impacts and pick an AI knowledge management solution that satisfies its distinct needs for privacy, accuracy, and security.

That said, there are a few more best practices to support successful adoption:

Start small and scale up: It's best to start with a small generative AI knowledge management pilot project, then scale up as you gain experience.

Get buy-in from stakeholders: It's important to get stakeholder buy-in before deploying generative AI to assure it's used effectively and its outputs are trusted. 

Continuously improve the model: It's vital to continuously enhance your AI knowledge management model by retraining it on new data and addressing any potential problems that might crop up.