There has been growing interest in the capabilities of generative AI since the release of tools like ChatGPT, Google Bard, Amazon Large Language Models and Microsoft Bing. With the hype comes concerns about privacy, PII, security and, even more importantly, accuracy. And rightly so.

Organizations are treading cautiously with their acceptance of generative AI tools, despite seeing them as a game changer. Many businesses are trying to find the sweet spot that enables them to capitalize on the benefits now, while identifying more strategic uses for generative AI for the future; all without compromising on security.

One area where immediate gains can be obtained within an organization is their knowledge management. This initiative has been challenging for many organizations, yet it’s one that can drive employee productivity and deliver significant benefits to support teams, who'd typically maintain knowledge manually. 

How generative AI and knowledge management intersect

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

Knowledge management, on the other hand, 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 when they need it.

Many organizations manually maintain their knowledge management, opening the door for out of date or poorly written content. By automating many of the tasks involved in knowledge management, generative AI can help improve the efficiency and effectiveness of your knowledge management processes.

Some of the specific ways in which generative AI can optimize knowledge management include:

1. Automating the creation of knowledge articles

Generative AI can automatically create knowledge articles from existing data sources, such as product documentation, customer support tickets and employee training materials. This automation can free up IT professionals to focus on more strategic tasks, such as developing new knowledge management initiatives and improving the quality of existing knowledge articles.

2. Improving the quality of knowledge

Generative AI can improve the quality of knowledge by identifying and correcting errors, archiving old information, as well as by adding context and additional information to knowledge articles. This can help ensure that employees have access to accurate and up-to-date information.

3. Generate new ideas and insights 

Generative AI can generate new ideas and insights by combining existing knowledge in new ways. For example, HR, facilities and IT all have articles that talk about onboarding and offboarding employees with an organization.

Generative AI may look at these and produce a merged knowledge article that discusses the end-to-end process of onboarding and offboarding across all three areas. This can save an employee from having to search across three different areas.

4. Solve problems more quickly 

Generative AI can quickly solve problems by identifying patterns and trends in data. This can help organizations make better decisions and improve their overall performance. 

For example, generative AI may look across IT incidents over a defined period of time and identify a common method of resolution for a group of common issues. Based on its findings, it can generate a knowledge article for service desk agents to resolve issues quicker, and for employees to resolve them themselves via self-service.

5. Create more engaging content

Generative AI can create more engaging content by personalizing it for each user, helping organizations improve their customer experience. Knowledge articles, particularly within HR knowledge, are personalized based on region or language. Being able to generate content unique to their persona will greatly enhance the use and experience to the employee.  

What are the drawbacks to generative AI?

Generative AI solutions paired with knowledge management have the potential to revolutionize many industries and fields. However, it isn't without its drawbacks, including:

1. Security and privacy

Generative AI systems used for knowledge management may contain sensitive or confidential information. So, it's crucial to ensure that they are secure and protected against cyberthreats. Additionally, there may be concerns around privacy, particularly if the AI is generating content that includes personal or identifying information.

For example, generative AI can create realistic-looking malware and phishing attacks. These attacks can be used to steal personal information, financial data or other sensitive information.

2. Quality and accuracy

While generative AI models can produce impressive outputs, their quality and accuracy can vary widely depending on the input data and the complexity of the task. The old saying of "garbage in, garbage out" still applies. It can also be difficult to ensure that the AI has access to accurate and up-to-date information, which can affect the quality of what it generates.
For example, training data for ChatGPT is collected from the internet and updated regularly. However, the current version of ChatGPT is trained on data that was collected up to September 2021. This means that ChatGPT may not be able to answer questions about current events or topics that have been in the news since September 2021.

3. Data bias

Generative AI models can inadvertently reflect the biases and prejudices present in the data they're trained on, leading to biased or inaccurate results. This data bias is especially concerning in knowledge management applications, where accuracy is critical.
For example, if a model is trained on a dataset of text that's predominantly from the United States, the model may be less likely to generate text relevant to people from other countries. 

Generative AI provides an organization with opportunities to enhance knowledge management through improved quality, engaging content and automation. But there are cautions along the way. Learn what you need to have in place to successfully use generative AI with knowledge management in your organisation.