How to Use Generative AI for Knowledge Management
In the blog “How Generative AI Can Benefit Knowledge Management”, we looked at the benefits of AI to knowledge management to enhance the quality, automating the creation of content and enabling more engaging content. In enabling generative AI to become part of the knowledge management framework introduces concerns about accuracy, data bias, privacy and security.
Now, it’s time to look at how we can make it work well together...
How to use generative AI with knowledge management
Despite concerns of using generative AI in daily operations, this technology has the potential to be a powerful tool to optimize knowledge management. By carefully considering the potential drawbacks and taking steps to mitigate them, organizations can use generative AI to improve their knowledge management practices.
Here are the five things to consider when using generative AI for knowledge management:
- Making sure to identify the type of data that will be used to train the generative AI model. Identification of the data type will help to ensure that the data used is accurate and reliable. Are you going to be using existing knowledge articles, incident data, problem data or combinations of all?
- Having identified the data type, generative AI is only as good as the data it's trained on. The old saying ‘garbage in, garbage out’ still applies. Ensure that the data you've identified above is accurate, complete and up-to-date.
- Monitoring the output of the generative AI model for signs of bias, misinformation, completeness and accuracy. This can help to ensure that the information generated by the model is reliable.
- Developing policies and procedures to manage the risks associated with using generative AI for knowledge management. This is an important step in ensuring the success of your project. These policies and procedures should address issues such as data security, privacy and ethical considerations. They should be designed to ensure that using generative AI for knowledge management is conducted in a responsible and ethical way.
- Putting an approval process in place before any knowledge information is shared publicly to ensure that the generated outputs are reviewed and authorized.
By taking these steps, organizations can use generative AI to improve their knowledge management practices while minimizing the risks.
Combine generative AI and knowledge management with caution
The effectiveness and impact of generative AI on knowledge management will depend on how it's used and implemented. It's important to carefully evaluate the benefits and risks before deciding whether to incorporate it.
Here are some potential pros and cons:
1. Automatic generation of relevant content
Generative AI can be used to automatically create knowledge articles from existing data sources, such as product documentation, customer support tickets and employee training materials.
With 32% of IT professionals reporting an increase in helpdesk tickets since the move to remote working, there’s a significant opportunity for enhancement of the knowledge base that can enable quicker and more effective issue resolution, freeing 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. Improved search accuracy
Generative AI can help improve search accuracy by personalizing the delivery of knowledge to employees, based on their individual needs and preferences. With an average employee spending 3.6 hours a day searching for information, any time savings in the way knowledge is delivered to them is a win.
Enabling easier and quicker access to information will ultimately enhance your employees’ digital experience.
3. Enhanced automation
Generative AI can assist in automating routine task – even if it's not directly related to the creation of knowledge management articles.
With 85% of IT professionals rating automation and AI investments as profitable ventures, identifying new ways of streamlining their processes can free up time for IT professionals to focus on more complex issues.
1. Risk of misinformation
Generative AI can potentially produce incorrect or misleading information, which can lead to serious consequences in the IT field. For example, the introduction of malware, or the incorrectly recommending turning off functionality which is used to secure the IT environment from malicious actors.
2. Dependence on AI-generated content
If companies become too reliant on AI-generated content, they may not prioritize the human-generated one or critical thinking skills, leading to a potential loss of expertise. Despite all the discussion around generative AI, human oversight is still required to validate accuracy and approve the generated information.
3. Ethical concerns
There are ethical concerns surrounding the use of generative AI, like potential bias in the data used to train the model, which can perpetuate existing inequalities.
There’s no doubt that generative AI can be a valuable tool for IT knowledge management and while a new exciting technology, there's still much to be learned about the benefits and pitfalls that it may bring.
Each organization needs to review the potential impact individually and choose an appropriate AI solution that meets their own need for privacy, accuracy and security.
Tips for implementing generative AI for knowledge management
Start small and scale up
It's better to start with a small pilot project and then scale up using generative AI as you gain experience.
Get buy-in from stakeholders
It's important to get buy-in from stakeholders before deploying generative AI in production. This will help ensure that the model is used effectively and that its outputs are trusted.
Monitor the model's performance
It's important to monitor the model's performance after it's been deployed in production. This will help identify any potential problems with the model and improve the model's accuracy.
Continuously improve the model
Generative AI models are constantly being improved. It's important to continuously enhance the model by retraining it on new data and addressing any potential problems that may occur.
Learn more about this topic – watch our webinar on Generative AI for InfoSec & Hackers: What Security Teams Need to Know.