Considering Generative AI for ITSM? Here’s What You Need to Know

In the world of IT service management (ITSM), people are often keen to understand and potentially try out new technologies. Hopefully, in the context of their business application and the benefits this brings. So what about using generative artificial intelligence (generative AI) for ITSM? It might be that it’s still on your to-do list, with a need to understand what it is and how it works in an ITSM context before dipping your toe into the generative-AI waters.

To help, this blog looks at what generative AI is and how it works (at a high level), offering ITSM-related examples for its use.

This @SysAid blog looks at what generative AI is and how it works (at a high level), offering ITSM-related examples for its use. #AI #ITSM #ServiceDesk Share on X

Generative AI technologies explained

Generative AI is an AI category where algorithms generate new data based on their training data, having learned the underlying patterns and distributions of the data in the large training data sets.

The most common types of generative AI technologies include:

• Generative Adversarial Networks (GANs) – GANs use a generator and a discriminator neural network to improve their data generation through a feedback loop, where the discriminator critiques the generated data. GANs are often used for visual tasks such as video creation, AI-generated art, and image resolution enhancement.
• Variational Autoencoders (VAEs) – VAEs learn the probabilistic mapping between the data space and the latent space (it’s best to Google it if you require more insight). Example use cases are drug discovery to create potential new drug molecules and generating new video game content by learning the underlying game structure and features.
• Recurrent Neural Networks (RNNs) such as LSTM (Long Short-Term Memory) – RNNs and LSTMs work with sequence data. These generative AI technologies are widely used in natural language processing (NLP) and can be trained on text to generate new text that mimics the style of the training data. They can also create new music in the same style as a music genre or particular composer.

Examples of GAN generative AI tool use cases for ITSM

GAN tools are usually focused on image creation, for example:

• StyleGAN2 – generates high-quality, realistic faces, but it can also be used for other kinds of images.
• Pix2Pix – image-to-image translation with paired data for tasks such as converting sketches to colored images or satellite images to maps.
• CycleGAN – image-to-image translation without paired data, for example, turning a horse into a zebra or a painting into a photo. This translation is without examples of what the transformation should look like.

However, GAN tools can also be applied to structured data, time series, and other types of data relevant to ITSM. Example GAN use cases for ITSM include synthetic data generation for testing and training purposes, such as:

• Load prediction and resource allocation – resource allocation planning strategies can be tested by simulating different scenarios.
• Network traffic simulation – to test the performance and resilience of network infrastructure under different load conditions.
• Improving incident classification – if certain types of incidents are underrepresented in real training data, GANs can generate synthetic data to help the training.

Here @SysAid looks at some examples of GAN generative AI tool use case for ITSM, and VAE generative and RNN generative use cases too. #ITSM #ServiceDesk #AI Share on X

Examples of VAE generative AI tool use cases for ITSM

With VAEs, we are closer to the technologies commonly employed within ITSM tools to improve IT operations and business outcomes. Examples include:

• Anomaly detection – VAEs are trained on normal behavior data, and when new data comes in the VAE can determine the likelihood that this data comes from the normal distribution.
• Ticket classification and routing – VAEs develop a model that generates meaningful representations of service tickets based on historical data. These can then be used to automatically classify and route new service tickets.
• Predictive maintenance – VAEs build models of normal machine and system behavior, and by monitoring their current status, the VAEs can predict future failures or maintenance needs.
• Chatbots and automated responses – a VAE can learn to generate human-like responses to common IT queries and issues through training on historical chat logs and ticket resolutions.
• User behavior analysis for security – VAEs can learn the typical user behavior patterns and identify unusual behavior that might indicate a security breach.

Examples of RNN generative AI tool use cases for ITSM

Finally, RNN technologies are the most likely to be seen in mainstream ITSM use cases. Again, built into ITSM tools and the commonly adopted ITIL processes:

• Incident prediction and alerting – RNNs can analyze logs and performance metrics over time to predict potential system failures or incidents before they occur.
• Automated ticket routing – for instance, like with VAEs, an LSTM can be trained to understand the context in the description of the service ticket and assign it to the appropriate department.
• Chatbots for IT support – RNNs can provide automated responses to common IT support queries, understanding natural language and providing contextually appropriate responses.
• Automated documentation and knowledge base generation – RNNs can generate documentation or knowledge base articles based on existing data and patterns.
• Resource allocation – RNNs can analyze time-series data such as CPU usage, memory consumption, or network traffic to predict future resource demands.
• Anomaly detection – RNNs, as with VAEs, can monitor sequences of logs or events to detect anomalies, identifying issues characterized by unusual sequences of events rather than single anomalous events.
• Root-cause analysis – RNNs can correlate different events to help identify the root causes of incidents and problems.

Are you using generative AI for ITSM? Please let us know in the comments!