What’s the Difference Between AI and GenAI in the Context of ITSM?

When considering adopting artificial intelligence (AI)-enabled capabilities in an IT service management (ITSM) context, it’s essential to appreciate the difference between traditional AI and generative AI (GenAI). This blog explains the key differences and how they impact possible ITSM use cases.

The distinction between traditional #AI and #GenAI relates to their capabilities, methodologies, and applications. This @SysAid blog explains. #ITSM Share on X

The key differences between traditional AI and GenAI

The distinction between traditional AI and GenAI relates to their capabilities, methodologies, and applications. 

Traditional AI employs algorithms and models designed to perform specific tasks or solve particular problems by following predefined rules or learning from data. These AI capabilities are typically task-specific and can’t generate novel content or solutions beyond their programming or training data. High-level work-related examples include classification, recommendation, and anomaly detection.

Traditional AI relies heavily on labeled datasets for training and validation and often uses supervised learning techniques where the model is trained on the labeled dataset to learn the mapping from input to output. This enables traditional AI capabilities to identify objects in images, convert spoken language into text, suggest products and content based on the preferences and past behavior of users, identify fraudulent activities in financial transactions, or power chatbots and virtual assistants that follow scripted responses.

Whereas GenAI capabilities are models and algorithms that generate new data or content that resembles the input data they were trained on. For example, GenAI systems can produce new content in the form of text, images, music, and video that didn’t exist in the training data.

GenAI systems employ advanced neural network architectures such as transformer-based models (like ChatGPT). They often use unsupervised or semi-supervised learning techniques, where the model learns patterns and structures from unlabeled data. GenAI models usually also have contextual understanding, allowing them to generate contextually appropriate content. GenAI-powered chatbots and virtual assistants are consequently more natural and can engage in context-aware conversations.

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How GenAI trumps traditional AI

Both GenAI and traditional AI have advantages and disadvantages. This makes each suitable for different applications and use cases. 

GenAI is good at content creation (including generating synthetic data to train other AI models) and has contextual understanding. It can learn complex patterns and structures from unlabeled data, reducing the dependency on large labeled datasets. In terms of potential issues, the most common for GenAI are ethical and security concerns. Therefore, there’s a need to ensure that the GenAI tool vendor provides capabilities to address these concerns. For example, the SysAid Copilot has several default Guardrails Rules (which an AI Admin can choose to edit, keep, or remove) that relate to:

• Inappropriate content 
• Embedded content or commands
• Any requests to change system instructions
• Any requests to change scripting, command language, language coding, etc.
• Attempts by the user to access data or functionality outside the intended query processing scope 
• User attempts to exploit potential system vulnerabilities
• Attempts to hide malicious content or intent 
• Requests to access sensitive or personal information
• Avoiding specific topics about payroll, religion, etc.
• Pretending to be someone else (an Admin, for example)

Another example is that the AI Chatbot “Monitor and Fine-tune” feature enables AI Admins to refine and validate AI Chatbot Q&A Sets for improved accuracy and quality. 

However, unlike GenAI, traditional AI cannot generate new content or innovate beyond its programming and training scope. Its task-specific nature makes it less adaptable to new or varying tasks without significant retraining or reprogramming. Plus, the dependency on labeled data for training predominantly relies on supervised learning techniques, limiting its ability to discover new patterns without labeled guidance.

How traditional AI and GenAI differ in ITSM use cases

In ITSM, traditional AI and GenAI capabilities can significantly enhance various processes and tasks. However, they do so differently, historically making traditional AI a better technology solution in some instances and GenAI better in others. However, GenAI use cases are rapidly increasing and now overlap and extend those of traditional AI.

The common traditional AI ITSM use cases were:

• Predictive analytics – analyzing historical data in order to predict future incidents or outages
• Predictive maintenance – predicting when hardware or software is likely to fail, allowing for proactive maintenance schedules
• Automated ticket routing – automatically categorizing and routing incoming tickets to the appropriate personnel or teams based on historical data and predefined rules
• Anomaly detection – detecting anomalies in network traffic, system performance, and end-user behavior
• System performance monitoring – continuously monitoring system performance metrics and generate alerts when deviations from expected performance are detected
• Resource optimization – analyzing resource utilization patterns to optimize the allocation of IT resources
• Chatbots and virtual assistants – handling routine inquiries, providing basic troubleshooting steps, and escalating more-complex issues to human agents.

However, GenAI can now deliver these in a manner that’s easier to implement and produces better results.

Did you know that #GenAI can now deliver the common traditional #AI #ITSM use cases in a manner that's easier to implement and produces better results? This @SysAid blog explains al.. Share on X

GenAI capabilities also offer additional ITSM use cases that include:

• Content generation – creating new knowledge base articles, FAQs, and troubleshooting guides based on existing documentation and end-user queries
• Content summarization – summarizing lengthy documents and support tickets, making it easier for IT staff to quickly understand the context and details of issues
• Automated documentation – automatically generating detailed incident reports based on the data collected during an incident
• Automated configuration documentation – generating and updating documentation for IT infrastructure configurations
• Advanced virtual assistants – creating more advanced virtual assistants to engage in more natural and context-aware end-user conversations; some of the key differences are covered in the next section
• Interactive troubleshooting – guiding end-users through complex troubleshooting processes by dynamically generating the next steps based on the end-user’s responses and system data
• Simulated scenarios – creating realistic incident simulation scenarios for training IT staff.

How GenAI advanced virtual assistants differ from traditional AI capabilities

GenAI chatbots can understand and generate human-like text. They can handle complex and nuanced language, making conversations with users more natural and engaging. In doing so, GenAI models can generate contextually appropriate responses, ask clarifying questions, and provide more relevant information. 

GenAI models can adapt to new topics and contexts more readily. They learn from their interactions and improve over time without requiring extensive reprogramming. They can also process a broader range of queries by leveraging vast amounts of training data. 

#GenAI brings new opportunities to improve #ITSM operations and outcomes and also quickly offers the automated enablement previously associated with more complex traditional AI capabilities. Here's how. #AI Share on X

Gen-AI can maintain context over more extended conversations, understanding the flow and nuances of dialogue. This allows for more coherent and relevant responses, improving user satisfaction. GenAI chatbots can also anticipate user needs and proactively provide solutions or suggestions. For example, they can identify patterns in user behavior and offer personalized assistance. 

Finally, GenAI can interpret and respond more effectively to unstructured data, such as free-text inputs. This is crucial for understanding user queries that don’t fit into predefined categories. It can even detect information from images. 

GenAI is now “the go-to AI” for ITSM tools

GenAI is increasingly used for what were the traditional AI ITSM use cases. For example, GenAI can be trained to identify complex, non-linear patterns in data that traditional AI predictive models might miss. Or with automated ticket routing, while traditional AI has been commonly used for this purpose, GenAI can add additional layers of sophistication and flexibility. It can also achieve higher accuracy by understanding context and intent more effectively than traditional rule-based systems. 

So, GenAI brings new opportunities to improve ITSM operations and outcomes and also quickly offers the automated enablement previously associated with more complex traditional AI capabilities. This means that your organization’s search for a new ITSM tool not only needs to inquire about the available AI capabilities but also the type of AI employed – with GenAI offering a far broader and more accessible portfolio of opportunities than traditional AI.If you want to find out more about why and how GenAI capabilities are changing the ITSM landscape, take a look here.