The Many AI-enabled Capabilities Available to ITSM and ESM

While many blogs talk about the opportunity of artificial intelligence (AI) and its benefits for IT service management (ITSM) and enterprise service management (ESM), it can feel a little like being told that you can now sand a wall after being given a bag of electronic do-it-yourself (DIY) tools. You know that technology can help you. You also understand that the right tool is in the bag. But you don’t necessarily know which piece of equipment is best suited to your need. Which AI-enabled capabilities are best for your organization?

To help, this blog post shares information on different types of AI technologies and capabilities and how they will benefit your ITSM and ESM operations and outcomes.

This @SysAid blog post shares information on different types of AI technologies and capabilities and how they will benefit your ITSM and ESM operations and outcomes. #ArtificialIntelligence #AI #ITSM #ESM Share on X

Categorizing AI technologies

Knowing how best to “slice and dice” the world of AI technologies into easy-to-understand groupings can be challenging. For example, a commonly seen way to separate AI capabilities is across four areas:

1. Reactive machines – basic types of AI systems designed for narrow, specific tasks and unable to learn or apply knowledge from one domain to another.
2. Limited memory – which learn from historical data and past experiences to some extent. Most current AI systems, including chatbots, are limited memory AI.
3. Theory of mind – AI systems that understand human emotions, beliefs, intentions, etc. This AI type is currently a concept rather than a reality.
4. Self-aware AI – an advanced AI that has its own consciousness, emotions, and self-awareness. Self-aware AI is theoretical and does not exist.

Three other AI groupings are commonly referred to:

• Artificial narrow intelligence (ANI) or “weak AI” – AI systems designed and trained for a specific task, such as voice assistants or facial recognition.
• Artificial general intelligence (AGI) or “strong AI” – AI systems that can apply intelligence to any problem, not just one specific problem. AGI is a theoretical concept and doesn’t exist in practice.
• Artificial superintelligence (ASI) – a hypothetical AI that surpasses human intelligence across all aspects. It’s where machines become self-aware.

However, most people will be more familiar with the main “flavors” of AI currently employed in delivering AI-enabled capabilities in ITSM and ESM scenarios:

• Machine learning – the creation of algorithms that learn from and make predictions or decisions based on data. The learning can be supervised, semi-supervised, unsupervised, or reinforced. A subset of machine learning, deep learning, involves neural networks with multiple layers and is used for complex tasks such as image and speech recognition (covered below).
• Natural language processing (NLP) – the interaction between computers and humans through natural language. NLP includes subfields such as sentiment analysis, language translation, and speech recognition.
• Speech recognition – the interpretation and transcription of spoken language into text by computers. It uses NLP and machine learning and is commonly experienced in personal-life voice-based assistants such as Google Assistant, Amazon’s Alexa, and Apple’s Siri.
• Computer Vision – teaching machines how to interpret and understand the visual world. For instance, to accurately identify and classify objects and then react to what they “see.” Examples include image and video recognition, facial recognition, and image processing.

The latter two “flavors” can be considered subsets of the first two.

Which ITSM & ESM processes can benefit most from machine learning? This @SysAid blog explores. #AI #ArtificialIntelligence #ITSM #ESM Share on X

Marrying AI technologies to ITSM and ESM capabilities – machine learning

Many ITSM and ESM processes (or practices if ITIL 4 is used) can benefit from machine learning; these include:

• Incident management – automated ticket classification, similar incident matching (suggesting possible resolutions), and incident prediction.
• Service request management – automated ticket classification and using chatbots and virtual assistants.
• Change enablement – risk assessment and impact analysis.
• Problem management – root-cause analysis and predictive maintenance.
• Knowledge management – knowledge article generation and knowledge retrieval through AI-powered search
• Service level management – monitoring service level agreement (SLA) target compliance and predicting SLA breaches before they occur.
• Asset management – resource optimization based on utilization.
• Monitoring and event management – anomaly detection.
• Capacity and performance management – resource planning (forecasting the demand for IT services and resources)
• Information security management – security threat detection by analyzing network traffic and system logs for unusual patterns.

Marrying AI technologies to ITSM and ESM capabilities – NLP

ITSM and ESM processes can benefit from NLP, often in conjunction with machine learning capabilities; examples include:

Incident management and service request management

• Enabling chatbots and virtual assistants to understand and process human language in handling routine queries, guiding end-users through troubleshooting steps, or automating common service requests such as password resets.
• Analyzing the text of incoming service tickets to determine their content and context to facilitate their automatic classification, prioritization, and routing.
• Real-time communication analysis, such as support chat interactions, to suggest solutions or relevant knowledge base articles to support staff.
• Language translation, where service tickets and other communications might be in different languages. This translation can be done in real-time using speech recognition.
• Generating human-like automated responses to end-user queries or service tickets, providing direct answers or links to self-help guidance.
• for opening service tickets, checking statuses, or receiving updates using voice commands.

Knowledge management

Enhancing search functionality by understanding the intent behind queries and delivering more relevant results. NLP can also help in the automatic tagging and categorization of knowledge articles.

Service level management

Sentiment analysis identifies how end-users feel about the service they receive. This insight can be from the text in service tickets, emails, and survey responses.

Measurement and reporting

Automated report creation or extracting key information, summaries, and insights from large texts.

From speech recognition to NLP, this @SysAid blog explores the many ways that AI-enabled capabilities can help your IT service management operations. #ITSM #ServiceDesk #AI #ArtificialIntelligence Share on X

Marrying AI technologies to ITSM and ESM capabilities – speech recognition

In addition to the voice-activated services already covered in the NLP section, ITSM and ESM service and support processes can also benefit from the following:

• Automated call routing – in a support call center, speech recognition can be used to understand the caller’s issue and automatically route the call to the most appropriate support team or agent based on the responses.
• Service provider voice-to-text ticket creation – where support agents use speech recognition to dictate service tickets, notes, and other documentation.
• Training and quality assurance – here, speech recognition transcribes support calls for training purposes or ensuring that quality standards are being met.
• Feedback collection – speech recognition can collect verbal feedback from end-users.

Here @SysAid explores the wealth of opportunities for ITSM and ESM operations to benefit from AI technologies. #ITSM #ServiceDesk #AI #ArtificialIntelligence Share on X

Marrying AI technologies to ITSM and ESM – computer vision

Finally, ITSM and ESM processes can also benefit from computer vision, including:

• Incident management – identifying potential data center issues such as equipment overheating or malfunctions or augmented reality (AR) support to guide remote staff through installations or fixes.
• Asset management – monitoring physical IT assets such as servers, workstations, and networking equipment through cameras.
• Information security management – detecting unauthorized access to restricted areas or facial recognition-based authentication.
• Supplier management – automatically extracting text from documents using optical character recognition (OCR).

This list isn’t exhaustive, but hopefully, it shows the wealth of opportunities for ITSM and ESM operations to benefit from AI technologies. If you’d like to learn more about SysAid in the context of AI-enabled capabilities, please get in touch.

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