There is good reason to believe AI for ITSM will be a game-changer. 

With modern organizations so reliant on IT—and, by extension, ITSM—it stands to reason that improvements to ITSM technology and related processes will positively impact how organizations operate. 

By introducing AI into ITSM, AI Service Management – or “AISM” – can help organizations create more agile, responsive, and efficient ITSM environments. 

From AI-enabled insight speeding up time to resolution of cases, to AI-curated self-help resources that help users troubleshoot issues independently, AI for ITSM’s potential is significant. 

This post will explore this potential by highlighting what AI can and can’t do, and the considerations organizations that wish to implement AISM should make. 

  1. What AI for ITSM Can’t Do
  2. What AI for ITSM Can Do
  3. Things to Consider When Implementing AI for ITSM
  4. ​​Enabling AISM for ServiceNow Users
AI for ITSM will revolutionize the workplace

What AI for ITSM Can’t Do

AI Can’t Think Like Humans

Due to the nuances involved in ITSM, AI is not a like for like replacement of human input. Instead, it should be seen as a supplementary initiative, supporting and augmenting employee’s duties, boosting operational efficiency and productivity as a result. 

While AI-enabled tools can automate routine workflows, they cannot match the uniquely human ability to interpret nuances and respond appropriately—skills essential for many IT support roles. 

AI cannot substitute the human expertise necessary for strategic tasks such as devising IT strategies, executing plans, or managing budgets. 

AI Can’t Work Outside of What It Was Programmed For

Generally, AI systems only perform tasks they have been specifically programmed to do. For situations with multiple variables (e.g., user issues/requests and communication methods), pre-programmed responses cannot always address every need accurately, and machine learning algorithms can’t cover all the possible outcomes and nuances of everyday life and business. 

As such, AI lacks the capacity to make moral judgments and is also inherently susceptible to programming biases. 

AI Can’t Fix Everything

Moreover, business leaders must recognize that integrating AI into ITSM will not magically resolve poor process designs or eliminate data silos within the organization. 

They must first identify the core challenges AI can solve and define expectations and outcomes before investing in AI-enabled ITSM tools.

What AI for ITSM Can Do

AI-powered solutions offer significant benefits to ITSM teams, supporting multiple areas of ITSM such as service desks, incident management, asset management, knowledge management, and security & compliance, to name a few. 

Here’s what AI for ITSM can do and support:

1. Chatbots

AI-based chatbots are designed to handle a high volume of requests simultaneously, significantly reducing response times and boosting CX. 

By accurately responding to user issues/requests, chatbots lessen the workload on IT personnel, allowing them to concentrate on more complex problems that demand human intelligence. 

When the request goes beyond what predefined solutions can currently account for, the request can be routed to a person to help solve the issue. 

2. Virtual Agents

These AI-enabled virtual agents serve as online service desk agents. They extend the capabilities of chatbots with machine learning enabled natural language processing (NLP).

This allows the agent to mimic human communication, interpreting and responding to human language inputs.

Virtual agents can efficiently handle inquiries and tasks, providing answers and guiding users to the right self-help resources. 

3. Machine Learning (ML)

Using statistical analysis techniques on data, ML algorithms can predict outcomes and adjust the predictions as new data becomes available. This makes it a useful tool in numerous areas of ITSM such as:

  • Automating service request approvals based on historical data relating to approvals.
  • Supporting incident resolution by predicting incidents and estimating resolution time.
  • Proactively preventing problems by identifying patterns in system malfunctions and downtime, and advising users as to how they can adjust.
  • Supporting asset lifecycle management by predicting performance degradation and automating replacement orders.

Things to Consider for AI-driven ITSM Operations

AI solutions and their output will only be as useful as the data they consume. 

As such, organizations seeking to capitalize on AI for ITSM will require a high-throughput data integration solution that can ensure large quantities of useful, correctly formatted data can be transferred from ITSM solutions and into AI solutions. 

As well as high-throughput, robust data security and reliable data quality are also critical.

Choosing the Right Integration Solution to Enable AI in ITSM

Large data volumes, data quality and security/privacy requirements are all factors organizations should consider before selecting an integration solution. This whitepaper will help you identify what’s right for you.

Choosing the right integration approach to enable AI for ITSM

Choosing the Right Integration Solution to Enable AI in ITSM

Large data volumes, data quality and security/privacy requirements are all factors organizations should consider before selecting an integration solution. This whitepaper will help you identify what’s right for you.

Enabling AISM for ServiceNow Users

While ServiceNow provides some AI-enabled capabilities, many users want more than the platform is equipped to offer. 

As such, they require a data replication and/or integration solution to transfer ServiceNow data into AI solutions, or systems better equipped to feed data into AI solutions.

Avoiding Performance Degradation is Key

The faster an integration solution can transfer useful, quality data, the faster the AI model can process and identify patterns within data and deliver valuable insight in real-time. 

However, traditional, third-party (and often API-based) approaches to integrating ServiceNow have presented a dilemma. The more requests for data and the higher the volume of data requested, the harder ServiceNow has to work to provide it. 

This means third-party integration solutions inevitably reach a point where the volume of integrated data starts to impact the performance of ServiceNow

Since ServiceNow’s operational bandwidth is straining to meet the data demand, this not only disrupts employees working on ServiceNow, but it also disrupts any employee and use case that requires ServiceNow data. 

Queries within the platform and data transfers out of the platform are both slowed – sometimes to an unmanageable extent. 

An Alternative to API that Avoids Performance Degradation

Fortunately, ServiceNow users have an alternative to third-party, API-based integration solutions. A ServiceNow-native data replication and integration solution, Perspectium.

Perspectium is a data replication and integration-as-a-service (IaaS) solution purpose-built for ServiceNow and avoiding the issues associated with extracting data from the platform including performance bottlenecks and data extraction limits. 

By leveraging Perspectium DataSync, users can transfer and replicate over 20 million records per day into external repositories (such as data lakes and or warehouses) and solutions from AI and ML, to reporting, analytics and business intelligence.

The enormous throughput is made possible because of DataSync’s approach to initiating data transfers.

Instead of relying on third-party mechanisms such as API calls, DataSync is installed natively within ServiceNow, leveraging efficient push technology to replicate and transfer data out of the platform. 

Data is initially pushed into the Perspectium Message Broker System (MBS), where it is queued for retrieval – with schema in-tact – by the target system. 

Unlike API, the process does not use the same data layer as ServiceNow users, and because data is retrieved from the MBS, ServiceNow performance remains unimpeded.

Data transfers via the MBS are supported by encryption (both at rest and in transit) and to protect data. The MBS also mitigates against data loss during transfers by preserving data within the queue, even amidst failures within target systems.

Do you want to learn more about how Perspectium facilitates ServiceNow-to-AI solution integrations and enables AISM? Talk to us!

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