Revolutionizing ServiceNow with Predictive Intelligence

Person using AI tool at job

AI and ML are here to complement and not necessarily replace humans as long as humans learn to utilize the new capabilities and increase productivity!

Significance of AI/ML in ServiceNow

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized various industries, including IT service management. ServiceNow, a leading provider of cloud-based IT service management (ITSM) and business process automation solutions, has embraced AI and ML technologies to enhance its platform’s capabilities. Below are some of the points to show how significant AI/ML has become for ServiceNow.

• Automation and Workflow Enhancement: AI and ML can be used to automate repetitive tasks and streamline workflows. For example, AI-powered chatbots can handle routine service desk inquiries, freeing up human agents to focus on more complex issues.
• Predictive Analytics: ML algorithms can analyze historical data to make predictions about future events, such as service outages or equipment failures. This helps in proactive problem resolution and minimizes downtime.
• Intelligent Routing: AI can intelligently route service requests and incidents to the most appropriate personnel based on factors like skills, workload, and historical performance, improving efficiency and customer satisfaction.
• Natural Language Processing (NLP): NLP can be used in chatbots and virtual agents to understand and respond to natural language queries. This enhances the user experience and reduces the burden on human agents.
• Recommendation Engines: AI-powered recommendation engines can suggest solutions or articles to users based on their past behavior and the context of their current issue. This can help users find answers more quickly.
• Asset Management and Optimization: ML can assist in managing and optimizing IT assets by predicting when hardware and software upgrades are needed, ensuring compliance, and reducing costs.
• Incident Resolution: ML algorithms can help identify patterns in incident data and suggest resolutions based on historical solutions, reducing the mean time to resolution (MTTR).
• Security: AI can be useful to detect security threats in real-time which can enable security teams to respond in time. ML models can analyze network traffic and user behavior to identify anomalies that may indicate a security breach.
• Performance Analytics: AI and ML can provide insights into the performance of IT services and infrastructure, helping organizations make data-driven decisions to improve efficiency and user satisfaction.
• Customization and Personalization: AI can be used to customize the ServiceNow platform for individual users or departments, providing a more tailored and efficient experience.
• Capacity Planning: ML can assist in capacity planning by analyzing historical data to predict future resource needs, ensuring that services are always available and responsive.
• Cost Optimization: AI can help organizations optimize their IT spending by identifying areas where resources can be reallocated or services can be consolidated.

In this article, we will explore the current state of Predictive Intelligence in ServiceNow and discuss how it is transforming the way organizations manage and deliver their services.

Current Challenges

Below are the major challenges being faced in ServiceNow currently, which Predictive intelligence aims to resolve:

• No assistance for the support team: Because of the lack of assistance in terms of available knowledge articles related to the issue or any existing resolved issues to refer to, agents end up spending a lot of time resolving the issue, thus leading to delays in the resolution.
• Delayed resolution of customer issues due to incorrect mapping: Often, the assignment groups are not correctly mapped to the issue. Also, the priority of the issue is not correctly mapped. This leads to tickets taking a lot of time to be mapped to the correct group and priority, and thus the overall time needed to resolve the issue increases.
• Difficulty in identifying issues that may cause outage/downtime in the future: No mechanism available to identify if the current issues, although less frequent, may become a major issue in the long term and cause a service disruption/outage.

Predictive Intelligence — Solution use cases

Below are the 4 frameworks available in ServiceNow for Predictive intelligence:

• Classification framework: The classification framework helps to set the field values using machine learning algorithms. For example, you can identify and set the value of Incident category, priority, assignment group, etc., based on the short description of the incident at the time of record creation.

Classification Framework

• Similarity framework: With the similarity framework we can train similarity solutions using the company’s historical ticket data. So, as soon as there is a new incoming ticket, the agent can quickly look up similar resolved incidents in the past and can refer to the resolution provided for the same. This way, the new incident can be quickly resolved. In the example below, the agent can see similar incidents resolved in the past and thus be able to quickly identify the problem and provide a resolution.

Agent Assist

• Clustering framework: Data is divided into categories through clustering, which can subsequently be utilized to spot patterns. Then, you can handle records collectively or identify data gaps. For instance, you can group together related new incidents to identify a major outage in the future.
• Regression framework: With the help of regression, a machine-learning framework, you can use historical data to forecast numerical outputs like the temperature or the price of a stock. Success can be quantified directly using regression models. Regression can be used, for instance, to calculate how long it will take to resolve an incident or a case. In the example below, based on the short description provided by the user, the system predicted the expected resolution time

Regression Framework

Benefits

Below are some of the main benefits of using Predictive intelligence in ServiceNow:

• Improved Efficiency: Predictive intelligence helps automate repetitive tasks and predicts potential issues before they occur. These efficiency gains come from automating workflows, reducing manual intervention, and ensuring that resources are utilized effectively.
• Enhanced Service Delivery: By predicting issues or customer needs before they arise, ServiceNow can proactively address these concerns. This proactive approach improves overall service delivery and customer satisfaction by preventing problems or addressing them swiftly.
• Cost Savings: Predictive intelligence can help organizations optimize their resources by forecasting demand and automating resource allocation. This optimization leads to cost savings by ensuring that resources are used efficiently and effectively.
• Better Decision Making: Predictive analytics in ServiceNow can analyze historical data and patterns to provide valuable insights. This data-driven decision-making helps organizations make informed choices about their services, resources, and strategies.
• Reduced Downtime: Predictive maintenance and issue prediction can help prevent unplanned downtime. By identifying potential problems before they escalate, businesses can take preventive actions, minimizing disruptions to operations.
• Enhanced Customer Experience: Predictive intelligence allows organizations to understand customer behavior and preferences. This knowledge enables personalized and proactive customer interactions, leading to an improved customer experience.
• Increased Productivity: By automating routine tasks and providing insights, predictive intelligence frees up employees’ time. Staff can focus on more strategic tasks that require creativity, critical thinking, and problem-solving skills, ultimately increasing overall productivity.
• Optimized Workflows: Predictive intelligence can analyze workflow patterns and suggest optimizations. By streamlining workflows based on predictive insights, organizations can ensure that tasks are completed in the most efficient and effective manner.
• Risk Mitigation: Predictive analytics can identify potential risks and security threats. By proactively addressing these risks, organizations can enhance their security posture and reduce the likelihood of security breaches.
• Scalability: Predictive intelligence in ServiceNow is scalable, meaning it can handle large volumes of data and provide insights for organizations of various sizes. This scalability ensures that businesses can continue to benefit from predictive analytics as they grow.

In summary, predictive intelligence in ServiceNow empowers organizations to deliver better services, optimize their processes, reduce costs, and enhance overall efficiency and customer satisfaction. By leveraging predictive analytics, businesses can stay ahead of the curve and make data-driven decisions that drive success.

Limitations

Predictive intelligence in ServiceNow, like in many other platforms, comes with its own set of limitations. Below are some of the common limitations:

• Data Quality: Predictive intelligence relies heavily on data. If the data within your ServiceNow instance is incomplete, inaccurate, or outdated, it can lead to poor predictions and recommendations.
• Data Privacy and Security: Handling sensitive data in ServiceNow requires strict adherence to data privacy and security regulations like GDPR or HIPAA. Predictive intelligence must comply with these regulations, which can limit the types of data that can be used and how it can be processed.
• Data Volume and Variety: To build accurate predictive models, you need a large and diverse dataset. In some cases, organizations may not have enough historical data to make reliable predictions.
• Model Accuracy: Predictive models may not always be accurate. They are based on historical data and assumptions, which may not hold true in the future. This can lead to incorrect predictions and recommendations, potentially affecting decision-making.
• Interpretability: Complex predictive models can be challenging to interpret. Users may not understand how a recommendation or prediction was generated, which can reduce trust in the system.
• Bias and Fairness: Predictive models can inherit biases present in the training data. This can result in biased predictions or recommendations, leading to unfair outcomes, especially in areas like HR or customer service.
• Implementation Complexity: Integrating predictive intelligence into ServiceNow can be technically challenging. It requires expertise in machine learning and software development.
• Maintenance and Retraining: Predictive models need regular maintenance and retraining to stay accurate. This demands ongoing effort and resources.
• Change Management: Employees may resist adopting predictive intelligence if they perceive it as a threat to their job security or decision-making authority.
• Cost: Developing, deploying, and maintaining predictive models can be costly. Smaller organizations with limited resources may find it challenging to implement predictive intelligence effectively.
• Performance: Running predictive models in real-time can be computationally intensive and may affect the performance of ServiceNow, especially during peak usage periods.
• Lack of Domain Expertise: Effective predictive modeling often requires domain expertise to ensure that the models are designed and deployed correctly. This can be a limitation if the organization lacks the necessary expertise.

To mitigate these limitations, organizations should carefully plan and execute their predictive intelligence initiatives, invest in data quality, transparency, and fairness, and continually monitor and update their models to ensure they provide value while minimizing potential drawbacks.

Conclusion

AI and ML technologies have become integral to ServiceNow’s platform, enhancing its capabilities across various areas of IT service management and business process automation. Predictive intelligence cannot currently eliminate the need for human intervention. However, it certainly frees the IT agent from the tedium and drudgery of their work and allows them to focus more on their company’s IT efforts. Early use of predictive intelligence provides companies with a huge opportunity to take their ITSM process to a higher level of maturity.

Reference

ServiceNow Product Documentation on Predictive Intelligence