Leverage artificial intelligence, machine learning, and automation on your ServiceNow platform
Employee experience is a critical component to on the job satisfaction, employee retention, and in the end, customer satisfaction. When employees need support from their internal teams in HR, finance, or IT they’re hoping for frictionless service that gets them the information they need, fast.
Employees reaching out for support can experience longer wait times created by human error on problem categorization, routing, and prioritization. On the flip side, help desk employees are often tasked with repetitive activities. Everyone suffers when critical systems fail, or staffing is not sufficient for peak service times because there’s no system in place to predict need or potential crisis. Internal teams can struggle to provide support quickly when they’re working with siloed data, disjointed legacy systems, and manual processes.
Platforms like ServiceNow help organizations create digital workflows to automate manual processes and increase productivity. ServiceNow integrates data from disparate service onto a single cloud platform (the Now Platform) on predesigned and custom workflows.
The goal of ServiceNow is simple: simplify complex workflows on a single cloud platform.
While many organizations are leveraging ServiceNow workflows to improve processes, they’re not taking full advantage of the native functionality of the platform. The next layer of automation leverages AI and machine learning, using existing data to automate case management and improve the quality of service. ServiceNow’s Agent Intelligence uses AI and machine learning to accelerate service resolution time. The machine learning algorithms recognize patterns in your data that humans miss, including specific context and language based on roles or departments.
Agent Intelligence helps organizations:
Categorize and route–Employee requests are assessed, categorized and automatically routed to the correct team to solve specific challenges. Service teams won’t have to manually triage request, saving time on both sides of the support equation.
Prioritize - High priority and business critical problems are bumped to the top of the queue, they don’t have to wait for manual prioritization by busy humans.
Decrease resolution time – Agent intelligence surfaces similar cases, helping agents solve problems faster and reuse existing material.
Predicts incidents – By recognizing patterns in past and current incidents, Agent Intelligence helps predict problems and make recommendations for critical actions.
Increase productivity – With AI handling manual tasks, employees can focus on higher-value or complicated tasks.
Scale – Help your internal teams scale as your overall organization, and its demands, grow.
Integrate Agent Intelligence in your cloud ServiceNow workflows:
As a native application, Agent Intelligence can be enabled to support several standard and custom steps in your management process . It’s designed to integrate with the data you have, making machine learning accessible across your organization.
Overall, Agent Intelligence uses supervised machine learning to automate prediction fields and trigger the correct actions. The AI in Agent Intelligence uses natural language processing (NLP) to parse incoming information; the similarity framework doesn’t require an exact word match to categorize the task.
Launching AI and machine learning requires careful attention to the business use case, data, and the algorithm. Best practices for machine learning include:
1. Clearly identify the root cause you’d like to solve. Agent Intelligence can be applied to OOB solutions and custom workflows but should be targeted to a clear business case.
2. Check and prep your data. To accurately train your model you need to have a robust historical data set. The size of the data set depends on the specific problem you’re trying to solve. Data should be both accurate and clean before you proceed. Select the right filters, remove inactive categories, and identify which cases will support your overall goal.
3. Choose your model. Select (or build) the right model to solve your specific problem.
4. Train your model. Use part of your historical data to train your model and test with the rest.
5. Optimize parameters. Once your solution is trained, tune to optimize for precision or prediction.
6. Launch – then reevaluate. Machine learning solutions need to change and evolve with your business needs. As you evaluate results your models will need to evolve over time to remain at the forefront of your business.
Automation through digital transformation is poised to unlock productivity across organizations. “Intelligent automation”, using machine learning to categorize, route, predict, and prioritize, amplifies the effect. People can work faster and smarter to get work done intelligently.
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Ben Kaely is a software architect at Logic20/20. He is a hands-on engineering leader who revels in bringing clarity to solutions with software and people.
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