ServiceNow Universal Request: When and How to Put it to Work

A new feature designed to enable cross-departmental collaboration, the ServiceNow Universal Request application can save many organizations time and money—but only if implemented correctly. To understand how the Universal Request tool works, consider this basic example.

Many end users go to a portal website with questions and requests. But of about 10,000 people submitting queries on the website daily, 1,000 are requests that either go to the wrong place or don’t fit any of the site’s existing routing options. Perhaps it's a request that spans departments, is poorly worded, or is outside the usual flow. Organizations can scale to the point that these ‘outliers’ demand a dedicated solution.

These organizations can place a Universal Request widget on the portal as a sort of “catch-all” for queries any 1,000 people can use to submit their inquiries. Universal Request is powered by predictive intelligence, which reads each issue and matches it with what users have asked many times before, allowing the widget to match the user to the correct department to solve their problem.

The widget weeds out several tickets, but not for issues that predictive intelligence can’t sort into an existing box. ServiceNow’s Universal Request widget uses trigger words in the ticket and natural language processing (NLP) to make suggestions and automate requests to departments. For example, it may respond: “This sounds like a technical problem, here’s the IT guidance people with similar issues have viewed before,” linking to the correct form, or “This sounds like a procurement issue, here is the form people have needed when they have said this before,” linking to the correct resource.

Best use cases for Universal Request

ServiceNow created this new feature to help break down interdepartmental silos and foster cooperation. Universal Request allows for cross-departmental teamwork on tickets.

For example, say ServiceNow generates a Universal Request and automatically assigns it to HR. The HR team realizes as they work on this ticket that they need the help of the IT team to solve the issue. Universal Request allows any team to see and work on the ticket at once.

Another excellent example of this use case is onboarding. A hiring manager may tag the HR team to set up employees on payroll and benefits, while the IT team shares the same ticket information to ensure new hires receive computers and other equipment when they start. The Facilities team can be tagged to ensure new hires have building access passes. If there are any changes, say the new employee can start a week early, the HR, IT, and Facilities teams are immediately alerted.

Because it is cross-functional, Universal Request is also an excellent place for questions and issues that need a safety net because they don’t have clear, single-department answers. So, it can come at the end of an automated agent flow that handles the most common questions and backstop tickets where there is an explicit need for cooperation, coordination, and multiple departments.

The educational sector is a classic use case for Universal Request because the end users (students, prospective students) are typically particular about what they are looking for. A predictive algorithm that looks at prior inquiries helps interpret specialist tickets. These tickets typically languish because they are so specific and challenging to route to a human agent with the necessary domain expertise. That’s no problem for an algorithm — it’s a strength.

But any company can benefit from Universal Request. It comes down to how willing your end-users are to put in the work to give the system a complete description of their requests since that is the data the predictive algorithm uses to make its recommendations. The dataset may not yield an accurate recommendation if the user requests are poorly worded or incomplete.

We should also touch on the timeline and implementation needs for Universal Request while we’re discussing where it’s a fit. The good news is that Universal Request is included within the ITSM subscription, so organizations that have already purchased ITSM also have Universal Request.

It requires configuration, however, and the timing varies depending on the goals. Broadly speaking, if the goal for the Universal Request widget is a way for multiple teams to work on one request simultaneously, that is a fairly standard setup and would take a month or less. That’s because it relies more on workflow definition than a robust corpus of request data to analyze.

But the timing depends heavily on data quality to implement the predictive intelligence piece as a safety net for the most complex tickets. Until the system can achieve a confidence index of at least 80%, for example, it will not perform as well as a human agent. Namely, manually looking at the tickets will be more accurate and yield a better outcome than the algorithm below that 80% threshold.

The challenge of data quality

Users who provide data about a vague issue can confuse the system. For example, a user might ask, “Where is the salary form?” as they look for an IRS W-4 form. But the organization’s HR portal is home to multiple forms, many with “salary” in the title. The amount and quality of the data the system has will largely determine the approach to this query.

Historically for this company, it may be that most employees are looking for the W-4 form, but others may be looking for a W-2 or W-9, so they may need to be given the correct information.

If the data set is poor, one solution is encouraging users to be more robust and detailed in their service requests. There may be more mandatory fields or additional stages to the request tickets to clarify their intent. Mandatory fields are suitable for the data quality but put more burden on the user.

The other solution is to train the predictive intelligence model to improve at catching even vague requests. Some feature engineering is available in Universal Request to improve the confidence index of a correct recommendation. But it’s relatively limited, as you’ll see below.

Capture more data and streamline the process

How many records does it take to finetune a predictive intelligence model enough to produce accurate and confident results every time? This is a good question, but it isn’t easy to answer. The reality is that between 10,000 to 300,000 data records may be needed — depending on how good the data is.

Even more than 300,000 poor-quality records will not yield a good recommendation — whereas 10,000 accurate, full-bodied case descriptions that have been correctly assigned will allow Universal Request to produce far better results.

To unpack this further, it helps to understand a little more about how Universal Request works. There are three predictive intelligence models in the ServiceNow world, and two are relevant here: classification and similarity.

For example, if an end user writes a Universal Request like the one above about the salary form, the system will parse the “Where is the salary form?” language. It may discover that the most often pairing in queries in the past has been between “salary form” and “W4” and that all of these requests went to the HR service desk.

If it can identify this kind of pattern, it may resolve the request that way. It may also identify knowledge articles with “W4” and “salary form” in the title and ask the end user if that resolves their issue.

Although much of the Universal Request solution’s accuracy rests on existing data quality, our experts can finetune the predictive intelligence model. Typically, 20 to 30% remains for improvement.

For example, a training model test of the phrase “W4 salary form” routes the user to the IT help desk and the correct form with a confidence threshold of 68%. Depending on the situation, you might want to recalibrate the system to see 68% as a positive result. In other words, based on the data you have from the “W4 salary form” query, the system will direct users to that result, but the system will need more information from the user below that threshold.

Positive interactions between users and virtual agents are another feature that can be overlooked. Technically, these sessions are a much better way for users to refine their search — because they are responding to questions they weren’t aware they needed to answer. This way, virtual agents can serve as the go-to, honing each query to be much more specific. That might help to resolve them, or it will give richer data to Universal Request as the backup.

The Universal Request process is an excellent step for organizations already using ServiceNow, burning many human employee hours on amorphous requests and tickets. Let virtual agents refine these tickets and assign them out before pooling them all as unresolved and in need of human eyes.

We can also help by parsing out the proper implementation combination of a virtual agent, AI search, and Universal Request. Using our previous example, if a user enters “W4 salary form” in the search box, AI search can autocomplete with the proper tax form based on responses to similar queries — reducing frustration and eliminating tickets while also building up the data set of good outcomes for predictive intelligence to use on other queries.

Another option is to create a “genius result” for searches that aren’t intuitive for end users — for example, one in which AI consistently fails to suggest the correct result in the top 10. Back to our “Where is the salary form?” example from above, end users are often confused about whether they need the W4, W2, or W9 because all three come up. Where the organization knows that searchers using this specific term are looking for a particular result, in this case, the W4, they can pin it to the top of the search box results, eliminating an obvious, repeated source of frustration — and an ongoing source of tickets.

NTT DATA can help you tune your existing system to file tickets exactly where they need to be and ensure the tools function as a stacked, interactive ticket funnel with the agent out front and the Universal Request widget as the final stop.

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