The steps to take before AI embeds ServiceNow challenges

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Key Takeaways

ServiceNow has evolved into an essential operating system for businesses, offering a low-code platform that allows rapid feature development and app creation, but this can lead to increased demand and potential bottlenecks.

Enterprises face significant challenges with inconsistencies across ServiceNow environments, leading to backlogs and a high manual workload for tech teams, which hinders their ability to meet business demands efficiently.

The introduction of AI into ServiceNow workflows holds promise for enhanced efficiency and automation, but without addressing existing backlog issues and ensuring data integrity, businesses risk compounding their problems rather than solving them.

Since its founding twenty years ago, ServiceNow has grown to become the operating system for running a business. Companies love it because no matter what they need to do, ServiceNow likely has a module for it. Whether the focus is customer, employee, tech or operations, ServiceNow has it covered.

Its low-code platform allows businesses to create features and build apps that extend their functionality. That means, in theory, enterprises can take something off-the-shelf and use it as they require very quickly.

The speed of development means that value can be realized rapidly. It’s the dream technology, the company says what it wants, and it can be done.

Expectation versus reality

Or at least, that’s the expectation. What often happens is that enterprises realize the potential and start demanding more and more from the platform. This puts their ServiceNow teams in a bind; requests for new apps and features must move through a delivery pipeline from development to production.

If companies don’t have the skills, capabilities and capacity to manage this explosion in demand, they could well throttle the potential impact, creating bottlenecks.

It’s not just a resource issue, either. To create something in ServiceNow, there are different environments, also known as instances. The same project will require different instances for development, quality assurance, infrastructure, etc.

But running multiple instances simultaneously wasn’t something the platform was initially designed for. That’s why, anecdotally, the chief issue for enterprises is inconsistencies between ServiceNow environments; time and time again, they are named the most significant inhibitor to delivery output.

As such, a backlog grows. To get around this, enterprise tech teams try and clone instances, but this often creates new problems; it’s a highly manual, time-consuming process with a propensity for errors. Therefore, teams have to spend time troubleshooting to fix mistakes and developing and delivering.

So, what should be a fantastic way for ServiceNow teams to deliver against business objectives evolves into another instance of non-tech functions expecting instance service and the departments tasked with meeting demand struggling to match expectations.

The advent of AI

Worryingly, it could get worse before it gets better. Why? Because artificial intelligence is coming to ServiceNow.

Again, the theory makes sense; recent AI announcements and partnerships target the user experience with promises of enhanced efficiency, streamlined processes and advanced predictive capabilities.

Tasks such as automatic ticket routing, intelligent virtual agents, AI-powered development, predictive incident management and proactive issue detection are all possible.

And it’s all based on ServiceNow’s huge volumes of customer data. It’s seen what the repetitive workflows are and it’s integrating AI into those areas to make it much more efficient for businesses. It should be a quantum leap forward, making the platform even more valuable to enterprises.

Embedding and accelerating existing problems

But what happens if we add AI to the current backlog issues? We get an explosion in demand and that growth brings intelligent automation. So rather than just problems, enterprises are now dealing with challenges that duplicate and expand rapidly as AI tries to figure out what teams are trying to do and comes up with multiple bad conclusions.

This puts the onus on ServiceNow teams to ensure their processes and data are honed, with no margin for error, before introducing AI-powered functionality.

Fixing the backlog with four focus areas

Put another way, they must fix their underlying backlog issues before they supercharge them.

How do they do that? They need to develop an approach that combines four core points, multi-environment visibility, controlled environment synchronisation, zero-touch deployments and enhanced governance.

Multi-environment visibility is knowing where things are and what version they are up to is critical to keeping projects on track, eliminating duplication of effort and ensuring that everyone is on the same page, no matter where the feature or app is in the development cycle.

The second core point is all about controlled environment synchronisation. Part of the problem with having multiple instances is that different versions of the same feature or app end up in circulation, and changes made in one instance are not automatically carried across to others. Communicating change constantly means that no matter the adjustment, it is translated across all other environments. So developers aren’t working with outdated libraries, QA teams aren’t reviewing wrong versions, and everyone can trust they have the latest iteration.

Zero-touch deployments keep developers focused on where they can add value rather than on tedious, mundane work such as mixing and matching spreadsheets filled with update sets, troubleshooting conflicting updates, and spending time on production releases.

The final core point is about enhanced governance. With so many moving parts, knowing what’s been changed and when and being able to provide a clear audit trail can be challenging. With better visibility and constant communication, ServiceNow teams can remain compliant, with full records to support development governance.

Realising value

ServiceNow has significant value to offer a wide range of enterprises, and with its integration of AI into workflows, demand could well explode. Yet businesses that do not get their house in order could find that rather than driving significant benefits, these new additions only embed and accelerate existing problems.

By deploying an approach that prioritises visibility and communication, businesses can free up their ServiceNow teams to focus on value-add activities, moving away from the troubleshooting that comes with trying to circumvent multi-environment issues manually.

In doing so, they can lay the groundwork for clearing their backlogs and harnessing AI’s full power on the ServiceNow platform.