One of the biggest issues any organization is going to struggle with, especially during challenging economic times, is determining the most efficient way possible to achieve a specific digital business transformation initiative goal. Most organizations are eager to both improve customer experiences and make employees more efficient, but the cost of acquiring all the platforms and applications required can be daunting.
In fact, after several years of investing in various digital business transformation initiatives, many organizations are realizing the total cost of maintaining multiple platforms and applications is becoming cost prohibitive.
ServiceNow has been making a case for an alternative approach that this week it expanded with the release of a Utah update to its Now software-as-a-service (SaaS) platform that among other capabilities adds process optimization to identify inefficiencies with multiple classes of workflows that are surfaced by machine learning algorithms.
In addition, ServiceNow has added ServiceNow Impact tools that make it easier to create dashboards that track the status of digital business transformation initiatives.
There’s also now a search capability that employs natural language and machine learning algorithms that makes it easier to surface customer service issues.
At the core of the ServiceNow strategy is a common data model that is employed across all the applications running on the Now platform. That approach substantially reduces the total cost of digital business transformation initiatives by eliminating the need to integrate separate business process management (BPM) applications, integration frameworks, process mining tools and low-code application environments, says Amit Saxena, general manager and vice president of Automation Engine for ServiceNow.
It also streamlines backend management processes because the same platform that is widely employed for IT service management (ITSM) can be extended to address a wide range of applications using the same data model, he adds. That allows IT teams that are ultimately responsible for managing applications to use a platform they already employ to manage IT processes, noted Saxena. “It enables hyperautomation using the same data model,” he says.
It also lays the foundation for applying AI more effectively using that same data model, adds Saxena. Instead of requiring data engineers to aggregate data in a way that data scientists can employ to train AI models, most of the data required is already made accessible via a single platform, he says.
It’s not clear to what degree an uncertain economy will drive digital business transformation leaders to revisit their strategies but there’s clearly a lot more pressure to deliver more results faster. The days when individual business units might invest in applications that in the final analysis create yet another island of automation are likely to be reconsidered as organizations look to reduce the total cost of IT, notes Saxena.
Platforms such as Now, of course, make extensive use of principles defined by the ITIL framework that define a more structured approach to delivering application services. It may take a little longer to get that application deployed but they provide the benefit of a proven model for delivering IT services at scale. The issue now is determining how and when to apply that model to digital business transformation initiatives in a way that delivers a more consistent set of predictable results that over time will continue to scale.