DxContinuum’s customers currently include Adobe, Cisco, Dell and VMware - and has to date mostly focused on predictive analytics using machine learning for CRM, sales and marketing automation.
However, ServiceNow will take these capabilities and use DXContinuum’s data models and apply them across its product portfolio.
ServiceNow started out as a cloud product for IT departments that wanted to improve their service management capabilities. However, it has quickly grown and expanded its offering, as the idea of ‘servitization’ has grown in importance amongst enterprise buyers.
Essentially, any task that is repeatable internally, or externally, could benefit from being streamlined within a platform that not only lets you make requests, but also gives you insight into who owns the task and its progress.
In the past I’ve compared it to Uber - which essentially allows users to make a request, declare their preferences for that request, and follow that request to completion. You can apply that process to endless tasks in a company, tasks which largely currently rely on email chains.
ServiceNow ties back end process to front end engagement, building an internal service management layer that speeds up tasks across the organisation - anything from an IT glitch to HR requests.
The more efficient ServiceNow can make these workflows, building in more intelligence, the more appealing it will be to enterprise buyers that are looking to strip out manual interventions that are costly and slow.
The introduction of DXContinuum sees ServiceNow taking this further, with the company claiming that hundreds and thousands of machine and manual work requests can “now be effectively and automatically categorized and routed” for each customer.
For example, McKinsey researchers recently calculated that 49 percent of time spent on work activities could be automated - this is what ServiceNow is going after. If successful, it could be the backbone of enterprises that are looking to streamline.
In a canned statement, ServiceNow said:
By applying DxContinuum’s machine-learning algorithms to each customer’s unique data set, ServiceNow can train machines on how to route IT, HR, customer service or other requests with a high level of accuracy.
For example, the models could set the category of the inquiry and assign the ticket to the right team, as well as calculate associated risks. When enterprises better predict outcomes and automate actions, they can reduce costs dramatically and speed time-to-resolution.
There is demand
I met with ServiceNow’s chief strategy officer, Dave Wright, in October last year, where he hinted that further automation, AI and machine learning could be expected from the company’s platform over the coming year.
At the time, Wright said that he couldn’t go to a customer meeting without the customer asking what ServiceNow has planned in this area. Wright said:
If you look at all the conversations I’m having at the moment, it’s all around machine learning, artificial intelligence, Then it’s around asking us what we are going to do from a management perspective for all these devices that are now IP addressable. And thirdly, people just want a completely different user experience.
And actually when you start to combine those together, it’s how can you build things like dynamic interfaces? How could you start to have systems that self analyse? If you’ve got the data of how something moves through the system, why couldn’t you use AI to analyse that process and start to say ‘it spends 70% of it’s time in this certain phase, maybe you should automate that phase’. Do you even need that phase? The machine being able to do self introspective is something that will be pretty cool.
We are starting the process of doing that now. It’s cool because [of the platform], we then we have got a learning engine that you can keep on processing that data and re-injecting it back into instances. So then you can start to do things like predictive categorization, which gives you predictive assignments, then you can look at how long it takes you to close those kinds of issues. So these are all things in the pipeline.
The ServiceNow platform already automates and streamlines processes effectively, but the more intelligence it can add in this area, the more appeal it will have to buyers. As Wright noted in October last year, it’s becoming increasingly common to speak to IT leaders that want to know how machine learning can be applied to their processes to gain further efficiencies.
Details are still lacking about whether or not this will simply be built into the platform for all customers to use, or whether it is an additional revenue stream for ServiceNow, but either way I can see it having appeal. The challenge now is for ServiceNow to integrate DXContinuum effectively so that customers see tangible benefits of the machine learning - and that it isn’t a buzzword just used for sales purposes.
We look forward to speaking to and hearing from customers about this one.