Artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are some of the hottest new technologies in IT service management. These technologies help companies streamline service management by automating business process and tasks within the ITSM framework.
Adobe provides a shining example. The creativity software maker has used AI, ML and NLP to help “change the dynamic within ITSM to allow for a better level of service to whoever that end customer is and to change the role of the ITSM professional to work on higher-level tasks instead of just ticket reduction,” says Cynthia Stoddard, senior vice president and CIO at Adobe.
The company’s intelligence-enabled ITSM makeover has helped Adobe not only support customer-facing digital media services but also improve productivity and efficiency inside the organization. Thanks to AI, ML and NLP, Adobe has improved ITSM processes, reduced errors and streamlined service management while also eliminating mundane and repetitive tasks for IT workers.
Here’s an inside look at Adobe’s shift to intelligent ITSM.
Eliminating repetitive tasks
When Adobe launched its ITSM overhaul, its first goal was to reduce its ticketing queue levels. By relying more on AI and ML, the company could “eliminate toil” for its staff, as ticketing systems can quickly become time consuming and repetitive for IT workers, Stoddard says.
This represents a common theme for companies modernizing ITSM these days. Technologies such as AI, ML, NLP and robotic process automation (RPA) provide ample opportunity for organizations to automate and streamline ITSM work.
“[They are] really about looking for problem patterns and repetitive tasks — because if you think about the discipline of ITSM, it’s all about organizing the work people do within operations and taking ITSM to the next level by saying, how do we change the type of work people are doing?” Stoddard says.
For Adobe, that meant using AI to find commonalities, trends and quick fixes in the ticketing system that could be addressed with scripts. Getting these tickets out of the queue faster has helped IT workers focus on issues that can’t be solved with AI. Now that Adobe’s IT teams aren’t bogged down by an overloaded ticketing system, they can spend more time writing scripts to help streamline service management.
“We’ve been trying to position our engineers and operations staff to do more of the scripting and higher-level work, versus what I would call more of the ‘traditional ticket workers,’” says Stoddard.
Building a self-healing framework
Adobe’s second goal for AI in ITSM was to develop a “self-healing framework.” This was born out of a desire to go beyond just automating repetitive tasks, but to find “operational issues and remediate them” to improve “time to recovery,” says Stoddard.
“We took it to the next level, and we said, ‘Things tend to break in any IT operation when you’re running services,’ so what we tried to do is look at — if they break — how can we automate it?” she says.
In some instances, the self-healing platform can find operational issues and automatically solve them, while also providing detailed data and stats to the IT team. In instances where the platform can’t heal itself, it can alert IT workers to issues faster and improve the time to recovery.
Adobe has a lot of “back shops that feed data across the organization to a lot of different places,” and when one of those systems failed, it used to take around 30 minutes to fix it. Since implementing the self-healing framework, Stoddard says the time from detecting a problem to fixing it and returning to service is down to 3 minutes.
Investing in talent
One of the ways Adobe has been successful with adopting AI, ML and NLP in its ITSM framework is by investing in talent. Adobe focuses on hiring outside candidates with the latest skills and training current IT employees inside the organization.
“AI amplifies what you could do as a human but the human activities don’t go away — in some respects, you’re actually creating opportunity for people,” says Stoddard.
Adobe has hired specific AI and ML talent, noting that recent graduates who come into the organization with open minds have brought some great ideas to the table. Stoddard credits this to how these young workers can apply “their new knowledge with no blinders” to real-life problems.
Stoddard has also focused on training internal employees and seasoned IT workers who already have an intricate knowledge of the company’s networks and systems. The company has rolled out a six-month technical AI and ML training program to over 5,000 engineers. It’s designed to “bring alive the data science component of each engineer.”
And it’s this mix of talent and freedom to experiment that created the self-healing platform to begin with.
“What we’ve found is that if you give people the time to learn and experiment, then they definitely can come up with ideas. So that’s what we’ve done, internally.”