For years, people have approached the need to scale support (or do more with less) by implementing self-service technology. They throw a few knowledge articles on a service portal, add the ability to log a ticket, and think they’re done. While the service portal is a key component of a holistic support approach, this is not the answer!
The Key: Solving Problems At Scale
The answer is not changing employee or end-user behavior; it’s changing how IT performs support. Managing problems at scale means taking a proactive approach to maintenance and incident management. It reduces the number of incidents experienced by end users and the load on IT. This relatively new strategic approach to scaling support leverages automation to perform manual tasks and to alert technicians to proactive work needed to avoid incidents. Unlike problem management, solving problems at scale means maintaining all assets at their expected configuration with all patches and software updates, and using artificial intelligence to determine which errors found by monitoring systems could impact users down to the user-device level.
Some examples of this include:
- Policy-driven, automated patch management: today’s class of patch management tools enables organizations to establish patch management policies by device class and at the individual device level. So when a set of patches must be deployed to all Windows servers, the deployment will begin automatically after testing, using policies to manage approved exceptions.
- Device health registers: Using a combination of automated patch management and monitoring that leverage machine learning algorithms, dashboards can be established that indicates devices falling below a particular health grade.
- Automated ticket logging: When an issue on a device is causing imminent failure, an incident ticket can be opened and assigned to the appropriate technician for resolution. In many cases, this may occur before the user has noticed anything more than the need to reboot more often or performance issues.
The difference between solving problems at scale and problem management is that while problem management looks for the root cause, solving problems at scale uses the results of problem management to power the machine learning algorithms that, combined with monitoring, enable mountains of data to be analyzed, looking for potential known issues before they impact the users. This is a use case where AI can help IT organizations improve and scale support in a way that humans never can.
Unlike scaling support by driving employees or customers to service portals to log tickets, scaling support with proactive management offers an excellent customer experience as it can virtually eliminate downtime.
Adopting a support-at-scale strategy has several foundational items that must be achieved and may require the integration of several existing tools and the potential of adding tools. It’s good to know what these are before beginning:
Discovery, asset, and configuration management are key! Completing these activities achieves two critical business outcomes:
- Successful security vulnerability and incident management
- Ability to proactively manage every device in the enterprise
ITSM tool needs predictive analytics or artificial intelligence capabilities to determine and calculate device health scores based on the following:
- Patches and software updates in compliance
- Updated anti-virus software signatures
- Errors detected by monitoring systems
Integration with workflow engines to kick off remote repairs or generate tickets to technicians for more serious conditions.
A mature patch management practice. As this work is policy-driven, policies that govern testing and deployment must be in place.
The Business Value of a Support-At-Scale Strategy
The business value of this approach is simple: reduced user downtime. While organizations have focused on service downtime for many years, they’ve only recently raised the bar on managing end-user downtime.
Looking at the finances, in an organization that can eliminate 50 end-user incidents a day, there are several layers of savings possible:
- Cost of service desk support: Contact reduction of only half this number represents a $625/day savings in service desk support.
- Lost productivity time: Every hour of downtime eliminated would save $3,250 in lost productivity time per day at a blended staff rate of $65.
Organizations can use this to build a business case, but it’s important to proactively open tickets representing every activity taken to manage the environment. This enables the organization to report results in downtime reductions and savings.