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How to Implement AI Into Your IT Service Management

Trent Waskey
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“AI adoption in IT service management can delight users, drive organizational productivity, and accelerate digital business initiatives.”

IT leaders and CIOs have adopted IT Service Management (ITSM) to simplify many of the tasks that most IT departments face. As they look to improve their processes, many are keen to throw AI into the mix. The problem is, not all AIs are equal, and implementing them incorrectly can lead to companies wasting money or not extracting the maximum value.

AI is the future of ITSM

Many companies are starting to invest in automating their ITSM, either with AI or Robotic Process Automation (RPA). When trying to build and deliver next-generation services, the aim should be to deliver a seamless support experience that offers users the ability to:

  • Interact with support across common communication channels (ex: Microsoft Teams, SMS, email, and phone)
  • Use self-service tools to resolve issues on their own
  • Have a unified search and knowledge portal across the business

With AI, it’s possible to offer all of this through a conversational interface where users can interact naturally and have requests resolved in seconds. This greatly reduces the workload on the service desk, allowing the IT team to work more efficiently and give the CIO options to invest those resources elsewhere. 

So how would an IT organization go about properly integrating AI into their service management?

Step 1: Start with clearly defined goals

As with any well-managed IT project, the key is to start with a clear definition of the requirements and success criteria. For example, here are a few common goals relating to improving the IT service desk:

  • Auto-resolve XX% of tickets with AI
  • Reduce the mean time to repair (MTTR) by XX%
  • Achieve a XX% ROI
  • Reduce service desk costs by XX%

By establishing these goals up front, this aligns all of the project stakeholders from Service Desk Manager up to CIO. When it comes time for budget and approvals, it should significantly speed up the process when there are measurable outcomes at stake.

Step 2: Engage with partners

There’s a good chance your organization doesn’t have a team of data scientists and software engineers waiting idle to build and implement an AI solution at a whim. In that case you will need to look externally.

Engaging with external partners, be they managed services providers (MSP) or specialist vendors, can provide domain knowledge, resources, and frameworks that can drive the project to success.

For companies wanting to build a world-class AI powered support experience, you can check out Cloud MSG (that’s us!). We’re a next-generation managed services provider that combines an AI service platform, a fully managed helpdesk, and end-to-end consulting services to ensure the application works perfectly for your business.

Step 3: Run a proof-of-concept (POC)

After partnering with a new client, we always recommend running a proof-of-concept to see how the AI would perform with real-world historical company data. This is a relatively simple operation, and usually only takes about a week to gather data and process it.

Ingesting Data

If you partner with Cloud MSG, we run a simple operation to ingest your existing ticketing history from apps such as SAP, ServiceNow, BMC, ZenDesk, JIRA service desk, or SFDC. Where native integration is not available, we can also manually export the data and ingest it into the platform that way. Similarly, connecting to your knowledge services, internal wikis, or other commonly used external knowledge sources is just as easy. This will provide the detail the AI platform needs to contextually understand and answer questions.

Progress Check-Ins

At this point, a check in can be held to understand progress and any issues is part of the process. We will help to identify any problem areas within the application, such as spurious data or invalid responses to questions. Lastly, a final progress check will ensure that all features are operational and that the robotic process automation (RPA) services fulfil their objectives to ensure a high level of automation and good-quality AI service.

Executive Sign-Off

Using the information from a successful test the CIO or IT team can make a presentation to the executive team for final sign off, highlighting the immediate and longer term benefits of automation and self-service, powered by AI. 

Step 4: Deploy Your AI-powered ITSM

Given final approval for deployment, the procurement process for vendors can conclude. Rollout should be planned with key departments gaining access first to provide a layer of live testing to prove that everything is battle tested.

While that would be the end point for legacy applications, with AI better quality information will surface regularly as the system continues to learn. So, constant monitoring of results and quality will be key to ensuring users are getting what they expect and that the business gets value for money.

Final Thoughts

AI adoption in IT service management can delight users, drive organizational productivity, and accelerate digital business initiatives. Having a clear set of goals, competent partners with domain expertise, and a well tested system will all but guarantee a successful rollout and adoption. 

If you’re looking to partner with a next-generation AI managed services provider, we’d love to talk to you!

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