The IT service community is rightly buzzing about the wealth of possibilities AI offers to automate service management operations and better assist customers — especially in this age of expanding Everywhere Work. But without sound data, the path to leveraging AI for IT service management becomes quite daunting.

With so many more people working outside a traditional office setting, more end users than ever are logging in more often to access employee services. According to Ivanti research, all this complexity affects IT significantly: 39% of IT professionals report too many logins, 47% report too many digital notifications and 42% report using too many tools/platforms.

So AI-driven service management is a welcome tool to help IT personnel serve their end users. Sound data for AI is the bedrock of such a solution.

Organizations thrive on accurate data for AI. Without it, your service teams can only be reactive. That means you have manual processes in the organization — and they are taking a ton of time away from people who should be focusing on outcomes within that organization.

Not only that, but poor data means your service resolutions are inaccurate because the information you have doesn’t match up to the right resolution. The result? Users are unable to get back to work because of slower ticket resolution. Mismatched data is also a hazard when information is transferred from one specialist to another within an organization.

Good data, bad data, no data

Data is the fuel on which AI runs. A data-savvy organization uses data to define a problem and ensure that it goes to the right process and speeds your resolution.
Conversely, an organization without well-managed data for AI has tons of blind spots. Not only does that increase that organization’s security risk, but it also drives up the cost of the service desk. Manual processes are costly; they require human input, and you have no way to control you spend. That type of organization also probably doesn’t know what software or hardware it has and is likely going to overspend there as well.

In IT operations especially, accurate and complete data for AI is critical, because AI requires significant training. If you don't have the right data, your AI is not going to give you the right answers. And those data needs are continuous as well. It's not enough to have a single data source feeding your AI. You constantly have to feed it with the right data so it can detect changes over time and react appropriately and differently.

Conflicting data within some data sources is another challenge you must resolve. As you bring data into a single data pool that you can learn from, you have to ensure that that data is reconciled.

Ultimately, all these hurdles slow digital transformation within the organization. Visibility into and accuracy of the data is vital to the success of any AI investment. AI needs to be trusted. If it's making recommendations and predictions — even suggestions about how you resolve issues — trust in those outputs is vital. If you leave this unaddressed — if the accuracy of your data for AI is not correct and the predictions are not correct — it's really going to impact your return on investment and hinder digital transformation in your organization.

The first 4 steps to implementing AI

If you are considering how to leverage AI for service management in your organization, you must address some critical initial steps:

  • Eliminate blind spots: Remove the data gaps within your company by getting better visibility into your data assets, then monitoring those assets. Collecting and understanding the data that drives AI is a continual process — this isn’t a one-time deal. You have to continually ensure that you have the necessary data feeds coming into your AI solutions.
  • Remove data silos: To achieve more powerful outcomes from the data you acquire, you should not have just a single view of an asset. For a fuller, more rounded view of an asset, you must combine your data silos. Those include your asset inventory, asset management software inventory, data center inventory service maps, tickets, information, knowledge bases and monitoring logs. Commonality exists among all these types of data — which is one of the reasons organizations are turning to the power of platforms that provide data seamlessly across different applications.
  • Protect privacy: Ensure that your data doesn't include sensitive or personal information in it, as that can be baked into the predictions and outcomes your AI delivers.
  • Create data governance: Many organizations are crafting governance frameworks, policies and processes to set a standard and guidelines for how they use data — and they’re including AI data governance in that process.

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