Why Your Organization’s AI Adoption Needs Experience Data

Both AI and experience management continue to be hot ITSM trends and are at similar stages of their introduction. IT organizations have considered “the art of the possible” with each ITSM trend and are now seeking to make the trends a reality. However, it’s likely that organizations have already committed more to AI adoption in terms of planning, early exploration, and pilot projects, given the inherent complexity and business demand.

This blog looks at why your organization’s AI adoption needs experience data, starting with the connectivity between these ITSM trends.

Making data-informed decisions on ITSM AI adoption

Hopefully, your IT organization isn’t simply introducing AI-enabled capabilities because it can. Instead, it’s likely tasked with investing in AI-enabled capabilities, including AI-enabled ITSM capabilities, in a way that delivers optimal business benefits. While senior IT personnel might have their own ideas about where AI will help IT and business operations and outcomes most, the people working in other areas of the business will know this best. 

So, when it comes to the investment in AI for better ITSM capabilities, employee experience data is required first to prioritize the available opportunities based on better IT and business operations and outcomes and second to quantify the relative success of AI introduction. These are reflected in the following five ways experience data improves AI adoption.

5 ways in which experience data will improve ITSM AI adoption

It’s not my intention to go into the details of what experience management entails and the associated benefits in this blog. If this insight is needed, please read this blog post – What IT Service Desk Managers Need to Know About Employee Experience.

Instead, the focus is on adopting AI – that it’s not simply a technological-based upgrade to the operational status quo and how experience data will help with the transformational shift that impacts various aspects of IT and business operations and decision-making. 

Your IT organization’s adoption of AI will need experience data for more reasons than you think, starting with ground that we’ve already covered: 

• Understanding and meeting end-user expectations and needs – as already mentioned, experience data offers insight into what end-users think of existing IT services and the issues they have with them (including which of the IT services and issues adversely affect their productivity most). This insight allows your organization to align and prioritize its ITSM AI investments with the areas that will make the most difference to end-users and the business operations they support. It’s a surefire way to ensure that AI capabilities aren’t being implemented for their own sake, with AI solutions designed to address real end-user needs instead.
• Measuring AI’s impact and performance – experience data can be used to prioritize where investments are best made, but we can’t assume that the changes will deliver the expected improvements. Instead, they could degrade end-user experiences. Hence, during and post-implementation, experience data can be used to measure the employee and operational impact of the new AI capabilities. For example, whether the AI improves efficiency and end-user productivity as intended.
• Facilitating planning and change management – experience data can also be used to understand the impact of potential AI-related changes. For example, AI adoption might require changes to workflows, roles, and responsibilities. Here, experience data can help decision-makers understand how the considered changes will likely affect different groups within the organization. Such that organizational change management tools and techniques can be leveraged where needed to minimize disruption and change resistance.
• Enhancing employee enablement – using experience data to assess how well end-users are taking to new AI capabilities not only identifies areas for “course correction” but also where additional training or support is needed. After all, there might not be an issue with the new capabilities but simply the failure to educate end-users on how best to use them.
• Driving continual improvement – AI capabilities will likely be self-learning, designed to evolve and improve over time. Hence, the second bullet’s need applies to this too. Experience data will provide ongoing feedback on any iterative improvements, whether AI- or IT-driven, that will help to ensure that the AI capabilities remain effective.

Hopefully, this blog has helped describe why your IT organization’s AI adoption needs experience data – from prioritizing where to introduce AI capabilities to ensuring that the expected benefits are met. If you would like to find out more about how AI adoption will improve your organization’s ITSM capabilities, please take a look here.