The Impact of AI on EUC: User Experience and Productivity
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It seems like everywhere we look AI is being touted as being added to one solution or another. AI in your email, AI in your hard drive, AI in your toaster! But where is AI really being seen and used directly by end-users or for end-users in the business environment? There are a lot of examples being given around AI being used by developers to write code, AI to create website content, or generate images. However, where is AI showing up in the daily life of a "typical" office end user and how is it improving their work, productivity and experience? Here are a few examples of how AI is impacting end-users:
The use of AI in shadow IT
To start things off, most, if not all, organizations will find AI being used by their users, whether officially supported by IT or not. Shadow IT refers to the use of unauthorized or unapproved software, tools, or systems within an organization, often bypassing the official IT department's oversight. When combined with AI, shadow IT introduces a unique set of effects on end users, both positive and negative.
Because many initial AI projects were more behind-the-scenes integrations, users began adopting AI into their daily workflows on their own. Bypassing slow internal processes to gain immediate access to advanced AI tools to enhance their productivity and speed up workflows before they were offered by IT. And even if offered by IT, users still may choose unofficial AI tools that better fit their specific needs, especially when the IT-approved solutions are limited or inadequate.
However, most user-initiated AI use results in data silos where important information is isolated in different platforms and outside the organization's central systems. This often makes it difficult to share data between teams, or if teams use different AI tools for the same problem, it may even lead to results from different tools that are incompatible or contradictory.
The largest risk of AI shadow IT to an organization is the risk of company data being exported outside of the organization's policies and controls. This can lead to data breaches, leakage of sensitive information, exposure to malware, or even violations of industry regulations such as GDPR, HIPAA or CCPA. It can also result in legal liabilities and penalties, especially if sensitive customer or business data is involved.
This is why the goal should be to have AI tools that are available and easy to use for users to accomplish their normal tasks. They must be competitive with external AI platforms and integrate seamlessly with common business processes, all while maintaining the security of the data and results within the organization.
Improving user workflows with AI integration in business applications
The good news is that AI is becoming part of almost all IT services deployed within organizations today. Some may provide actual meaningful improvement to the end-user workflows, whereas others are more of a bolt-on to say that they have AI capabilities.
AI can be integrated with common IT platforms to personalize user experiences by analyzing individual job roles, behaviors, and interactions. A company home page may customize the content feed to tailor to each individual end user to prioritize what they use the most. This level of personalization can enhance user satisfaction by ensuring individual users receive relevant and engaging content that aligns with their own interests and needs.
One of the most common use cases seen in typical productivity applications is their recommended actions. AI built into these tools suggests actions like replying to emails, scheduling meetings, or correcting grammar, helping users save time, be more efficient and improve communications.
Using a combination of AI and job roles, corporate communications such as email or push notifications can be limited to only those users who actually need it. For example, instead of company-wide service impact notification, AI can be used to customize the push to only users who have logged into the affected application in the last seven days and then to re-push the notice any time a user attempts to access the application. This strategy can reduce the communication fatigue that often causes users to ignore most, if not all, corporate messages.
Another impactful AI innovation for many business users is an AI summary assistant integrated into modern meeting platforms. These assistants listen in on a meeting and provide useful information after the call including attendance, summary of main speakers, key talking points and any action items. This was sometimes assigned to a user in the past but typically did not occur at all, which resulted in inconsistent details between meetings. Now, with AI, there can be a consistent record for all meetings where it is used.
To truly impact end-user workflows, AI, when implemented, should augment existing workflows by streamlining common tasks, reducing the "noise" of communications, or even completely replacing what was formerly a manual task by automating it.
The impact of AI on user experience
AI can have either a positive impact on a user's experience and perception of a company and its IT systems or a negative impact. Often, the difference between these is in how they are deployed. AI solutions that are deployed that make the user feel empowered and are proactive with the end-user reduce digital friction and are perceived as improving their experience.
AI is often deployed with a virtual assistant or "chatbot" to provide self-service to end users. The chatbot, when trained properly, can handle common IT or service-related queries, which reduces the workload for human support agents while speeding up response times for the end-users. Additionally, many organizations are leveraging the virtual assistant for a "universal search" across company resources to help users quickly find answers from a single place. A common complaint for new hires is not knowing where to go to ask questions about things such as HR policies, request access to new applications or help with device problems.
The chatbot can be used as the primary place for all users to go which helps improve the experience for both their new and existing users by spending less time searching for an answer. Or even just never asking because it was "too hard" and complaining about it to peers and friends.
Behind the scenes, AI integrates as part of digital employee experience (DEX) strategy and tooling for the teams that support those end-users. Digital Experience Monitoring (DEM) solutions utilize AI to help IT teams identify and prioritize system issues affecting end-user experience by offering faster root-cause solutions and automated remediation. The solutions help build a DEX score that tracks whether user experience is improving or decreasing.
As the AI analyzes the issues and trends across devices it can help rapidly bring to IT's attention trends across their users or even how they compare to industry peers. AI further enhances these tools to help build impactful dashboards and automations using natural language prompts for a no-code / low-code experience.
When DEM solutions and AI assistants are integrated, they can be used to proactively send users notifications at the time that a system or application issue is observed. They can provide options to the end user right at the point of impact to initiate a scripted self-service remediation or to automate opening a service desk ticket on behalf of the user, attaching all of the relevant information and logs. Not only does this proactively let the user know that IT is reaching out to help with their issue, but it also gives them the ability to immediately solve the problem with minimal effort on their part.
As a final impact on end-user experience, many AI features are moving to process more locally on the users' endpoint. This has the benefit of speeding up response times, lowering network usage, and decreasing data center costs. All great benefits to IT, however this comes at the cost to the user device by putting a higher load on their system potentially impacting other applications and their experience. Devices that are designed as an AI PC typically have neural processors and higher spec CPU/GPU/RAM/Disk that optimize this experience. However, these are not widely deployed yet due to availability and cost, so they are typically only deployed for small pilot use cases. For the typical user device, most local AI processing utilizes the existing resources and is best implemented alongside a digital experience monitoring tool to help ensure that users' experience is not negatively impacted or if they need an upgrade.
More resources on the impact of AI on EUC
If you would like to learn more about the impact that AI is having in End-User Computing (EUC), check out these other articles in this series: