How AI Changes End-User Experience Optimization and Can Reinvent IT

Originally published by CXO Revolutionaries.

Written by Christopher Jablonski, Director, CXO REvolutionaries & Community.

Improving the user experience is a top priority as businesses adapt to hybrid work, increase usage of SaaS applications, and new business demands. Everyone — employees, partners, and customers — seems to expect the digital world to operate flawlessly. But from an IT perspective, it’s a daily struggle. The good news is that solutions are emerging that use AI to identify issues faster, triage more efficiently, and create a frictionless user experience.

The challenges of today’s IT environments

In modern IT environments, applications move from traditional data centers to the cloud. A hybrid workforce uses different devices to access these applications. IT teams juggle many devices, networks, and applications in this landscape while ensuring a positive and reliable user experience.

Traditional monitoring tools designed for static data centers fail to provide the visibility and agility required today. The lack of end-to-end visibility over user experiences can lead to prolonged troubleshooting, a never-ending stream of help-desk tickets, and frustrated users. Ensuring a consistent experience in fragmented environments requires innovative solutions that provide comprehensive insights and rapid response capabilities.

Using AI for enhanced IT operations and efficiency

AI-driven digital experience monitoring (DEM) solutions can help you manage and optimize digital experiences. They let your IT teams quickly find patterns and unusual things by processing real-time data.

For example, if a significant number of users report slow performance on a specific application, AI can correlate this with network traffic data, recent software updates, or external factors like regional internet outages. This holistic approach enables faster and more accurate problem resolution, leading to a more efficient support team and a better user experience. If every member of an IT team were to have access to such a tool, they could become instant service and support experts.

Teams can use AI to automatically detect network anomalies, suggest potential fixes, and even begin remediation actions. This proactive approach ensures issues are addressed before they escalate, maintaining high user satisfaction and reduced workloads for support teams.

AI-driven solutions also improve the overall efficiency of IT teams by streamlining workflows and reducing manual intervention. The ability to interact with the system using natural language prompts allows IT staff to quickly gather information and make informed decisions, further contributing to productivity gains. These solutions use large language models (LLMs) to get data from time-series metrics, like web probes, device events, process statistics, and hundreds of others. This helps you to analyze problems faster and more accurately.

Automating routine tasks and providing precise diagnostics frees up resources across compute, network, security, and application teams, translating into more cost savings.

Real-world impact of AI+DEM in action:

• A sudden spike in help-desk tickets related to poor application performance: Pinpointing the root cause of slow-to-respond applications is often complicated and time-consuming. With AI, however, the process becomes streamlined and efficient. An IT administrator can ask: “What is causing the slowdown in our Microsoft Teams performance?” AI analyzes data from various sources, identifies the root cause — be it network latency, device issues, or application glitches — and provides actionable insights that help resolve issues.
• A global enterprise with a distributed workforce that has deployed a new application across multiple regions: While users in, say, Europe report excellent performance, those in Asia experience substantial delays. Hosted monitoring services that operate continuously across multiple worldwide vantage points can help IT teams observe and benchmark network performance, giving them a comprehensive overview of their network's health and activity. They can also configure probes for specific locations and groups, such as business-critical applications in finance or operations. AI can analyze the network paths, server response times, and local device metrics to identify the bottlenecks affecting some users and not others. These insights enable you to implement targeted improvements, such as optimizing routing or increasing server capacity in specific regions.
• Frequent video conference connectivity issues: Traditional troubleshooting for this issue involves multiple teams and can take a ton of time. AI enables IT administrators to ask specific questions about network performance, device status, and application health. The AI assistant does a detailed analysis, finding the cause and suggesting quick actions like changing network settings or updating software. While AI streamlines data processing and automated responses, human teams handle higher-level analysis, decision-making, and strategy.

A future-ready approach to digital experience

AI’s role in DEM will continue to grow. In the near future, we can expect to see AI assistants with built-in predictive analytics to anticipate issues and integrate with other AI systems for a holistic IT view. Staying at the forefront of these developments ensures organizations remain agile, competitive, and prepared for digital challenges.

AI-based user experience optimization has transitioned from a futuristic concept to a vital present-day reality. Using AI in IT operations can help you save money and work better. It can also make users happier and reduce downtime. By using the capabilities of AI while freeing up human ingenuity to handle bigger challenges, businesses can create robust and adaptive IT environments that deliver exceptional user experiences.