Improve Digital Experiences With Observability and AI

From seamless e-commerce to life-critical healthcare apps, every click, tap and interaction reflects on the brand behind the experience. IT and engineering teams work behind the scenes to make sure your brand’s reputation carries through to every digital interaction. With digital experiences becoming more central to how we live and work, IT teams are feeling the pressure to keep systems running without disruptions.

Observability platforms and artificial intelligence (AI), including machine learning (ML), can help your organization improve digital experiences and gain deep insight into the health and behavior of increasingly complex systems.

Observability refers to the ability to measure and understand what’s happening inside a system based on the data it produces, such as logs, metrics and traces — enabling teams to detect and resolve issues quickly. Unlike traditional monitoring, which offers a limited snapshot, observability platforms deliver a unified, actionable view across services, infrastructure and user interactions.

ML takes observability beyond surface-level insights by identifying patterns, predicting anomalies and automating root cause analysis. This empowers developers to become active contributors in shaping exceptional customer experiences while also providing valuable behavioral insights to teams focused on the broader customer journey.

Nearly 75% of IT outages could have been prevented with earlier detection, according to the Uptime Institute. Yet, many IT teams are still on call, waiting for alerts or user complaints before acting. Instead of reacting to outages, teams can transition to a predictive model featuring real-time anomaly detection, automated root cause analysis and facilitate self-healing responses.

Issues like sudden login failures or payment processing delays rarely occur without early signs. AI-powered observability can detect subtle changes in user behavior, like increasing response times or growing error rates on a checkout application programming interface (API), and then flag them before they cascade into outages. With machine learning analyzing these trends, the platform can trigger alerts, initiate automated responses like rerouting traffic or even scale infrastructure preemptively.

The result? Fewer disruptions, faster recovery and teams focused on innovation rather than firefighting.

Customers who enjoy a company’s digital services and whose expectations are met are more likely to engage with the brand, stay loyal and recommend it. Organizations across various industries have made it their mission to continuously improve the customer experience through innovative digital experiences and, as a result, are seeing how this directly affects the success of their business.

AI-driven incident response helps organizations detect anomalies, uncover root causes and take corrective action before users ever notice an issue. This approach maintains uptime, protects customer experiences and helps build brand trust and digital security.

By identifying performance issues like slow API calls or misconfigured systems, real-time insights enable teams to resolve performance issues fast. The same speed applies to emerging security threats, like suspicious logins or potential breaches, without waiting for manual intervention to keep experiences seamless.

Use case: A financial institution uses AI to detect failed login attempts on Azure Active Directory over a five-minute interval, triggering a security review and preemptively locking accounts to avoid breaches.

AI-powered root cause analysis (RCA) uses trace-based insights to pinpoint failures, accelerate recovery times and help avoid finger-pointing between teams. When your organization knows the exact source of an issue, whether it’s a failed extract, transform and load (ETL) job in a data pipeline, a software bug traced through logs and code or an infrastructure problem like a server outage or memory leak, they can move beyond guesswork to deliver fast resolutions before the issue turns into something bigger.

Use case: A healthcare company uses RCA to identify excessive hops per route in a network monitored by Cisco Meraki and SolarWinds, leading to faster diagnosis and improved application speed for patient portals.

Digital systems run on data, but when data pipelines break down, everything from insights to outcomes comes down with it.

Lost in the noise of busy systems, a single issue can spiral and lead to performance problems or data loss. Observability allows you to trace back through logs, replay events and uncover the root issue before it impacts the larger system. Whether it’s monitoring jobs for accuracy, spotting anomalies in model training, feature engineering or ensuring control across the entire pipeline, observability ensures teams can trust their data.

Use case: A retail analytics company implements a unified observability pipeline to detect data drift and model training failures before they impact weekly product recommendation rollouts.

Deploying a model is just the start. Organizations need models to remain accurate, fair and reliable as real users interact with them. Ongoing performance monitoring helps track metrics like precision, recall and F1-score, ensuring that the model’s predictions remain trustworthy.

Model accuracy isn’t just a concern for data scientists; it directly affects customer experiences, whether it’s personalized content recommendations or fraud detection systems. Even small improvements in model speed can have a huge business impact.

Use case: Sellers of private mortgage insurance found that reducing quote delivery time by just 10 milliseconds led to a 20% increase in conversion rates —a true testament to how AI/ML with observability can directly affect revenue growth.

Implementing AI-driven observability comes with challenges. From fragmented toolsets and siloed data to undefined processes and vendor lock-in, organizations often struggle getting started. CDW offers comprehensive observability services, from assessments and architecture planning to telemetry integration and platform deployment.

Our observability maturity model guides you through each phase of transformation, from basic monitoring to predictive, business-aligned insights. With deep expertise in multi-cloud and hybrid environments, strategic workshops and proven methodologies, CDW helps you overcome obstacles and achieve a smarter, insight-driven operations model.

Need help getting started? CDW’s Lab-as-a-Service (LaaS) offerings and Proof of Concept services (POC) allow your team to explore observability use cases and test integrations before going all-in.

Traditional monitoring tools look at telemetry to flag application issues or outages but understanding how applications perform from a user perspective allows teams to dig deeper and improve their applications beyond issue remediation to improve customer experiences and help achieve broader organizational goals.

Every delay, error or outage impacts customer perception and trust. Observability means businesses can track customer interactions across various digital touchpoints, gaining insights into how customers engage with digital systems, identifying pain points and working to craft processes designed to delight customers.

In healthcare, real-time monitoring of connected devices like heart monitors helps protect patient safety by identifying failures before they escalate. In retail, a broken loyalty card system can mean missed savings for customers, damaging both the customer experience and the brand’s reputation for helping customers get the most value for their money. With AI-driven observability, these issues can be detected before they lead to customer complaints.

By implementing observability across every touchpoint, your teams can take part in more meaningful work, like helping align technology with the overall business goals and transforming the customer experience.