From Experience Management to Experience Engineering: How AI Is Reshaping DEX in IT’s Next Great Transformation

Over the past decade, the digital workplace has evolved steadily, but the current wave—driven by generative and agentic AI—is a rapid and fundamental shift. In 2023 and 2024, generative AI grabbed headlines as organizations experimented with pilots. By 2026, we’re seeing AI move beyond answering questions to executing work, ushering in a new operating model for the digital employee experience (DEX).

This shift is about more than just new tools or services; it’s about redesigning organizations and connecting digital workplace efforts directly to business outcomes. As with previous IT transformations—from client-server to SaaS, from fixed desktops to hybrid work, from on-premises to cloud—the impact is profound. But this time, the change targets the very nature of operational work in IT.

DEX is now on a journey similar to what infrastructure and application operations have undergone: moving from reactive support to measurement, to reliability engineering, and now toward autonomy. This journey requires new definitions of value, new service models, and a tighter link between digital workplace outcomes and business results.

DEX is now on a journey similar to what infrastructure and application operations have undergone: moving from reactive support to measurement, to reliability engineering, and now toward autonomy.

Redefining Value in the Digital Workplace

The digital workplace is always evolving. New tools emerge, platforms mature, and operating models are updated. Still, real progress is measured not by improved metrics alone, but by delivering meaningful business value. Earlier, outsourcing focused on lowering costs through standardization—”your mess for less”—in response to complex, sprawling IT environments.

However, as hybrid work became the norm, collaboration tools became essential, device diversity grew, SaaS applications updated unexpectedly, and employee expectations rose. Cost savings alone are no longer enough; value has shifted toward creating better outcomes with less friction.

As a result, service metrics evolved. The focus moved from traditional SLAs (tracking response times and ticket closures) to XLAs (experience-level agreements), measuring factors like login speed, app stability, and disruption frequency. This happened because leaders recognized that digital friction has a cost—not just in helpdesk expenses, but in lost productivity, delayed decisions, employee frustration, and even lost revenue.

Now, experience in the workplace is something to be engineered, not just observed. AI accelerates this shift, moving DEX from visibility to autonomy. AI contextualizes data, predicts potential problems, explains root causes, and can initiate remediation automatically—turning XLAs from static scorecards into real-time, predictive guides for action. The goal is to move from “observe and react” to “anticipate and prevent.”

What Desktop Support Teaches You About Outcomes (And Why This Moment Feels Familiar)

In desktop support, the hardest issues are often those that seem random, with multiple root causes and fixes that may not work consistently. For years, EUC (end-user computing) leaders have faced the challenge of manual triage, often spending more time on diagnosis than on prevention.

Modern digital workplaces are dynamic, with constantly changing devices, networks, apps, and security policies. AI can help by reducing the cost of sense-making and orchestration, but reaping the benefits requires evolving the operating model as well as the technology.

From XLAs to SLOs: The New Language of Reliability for Experience

One major change is that XLAs are becoming more like SLOs (service level objectives). While XLAs forced organizations to pay attention to user experience, many programs failed because they were treated as mere measurement overlays. SLOs, from the reliability engineering discipline, are more than targets—they’re frameworks for tradeoffs, continuous measurement, and investment based on error budgets and business priorities.

While XLAs forced organizations to pay attention to user experience, many programs failed because they were treated as mere measurement overlays. SLOs, from the reliability engineering discipline, are more than targets—they’re frameworks for tradeoffs, continuous measurement, and investment based on error budgets and business priorities.

Applying SLO thinking to DEX means setting concrete, business-relevant experience targets for different user groups (e.g., “95% of contact center logins must complete in under X seconds during business hours”). This bridges technology services with business outcomes, connecting EUC with SRE (site reliability engineering) concepts.

Why EUC Is Adopting SRE-Like Frameworks (And Why That Is a Good Thing)

In the recently published Gartner research note “Adopt Site Reliability Engineering Principles to Get Digital Workplace Operations AI-Ready“, analyst Stuart Downes wrote that heads of I&O must adopt SRE principles to boost reliability, cut costs, and enable AI automation. We agree. Adopting its principles makes sense as complexity outpaces manual operations, just like in DevOps. In DEX, SRE-like practices include:

1. Defining business-critical experience objectives: Not all metrics matter; focus on those that do.
2. Instrumenting context: Move from subjective ticket reports to evidence-based telemetry and baselines.
3. Reducing toil: Automate repetitive diagnosis and create runbooks for what can’t be automated.
4. Running blameless learning loops: Treat systemic issues as learning opportunities to prevent recurrences.
5. Engineering for change: In a world of constant updates, detect regressions early, isolate issues, and remediate safely.

In short, EUC is progressing from support to operations to engineering, with AI accelerating the shift and raising the bar for clear objectives, trusted data, and safe automation.

Agentic AI and the Rise of “Autonomous IT”

GenAI enabled conversational interfaces, but agentic AI goes further—turning intent into action. “Autonomous IT” (or “headless IT”) means remediation no longer depends on human navigation through siloed tools. Orchestrated workflows, guided by policies and validated by outcomes, execute work automatically.

Current examples include systems that automatically validate and resolve Wi-Fi issues, distinguish between endpoint and network outages, or contain hardware-specific performance regressions before tickets spike. The digital workplace is evolving into an autonomous system.

To enable this, organizations need:

1. A trusted experience data foundation: High-fidelity telemetry and clean baselines for AI to reason over.
2. A workflow layer that takes action: Orchestration, APIs, automation platforms, and cross-tool integrations.
3. Governance and safety mechanisms: Policy-driven, auditable, and observable actions with appropriate oversight.

The Service Model Shift: From Labor to Leverage

AI is changing the economics of managed workplace delivery. Previously, value was tied to labor; in the XLA era, outcomes mattered more, but labor remained central. In the agentic era, value comes from leverage—delivering better outcomes without scaling headcount in lockstep with device numbers, app complexity, or ticket volume. This requires investment in data and automation, shared objectives, and mature mapping of business outcomes to digital experience. The move from XLA to SLO provides a clearer contract for what matters and a way to prioritize work rationally.

What Modern EUC and DEX Leaders Should Do Next

For EUC, digital workplace, or DEX leaders, here are the recommended actions:

1. Choose experience SLOs: Identify key workflows and user groups, define measurable objectives that link to business outcomes.
2. Strengthen data credibility: Build trust in telemetry sources, baselines, and explanations for variance.
3. Design for orchestration: Prepare for cross-tool, automated operations with clean interfaces and safe automation.
4. Build governance for agentic work: Decide what can be automated end-to-end, what needs human review, and what must be blocked, ensuring auditability and feedback.
5. Invest in change detection: Early detection and containment of regressions prevents ticket storms.
6. Treat your service model as a partnership for outcomes: Align objectives and mechanisms with service providers, focusing on reliability and improvement over time.

The digital workplace now behaves like a dynamic, interconnected software system, and operational discipline must evolve accordingly.

Closing: A Transformation Worth Getting Right

As highlighted in Gartner’s Digital Workplace Summit agenda, generative and agentic AI are moving from pilots to broad adoption. Success will require better governance, data quality, and AI skills. The digital workplace is where these changes become real; when AI can both diagnose and remediate issues safely, DEX shifts from reporting to engineering advantage.

The winning organizations will build strong data foundations, adopt reliability-focused operations, and create safe, autonomous workflows that minimize friction and drive better business outcomes.

Ready to Explore DEX Engineering?

Connect with us at Gartner Digital Workplace Summit in San Diego (March 24-25) and London (April 27-28). Use priority code PCC26EDC to save $400 on registration.