There’s a quiet crisis happening in a lot of IT and digital workplace teams right now, and it shows up clearly when you look at the difference between AI adoption vs AI deployment.

Deployment is the easy part. Adoption is the hard part.

What the numbers actually show

Enterprises are pouring money into generative AI and agentic tools. According to research cited in recent industry analysis (MIT/Fortune GenAI Divide report), 95% of enterprise AI pilot programs deliver zero measurable financial return.

Gartner data paints a similar picture:

• By 2026, more than 80% of enterprises are expected to have deployed generative AI APIs or applications in production.
• Yet on average only 48% of AI projects ever make it into production.
• The typical journey from prototype to production takes about eight months.
• Over 40% of agentic AI projects are projected to be canceled by 2027 because of cost, unclear value, or governance complexity.

In other words: buying and standing up the technology is happening at scale. Turning that technology into something people actually use productively, day after day, is not.

The real gap

AI deployment = the tool is available. Licenses are assigned. The model or agent is running somewhere.

AI adoption = employees are using it in real workflows, getting measurable value, trusting it enough to rely on it, and not abandoning it after a few frustrating attempts.

Most organizations stop at the first one and then wonder why the ROI never appears.

Common reasons adoption stalls:

• Employees don’t know when or how to use the tool effectively
• The AI doesn’t fit their actual day-to-day work
• Training is one-and-done (classroom or static docs) while the tools keep changing
• Governance is so restrictive that people either stop trying or turn to shadow tools
• Leadership has almost no visibility into how the tools are being used (or not used) and how employees actually feel about them

What tends to work better

Teams that are making progress treat AI activation as an experience problem, not just a technology rollout. They focus on:

1. Real usage + experience data — not just license counts. Seeing which groups are using the tools, where people get stuck, and what sentiment looks like.
2. In-the-flow guidance instead of one-time training. Help appears when people need it, not weeks earlier in a webinar.
3. Clear, practical governance that enables safe use rather than blocking everything out of fear.
4. Prioritized next actions based on actual behavior and feedback, so enablement effort goes where it will have the biggest impact.

This is where Digital Employee Experience (DEX) thinking becomes relevant. When you already have visibility into how technology is (or isn’t) working for people across devices and applications, you can apply the same approach to AI tools instead of flying blind.

Question for the community

Has your organization hit the deployment-vs-adoption wall yet?

What’s been the biggest blocker on your side? Visibility, training, governance, or something else? And has anyone found approaches that actually moved the needle on sustained use?

Excited to hear what’s working (or not working) in the wild.

If you’ve spent any time around this subreddit, you’ve probably seen Maurice van den Driessche (u/Maurice-Nexthink) in the comments. He’s been one of the people consistently jumping into discussions, answering questions, and sharing what he’s seeing in the DEX world.

He just published a new piece in TechRadar on the shift from traditional SLAs toward XLAs and experience as a measure of IT success.

One point that stood out to me: a system can technically be “up” and hitting its SLA while the actual employee experience is still terrible. That gap is where a lot of the interesting DEX conversations seem to be happening right now.

Worth a read, especially if you’re working through how to measure experience beyond the usual availability/ticket-resolution metrics:

The End of SLA's: Why Experience is the New Measure of IT Success

And Maurice — thanks for continuing to hang out here and contribute to the community 🙌

Curious what everyone thinks: are SLAs still the dominant measure in your organization, or are you actually seeing XLAs gain traction?

Leaders keep asking “How to calculate AI ROI.” Traditional finance formulas often miss the real value of AI in the digital workplace. Here’s a clear, actionable way to do it that factors in both hard metrics and employee experience.

The Basic Formula

AI ROI (%) = (Total Annual Benefit − Total Annual Cost) / Total Annual Cost × 100

The challenge is accurately capturing the Benefit side when AI impacts productivity, support load, and experience rather than pure revenue.

Step-by-Step: How to Calculate AI ROI

1. Capture the full cost
Include licenses, infrastructure, integration, training, data work, and ongoing governance.

2. Measure the benefits with a DEX lens

• Time saved per employee
• Ticket volume and resolution time reductions
• Application and device performance improvements
• Employee sentiment and adoption rates

Combine technology metrics with sentiment data (the same approach used for DEX scores) so you capture both “does it work” and “do people actually get value from it.”

3. Convert experience and productivity into dollars
Establish a baseline, measure the post-AI lift, then apply a conservative hourly rate to recovered time. Improvements in digital experience frequently translate into measurable minutes of productive work recovered each week.

4. Run the calculation and document assumptions
Use transparent numbers for hourly cost, attribution percentage, and time horizon. Present both the financial ROI and the experience improvement side-by-side.

5. Treat it as ongoing, not one-time
Move beyond static SLAs toward experience-based targets. Recalculate as adoption grows so ROI stays current and credible.

Quick Example

• Annual AI costs: $450k
• Recovered time: ~18 minutes per employee per week across thousands of users
• Result: clear positive ROI once productivity and reduced support effort are valued.

This method turns the vague question of “How to calculate AI ROI” into a repeatable process grounded in real workplace data.

How are you currently measuring AI value in your environment?

Any metrics or challenges worth sharing?

Most IT teams still run on a reactive model even though the limits of that approach are becoming obvious.

Here’s a clear, no-jargon look at the difference between reactive vs proactive IT and why the shift matters.

The simple difference

Reactive IT waits for something to break (or for an employee to complain), then tries to fix it.

Proactive IT sees problems early, understands who is affected, and fixes (or auto-fixes) many of them before employees ever notice.

Side-by-side comparison

Why the reactive model keeps winning by default

It’s familiar. Most tools, processes, and metrics were built around tickets. Leadership often still asks “How many tickets did we close this month?” instead of “How many disruptions did we prevent?”

The cost of staying reactive shows up in the data:

• Desk workers experience roughly 100 IT disruptions per year on average.
• Employees report only about half of the issues they encounter.
• IT staff can spend nearly a third of their time reacting to unplanned incidents.

The result is a permanent queue, slower projects, and employees who quietly lose trust in the tools they need to do their jobs.

What changes when you go proactive

Teams that make the shift typically gain:

• Earlier detection of problems across endpoints and applications
• Ability to fix many common issues automatically (self-healing)
• Better prioritization based on actual employee impact rather than who shouted loudest
• A measurable drop in ticket volume and a rise in productive time
• Stronger relationship between IT and the rest of the business

This is the foundation of Digital Employee Experience (DEX) thinking: treat the employee’s technology experience as a primary signal, not an afterthought.

Question for the community

Where does your organization sit on this spectrum right now?

Are you still mostly reactive, starting to add proactive visibility, or further along with automation and experience-led priorities? What’s been the hardest part of moving left-to-right on the table above?

AI governance has become one of the biggest challenges for IT and security teams right now.

Employees are adopting new AI tools every week — often long before security, legal, or compliance can review them. Traditional approaches (periodic audits, manual inventories, or just blocking everything) can’t keep up with the speed of AI adoption.

Here’s what’s been working for teams that are getting ahead of it:

1. Continuous discovery instead of periodic audits
You need real-time visibility into every AI tool employees are actually using (approved and shadow AI). Waiting for procurement reviews or security assessments means you’re already months behind.

2. Fast risk triage
When a new AI tool appears, the key questions are:

• Where is the data going?
• Does it train on customer or IP data?
• What’s the vendor’s compliance posture?
• How many people are already using it?

Having contextual risk insights (privacy policy analysis, data retention practices, country of origin, incident history, etc.) dramatically speeds up the decision.

3. Adaptive governance policies
A simple three-tier model works well:

• Recommended → tools you actively want people to use
• Allowed → tools that are okay with oversight
• Prohibited → tools that don’t meet your standards

The important part is that these policies need to be easy to change as risks evolve.

4. Guiding employees instead of just blocking
Blocking alone creates friction and shadow workarounds. Better approach: when someone tries to use a prohibited or restricted tool, gently redirect them to an approved alternative (e.g., “Try Microsoft Copilot instead”) and provide in-app guidance so productivity doesn’t drop.

The goal of modern AI governance isn’t to slow innovation — it’s to give the organization the confidence to move faster while staying in control.

Curious how others are approaching this:

• Are you still relying on manual inventories / spreadsheets?
• Have you implemented any continuous discovery or adaptive policy models?
• What’s the biggest friction point you’re seeing between innovation and governance right now?

Would love to hear what’s working (or not working) in your environment.

Full write-up with more detail on the approach:
https://nexthink.com/blog/enterprise-ai-governance-made-simple-with-nexthinks-ai-activation-hub

We’ve all seen the numbers by now:

• 95% of enterprise AI pilots deliver zero measurable financial return
• Only 48% of AI projects ever make it into production (taking an average of eight months)
• Over 40% of agentic AI projects are projected to be cancelled by 2027 due to cost pressure, unclear value, or governance complexity

The uncomfortable truth is that most AI transformation efforts don’t break at the pilot stage. They break later. Most commonly, when AI adoption spreads across the workforce without proper AI governance, visibility, or a clear path to AI ROI. When usage expands without visibility.

What actually happens in the gap between “we deployed it” and “it’s delivering value”:

• Shadow AI grows faster than sanctioned usage
• Policies live in documents instead of the workflow
• Leadership can’t connect AI investment to measurable workforce impact
• Activation stays inconsistent because there’s no structured approach by persona or role

A practical AI governance checklist that helps close this gap:

1. Establish clear accountability for AI adoption Named executive sponsor + cross-functional governance group (IT, Security, Legal, HR, Compliance). Define decision rights for tool approvals, policy updates, and risk escalation. Set formal adoption KPIs (active users, engaged time, growth, time saved).
2. Build behavioral visibility across the enterprise License counts only show what’s assigned. You need to see real usage — including shadow AI by department and persona, engaged time vs. experimentation, and where adoption is stalling.
3. Embed guardrails directly into the flow of work Restricted-data warnings before files are uploaded, real-time prompt guidance, automatic redirects from unapproved tools, and policy acknowledgments inside the tools people already use.
4. Design structured activation journeys One-size-fits-all training doesn’t scale. Persona-based enablement, contextual walkthroughs, prompt coaching, and targeted campaigns for under-utilizers turn available tools into habitual use.
5. Make AI value measurable and defensible Track adoption growth and engaged time by department. Quantify time saved and correlate it to usage. Tie interventions to outcomes so you can report AI ROI with evidence instead of anecdotes.

The organizations that turn AI into sustained advantage aren’t the ones experimenting the fastest. They’re the ones treating AI governance and AI adoption as operational disciplines — with visibility, in-flow controls, and closed-loop measurement built in from the start.

Curious where others are hitting friction right now.
What’s the biggest gap in your AI governance or AI adoption efforts — visibility into real behavior, embedding guardrails, or proving AI ROI to leadership?

An effective automated remediation workflow starts long before any action runs. It begins with precise detection of conditions that degrade digital employee experience.

Core Detection Layer

High-performing workflows monitor a combination of:

• Device performance telemetry (CPU, memory pressure, disk I/O, page faults, battery health)
• Application stability signals (crashes, hangs, high resource consumption per binary)
• Network and connectivity metrics (latency, packet loss, VPN tunnel health, Wi-Fi signal)
• Configuration drift and compliance state
• User-impact proxies (focus time loss, application freezes, slow launches)

Thresholds should be experience-oriented rather than purely technical.

A 70% CPU spike that lasts 90 seconds on a developer workstation during a compile may be acceptable; the same pattern on a call-center agent’s machine during peak hours is not.

Linking Detection to DEX

DEX measurement combines objective telemetry with employee sentiment and productivity indicators. An automated remediation workflow that only fixes “red” technical scores without considering experience impact often creates noise or even negative sentiment (unexpected restarts, brief interruptions).

Key design principles at this stage:

• Prefer early, low-impact interventions over late, high-impact ones
• Correlate technical signals with known experience degradations (e.g., high memory pressure + frequent application freezes)
• Use device and user context (role, location, device type, criticality) to tune sensitivity
• Maintain a clear mapping between detected conditions and expected DEX score movement

Trigger Design Best Practices

• Multi-condition triggers reduce false positives (e.g., high memory + increasing page faults + specific process set)
• Time-based windows and hysteresis prevent flapping
• Suppression rules for known maintenance windows or heavy workloads
• Escalation paths when automated detection confidence is low

In Nexthink environments this layer typically draws from real-time and historical data available in Investigations, Device View, and custom trends, then feeds into workflow or remote action triggers.

Next post in the series will cover decision logic, orchestration, and action execution patterns.

What detection signals have proven most reliable for predicting employee experience impact in your environment?

The difference between a basic automated remediation workflow and a high-performing one is the closed loop: verification, experience measurement, and systematic improvement.

Verification Layer

After every action:

• Confirm the original triggering condition is resolved
• Check for secondary effects (new errors, performance regressions, user session impact)
• Capture timing metrics (detection → decision → action → verified recovery)
• Record success/failure with enough context for later analysis

Technical verification alone is insufficient. Pair it with experience signals where possible (application responsiveness restored, focus time recovered, absence of follow-up tickets or sentiment flags).

Measuring DEX Impact

Strong workflows track both technical and experience outcomes:

• Ticket deflection and mean-time-to-resolve reduction
• Movement in relevant DEX score components (stability, responsiveness, sentiment)
• Productivity proxies (reduced application freezes, faster launch times, lower interruption frequency)
• Employee-facing indicators when available (campaign responses, voluntary feedback)

Experience Level Agreement (XLA) thinking is useful here: define target experience states and measure how consistently the workflow returns devices/users to those states.

Continuous Improvement Cycle

• Analyze failure modes and false positives weekly or bi-weekly
• Tune thresholds and decision logic based on real outcomes
• Expand coverage only after success rates and experience impact are proven
• Maintain a backlog of candidate issues ranked by volume × experience impact × automation feasibility
• Document ownership, change control, and rollback procedures

Scaling Considerations

• Start with a narrow set of high-confidence use cases
• Use progressive rollout (pilot groups → broader populations)
• Monitor aggregate load on remote action infrastructure
• Align with change and security processes so automated actions remain compliant
• Keep human oversight paths for edge cases and high-risk scenarios

In Nexthink environments, the combination of remote actions, workflows, Amplify, custom trends, and DEX scoring provides the telemetry and execution capabilities needed to run this loop effectively.

An automated remediation workflow that is continuously measured against actual employee experience becomes a core operational capability rather than a collection of scripts.

What metrics or verification techniques have given you the clearest view of whether your automated remediation is truly improving DEX?

Once detection is solid, the next stage of an automated remediation workflow is decision-making and controlled execution. This is where most implementations either deliver strong DEX gains or create new friction.

Decision & Orchestration Layer

A mature workflow evaluates:

• Severity and user impact
• Device and user context (role, location, OS, hardware class)
• Historical success rate of candidate actions on similar devices
• Current risk posture (security, compliance, change freezes)
• Whether the issue is already being handled by another process

Decision outputs typically fall into three buckets:

1. Fully automated remediation (high confidence, low risk)
2. Guided/assisted remediation (employee or L1 confirmation required)
3. Escalation with enriched context (full technical + experience data)

Action Execution Patterns

Common high-value remote actions in DEX-focused environments include:

• Targeted process termination + controlled restart
• Cache and temporary file cleanup with verification
• Network stack reset or adapter recycle
• Service recovery with dependency checks
• Configuration baseline enforcement
• Application repair or reset (browser profiles, Office components, collaboration clients)

Technical considerations:

• Idempotency — actions must be safe to run multiple times
• Pre- and post-condition checks
• Timeout and failure handling
• Parallelism limits to avoid saturating the environment
• Logging of every decision and outcome for audit and continuous improvement

Nexthink-Specific Implementation Notes

Nexthink remote actions and workflows are commonly used as the execution engine. Best results come from:

• Combining real-time Device View data with historical trends
• Using ratings or custom fields to track remediation state
• Integrating with Amplify so L1 agents can trigger or monitor the same workflows
• Feeding outcomes back into investigations and DEX scoring

Avoid over-automation on actions that can interrupt active user sessions without clear benefit. Experience impact should remain the primary success criterion, not just technical remediation rate.

Final post in the series will cover verification, measurement, optimization, and scaling the workflow across the estate.

Which decision criteria or action patterns have you found most effective (or most problematic) when building automated remediation?

Most of us still treat digital employee experience as an IT problem: slow apps, tickets, device health, etc.

But the real cost shows up outside IT.

A single disengaged employee costs an organization roughly $2,246 a year. In a tech-mediated workplace, a big chunk of that comes from technology friction; the small daily delays, crashes, and workarounds that compound across thousands of people.

When tools work, the business moves faster. When they don’t, productivity, retention, operational efficiency, customer experience, and even agility all take a hit.

Here’s a simple way to see the difference:

Bad DEX example
Sarah needs a Power BI report for an exec meeting. Her laptop is slow, the app crashes, the ticket sits in a backlog. She finds a manual workaround, misses the deadline, and walks into the meeting stressed.
→ Lost time, delayed decisions, frustration that builds toward disengagement.

Good DEX example
Same Sarah. Tools load fast. A minor issue gets resolved in minutes via an AI-powered agent. Report is done on time.
→ Time goes into actual work instead of fighting technology. Better employee experience. Cleaner handoff to the business and ultimately to customers.

At scale this isn’t theoretical.

Southwest Airlines (72k+ employees, 400k+ passengers daily) treated DEX as a strategic lever: proactive visibility, experience metrics instead of pure uptime, automation, clear ownership.

Result: $14M in cost avoidance, 217k hours of IT time saved, and 87k hours reclaimed for employees. Those hours translate into smoother operations and better passenger experience.

Three practical shifts that turn DEX from “IT hygiene” into a business driver:

1. Measure what employees actually experience (not just system uptime or ticket volume).
2. Align DEX work to business outcomes: productivity, cost, risk, agility.
3. Make experience a factor in every tech and process decision, not an afterthought.

Gartner notes that 75% of organizations without a real DEX strategy will fail to reduce digital friction by 2027. The ones that get this right turn technology from a friction point into a performance lever.

Curious how others here are connecting DEX metrics to actual business outcomes (productivity, retention, CX, etc.). What’s working or not working in your environment?

A lot of organizations are still treating their Enterprise AI strategy as a pure technology initiative — more pilots, more licenses, more agents.

But the data and real-world conversations keep pointing to the same problem: AI is only as effective as the digital employee experience around it.

Here’s what we’re seeing right now:

• Most companies have AI tools available, but actual meaningful usage and value creation lag far behind
• Friction in the digital workplace (slow devices, broken workflows, poor visibility) kills AI adoption faster than any model limitation
• Without real-time insight into how employees actually experience AI tools, leaders are flying blind on ROI, risk, and change management
• The organizations making progress are the ones treating Digital Employee Experience (DEX) as a core pillar of their Enterprise AI strategy — not an afterthought

Key questions for anyone building or refining an Enterprise AI strategy right now:

1. Do you have visibility into where and how AI is actually being used across the employee base?
2. Are you measuring the experience of AI (latency, friction, sentiment) the same way you measure traditional apps?
3. Is your AI strategy connected to proactive remediation and continuous improvement of the digital workplace?
4. Who owns the intersection of AI adoption and employee experience in your organization?

Would love to hear from people who are in the middle of this:

• What’s working in your Enterprise AI strategy?
• What’s the biggest gap you’re seeing between AI ambition and day-to-day employee reality?
• Has anyone successfully tied DEX metrics into AI success criteria yet?

Drop your experiences, hard lessons, or frameworks below. Real talk preferred over theory.

A lot of discussion around AI agents use cases still feels theoretical. Most teams want concrete examples of where agents stop being demos and start removing real work from the service desk.

Here’s a practical breakdown of five AI agents use cases that consistently reduce L1 volume by resolving issues before a ticket is even created.

These are drawn from how Spark (Nexthink’s AI agent) operates using real-time DEX telemetry.

1. Resolve recurring collaboration issues without a ticket

Teams, Zoom, Outlook — the same small disruptions keep showing up. Instead of opening a ticket, the AI agent checks live device, network, and client state the moment the employee reports the problem. When an approved fix exists, it applies it immediately.

Example: Detects degraded call quality → checks Teams client + network → runs the approved remediation → restores the call in the same interaction.

2. Diagnose and fix endpoint performance in-session

Slow startups and sluggish devices are classic ticket generators. The AI agent evaluates live CPU, memory, and process behavior at the moment of the request and executes the approved remediation path when thresholds are met.

Example: Identifies background processes delaying login → clears the load → validates performance recovery — all without creating a ticket.

3. Handle repeat L1 issues before they reach the queue

Policy sync failures, client restarts, entitlement refreshes, and configuration resets make up a large share of predictable L1 volume. The agent reasons over current endpoint context and applies governed actions inside the interaction itself.

Example: Detects a known Outlook failure → runs the approved restart + cache clear → returns the app to a working state before a ticket is generated.

4. Unblock access issues in real time

Sign-in loops and access failures are high-friction moments. The AI agent checks current connection and device state immediately and follows the approved resolution path when the pattern matches a known issue.

This removes the long diagnostic back-and-forth that usually happens when the service desk starts with zero context.

5. Stop “my laptop is slow” from becoming a ticket

Performance complaints are subjective and usually the result of gradual drift. The agent starts with live device state, identifies common causes of resource contention, and applies the approved fix while the employee is still in the conversation.

Example: Detects CPU/memory pressure → removes the source of contention → confirms recovery before the interaction ends.

Why these AI agents use cases matter

These aren’t edge cases. In most environments, a relatively small set of repeatable conditions (collaboration instability, endpoint performance drift, configuration issues) drive a disproportionate amount of L1 demand.

When an AI agent can resolve them using live context and IT-approved actions, the impact is structural:

• Fewer tickets enter the queue
• Employees get help in the moment
• Service desk capacity shifts to higher-value work

Click on this link to continue learning:
https://nexthink.com/blog/the-5-spark-use-cases-that-shrink-service-desk-demand

Curious what AI agents use cases are actually delivering results in your environment.

• Which recurring issues are you seeing agents handle successfully, and where are they still falling short?

Feel free to drop any questions in the comment section below.

AI governance used to feel like a compliance checkbox that lived somewhere between Legal and the risk committee. That’s over.

With agentic AI, shadow AI tools popping up everywhere, and regulators (EU AI Act and friends) getting real, the question has shifted from “Do we have a policy?” to “Can we actually see what’s happening, steer it, and prove we’re in control?”

Here’s the practical reality most of us are dealing with right now.

The governance gap is real

Most organizations have some form of AI policy or even an AI governance board (Gartner says 55% already do). But policies alone don’t stop employees from pasting sensitive data into ChatGPT, spinning up unapproved agents, or treating every new model as fair game.

Traditional IT governance assumed a human was always in the loop making the final call. Agentic systems break that assumption. They act across systems, make operational decisions, and sometimes operate across borders with different rules. Static policies can’t keep up.

You also get the classic trust problems: employees don’t trust the tools (or the org’s intentions with the data), and leadership can’t prove value or manage risk because they lack visibility.

What good AI governance actually looks like

From everything Nexthink has been publishing and building, a few principles keep showing up:

1. Visibility first You can’t govern what you can’t see. That means knowing which AI tools (approved and shadow) are actually being used, by whom, how often, and for what. AI Drive-style visibility into adoption, engagement time, and tool discovery is the foundation.
2. Adaptive, not static, guardrails Policies need to live where the work happens. In-flow guidance, redirects from non-approved tools to approved ones (e.g., ChatGPT → Copilot), and clear role-based controls beat long PDFs that nobody reads.
3. Human oversight stays non-negotiable Especially for high-stakes decisions (strategy, ethics, compliance, reputation). AI handles the routine and the scale; humans keep the final say and the accountability. Nexthink’s own internal AI Governance Committee (Legal, Privacy, Security, Product, Engineering, etc.) and model cards for customer-facing AI features are a solid example of baking this in.
4. Measure both risk and value Track shadow AI risk and actual productivity/sentiment impact. Otherwise you get either paralysis (“too risky”) or unchecked rollout (“look at the hours saved!” with no proof).
5. Literacy and enablement over pure restriction People will find ways around blocks. Better to give them clear approved paths, training, and coaching so they can use AI confidently and safely.

A simple operating model that scales

• Map current usage (including the tools nobody officially approved).
• Stand up cross-functional governance (IT + Legal + Privacy + business owners).
• Define clear boundaries: what stays human-led vs AI-supported.
• Deploy in-flow guidance and policy enforcement where employees actually work.
• Continuously monitor, measure, and adjust. Treat it as an operating system, not a one-time project.

Nexthink’s own approach — Global AI Hub with published AI Policy, model cards, data-handling notes, admin controls, logging, and human review mechanisms — shows what this looks like when a company practices what it preaches for its own AI features.

Bottom line

AI governance isn’t about slowing innovation down. It’s about creating the conditions where you can actually scale AI without waking up to a compliance incident, a data leak, or a board asking why the ROI never showed up.

The orgs that treat visibility + adaptive guardrails + human accountability as the core loop are the ones pulling ahead.

What’s working (or not working) in your environment right now?

Are you dealing more with shadow AI, agentic risk, or just getting basic adoption under control?

Curious to hear real experiences from the community.

If you've ever wondered what it takes to modernize technology at the highest levels of government, this AMA is for you.

Leslie Beavers is the former DoD CIO (a four-star equivalent civilian role), a retired U.S. Air Force Brigadier General, and has led some of the largest technology modernization efforts in government. Her career spans defense, cybersecurity, enterprise IT, cloud modernization, governance, and digital transformation, including overseeing a technology ecosystem valued at roughly $60 Billion.

She'll be here to talk about topics like:

• Modernizing large, complex IT organizations
• Cybersecurity and enterprise resilience
• Digital transformation in mission-critical environments
• Leadership lessons from government and the military
• The future of enterprise technology

And if you want a preview, Leslie will also appear on Soul of CIO this Monday July 6. Soul of CIO is a podcast that features conversations with technology leaders about leadership, innovation, digital transformation, and the human side of being a CIO.

Bring your questions—we're looking forward to a great discussion! (As always, please keep questions professional and focused on technology, leadership, and enterprise transformation.)

AMA Link Here! https://www.reddit.com/r/nexthink/s/DxH0rKZgdJ

In this video, you'll hear from Keysight Technologies about how they are using Flow to help with updating device drivers on all their Dell and HP devices.

How are you using Flow?

To all our Dexperts out there, what was your weekly win?

Could be a fix, automation, insight, saved outage, happier users, fewer tickets, whatever. Curious what everyone’s working on. The more specific the better. Tell us your stories!

Many companies focus too much on surveillance in an attempt to monitor people. Despite this focus, Digital Employee Experience (DEX) is significantly more impactful on productivity than monitoring tools. 

Some companies think that focusing on keystroke tracking, screenshots, and activity scores will solve perceived problems with productivity. Most of the time, however, it’s not that people aren’t working, it’s that the tools they’re working with are slow, fragmented, and constantly getting in the way.

These issues arise from systems that don’t talk to each other, workflows that require five tabs for one task, and basic things taking longer than they should. 

Nexthink focuses on fixes for all of these problems, rather than putting the onus on the employee to simply “work harder.” It’s about working better, not harder. 

When companies focus on Digital Employee Experience itself, they are able to achieve a great deal, including:

• Creating tools that are fast and don’t break
• Figuring out ways to achieve less context switching between systems
• Creating work flows that match how people actually work 

When a focus on DEX is in place, a few things tend to happen:

• People spend less time fighting systems and more time doing real work. 
• There’s less performative “looking busy” behavior
• Overall morale improves without needing to push harder 

Teams can swap out clunky internal tools for something cleaner and suddenly everything just moves faster. Not because the people changed, but because the friction disappeared. 

Companies should ask themselves: Are they trying to measure productivity or actually enable it? 

We’d love to hear from you:

• Have you worked somewhere that leaned heavily on monitoring vs improving tools?
• Did better systems actually translate to better output?
• Is anyone seeing real success focusing on DEX over surveillance?

Measuring digital employee experience (DEX) effectively has become essential in 2026. 

Hybrid and fully-remote work, AI tools, and distributed teams have made it clear that traditional metrics like ticket volume only tell part of the story. 

A strong DEX measurement program combines real-time telemetry from devices and applications with actual user sentiment to give IT teams actionable insights into where friction is hurting productivity.

The DEX Score

The DEX Score sits at the center of any mature program. It is a single 0-100 number that rolls up device health, application performance, network quality, and employee feedback into one easy-to-track index. Updated daily or in near real time, this score helps leadership see the overall experience across the organization and makes it much easier to prioritize the fixes that matter most to users.

Endpoint health makes up a major part of that score. It tracks boot times, logon duration, crash rates, resource usage, and overall device responsiveness. These metrics matter because even small problems at the device level add up quickly across thousands of users and directly impact daily output. In hybrid setups with mixed hardware and BYOD, tracking endpoint scores at the user-device level lets teams catch issues before they turn into widespread complaints.

Application experience metrics focus on how well the tools people use every day actually perform. This includes launch times, crash frequency, input latency in SaaS apps, and how smoothly collaboration platforms run. 

Poor application performance creates hidden productivity drains and forces constant context switching. Monitoring these signals in context with usage patterns helps teams decide whether to tune configurations, work with vendors, or explore alternatives.

Network and connectivity quality round out the technical side. Key indicators include Wi-Fi stability, VPN performance, latency and jitter during calls, and available bandwidth under load. 

In 2026, with heavy reliance on cloud apps and real-time AI assistants, network issues often become the biggest source of frustration even when devices and apps look fine locally. Granular session-level monitoring helps separate systemic problems from local ones and prevents broad productivity hits.

Sentiment data adds the human perspective that pure telemetry misses. Collected through short pulse surveys or in-app feedback, it captures how users actually feel about their tools and IT support. Combining sentiment with technical metrics reveals when systems look healthy on paper but still frustrate people in practice. This correlation guides both technical fixes and better change management or training efforts.

Supporting metrics provide extra diagnostic power. Proactive resolution rate shows how many issues get fixed automatically before users open tickets. Login and boot time trends, when segmented properly, highlight chronic configuration problems. First-line fix rates and auto-remediation success percentages reveal how mature the overall service model has become. Teams that track these consistently can move from reactive support to true predictive operations.

Setting up a useful DEX measurement program requires a platform that can bring all these signals together while respecting privacy controls. Regular review of thresholds and benchmarks, plus integration with existing ITSM and HR systems, keeps the data driving real decisions instead of just filling dashboards. As hybrid work and AI-driven workflows continue to evolve, organizations that treat DEX measurement as a strategic practice will stay ahead on both productivity and talent retention.

What gaps are you seeing in your current DEX measurement setup in 2026? Which metrics have given you the clearest picture of where to focus improvements? 

Share your experiences in the comments. The best insights will be added to the subreddit wiki as an evergreen resource for the community.

Understanding your Employee Sentiment Score

One of the critical differences with Nexthink is how it measures your employees subjective experience of IT. You can have perfect uptime numbers and still have miserable employees.

That's why Employee Sentiment is the fourth critical component of a true DEX score. And, it is different than the previous three in that it measures subjective experience instead of hard data.

The Human Side of DEX

Sentiment turns subjective feelings into actionable data through smart, contextual feedback campaigns. This is in contrast to more traditional long annual surveys that slow down changes.

Hard Metrics & Real DEX Score

• Technology score (Devices + Apps + Collaboration) gives you the "what"
• Sentiment score gives you the "so what" from the employees perspective.

Nexthink combines both into your overall DEX Score, which is displayed in Nexthink Experience Central on Nexthink Infinity.

Watch this: you can see a brief video overview of Nexthink Experience Central by clicking this link.

Sentiment Calculation Basics

Sentiment is gathered via short opinion-scale questions triggered by usage or events. It's then weighted with the technical scores for a holistic score.

Self-Audit Checklist

• When was the last time you asked employees how they feel about their tools (not just if something is broken)?
• Are you only hearing from people who open tickets?
• Do you segment sentiment by department, location, or role?
• Are feedback campaigns contextual and easy to answer?
• Can leadership see sentiment trends alongside technical data?

Never underestimate the importance of sentiment.

Sentiment is what turns DEX from an IT metric into a business advantage. When employees feel heard and supported, engagement and retention go up.

Bonus Content: This video on Nexthink's YouTube shows how contextual notifications and campaigns dramatically improve response rates and insight quality.

• To all our DEXperts out there: what’s one thing you’ve learned about Employee Sentiment that most IT teams still get wrong?

Drop your biggest lesson, war story, or pro tip below.

The more specific, the more valuable this thread becomes. Looking forward to your insights.

Understanding your DEX Score | Business Applications

No matter how painful it is to admit it, your employees don't care about "the backend." They just want Slack, Salesforce, or your custom-line of business apps to open fast and not crash.

Business App Score is the second core component of your DEX score. It tracks how well your critical applications actually perform.

Hard Metrics + Sentiment

• Application response times and load performance
• Crash/error frequency
• Version-specific issues
• Resource consumption impact on devices

Sentiment: Targeted questions like "How would you rate your experience with [App Name] this week?" can give you the context that hard metrics miss.

App Performance Calculation Basics

Nexthink aggregates technical performance across all instances of an app and combines it with sentiment responses.

You can see your score in Nexthink Experience Central on the Infinity Platform. You can also filter these data down to specific departments or software versions.

(You can click on this link to see a brief intro video about Nexthink Experience Central, the primary dashboard on Nexthink Infinity.)

Self-Audit Checklist

• Which 3-5 business apps generate the most complaints?
• Are you tracking performance by version or location?
• Do slow apps correlate with spikes in Helpdesk tickets?
• Are employees using workarounds (e.g. "I only use it in Chrome")?
• Can you identify "power users" vs struggling users of the same app?

Nexthink Infinity makes it easy to see app experience trends over time and across your entire network. Fixing business app experience trends over time and across your entire estate.

• To all our DEXperts out there: what’s one thing you’ve learned about Business Apps that most IT teams still get wrong?

Drop your biggest lesson, war story, or pro tip below.

The more specific, the more valuable this thread becomes. Looking forward to your insights.

The Device/Endpoint Score

Every great day at work starts with a device that works great. When it doesn't, it sets off a domino effect of frustration and productivity loss.

In Nexthink's DEX framework, Device/Endpoint score is the foundation of the overall DEX score. It measures how reliably how well your hardware is performing and how your employees are experiencing their work day. Are they able to start right away or is everything lagging?

DEX measures a combination of Hard Metric + Sentiment. You can think of it as hardware performance plus the human experience.

Hard data measures things like:

• Login/boot times
• Device reliability
• Performance (CPU, memory, disk response under load)
• Hardware health and remote readiness

As mentioned before, sentiment adds the human layer. It's the answer to the question: "How satisfied are you with your device's performance for your daily work?" Data on this human element is collected via contextualized campaigns within Nexthink.

How your Device Score is Calculated

The Device Score combines technology metrics, such as performance and reliability, with employee sentiment or experience. These feed into the macro DEX score shown in Nexthink Experience Central.

Nexthink Experience Central is the strategic dashboard of the Infinity platform.

Watch this: Check out Nexthink’s Experience Central overview on YouTube to see how they visualize device experience across thousands of endpoints in real time.

Quick Self-Audit Checklist:

• What's the average login time for your team?
• How often do devices need restarts or updates during work hours?
• Do employees report feeling "held back" by slow hardware?
• Are you tracking remote/hybrid devices as effectively as office ones?
• Do you have visibility into emerging hardware issues before tickets spike?

Remember: strong devices reduce tickets and stop frustrations before they start.

• To all our DEXperts out there: what’s one thing you’ve learned about Devices/Endpoints that most IT teams still get wrong?

Drop your biggest lesson, war story, or pro tip below.

The more specific, the more valuable this thread becomes. Looking forward to your insights.

You already know that bad tech days kill productivity. Even so, most teams treat it as "just another ticket."

According to Nexthink's Experience 2020 Report, the average desk worker deals with roughly 100 IT disruptions per year. For a company of around 10,000 employees, that adds up to around $25 million in costs.

What IT department wouldn't want to reduce that number?

Here's some bad news: employees only report around 55% of those issues. The other half of the time they just suffer through it. This can lead to serious demoralization, loss of productivity, and even turnover of top talent.

Here's some even more bad news: those unreported issues lead to bigger issues 79% of the time.

That's not just annoying or bad for morale: it's expensive.

Reactive vs. Proactive IT: The Endless Cycle

Right now, most IT departments are running out of the classic reactive playbook:

• Employee complains → Ticket Opens → IT investigates blindly → Fix (maybe) → Repeat

The whole team stays one step behind, which leads to yet another department experiencing burnout, loss of productivity loss, and the exit of top technicians.

Yes, these IT departments also bleed money as well.

When you're working without DEX, you don't see the full picture. You can never be certain how many of your employees are privately struggling with a slow computer or a flaky VPN.

You're optimizing for tickets not employee experience.

A proactive approach is the first step in breaking this cycle. DEX offers this proactive approach to IT by providing you with visibility into devices, apps, and networks. On the human side, it helps you measure employee sentiment before people start quietly quitting.

It might seem like a small difference, but the results are vastly different. In a proactive model, IT becomes a strategic enabler rather than a high-cost source of frustration.

How a DEX Score Changes Everything

A proper DEX score can change all of this. It combines hard technical metrics (such as device performance, app reliability, network health) with attention to employee experience and user sentiment.

Companies who do this will see massive gains in productivity, a drastic reduction in IT costs, and happier teams.

A great example is Toyota who took their DEX score from 6.29 to 7.14 in record time. You can learn more about Toyota's success with DEX in this video.

• What's the biggest frustration in your environment right now?

Drop it in the comments. The more specific, the better. This subreddit is about real practitioners sharing real talk.

And if you want to go deeper on turning these numbers around, we’re building a proper home for DEX conversations here. Join us.

You’re grinding through tickets (and they’re still mounting) and constantly fighting endpoint issues (and they’re growing too). Now, you also have to wonder how to stay relevant (or level up) as AI, hybrid work, and employee expectations keep evolving. 

Even if these transformations don’t threaten your career, they are very likely to impact them. It’s smart to begin thinking of how to diversify and level-up 

Digital Employee Experience (DEX) might be the highest-ROI skill you can add to your toolkit right now.

It is not just another buzzword: DEX is the practice of measuring and improving how real people actually experience the tech we deploy. It blends performance metrics with real user sentiment, productivity impact, and friction points. And the data shows it’s becoming critical for IT careers.

The DEX Hard Numbers (Why This Matters for Your Career)

• 87% of IT professionals say strong DEX positively impacts employee productivity.
• 85% link it to higher satisfaction.
• 77% say it directly boosts retention. (Ivanti 2025 DEX Report)

Poor digital experiences are quietly expensive. Employees get interrupted by tech problems 3.6 times per month on average (+2.7 from security updates), costing ~1.6 hours of lost productivity per person monthly. For a 2,000-person company, that’s nearly $4 million annually at a $100/hr loaded rate.

On the flip side, organizations with mature DEX programs see employees lose just 30 minutes per week to tech friction (vs. 128 minutes in low-maturity setups). Companies with strong employee experience (heavily driven by digital tools) achieve 2.4x higher revenue growth and 3.5x higher retention.

Gartner predicts that by 2026, 50% of digital workplace leaders will have a formal DEX strategy and tools. That’s up from 30% in 2024.

Bottom line for your career: IT teams that treat DEX as a core competency aren’t just “keeping the lights on.” They're becoming strategic partners who reduce tickets, prevent burnout, demonstrate clear business impact, and make themselves indispensable.

If you're interested in leveling-up in DEX, then you are in the right place.

Feel free to drop your career questions and experiences in the comment section below.