In the mainframe era, computing was centralized. Hardware, software, and workloads were tightly coupled, and the vendors you could choose from were exceedingly few. Seriously, you really had only two choices, and they were both wildly expensive.

As a result, these contracts were usually large, and the pace of change was slow, but at least (your perception of) control was inherently high. You bought capacity, ran the business, and went home. It turns out that IT, finance, procurement, and users were inadvertently aligned because there really was only one way to consume the available technology.

Then came the early 1990s. The PC revolution moved computing from the data center to the desktop. Client/server architectures took off. Software was no longer monolithic; it was modular, installable, and increasingly decentralized. That changed IT procurement almost overnight. Departments and divisions began buying their own software. Licenses were purchased in bulk (often based on overly optimistic growth projections), and shelfware became routine. Deployment planning was also manual, slow, and imprecise. Asset tracking relied on spreadsheets (if you were lucky).

For the first time, enterprises faced a new problem of visibility. What was installed? What was actually used? What was compliant with policy? What is the policy? What could be rationalized? And something else happened too: users gained autonomy while corporate IT lost a little control. Procurement tried to standardize and finance attempted to forecast despite the futility. Naturally, everyone’s respective incentives began to diverge. Users wanted speed and functionality while IT wanted stability and supportability. Procurement wanted leverage and discounts while finance wanted predictability (and also discounts). The gaps between everyone widened and “experience” started to mean different things depending on who you asked.

This period gave rise to early software asset management (SAM) and deployment intelligence tools. Vendors like Lakeside Software were among those helping enterprises understand endpoint environments; not just what they owned, but what they needed. Instead of guessing about upgrade cycles or Windows migrations, organizations could use data to plan. The challenge had shifted from centralized scarcity to distributed complexity, and increasingly, to organizational misalignment.

Virtualization, SaaS, and Subscription Economics

The next phase (roughly the 2005 to 2015 window) accelerated the fragmentation. Virtualization and containers abstracted infrastructure, SaaS abstracted deployment, and cloud abstracted ownership.

Procurement models evolved. Perpetual licenses became subscriptions, CapEx shifted to OpEx, device-based licensing morphed into user-based licensing, and enterprise agreements evolved into cloud consumption commitments.

Looking back, shelfware wasn’t really the same level of problem. Now we had redundant SaaS tools, underutilized subscriptions, shadow IT, poorly aligned consumption forecasting, and cloud cost overruns.

In short, visibility became far more difficult and the disconnects deepened. Users could input a credit card for enterprise IT services. Business units could adopt SaaS without permission from IT. Finance saw spend accelerate and procurement negotiated contracts that didn’t always reflect real usage. Adding insult to injury, IT was often asked to secure and support tools it didn’t select. Everyone had dashboards but nobody had shared truth (sound familiar?).

Marketplaces, MACC, and Ecosystem Economics

We’re now in a different era again. Cloud marketplaces have become procurement engines where enterprises increasingly purchase ecosystem software. Constructs like Microsoft’s Azure Consumption Commitments (MACC) tie software procurement directly to cloud spend, which changes the incentives. Software companies (ISVs) align with those marketplaces so that customers can optimize purchases against existing commitments. Procurement, finance, and IT are pushed to coordinate.

Indirectly, software selection has now become ecosystem selection. Marketplace purchasing isn’t just a transactional convenience. It’s structural alignment. It forces finance, procurement, and IT to operate against a common consumption model.

At the same time, AI is adding another layer of volatility. AI isn’t just a feature; it’s becoming an infrastructure multiplier. Agents consume APIs, invoke services, and generate unforeseen compute. AI agents require data pipelines, so poor software choices now have compounding effects. In a world of elastic cloud workloads and accelerating automation, procurement isn’t just about price or feature lists. It’s about telemetry, integration depth, ecosystem compatibility, consumption alignment, and operational intelligence.

If you think about it, the same core problem from the 1990s still exists: visibility and optimization. The difference is that the surface area (and associated risk) is exponentially larger. The organizations that reduce friction between users, IT, finance, and procurement, and operate from shared telemetry instead of assumptions, will move faster and waste less.

This is where modern endpoint intelligence platforms and marketplace-based procurement converge: operational truth alongside financial and contractual alignment. Together, they reduce the historic disconnect.

For us at Lakeside, this is also where SysTrack fits naturally. If you are buying through a marketplace and applying spend to MACC, you still need to know what is actually happening across endpoints: what is installed, what is used, what is degrading experience, and what can be optimized. Without that shared truth, you are just moving spend through a different channel. With it, marketplace procurement becomes a lever for both governance and outcomes.

Full Circle (again?)

Looking back, mainframes were centralized and controlled. Then the PC era decentralized everything and created sprawl. The cloud re-centralized infrastructure but further decentralized the services.

Today’s marketplace-driven enterprise now sits somewhere in between. We have distributed workloads running on centralized platforms, purchased through ecosystem commitments.

The enterprises that win won’t simply buy software. They’ll buy into the right ecosystem with the right data, the right telemetry, and the right partners to continuously optimize what they consume.

Procurement has evolved from purchasing capacity, to managing licenses, to governing consumption, to orchestrating ecosystems. The difference now is that the tools exist to reconnect the user experience, operational reality, and financial model into a single system of truth. We still have work to do.

If you are sitting on MACC, the pragmatic question is not whether you should use it, but whether you are using it to drive measurable operational outcomes. If you’d like to evaluate SysTrack through the Azure Marketplace, we can help you map marketplace procurement to endpoint-level reality so commitments translate into utilization, performance, and continuous optimization.

Getting Started

If you’re exploring how to turn marketplace purchases and MACC commitments into real operational outcomes, start by grounding decisions in endpoint‑level truth.

Learn how Lakeside helps organizations connect Microsoft Marketplace procurement to visibility, optimization, and measurable results—and take the next step when you’re ready.

The post From Mainframes to Marketplaces: The Evolution of Enterprise IT Procurement appeared first on Lakeside Software.

“Proactive IT” (resolving issues before users notice them) is far from new, but the latest innovations in end-user computing make it possible to detect, diagnose, and remediate problems faster than ever—zero user disruption, zero IT tickets.

Last week, Intel announced enhancements to their vPro platform for IT management and security as part of their commercial client portfolio. One of these new capabilities is Intel Device IQ, which detects PC health signals at the platform level, enabling earlier detection before issues show up at the OS or application layer.

Lakeside Software has expanded our technology collaboration with Intel to integrate Device IQ with SysTrack, our digital employee experience engineering platform. As a vPro Certified App, SysTrack collects and analyzes more endpoint data—more frequently—than other DEX platforms. And we don’t just send that data to the cloud: we also store it locally on each device (at the edge) using available endpoint resources. This patented approach gives IT an unbroken record of endpoint history for accurate, end-to-end context. That context supports deeper root cause investigation and more opportunities for automation.

Now with Intel Device IQ, SysTrack captures additional on-device intelligence, including performance degradation, resource contention, thermal and power anomalies, and abnormal system behavior. Once detected, SysTrack surfaces insights for proactive IT intervention and self-healing automations for frictionless digital employee experiences.

What Is Intel® Device IQ?

Intel Device IQ delivers continuous, on-device intelligence that detects platform-level performance degradation, resource contention, thermal or power issues, and abnormal system behavior before they impact users. By identifying responsiveness degradation early and enabling near-real-time remediation, it prevents user-visible disruptions during active workflows and maintains a smooth user experience.

How Does the SysTrack and Intel Device IQ Integration Work?

New, advanced Intel processors are far more complex than earlier generations, packing more cores, threads, power-saving states, and dynamic performance features into a single chip. This complexity means that traditional metrics like basic utilization, temperature, or queuing no longer provide a complete view of performance. To increase reliability, manage power and thermals, and quickly diagnose issues, we’ve worked with Intel to integrate Device IQ signals into SysTrack. Through this integration, we capture advanced monitoring metrics such as:

• Per-core behavior
• Cache and memory efficiency
• Power delivery
• Throttling events
• Workload characteristics

With these deeper, architecture-aware insights, IT can spot hidden bottlenecks and improve system reliability under real-world workloads.

Once a responsiveness threshold is breached but before a user experiences slowdowns, SysTrack surfaces the signal for proactive intervention. With full root cause context and intelligent event correlation, IT can accurately diagnose what triggered the issue and address the core problem (if not already remediated automatically by a triggered SysTrack automation).

Key Benefits of SysTrack and Intel Device IQ

As our CEO and founder, Mike Schumacher, puts it, “Imagine if every device shipped with its own IT admin. That’s the power Intel Device IQ and Lakeside SysTrack provide: Early detection, proactive resolution, and productive digital employee experiences at the edge.”

“Imagine if every device shipped with its own IT admin. That’s the power Intel Device IQ and Lakeside SysTrack provide: Early detection, proactive resolution, and productive digital employee experiences at the edge.” — Mike Schumacher, CEO, Lakeside Software

Getting Started

Ready to try early detection, proactive resolution, and productive digital employee experiences at scale?

Learn more about how Intel Device IQ and Lakeside work together to improve organizational productivity and reduce IT support costs in our solution brief and contact us to get started.

The post Lakeside Software and Intel: Monitoring Emerging PC Health Issues to Improve DEX with SysTrack and Intel® Device IQ appeared first on Lakeside Software.

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.

The post From Experience Management to Experience Engineering: How AI Is Reshaping DEX in IT’s Next Great Transformation appeared first on Lakeside Software.

EUC operations haven’t lacked for smart people or tools. What’s been missing is a way to connect decisions across teams without paying the swivel-chair, multi-console “UI tax.” SysTrack has always been the deep, first-party view of what’s happening on endpoints. SysTrack AI takes that truth layer and makes it useful everywhere: on the CIO’s laptop, at the helpdesk, in a user’s chat window, and inside the agentic apps you already rely on like ServiceNow and Microsoft Intune. The goal isn’t another pane of glass; it’s to make every pane you already own smarter and more honest.

Think about the kinds of decisions EUC teams make.

Investment decisions come first. Should we shift on-prem VDI to Windows 365? Which devices need to move to Windows 11, and what will that change, break or improve? When do AI PCs make sense? Those aren’t guesses; they’re bets. Good bets start with a baseline. With SysTrack you see the real fleet: hardware, drivers, apps, policies, utilization, and the experience people are actually having. You can ring changes, validate that a cohort stays healthy for a defined window, and expand with confidence. When someone asks whether an AI-capable device program will pay off, you can point to workload characteristics and measured experience deltas rather than a slide. Invest where the data says you’ll see an outcome—not where the demo sizzles.

Then there are support decisions. “My computer is slow” isn’t a category; it’s a state. The fastest way to close that ticket is to know what changed just before “slow” appeared: process spikes, disk contention, policy updates, a driver roll-out, a network issue. SysTrack captures that pre-failure context at the edge so you don’t waste cycles triaging symptoms. Edge vs. cloud isn’t a debate for us; it’s a design. Collect high-cadence facts on the device so you can act quickly; condense and normalize to the cloud so you can see trends, prove compliance, and plan. And then you automate: trigger on deterministic signals and enrich the action with the right context. That’s how you reduce the manual triage tax and keep false positives from becoming tomorrow’s backlog.

Finally, outcome decisions. What’s the best way to measure end-user experience? Is quantitative or qualitative sentiment more useful? The honest answer is “both.” Telemetry tells you what happened; feedback tells you how it felt. SysTrack AI blends device-level facts with light, privacy-conscious prompts so you can tie actions to experience by cohort, location, device class, and application. If a change didn’t improve DEX for real people, we shouldn’t call it a win. Around here we say, “If we can’t prove something, we didn’t improve anything.”

How does SysTrack AI actually break the silos? We kept it to three moves. First, collect trustworthy facts at the endpoint: wide coverage, high cadence, and identity you can rely on. Second, trigger and enrich flows on those facts; deterministic sensors to start, rich context (apps, dependencies, recent changes) to guide what happens next, whether the doer is an engineer, an end user, or an agentic app. Third, verify and measure the outcome; a validation window and an audit trail you can report back on from our platform or any other platform you choose. We want to be your EUC agent of record. When you run operations this way, UI silos stop mattering because the evidence travels with the work.

This approach is exactly why we play nicely with others. In fact, SysTrack AI is designed to run equally well “headless”. Orchestration platforms and managed services bring great playbooks; we bring the endpoint truth that makes those playbooks precise. We’re grateful for the partners who build with us, and we design SysTrack AI to be neighborly: Teams or Slack or even voice as a front door for end-users if you want it, ITSM systems enriched rather than superseded or bypassed, and a fresh, clean new GUI so you don’t need to rewire what’s already working.

There are a few things we’ll never do, and I want to be clear about them. We won’t guess without evidence. Anomalies without context create work, not value. We won’t skimp on data quality. Provenance matters to operations, to compliance, and to trust. And we won’t hide how diagnoses and decisions are made. If you can’t prove the chain from trigger to action to outcome, you can’t get better.

If you’re looking for a place to begin, pick one decision and let’s prove the delta together: Let’s start by turning “slow device” from a generic complaint into an evidence-based fix with measurable time saved based on real-time contextual data from the device rather than scraping knowledge bases and the web. Let’s analyze which users could benefit from an AI PC. Let’s measure the impact of Windows 11 on different user personas. We’ll meet you where you are. Bring your orchestration; we’ll bring the facts.

To the practitioners who carry the pager and the partners who help them succeed: thank you. We built SysTrack AI the way you work: carefully, with evidence, and with respect for your time. Because IT matters.

The post Why We Built SysTrack AI to Break EUC Ops (in a Good Way) appeared first on Lakeside Software.

My conclusion: IT needs partners to get IT done. The VARs who design and deliver, the MSPs who live in the trenches of day-to-day operations, the GSIs who stitch everything together at global scale, and the ISVs who keep pushing the ecosystem forward. You’ve trusted Lakeside as a core part of your toolbelt, and I want to say thank you for that trust as we help customers navigate the AI transformation of the digital workspace and all the services that orbit it.

From Projects to Practice: How EUC Quietly Became SRE-Like

If you step back, end-user computing has gone through a quiet but massive shift.

We started in a world of “projects”: Windows migrations, VDI rollouts, hardware refresh cycles. Success was often defined by go-live dates and under-budget delivery.

Today, the customers we support are moving toward something much closer to Site Reliability Engineering (SRE) for the digital workspace:

• defining experience SLOs instead of just uptime
• instrumenting the edge to understand what users are actually experiencing
• building continuous improvement loops between EUC, ITSM, SecOps, and the business

IT directors are adapting DevOps notions of continuous improvement to EUC and ITSM. They’re no longer satisfied with, “Are we at five nines?” They want to know, “Are our employees actually able to do their jobs without friction, and are we getting better every quarter?”

That mindset shift is exactly where SysTrack and our partner community meet.

For years, SysTrack has been the system of record for EUC; the place you go when you need the real story about devices, apps, resource utilization, and user experience at the edge. It’s the data that underpins your assessments, your transformation projects, your managed services.

SysTrack AI is about taking the next step in that journey.

The Agentic Evolution of All Things IT

We’re all watching the same movie right now: AI agents are starting to appear everywhere in IT.

Service desk analysts have copilots. Operations teams are building runbooks that can execute automatically. Workflow tools are becoming more “agentic”; able to notice something, reason about it, and act on it without waiting for a human to click every button.

That’s exciting. But it also introduces a new responsibility: those agents are only as good as the data and context we feed them.

If an AI agent is going to:

• open or enrich a ticket
• push a policy change
• trigger an automated remediation
• or even just recommend a course of action to a human

…then the underlying telemetry had better be reliable, rich, and grounded in the reality of what’s happening at the endpoint.

That’s where our “first-party edge data” story becomes central, not peripheral.

From System of Record to Agent of Record

The way we think about SysTrack has evolved.

We will always be a system of record for EUC, that’s our foundation. But in our rapidly AI-driven world, we see SysTrack AI becoming something more: an agent of record.

By that, I mean:

• the trusted source of truth that AI agents use when they make decisions about the digital workspace
• the contextual layer that tells an agent, “This isn’t just CPU at 95%. This is a CxO on a latency-sensitive app, right before a critical meeting.”
• the common data fabric that connects EUC, ITSM, DEM, SecOps, collaboration platforms, and endpoint management tools so that agents aren’t working from conflicting views of reality

You’ve seen this pattern before. We instrument first, then we build dashboards, then we automate. Now we’re entering the “agentic” phase, where automation gets wrapped in reasoning.

Our commitment is that SysTrack AI will always reason from evidence from real, continuous, first-party telemetry at the edge, not from guesses, scraped data, or opaque signals. And that commitment exists because you, our partners, have held us to a high standard for years.

What This Means for VARs, MSPs, GSIs, and ISVs

For our partner community, SysTrack AI is an accelerator for things you’re already doing well:

• If you’re a VAR, it helps you design and prove out architectures where experience and reliability are first-class requirements, not afterthoughts. You can connect SysTrack AI’s insights directly into customer workflows and show how AI-enhanced operations still rest on solid data.
• If you’re an MSP, SysTrack AI gives you a way to scale your expertise. The playbooks your teams have honed over years can now be embodied in agents that act consistently and explain their actions — backed by the telemetry you already trust.
• If you’re a GSI, SysTrack AI becomes a key building block in your reference architectures for digital workplace, observability, and AIOps. It’s the layer that ensures every agent, across every tower, is working from the same, accurate view of the edge.
• If you’re an ISV, SysTrack AI is an opportunity to enrich and trigger your own AI and automation with deep, contextual EUC data without having to reinvent endpoint telemetry from scratch or trust data of unknown freshness or provenance.

In all of these cases, our goal is not to replace the services and IP you’ve built. It’s to amplify them with better data and more transparent, explainable AI.

Thank You for the Trust (and What’s Next)

As COO, I spend a lot of time thinking about execution: can we deliver, can we scale, can we be the kind of partner you’re proud to put in front of your customers?

SysTrack AI raises the bar for us in all of those areas. It also raises the bar for how we show up for you:

• being honest about where AI is ready for production and where it still needs a human in the loop
• giving you the tools, integrations, and guidance you need to embed SysTrack AI into your offerings
• continuing to invest in first-party edge telemetry as the bedrock of everything we do together

Most of all, it means staying humble. We know that our success is tightly coupled to yours. You’re the ones translating technology into outcomes, keeping enterprises running, and now helping them navigate the agentic evolution of all things IT.

On behalf of everyone at Lakeside: thank you for getting us here — and for trusting us as we take this next step together, from system of record for EUC to agent of record for the AI-driven digital workspace.

If you’d like to go deeper on how SysTrack AI can plug into your services or platform, I’ve always got time for you. Let’s keep building together.

The post To Our Partners: SysTrack AI Wouldn’t Be Here Without You. appeared first on Lakeside Software.

I’m excited to share that I am returning as CEO of Lakeside Software, the company I founded in 1997. I am energized to rejoin in this capacity to propel Lakeside’s mission to be the market leader in Digital Employee Experience (DEX) software. 

Why Now? 

The future of enterprise IT depends on an elevated focus on AI-driven innovation. As a leading DEX tool for enterprise IT teams, Lakeside SysTrack is ripe for rapid advancement of our product and technology as we continue to seize the value of our depth, breadth, history, and quality of data to deliver on all the promises of AI.  

As SysTrack’s original inventor and a pioneer in the DEX space, I return to the CEO role with a primary focus to accelerate innovation, scale Lakeside’s AI capabilities, improve the SysTrack user experience, and deepen Lakeside’s business-centric impact across enterprise IT. During this transition, I want to express my sincere gratitude to Dave Keil for his contributions and leadership over the past three years during an important chapter of Lakeside’s growth.  

I’m returning at an exciting time in the market as enterprise IT teams mature their DEX strategy and implement AI into IT operations, and I am encouraged by the opportunities Lakeside has as a leader in this exciting space.  

Connecting With the Lakeside Community 

Over the next few weeks, I will be spending time with many of our employees, partners, and customers to reengage and hear directly where we have the greatest opportunities to innovate and grow together. 

Moving forward, I plan to spend significant time in the Ann Arbor, Michigan, office with our world-class engineering team. There, I’ll focus on working directly with the team to lean in to our company value of boundless ingenuity and curiosity, taking a hands-on role in driving innovation and increased collaboration across the entire organization. 

Our Path Forward 

Looking ahead, I will say that one of our primary goals is to continue scaling the company to best serve our customers—that is, enterprise organizations across all sectors, including financial services, retail, healthcare, airlines, and more. There’s still significant work to be done in the DEX space, both for our customers and partners. 

Lakeside remains firmly committed to listening to our customers and partners because that’s where we learn about the real-world problems they need to solve. By understanding their challenges, we gain insights that inspire creativity to develop solutions to better meet their needs. I stand by the 10+ patents Lakeside already has, including our recent patent to monitor consumer-facing kiosks in the retail space, and I am energized to see what innovations await our team.  

For example, our patented product architecture (that is, our Intelligent Edge) positions us to excel in scalability, mobility, cloud, and privacy. Our long-standing, unique edge architecture is especially exciting as AI PCs are fast entering enterprise fleets and require device performance monitoring capabilities with low latency. This architecture gives Lakeside a distinct advantage in delivering the highest-quality endpoint data, and it will allow us to build on this foundation to unlock even more potential for our customers, especially as AI integrations become ubiquitous across enterprise processes, products, and employee productivity tools. 

The Future of Digital Employee Experience 

It’s not the 1990s anymore; traditional, reactive IT is only holding back innovation, business-driven tech advancements, and end-user productivity. The standard for a high-quality digital experience is rapidly rising. People now expect—and deserve–a seamless, consumer-like experience with their work tech. As such, IT teams are moving toward a more AI-enabled, proactive approach. This shift aligns perfectly with SysTrack’s ability to improve both end-user satisfaction and organizational productivity, creating a win-win scenario. Companies want to deliver the best experience at the most effective cost, and that’s exactly what DEX is all about. 

I am particularly encouraged by our continued momentum in forming strategic partnerships to drive transformative change in the DEX and proactive IT space. Our recent reinvigoration of our Customer Advisory Board, our realignment of Strategy & Products, and the evolution of our partner program—highlighted by our second annual partner awards—all demonstrate our commitment to deepening customer relationships and expanding our partner ecosystem. 

A Personal Note 

Returning to lead Lakeside as CEO feels like coming home. While I’ve never truly been away from the company as I’ve remained an integral part of the board and advised on Lakeside’s strategic direction, stepping back into the CEO position renews my commitment to the vision that started it all. I’m looking forward to listening, learning, and collaborating with all of you as we forge a new era of IT, together. 

The post Accelerating Innovation: Reimagining DEX for the AI Era appeared first on Lakeside Software.

But what happens when an L3 incident does require specialized expertise or when there are deeply rooted system issues? You simply cannot use an L1 tool to solve many L3 incidents, and “shifting less” is impossible. That’s like trying to start your broken-down, old car with a 2024 Maserati key. The likely result is that your car will be stuck in the driveway for quite some time. 

So, when it comes to complex L3 incidents, robust datasets and machine learning (ML) are imperative. By using advanced analytics and RCA based on ML models, IT departments not only resolve these challenging L3 incidents faster but also learn from them to prevent future occurrences. Tools with the capability to resolve only L1 issues do not have the data and visibility needed to see issues across the entire IT estate and pinpoint the root cause of more nuanced problems. 

When an L3 issue escalates, IT service desks need powerful diagnostics to help get to the root cause quickly. The ability to conduct root cause analysis empowers service desk teams to accelerate troubleshooting for the most complex technical issues, enhancing the digital employee experience, engagement, and productivity.  

Lakeside’s L3 root cause analysis capability in SysTrack combines historical and real-time data across the IT estate to run automated diagnostics and provide detailed drilldowns for L3 technicians to triage either physical or virtual desktops. SysTrack’s L3 root cause analysis centralizes both the collection and analysis of data related to system performance, application behavior, and environment bottlenecks, by using:  

• Automated diagnostics 

• 1,300+ smart sensors 

• Dependency mapping 

• Powerful visualizations 

This built-in L3 expertise stands out among DEX solutions in the market today. In fact, in the Forrester Wave: End User Experience Management, Q3 2024, customers cited Lakeside’s “exceptional support and extremely robust RCA capabilities” and “significant cost savings from using (SysTrack).” The report also noted SysTrack’s “real-time reporting and market-leading data retention policies to enable deep historical analysis, making it an excellent tool for level-three RCA.”

Understanding L3 Incidents and the Importance of Root Cause Analysis 

L3 incidents typically involve the most complex and critical issues in an IT environment. These could range from system outages such as a Blue Screen of Death or software malfunctions that require a high level of technical expertise to diagnose and fix. Unlike L1 or L2 incidents, which often are resolved through predefined scripts or automations, difficult L3 issues demand deep investigation into the underlying causes of the problem. 

Root cause analysis is critical in these cases. Rather than treating the symptoms, RCA aims to identify the core issue that triggered the incident in the first place. Conducting RCA for L3 incidents, however, can be time-consuming, requiring collaboration among multiple teams and sifting through vast amounts of data. Accordingly, a DEX tool that may thrive when it comes to L1 ticket resolution may not be the best option for solving complex L3 issues. What’s more, machine learning can make a significant impact by automating parts of the RCA process, enabling faster and more accurate resolution. That is why the depth, breadth, history, and quality of data matter. Lakeside SysTrack, for example, collects more data than any other DEX tool on the market. Specifically, it collects 10,000 data points every 15 seconds from an endpoint.  

Using Lakeside SysTrack, a U.K.-based global law firm’s IT team, for example, was able to: 

• Detect three sensors going off thanks to ML-based anomaly detection, impacting 800 machines in the environment, or nearly 10% of staff. 

• Investigate the root cause of the spiking CPU and discover the culprit was a common video driver. 

• Resolve the issue with a driver update before the issue hit the whole firm and affected employee 

How Machine Learning Enhances Root Cause Analysis 

Why does Lakeside SysTrack stand out as a go-tool DEX tool for L3 incident response (in addition to solving L1 and L3 tickets)? The differentiators boil down to two things: data and AI based on machine learning. Machine learning, when applied to IT operations, enables systems to analyze large datasets, identify patterns, and predict potential issues before they escalate. For L3 incidents, ML models can be trained on historical incident data to recognize trends, common failure points, and correlations between various system events. Here is how ML-powered RCA works in practice: 

1. Anomaly Detection: ML models can continuously monitor system behavior to detect anomalies in real-time. These anomalies, whether they are unexpected spikes in network traffic or unusual application response times, often serve as early indicators of larger problems. Identifying these anomalies early allows IT teams to focus their investigation on specific areas, reducing the time needed for RCA. 

2. Automated Correlation: When an L3 incident occurs, a major challenge is understanding its relationship with other system events. ML can automate this process by correlating the incident with other events across the infrastructure, such as recent software updates, configuration changes, or performance degradation in related systems. This automated correlation narrows down the possible root causes, enabling IT teams to take more targeted action. 

3. Historical Analysis: ML can analyze past incidents to discover recurring patterns. For example, if similar issues have occurred in the past due to a specific network configuration, the ML model will suggest this issue as a probable cause when a new incident arises. Over time, as more incidents are logged and resolved, the model becomes increasingly accurate in its predictions. Here, data history matters; it is why Lakeside SysTrack stores data for up to three years. Other DEX tools simply do not take this extra step related to endpoint data collection. 

4. Proactive Recommendations: Beyond simply identifying the root cause, ML can provide proactive recommendations based on historical data and predicted trends. If the ML model predicts that a similar incident is likely to happen again due to recurring system issues, it can suggest preventive measures such as software patches or system reconfigurations. 

Business Value of Using Data and Machine Learning for L3 Ticket Resolution 

The integration of machine learning into the RCA process for L3 incidents offers several significant business benefits: 

• Faster Incident Resolution: By automating parts of the investigation process, IT teams can resolve L3 incidents faster, reducing downtime and minimizing the impact on business operations. This improved efficiency reduces mean time to resolution (MTTR). 

• Cost Efficiency: L3 incidents are typically the most expensive to resolve due to the high level of expertise required. Machine learning reduces the need for manual investigation, allowing organizations to save on operational costs while still maintaining high-quality resolutions. 

• Better Digital Employee Experience: RCA allows for troubleshooting without interrupting end users, allowing IT teams to maintain strong digital employee experience. 

• Continuous Improvement: Machine learning models improve over time as they are trained on more incident data, leading to increasingly accurate RCA and more proactive incident prevention. 

• Increased IT Resilience: Proactive identification of root causes and early anomaly detection enhance the overall resilience of the IT environment, allowing businesses to avoid outages and maintain service availability. 

As IT environments grow more complex, managing L3 incidents through traditional methods is no longer sufficient. Leveraging data and machine learning for root cause analysis transforms how organizations approach these complex issues, allowing for faster, more accurate resolutions and preventing future incidents from arising. Data provides the big picture and granular visibility RCA needs to resolve complex L3 issues related to endpoint devices, software, networks, web applications, and the historical performance of the device or IT estate.   

IT leaders who invest in ML-powered solutions for L3 tickets such as SysTrack not only will reduce operational costs but also build a more resilient, future-ready IT infrastructure. Because SysTrack also is a powerful tool for resolving L1 and L2 tickets, why not choose the DEX solution that does IT all? 

The post Think about IT: Why You Can’t Use an L1 Tool to Solve an L3 IT Incident  appeared first on Lakeside Software.

In today’s rapidly evolving workplace, where remote and hybrid work models have become the norm, organizations are increasingly relying on technology to keep their employees productive and engaged. This shift has highlighted the critical need for effective monitoring and management of digital experiences. Let’s explore the concept of Digital Experience Monitoring (DEM) and its evolution into Digital Employee Experience (DEX).

What Is Digital Experience Monitoring (DEM)?

Digital Experience Monitoring (DEM) is a technology-driven approach to understanding and optimizing how end-users interact with various digital platforms and services within an organization.

The four key aspects of DEM include:

1. Continuous data collection from endpoints devices
2. Analysis of health and performance metrics
3. Identification of device-performance issues affecting user productivity
4. Proactive problem-solving and optimization of digital environments

DEM goes beyond traditional IT monitoring by focusing on the end-user’s perspective, providing insights into how technology impacts their day-to-day work experience.

The Transition from Digital Experience Monitoring to Digital Employee Experience (DEX)

As organizations recognized the broader implications of digital experiences on employee satisfaction, productivity, and retention, the concept of Digital Employee Experience (DEX) emerged. In fact, Gartner® says, “By 2026, 50% of digital workplace leaders will have established a DEX strategy and tool, up from 30% in 2024.”

DEX expands on the technical focus of DEM to encompass a more holistic view of how employees interact with and perceive their digital work environment.

DEX incorporates:

1. Technical performance metrics (from DEM)
2. User sentiment and feedback
3. Broader workplace factors (e.g., digital literacy, IT support quality)
4. Alignment with business goals and employee needs

This evolution reflects a growing understanding that technology is not just a tool but a fundamental part of the employee experience in modern workplaces.

Key Differences between Digital Experience Monitoring and Digital Employee Experience

While DEM and DEX are closely related, there are some important distinctions:

Analysts have identified several key differences between Digital Experience Monitoring (DEM) and Digital Employee Experience (DEX). Here’s an overview of the core distinctions:

1. Scope:

– DEM: Primarily focuses on technical performance and user interactions with specific digital touchpoints.

– DEX: Encompasses a broader view of the entire digital workplace experience and how employees interact with technology.

2. Stakeholders:

– DEM: Mainly involves IT departments and operations teams.

– DEX: Engages multiple stakeholders including IT, HR, facilities management, and business leaders.

3. Approach:

– DEM: Takes a more reactive approach, often focusing on troubleshooting and resolving technical issues.

– DEX: Adopts a proactive stance, aiming to continuously improve the overall digital work environment.

4. Outcomes:

– DEM: Primarily aims to optimize technical performance and reduce downtime.

– DEX: Focuses on enhancing employee satisfaction, productivity, and overall business outcomes related to employees’ use of digital tools.

5. Technology focus:

– DEM: Emphasizes monitoring and analyzing specific technologies and applications.

– DEX: Considers the entire technology ecosystem and how it integrates into employees’ workflows.

6. Timeframe:

– DEM: Often deals with real-time or near-real-time data for immediate issue resolution.

– DEX: Considers both immediate issues and long-term trends to drive strategic improvements.

The post From Digital Experience Monitoring to Digital Employee Experience: An Evolution in Workplace Technology appeared first on Lakeside Software.

But what does DEX really mean for your business? And why should you care?

This blog was originally published on June 12, 2020, and updated Sept. 24, 2024, with the latest data from industry analysts on the digital employee experience.

The post Digital Employee Experience: What You Need to Know About DEX appeared first on Lakeside Software.

In today’s always-evolving IT landscape, enterprises are under immense pressure to deliver seamless and efficient services to support employee productivity. At the heart of this effort lies the Digital Employee Experience (DEX), which encompasses how employees interact with the IT systems and digital tools provided to them. A poor DEX can significantly hinder employee efficiency, slow down workflows, and introduce bottlenecks that could harm overall business resilience. 

During a recent discussion on IT resilience and digital employee experience, Simon Salloway, an expert in IT infrastructure and lead solutions architect, EMEA, at Lakeside, explored why a DEX strategy is critical for IT resilience and how IT teams can proactively manage and improve it. Let’s delve into the key insights from that conversation. 

The Shift from Technical Metrics to User Experience Metrics 

Traditional IT monitoring has largely been focused on technical metrics—system uptime, network performance, hardware status, and other backend processes. While these are important, they don’t always tell the full story of how users experience IT services. As Salloway mentioned: “While most of these tools give you quite an assortment of technical metrics, none of them really give you a good sense of the IT service that you’re delivering to your business.” 

The rising importance of an enterprise DEX strategy, as discussed by both Gartner® and Forrester industry analysts in their recent market reports, illustrates that the technical side of IT infrastructure is just one piece of the puzzle. The firsthand employee experience matters, especially in varied working environments—whether purely remote, hybrid, or in an office. Employees depend on a variety of digital tools and applications to perform their tasks — from cloud-based software to various types of laptops or virtualized desktops and connectivity solutions. Delivering a great digital experience, then, is not just about whether systems are technically running; DEX is about how well they are supporting users in doing their jobs efficiently.  

Why DEX is Critical to IT Resilience? 

At its core, DEX is about ensuring that the IT environment facilitates productivity rather than hindering it. Salloway used a fitting analogy to describe what it fundamentally means to employees to have a great digital experience: “If you’re in a room, and the air temperature is right, you don’t really notice it. But if it’s too hot or too cold, you immediately notice. Similarly, when IT systems are running smoothly, employees are able to perform their tasks without disruption, often without even thinking about the IT systems behind the scenes. But when things go wrong — be it a slow system, faulty application, or poor network performance — the IT issue becomes painfully obvious. Such disruptions can slow down employees, frustrate them, and reduce their overall productivity. 

The resilience of an IT organization is reflected in its ability to prevent and quickly address these issues. That’s proactive IT. If IT professionals can anticipate potential problems by monitoring user experience data and identifying root causes, they can improve the overall resilience of the IT organization. IT should not be a barrier; it should be invisible, quietly doing its job so employees can focus on theirs.

How IT Can Measure and Quantify DEX? 

One of the most important steps in managing DEX is measuring it. Quantifying user experience with a DEX score can help IT teams track the impact of systems on employee productivity. This use of a score goes beyond simply knowing that an application is up and running; instead, it also involves understanding how well that application is performing for each user and whether it is slowing them down in any way. For instance, as Salloway pointed out: “If you could measure and quantify… and not only have a measurement but understand what is specifically impacting those users, you’d have the detailed data behind that score to be able to improve it.” 

This data-driven approach gives IT teams the ability to be proactive rather than reactive. By capturing detailed information about the root causes of device or application performance issues — whether slow Wi-Fi, latency, or a specific application problem — IT teams can identify patterns and resolve common issues before they become widespread. 

Leveraging Data to Improve DEX 

When it comes to large enterprises and organizations, issues that may seem isolated can quickly compound and affect a significant portion of the workforce. Being able to analyze DEX data at scale across an entire enterprise is key to ensuring IT resilience. The famous 80/20 rule comes into play here, as Salloway explained: “If you can fix that 20 percent of things that affect 80 percent of the impact, you can make dramatic improvements in your IT service level that you provide to the business.” 

This ability to prioritize and tackle the most pressing issues affecting the digital employee experience can result in significant improvements in employee productivity and satisfaction. It also strengthens IT resilience, ensuring that common problems don’t spiral into major disruptions that could affect business operations. 

The Business Impact of DEX 

One of the most significant impacts of a good DEX strategy is improved employee productivity. Every time an employee encounters a system failure or slowdown, that issue directly affects their ability to do their job. Over time, these small issues can accumulate and have a significant negative impact on the business. As Salloway noted: “If a device or app is not performing, people notice, because it affects their ability to do their job, in turn affecting their productivity.” 

A strong DEX strategy ensures that IT services are optimized to support employees in doing their jobs without unnecessary interruptions. It also reduces the risk of IT-related bottlenecks, contributing to the overall resilience of the organization. 

A Strong DEX Strategy for IT Resilience  

In the evolving world of IT, where technology plays an increasingly critical role in business operations, DEX has emerged as a crucial factor in ensuring IT resilience. By moving beyond technical metrics and focusing on how IT services impact employee productivity, IT professionals can better support their organizations. Measuring DEX, understanding its root causes, and acting on that data allows IT teams to be proactive, efficient, and, ultimately, resilient in the face of challenges. 

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