The ITSM Reset: Why AI Can’t Scale Without Strong Service Management Foundations
In 2026, artificial intelligence (AI) is mainstream in IT service management (ITSM) (as long as you don’t look too closely). Autonomous workflows, predictive operations, and conversational experiences are already in production across many organisations. Sounds fancy, right? Now, the more important question is whether the operational foundation underneath those AI initiatives is capable of supporting them. Or do we need an ITSM reset?
AI Adoption in ITSM Has Reached a Tipping Point
Our latest global research suggests that many organisations still have work to do. I know. Here we were thinking, “The AI is going to do all the work for us!” Well… not quite, as it turns out. While AI adoption in ITSM is nearly universal, the maturity of the underlying frameworks on which ITSM operates is still lacking. In fact, ITIL maturity is – candidly – rather low, globally. This is creating a gap between what organisations hope that AI will achieve and what their underlying service management environment can realistically support.
Let me just come out and say it: work must be documented digitally for AI to do the work.
Great AI outcomes require strong data and process rigor, and those are two things ITSM orgs have struggled with for 3+ decades. The struggle didn’t magically go away because “We’ve integrated ChatGPT”.
This is why I believe 2026 is shaping up to be the year of the ITSM reset.
The AI Adoption Question Has Already Been Answered
Based on our research, it’s abundantly clear that AI is no longer an emerging technology in ITSM. It’s now part of everyday operations.
Among the more than 1,000 IT professionals we surveyed, 95% reported using AI to improve ITSM processes. Organisations are applying AI across a wide range of activities, and the use cases with the highest adoption rates include IT asset tracking and reporting (41.5%), AI-powered chatbots (39%), task automation (38%), trend analysis (38%), and incident prediction and prevention (37%).
Importantly, these IT leaders say they are generally satisfied with the results. Across the 11 use cases referenced, AI is largely meeting or exceeding expectations. Respondents gave the highest satisfaction scores to translating service responses and knowledge articles (54.5%), and to task automation, IT asset tracking and reporting, and incident prediction and prevention (all at 53%). The dissatisfaction scores reported are relatively low (less than 10% in most cases); however, there don’t seem to be any known answers across the industry about what value is being achieved and how it is measured.
These results are encouraging for service management teams and signal that AI is delivering. But that’s only part of the picture. One that hides the need for an ITSM reset.
The Hidden ITSM Maturity Gap Behind AI Initiatives
While AI adoption is widespread, our research also shows that relatively few organisations describe their service management practices as mature. Only 12% of respondents say their ITSM approach is proactive and fully mature. By comparison, 31% report operating in partially structured environments, while 11% describe their approach as ad hoc and reactive.
In other words, more than four in ten organisations are still working in environments where processes, workflows, governance, and operational consistency are largely absent, or still a work in progress. This creates a notable contradiction. It means that organisations are moving ahead with AI while underlying service management issues, probably ones that they have struggled with for years, go unresolved. Importantly, do they know they need an ITSM reset?
It’s also understandable. AI is hard to ignore. New capabilities are appearing in the platforms organisations already use and are increasingly present in the tools people use outside of work. When technology is advancing this quickly, it’s easy for attention to shift to new capabilities before longstanding operational issues are fully addressed. So, I understand why we’re here, but it also makes the incoming challenges pretty easy to predict.
Why AI Amplifies Process Issues Instead of Fixing Them
While AI can support teams in many useful ways (automating tasks, surfacing insights, improving the end-user experience), it cannot compensate for fragmented processes, disconnected systems, or inconsistent data. If your knowledge articles are still providing steps to resolve the issue on Windows XP, the answer the LLM retrieves will still be stupid.
In many cases, AI reflects the environment in which it operates. If the underlying process is efficient, AI can help scale that efficiency. If the process is inconsistent, AI can make those inconsistencies more visible and spread them faster. AI isn’t creating these issues, but it is revealing them very quickly, conveniently, and fluently. AI is showing up the need for an ITSM reset.
The Operational Challenges Holding Back AI Success
One of the most interesting findings in the research shows that AI deployment challenges closely mirror broader ITSM challenges.
When asked about the biggest obstacles to deploying AI, respondents cited data privacy and security concerns (23%), integration challenges (18%), a lack of expertise (14%), and costs (13%). When asked about their biggest challenges in delivering IT services effectively, the same areas topped the list: ensuring secure and compliant IT operations (39%), budget constraints (38%), a lack of skilled personnel (36%), and a lack of integration between IT tools (35%).
These are not separate challenges. The same operational issues that make service management difficult also make it harder to scale AI successfully.
This matters because many organisations continue to view AI as a technology initiative. In reality, the factors that limit AI are often operational. The challenge is often less about finding the right AI capability and more about creating the conditions, the environment where it can deliver consistent results. Those same conditions would also have helped the team of humans perform better.
You can choose the most advanced or sophisticated AI technology on the market, and that won’t be able to compensate for the friction that disconnected tools, poorly documented workflows, fragmented data, and inconsistent governance will create.
What an ITSM Reset Looks Like in 2026
For many organisations, the next phase of AI success will not come from deploying some leading-edge new AI tool. Instead, the next phase of AI success will come from strengthening the operational foundation on which AI relies.
ITSM Reset #1 – Simplify and Standardize Core Processes
It’s only natural that service management environments accumulate clutter over time. Unnecessary approvals, overlapping workflows, disconnected tools, and redundant steps are really inefficiencies that create complexity for employees and make automation less effective.
ITSM Reset #2 – Reduce Tool Sprawl and Improve Integration
It’s no surprise that integration is one of the most persistent challenges organisations face. AI works best when it can access reliable information across connected systems. Information spread across disconnected tools and data sources makes this much, much harder.
ITSM Reset #3 – Strengthen Governance for AI-Driven Operations
As organisations expand their use of AI, they need confidence that processes are documented, decision-making responsibilities are clear, and data is being managed properly. Governance is sometimes viewed as bureaucracy, but it provides the structure that enables innovation to scale safely and responsibly.
ITSM Reset #4 – Build Security and Compliance into Workflows
Given that security and compliance is the top operational challenge cited by respondents, organisations cannot afford to treat them as separate initiatives.
ITSM Reset #4 – Measure AI Outcomes Consistently
While many respondents report positive experiences with AI, organisations need consistent ways to evaluate its business impact over the long term. Without clear measurement, it will be difficult to understand where AI is delivering results and where adjustments are needed.
Building Agentic Readiness Through ITSM Maturity
But this is a list of needs and constraints… which can feel a bit overwhelming. So let me be clear: The single most useful thing IT teams can do is think LEAN. Simplify down. Get back to the basics.
Identify the core processes and focus on getting them right. Find SIMPLE, obvious use cases for AI, and deploy it for one specific use case at a time. Then check back on it, see how it’s going, and make improvements. From there, when solving issues, seek out the single bottlenecks and tackle them one at a time.
Every day, we do slightly better than the day prior. This is how the whole industry is moving forward with AI.
The Next Competitive Advantage: Operational Excellence Before AI Scale
The next challenge is no longer about whether AI can improve service management. We are already seeing evidence that it can. The question now is: Do organisations have the operational maturity to expand those improvements across the enterprise?
The organisations that get the greatest value from AI over the next several years may not be the ones that deploy the fastest. I suspect they’ll be the ones that spend time strengthening their service management practices first. I’m referring to this as investing in “agentic readiness” instead of buying AI tools, crossing your fingers, and hoping for the best.
For IT leaders, service desk managers, and ITSM practitioners: it’s time to take a hard look at the fundamentals. Consider process design, integration, governance, security, and measurement. AI cannot create operational maturity on its own. It depends on the system of record it is placed on top of. As organisations pursue larger-scale automation and more autonomous service experiences, the strength (or weakness) of that foundation may prove to be the most important differentiator of all.
ITSM Reset FAQs
Why can’t AI scale effectively in ITSM without strong foundations?
AI in ITSM depends heavily on structured, consistent, and well-governed operational data. In ITSM environments where processes are fragmented or poorly documented, AI struggles to deliver reliable outcomes at scale because it simply amplifies the quality (or lack of quality) of existing systems.
What is the “ITSM maturity gap” in relation to AI adoption?
The ITSM maturity gap refers to the difference between high levels of AI adoption and relatively low levels of operational maturity. Many organisations are actively using AI in ITSM, but still operate with inconsistent processes, limited governance, and fragmented workflows, which prevent AI from reaching its full potential.
Is AI actually delivering value in ITSM today?
Yes. Many organisations report that AI is meeting or exceeding expectations in areas such as chatbots, task automation, asset tracking, and incident prediction. However, while satisfaction is generally high, many organisations still lack consistent methods for measuring the true business value delivered.
What are the most common use cases for AI in ITSM?
The most widely adopted AI use cases in ITSM include IT asset tracking and reporting, AI-powered chatbots, task automation, trend analysis, and incident prediction and prevention. These are typically focused on efficiency, speed, and improved end-user experience.
Why does AI amplify existing ITSM issues instead of fixing them?
AI does not fix broken processes or poor data quality. Instead, it reflects and accelerates what already exists. If ITSM processes are inconsistent or poorly documented, AI will replicate and scale those inconsistencies rather than resolve them.
What operational challenges most affect AI success in ITSM?
The biggest barriers include data privacy and security concerns, integration challenges, lack of skilled personnel, and budget constraints. These are the same challenges that already impact ITSM effectiveness, showing that AI issues are often symptoms of broader operational weaknesses.
Why is ITSM integration so important for AI?
AI relies on access to accurate, connected data across multiple systems. When tools and workflows are disconnected, AI cannot reliably interpret or act on information. Strong integration is essential for enabling consistent automation and decision-making.
What does an “ITSM reset” mean in practice?
An ITSM reset involves simplifying and standardising processes, reducing tool sprawl, improving integration, strengthening governance, embedding security into workflows, and establishing consistent outcome measurement. It focuses on operational maturity before scaling AI initiatives.
What is “agentic readiness” in ITSM?
Agentic readiness refers to the ability of an organisation’s ITSM environment to support autonomous or semi-autonomous AI agents. It depends on clear processes, reliable data, strong governance, and integrated systems to ensure AI can safely execute actions across the service landscape.
How should organisations start improving ITSM maturity for AI?
A practical starting point is to adopt a lean approach: simplify core processes, focus on high-value use cases, reduce complexity, and improve one area at a time. Incremental improvements in process design, integration, and governance lay the foundation for scalable AI success.
Written by Keith Andes, previously published on ITSM.tools.
DON’T IMPLEMENT AI AGENTS WITHOUT A STRONG ITSM AND ITOM FOUNDATION
Organizations are shifting rapidly to implement AI in the enterprise to improve operational efficiency, productivity, and the customer experience. In 2024, organizations invested $252.3 billion in AI, yet the impact is mixed. While most organizations see positive financial impacts, most reported cost savings of less than 10 percent and revenue increases below five percent. Forward-looking leaders aren’t asking if agentic AI will reshape their business, but how they can prepare their service operations environments to deploy it safely and effectively.
Agentic AI relies on continuous, high-quality context and predictive and casual AI insights. Collectively, these capabilities enable agentic AI systems to observe and explain system behavior in real time, and recommend remediation plans effectively. This in turn helps human IT teams make better decisions.
Context in agentic AI
For an agentic AI agent, context is far more than just the user input. It is the full operational environment that the AI agent uses to reason and coordinate and execute complex workflows. Context allows AI agents to make decisions that go beyond simple rules.
An AI agent triaging an application performance issue, for example, will use context about the relationship between the application and the supporting infrastructure, log insights, recent deployments, and knowledge articles before acting. This requires reconciliation of application,network, and infrastructure topology, which is very difficult to do. Starting with a strong service modeling strategy—the practice of creating a service infrastructure model that stays up to date in real time—provides teams with a single source of truth for end-to-end observability. The first step is to ingest topological data from sources across your environment, including third-party monitoring tools such as AppDynamics for application topology discovery and mapping.
Our world-class BMC Helix Discovery product continuously identifies and catalogs all hardware, software, and services in your environment. This includes servers, storage, network devices, container platforms like Kubernetes, and cloud services from providers like Amazon Web Services (AWS) and Microsoft Azure. Next, the topology reconciliation capability merges all of your ingested data into a single, unified model of your service environment. Reconciliation identifies differences in topology reports and standardises element names and relationship representations. What began as a group of disconnected topology reports is merged into a unified model that clarifies dependencies and relationships.
Figure 1. BMC Helix’s multi-agent system approach for reducing service downtime in ServiceOps environments.
After reconciliation, dynamic service modeling uses blueprints to build the data structure of the services, including information about configuration items (CIs) and their relationships. These modeled services provide critical context for AI to perform root cause analysis, continuous optimization, and continuous compliance. Service models also allow site reliability engineers (SREs) or DevOps engineers to pinpoint an application performance degradation to the network interface flapping.
Building on predictive and causal AI
Building on the predictive and causal AI capabilities of BMC Helix AIOps extends its foundation of historical data and machine learning (ML) to generate advanced insights, simulations, and recommendations. Integrating causal reasoning limits or eliminates hallucination and the use of outdated information. This helps agentic AI systems deliver consistent, precise, and reliable outcomes that are easier to trust, explain, and verify in real-world applications.
Causal AI is a branch of ML that emphasizes the understanding of cause-and-effect relationships versus solely processing patterns in data. Causal AI integrates knowledge-graph-based and transformer-based AI techniques to understand and model relationships across telemetry data variables. Casual AI can reason about casual relations or patterns using topological data. A knowledge-graph–based causality analysis analyzes how causal relationships change, depending on how the variables influence one another. This is crucial because, without causality, AI agents will not perform well in dynamic environments.
On the other hand, predictive AI uses historical data, ML, and statistical algorithms to forecast potential issues like performance bottlenecks and capacity shortages before they disrupt services. By integrating with predictive AI, agentic systems can use forecasts to act, often correlating a series of actions to complete the task, like optimizing cloud capacity based on resource shortage prediction.
Context and a composite AI approach
The next step in AI requires a composite AI approach that complements agentic AI with predictive, causal, and generative AI. We expect enterprises to deploy ecosystems of large language models (LLMs), knowledge graphs, and predictive and causal models that architecturally augment each other to create capable agentic AI systems.
Enterprises looking to deploy AI agents must have strong IT service and operations management (ITSM/ITOM) capabilities and accurate data to extract value from AI agents, including:
- CMDB: Make sure your configuration management database (CMDB) is accurate.
- Reconciliation: Ensure your reconciliation rules are protecting your CIs.
- Service models: Provide domain-specific knowledge as input to the AI pipeline.
It’s also important to have enough surrounding information to understand why systems are behaving the way they are, like knowing that high latency in service A happened right when service B released a new version. Without that context and a composite AI approach, AI agents won’t be effective digital partners.
Written by ServiceOps.
HappySignals’ Global Benchmark 2026: Employees Lose More Than Three Hours of Productivity Per IT Incident
New analysis of 1.77 million employee responses reveals that resolving tickets doesn’t necessarily make people happy, and that a small percentage of support cases create the majority of productivity loss. Solving IT issues is just the baseline. What drives satisfaction is how the ticket is handled.
HappySignals has released its Global IT Experience Benchmark 2026, the world's largest continuous dataset on employee IT experience, based on almost 2 million employee responses collected across more than 130 countries. The findings challenge several long-held assumptions about IT support and reveal high hidden costs in employee productivity.
According to the report, employees lose an average of 3 hours and 18 minutes of productive time per IT incident. The findings suggest that while organizations continue to invest heavily in AI, automation, and digital transformation, workplace productivity is still significantly impacted by unresolved friction in day-to-day IT services.
"The AI era is forcing organizations to rethink how they measure IT success," said Sakari Kyrö, Product Strategy Lead at HappySignals.
"Traditional operational metrics tell us whether systems are working. Experience data tells us how people feel about IT."
The Global IT Experience Benchmark 2026 analyzes employee feedback collected directly after IT incidents, service requests, and digital workplace touchpoints, providing one of the most comprehensive views of how employees experience enterprise technology.
Key findings
Employees care less about ticket resolution than IT leaders think
One of the report's most surprising findings is that only 6.6% of positive employee feedback directly addresses the technical issue itself. Instead, employee experience is more strongly influenced by communication, empathy, speed, and ease of getting help.
"The technical fix is only part of the experience," commented Kyrö. "Employees remember how support was delivered just as much as whether the issue was resolved."
A small number of tickets causes most productivity loss
The report found that 13.3% of support tickets involve multiple reassignments, with each handoff increasing employee frustration and productivity loss. Previous benchmark analysis has shown that a relatively small proportion of tickets accounts for the majority of lost work time, suggesting organizations may achieve outsizedgains by focusing on the most disruptive cases rather than average performance metrics.
Experience differs dramatically between employee groups
The research highlights substantial differences in how employees experience IT support depending on their role and work style. For example, the proportion of "Doers" — employees focused on completing tasks quickly and efficiently — varies significantly between regions, from 62% in Western Europe to considerably lower shares elsewhere.
The finding challenges the idea of a universal benchmark for employee experience. "The same IT service delivered in two organizations can generate very different outcomes because employee expectations differ," said Kyrö. "Context matters more than many organizations realize."
Human interactions continue to outperform digital channels
Despite years of investment in self-service and automation, employees continue to rate direct human support highly. The report found a substantial experience gap between support channels, with walk-in support achieving significantly highersatisfaction scores than portal-based interactions. The findings raise important questions as organizations increasingly rely on AI agents and digital support models.
Why this matters in the AI era
As enterprises push forward with AI transformation programs, HappySignals argues that employee experience will become an increasingly important business metric.
Organizations can deploy AI tools at scale, but productivity gains will only materialize when employees can successfully adopt and use those tools in their daily work. The report suggests that understanding employee experience may become one of the most important leading indicators of successful digital transformation.
Written by HappySignals.
SITS Celebrates its 30th Edition at Excel London
SITS – The Service Desk and IT Support Show, and the MSP Show returned to Excel London on the 13-14 May 2026 with a fresh wave of new exhibitors, and two days of high quality connections that left the community already looking forward to next year.
‘It’s always valuable to spend time with fellow ITSM professionals discussing best practices, emerging trends, and the challenges we’re all navigating’
SITS remains the only event in Europe offering a completely free ITSM education programme, spanning seminars, panels, Breakfast Briefings and Hot Topic workshops.
The largest ITSM-focused event in Europe, this year’s show brought together 140 vendors showcasing the latest in the IT service management, service delivery and support.
‘The energy at this show never disappoints. Neither does the community’
Event Director, Alice Fulton said: “Walking the show floor this year, the energy was hard to miss. People were genuinely engaged, having real conversations and finding real answers. That is what SITS is all about.”
Alice said “Reaching 30 editions is a milestone we are incredibly proud of. SITS has always been about serving this community, and the fact that it keeps growing tells us that the need for a dedicated space like this is as strong as ever.”
‘Everywhere I turned, there were conversations about automation, service operations, integrations, AI, security, ITSM, and data quality… I was genuinely in my element’
Exhibitors ranged from household names to emerging start-ups, including Alvao, Xurrent, NinjaOne and SysAid. Solutions covered everything from managing AI risk and leadership to employee experience and autonomous ITSM.
Beyond the sessions, the show floor had plenty to keep attendees entertained. This year's highlights included a KITT car and a McLaren on display, the Mandalorian and Grogu roaming the floor for photos, a dedicated photo wall. There was something for everyone between sessions.
“Nothing replaces a conversation with a real IT pro who looks you in the eye and tells you what they think. About the product. About the marketing. About the people. That feedback loop is worth every square meter on that show floor.” Comments David Protzmann, VP Marketing at NinjaOne.
The seminar programme ran across three theatres and a keynote and packed in 60+ sessions over two days. Speakers from across the industry tackled topics ranging from managing AI risk and leadership to employee experience and autonomous ITSM.
‘The programme was full of insights, my brain was so full of ideas afterwards!’
'The MSP Show is the perfect addition to SITS’
The third edition of the MSP Show took place alongside SITS, and by every measure it was the strongest edition to date. Attendance grew to 1,800+ MSP professionals, while exhibitor numbers increased by 10%. The response from attendees and vendors alike has been overwhelming, with feedback consistently describing it as the best MSP Show yet.
SITS Netherlands returns for the second year
SITS and the MSP Show Netherlands return to Jaarbeurs, Utrecht on 22 September 2026. Both events will be bringing the best of the London show to a Dutch audience, with a full programme of technology, education and networking built for the ITSM and MSP communities.
SITS, The Service Desk & IT Support Show and the MSP Show are back 19-20 May 2027 at Excel London.
Join the mailing list to find out more.
Why Your Service Desk Is Flying Blind Without ITAM
The cost of operating without full visibility
Endpoints, applications, and devices multiply across distributed networks faster than most IT teams can track. They can’t secure what they can’t see, and relying solely on agent-based tools means unmanaged devices and unpatched systems go undetected, widening security exposure.
When asset records are incomplete:
IT teams run through generic troubleshooting steps with no visibility into recent device changes.
Service disruptions take longer to resolve, and ecosystem policies may vary.
Tangible costs from untracked assets increase, including hardware expenses, software license overages, and maintenance costs.
IT teams and MSPs need unified visibility for every IT asset.
How ITAM changes issue resolution
IT Asset Management (ITAM) covers the processes an organization uses to manage IT assets, including all hardware and software. It centralizes every asset, managed or unmanaged, into a continuously updated inventory that replaces static spreadsheets with an accurate record of what exists, where it is, and what state it is in.
When asset data is connected and current, IT teams and MSPs spend less time gathering information and more time resolving issues.
Tickets are automatically populated with critical asset information, and endpoint health alerts help create, route, and escalate tickets. Root cause can be traced to a specific device or configuration, and asset dependencies can be reviewed before an update is pushed, reducing the risk of cascading failures.
Why ITAM and ITSM are stronger together
Many IT teams treat asset management and service delivery as separate workflows. Asset records live in one system; tickets get handled in another. The data exists, but it never reaches the person resolving the issue. The gap between the two is where resolution slows down.
Closing the gap starts with understanding what each discipline contributes. ITAM tracks every asset across its full lifecycle, from procurement to decommission. IT service management (ITSM) governs how IT services are planned, delivered, and improved to meet business goals.
The difference becomes tangible in an incident. Without asset context, a technician starts from scratch: standard questions, generic troubleshooting. With ITAM connected to the service workflow, the ticket surfaces that the device is three patches behind, disk usage spiked two days ago, and a configuration change was pushed last week. The technician instantly knows where to look.
For IT teams and MSPs, that connection means fewer handoffs and faster resolution. It's the most immediate efficiency gain available to teams already running ITAM.
Building the practice
ITAM is a process, not a one-time project. Every new device should be added to the inventory at deployment, with monitoring software installed immediately. Periodic scheduled scans and automatic alerts keep asset records current without manual effort. Connecting ITAM with an RMM or endpoint management tool makes the practice more impactful and scalable.
The result is an IT operation that can see its entire environment and act on what it finds: lower resolution times, fewer repeat incidents, and better control over cost and risk.
For a practical guide to building a stronger ITAM foundation, read Fixing IT Asset Chaos.
Written by Amanda Kaza, NinjaOne.
Find NinjaOne on stand 451.
Why Automated IT Distribution Is Becoming a Service Desk Essential
As organisations support hybrid working, mobile staff and shared device models, IT distribution has become a critical, and often underestimated, service desk challenge. Laptops and peripherals are no longer static assets issued once and forgotten; they move continuously between users, locations and support teams. For service desks and MSPs, this has increased pressure to deliver devices quickly, securely and with minimal manual effort.
The Growing Complexity of Device Logistics
Many organisations still rely on traditional methods such as scheduled device collection, manual handovers and decentralised storage. While familiar, these approaches do not scale well. They often require staff to travel to collect or return equipment, reduce visibility of assets and place additional strain on already stretched support teams.
Industry insight consistently shows that poor asset visibility and inefficient lifecycle management contribute to longer resolution times, duplicated effort and unnecessary device purchases. In environments where staff depend on timely access to technology, such as healthcare, education and large enterprises, delays in device availability can directly affect service delivery.
Automation as a Practical Response
To address these challenges, service desks are increasingly treating IT distribution as an integrated operational process rather than an isolated task. Automating device distribution through service management, asset and identity systems enables organisations to standardise how equipment is issued, returned and redeployed.
This approach reduces manual handling, improves auditability and creates a more consistent user experience. For MSPs managing multiple customers or sites, automated distribution models also provide repeatability and control, helping teams scale services without increasing complexity.
Evidence from the Front Line: RDaSH
The experience of Rotherham, Doncaster and South Humber NHS Foundation Trust (RDaSH) illustrates the measurable impact of modernising IT distribution. Supporting more than 155,000 patients annually across 128 sites, the Trust faced significant inefficiencies in how devices were issued and maintained for its geographically dispersed workforce.
During a proof of concept, RDaSH distributed over 1,000 laptops through automated locker based distribution and reported savings of more than 2,000 clinical hours, the equivalent of a full time nurse. The Trust also eliminated regular on-site visits across three major sites, reducing travel time and face to face dependency for both clinicians and IT staff. Device swaps that previously took hours were completed in less than an hour, directly improving staff productivity and service continuity.
Turning IT Distribution into a Measurable Service Outcome
Rather than being a logistical afterthought, IT distribution is increasingly recognised as a strategic service desk capability. When distribution is automated, measurable and aligned with wider service workflows, it supports faster onboarding, better asset utilisation and improved user satisfaction. For service desks and MSPs alike, modern IT distribution is becoming a key enabler of scalable, resilient and user focused services.
Contact email: [email protected]
Contact phone: 0800 130 3456
Written by Kay Tilbury, LapSafe.
Find LapSafe on stand 560.
Meet the Brand: SysAid
SysAid is on a mission to put AI to work for organisations and their people. Built on a robust ITSM platform, SysAid's Agentic AI automates repetitive tasks and frees teams from reactive work. AI Agents take the first action, so IT pros intervene only when truly needed. Organisations can go live in weeks with rapid onboarding and no heavy migrations, with security and governance built in by design.
What does SysAid do?
SysAid is an AI-native Enterprise and IT Service Management platform built for organizations that are ready to move beyond reactive IT.
At the heart of SysAid's platform is Agentic Service Management, in which autonomous AI Agents take the first action throughout the full service lifecycle. Incidents, requests, workflows, asset management, remote support, and AI Agents handle it end-to-end, across the tools your teams already use, without waiting to be told. IT professionals step in only when genuinely needed, freed to focus on the work that actually requires human judgment.
With over 100 prebuilt AI Agents available across platforms, including Microsoft Teams, Jira, and Splashtop . and an AI Agent Builder that lets teams create and deploy custom agents in days, SysAid makes agentic ITSM practical, not theoretical. Security and governance are built in by design, and organisations go live in weeks with no heavy migrations required.
With over 5,000 customers, SysAid partners with organizations from small businesses to Fortune 500 enterprises across 140 countries.
How did you get started?
SysAid's current chapter represents a fundamental shift in what service management can be.
Rather than adding AI at the edges of an existing product, SysAid rebuilt its vision around autonomous operations. In March 2025, a full Agentic AI Platform with ready-to-deploy AI Agents became generally available, making SysAid one of the first ITSM vendors to put true agentic capability directly in the hands of IT teams. By late 2025, SysAid went further, introducing fully agentic coding into its AI Agent Builder: the AI autonomously interprets requirements, generates production-ready code, and validates its own outputs, dramatically accelerating how quickly teams can build and deploy custom agents.
Recognition has followed. SysAid was named in the 2025 Gartner® Magic Quadrant™ for AI Applications in IT Service Management, achieved the highest overall score in the Gartner® Peer Insights™ Voice of the Customer report, and was named AI Company of the Year at the 2025 Globee® Awards for Artificial Intelligence.
What are you looking forward to most at SITS 2026?
SITS is where the real conversations happen. where IT leaders talk frankly about what's working and what's still a struggle. We're especially looking forward to discussing:
- What agentic ITSM looks like running live in production, not in a sandbox
- How organisations are governing AI Agents at scale, safely, and with confidence
- The practical path from reactive service management to truly autonomous operations
- What IT team roles look like when AI Agents handle first response by default
We're most excited to meet leaders who are ready to stop evaluating AI and start running it.
Why should visitors come to your stand?
Booth 211 isn't going to feel like a booth. We're doing something different at SITS this year, bringing the energy, the fun vibes, great swag, an unforgettable experience, and even the chance to win a trip for 2 to the Canary Islands
Come to see Agentic AI running live. Stay for the atmosphere. Here's what you'll find
- Agentic ITSM in action: Live demonstrations of AI Agents autonomously resolving tickets, orchestrating workflows, and executing tasks end to end across your existing tool stack.
- Honest, experience-led conversations: Talk to SysAid practitioners about where to start, what governance looks like in practice, and how to build a roadmap that delivers measurable outcomes.
Find SysAid on stand 211.
SITS - Service Desk & IT Support Show, taking place at Excel London 13-14 May 2026.
For further information, please visit www.servicedeskshow.com.
The hidden cost of institutional knowledge in IT
There’s a person on almost every IT team who just knows things.
They know why the integration between the CRM and the billing system behaves strangely on the last day of the month. They know which server can’t be patched yet because of a dependency no one properly documented. They know that when a particular error code appears, you don’t follow the runbook; you call Dave.
This person is invaluable. They’re also a ticking clock.
What is institutional knowledge, and why is it dangerous?
Institutional knowledge is the informal expertise that lives inside people’s heads rather than in documented systems. It builds up naturally over time in any IT environment. A workaround gets implemented under pressure and never properly written up. A vendor issue gets resolved through a conversation that no one thought to log. A configuration decision gets made for good reasons that were never recorded anywhere.
None of this feels dangerous in the moment. In fact, it often feels like efficiency. Why document everything when the person sitting next to you knows the answer?
The problem is that IT environments are in constant flux. People get promoted. People leave. Teams grow. And every time one of those things happens, a piece of that invisible knowledge either walks out the door or gets stretched too thin trying to cover too many gaps.
What this actually looks like in IT
It’s worth being concrete about what institutional knowledge looks like in practice, because it rarely announces itself. It tends to accumulate in a handful of recurring places:
Undocumented workarounds. A specific script gets run manually every Sunday night to prevent a downstream reporting failure. It’s not in any runbook. The engineer who wrote it left eighteen months ago. The person currently running it learned it from them informally and isn’t entirely sure why it works.
Vendor and integration quirks. The API connection between two SaaS platforms occasionally drops authentication tokens under a specific load condition. The fix takes thirty seconds if you know it, and hours to diagnose if you don’t. That fix exists in one engineer’s head, not in the documentation.
Patch and update exceptions. Certain servers can’t be updated on the standard cycle because of a dependency introduced years ago during a rushed migration. No ticket captures this. It’s kept alive purely through institutional memory, and each time a new team member nearly triggers the issue, someone catches it in the nick of time.
Access and permission logic. In many environments, the actual permission structure in production diverges significantly from what the diagrams show. Shadow admin accounts, legacy groups, role exceptions granted during incidents, someone knows how these work. The org chart doesn’t.
Read the full blog here.
Written by Oded Moshe, SysAid.
Find Sysaid on stand 211.
Why Integration Is Essential for Service Desks and MSPs
As service desks and managed service providers (MSPs) take on broader operational responsibility, the complexity of their toolsets continues to grow. Asset management platforms, IT service management (ITSM) tools, identity systems and booking platforms often operate in parallel, creating silos of data and increasing reliance on manual intervention. For many teams, integration is no longer a “nice to have”; it has become a critical enabler of efficiency, accuracy and scalability.
Reducing Manual Work Through Connected Systems
One of the most immediate benefits of integration is the reduction of duplicated effort. When systems operate independently, service desk teams are often required to update asset records, user permissions and device statuses in multiple places. This not only consumes time but also increases the risk of errors and inconsistencies.
By connecting operational systems, such as asset management platforms, ITSM tools and access controls, updates can be automated and synchronised. Device check-ins, status changes or user allocations can be reflected in real time, reducing administrative overhead and allowing analysts to focus on higher-value tasks.
Improving Visibility and Decision-Making
Integrated environments also provide clearer visibility across the service landscape. When live operational data feeds directly into reporting tools, service desks gain insight into usage patterns, peak demand, device performance and recurring issues. This level of transparency supports more informed decision-making, from capacity planning and budget forecasting to identifying areas for service improvement.
For MSPs managing multiple client environments, centralised reporting can be particularly valuable. A consistent, integrated data view enables faster issue resolution, clearer client reporting and stronger evidence for service recommendations.
Supporting Compliance and Audit Readiness
With increasing regulatory and security expectations, maintaining accurate audit trails is essential. Integrated systems help ensure that asset movements, user access, and device usage are automatically recorded and time-stamped. This creates a reliable source of truth that supports compliance requirements without adding manual documentation work for service desk teams.
A Practical Example of Integration in Action
In real-world scenarios, integration often plays a critical role during system migrations or service changes. For example, when organisations transition to new library, asset or management platforms, seamless integration can help ensure continuity of service and accurate data flow from day one. Successfully aligned systems reduce disruption for end users while maintaining operational control behind the scenes.
Smart Locker Integrations
Smart locker integrations help organisations automate device management securely and at scale. The LapSafe® cloud based platform, ONARKEN®, integrates with systems teams already use, including identity management tools such as Microsoft Active Directory and IT service management platforms like ServiceNow®. Full API support enables automation across operational workflows. IT teams can trigger device swaps and collections through tickets, while estates and facilities teams manage locker allocation via existing booking systems. Administrators gain visibility through integrated dashboards showing usage, issues and trends. Built around permission based access and secure data handling, these integrations support governance and compliance, enabling service desks and MSPs to scale consistent, user focused services across departments, sites and customer environments without added complexity overall.
Written by Kay Tilbury, LapSafe.
Speaker Q&A: Dean Clayton
Dean Clayton, Senior Product Manager, Service Management at OpenText will be taking to the stage at SITS for Teach Your Workflows to Think (So You Don’t Have To).
What’s the most rewarding aspect of your job?
Talking with customers and seeing their reaction when our AI capabilities in OpenText Service Management start delivering real results.
There’s a genuine moment of excitement when they see it working in their own environment making a measurable difference.
If I’m honest though, it’s what happens next, those moments, when they start connecting the dots and imagining what else they can automate with Aviator Agent workflows, are always incredibly rewarding.
What's one workflow that you've seen AI transform in a way that genuinely surprised you?
We’ve seen some impressive ideas emerge in product development — from chatbots that can fulfil requests end to end without human intervention, to AI helping accelerate the product development process itself. But I’m going to give an example from one of our customers.
They had recently completed training on our Aviator agent workflow capabilities and were eager to put in action, specifically, removing manual effort involved to assign tickets to the right teams. After an initial sharing of ideas and approaches, they moved incredibly quickly and built an Aviator Agent workflow for ticket assignment.
What made it particularly interesting was that they didn’t rely solely on looking at similar historical tickets.
Instead, they took a document-centric approach: documenting what each team is responsible for and then using AI to interpret that information and determine the most appropriate assignment.
For me, that was a great example of where this technology is heading. Document-centric configuration feels like the next step beyond traditional low-code or no-code approaches, helping organisations get value faster, while giving business / service owners more direct control over how their services are delivered.
How did you get into agentic AI and what made you realise rule-based automation wasn't enough?
As a Product Manager, we started looking at how to apply GenAI in our product back in early 2023. At the time, many of the demonstrations in the market were focused on relatively simple chatbot scenarios, but it was clear to us that the potential went far beyond that. We could see an opportunity to use AI not just to answer questions, but to actively streamline and improve support operations.
In 2024, we released our first iteration of autonomous AI capabilities, designed not just for user interaction through a chatbot, but to enable AI to take action and get work done independently.
Initially, this was focused on simpler tasks, but even then, it was obvious that support operations were entering a new era.
Rules are useful when every step is predictable, but real support environments are often more dynamic and nuanced than that. Agentic AI opens the door to handling those complexities in a much more flexible and scalable way.
What are you most looking forward to at SITS?
For me, it’s all about the conversations. I’m really looking forward to meeting people at the booth and in my session, hearing about the challenges they’re trying to solve, and understanding what they want to achieve with AI.
Those interactions are incredibly valuable. Of course, they’re a chance to show how our products can help but just as importantly, they often spark new ideas. Some of the best use cases come directly from those discussions, where a real-world challenge opens up a new way of thinking about how AI can be applied.
What do you want visitors to take away from your session?
That AI doesn’t have to be intimidating. Every organisation has its own path when it comes to trust and adoption, and there are plenty of ways to start small, move at the right pace, and still see meaningful value.
SITS - Service Desk & IT Support Show, taking place at Excel London 13-14 May 2026.
For further information, please visit www.servicedeskshow.com.