Knowledge management for IT Service Desk and Support teams : the complete guide

The hidden cost of scattered knowledge: when searching costs more than solving

9.3 hours per week lost searching for information

McKinsey research reveals a brutal reality: employees spend an average of 1.8 hours every day—9.3 hours per week—searching for the information they need to accomplish their tasks. For an IT team of 25 professionals with an average loaded cost of €60,000 per year, this represents between €450,000 and €600,000 wasted annually on search time.

For IT service desk teams managing hundreds of incidents daily under strict SLA requirements, this lost time directly impacts resolution rates and customer satisfaction. However, the real cost extends far beyond simply lost time.

Damien Houllegatte, head of the studies and methods office at the IT department of the Orne Département, states it bluntly:

We had documentation everywhere – Word documents on shared drives, procedures in Alfresco, incident resolutions scattered across OneNote.

This dispersion isn’t an isolated case. According to research, 54% of companies acknowledge that their customer service operations are siloed, and only 25% of executives believe their teams share knowledge effectively.

When obsolete procedures become security risks

Time lost searching represents an efficiency problem. Obsolete procedures create critical security risks.

Without an adapted review process, old procedures remain active. Teams risk following outdated approaches or missing critical security updates. When managing IT infrastructure for services affecting thousands of users, obsolete instructions aren’t just inefficient—they’re dangerous.

Statistics on the cost of technical debt confirm the problem’s magnitude: 40% of IT budgets are consumed by the consequences of technical debt, a large portion stemming from obsolete or missing documentation. Between 20% and 40% of the average technical stack constitutes pure technical debt—code and procedures that slow the organisation without creating value.

The tribal knowledge trap: when the expert goes on holiday

The IT department of the Orne Département oversees infrastructure for 1,800 staff distributed across Normandy: from managing the department headquarters to social services and maintaining thousands of kilometres of departmental roads.

With 25 IT professionals managing about a hundred business applications and approximately 3,500 IT equipment pieces, each expert holds critical knowledge. A colleague on leave? Good luck finding their solutions.

This fragility—knowledge locked in heads rather than accessible systems—transforms each absence into a potential crisis. Organisations lose up to 20 hours monthly per team due to non-integrated or non-centralised knowledge management tools.

The five specific challenges of IT documentation

1. Tool multiplication kills productivity

According to industry data, 54% of organisations rely on more than five different platforms for information sharing. Word, SharePoint, OneNote, Teams, Outlook, internal wikis, Alfresco, Confluence—each tool solves a problem but creates a new silo.

The result? 31% of employees don’t even know how many knowledge management tools their organisation has deployed.

A global manufacturing company discovered over 15,000 inactive SharePoint sites consuming terabytes of storage, with maintenance costs—storage and backup included—exceeding €68,000 annually. A financial services company had to allocate an entire quarter to a cleanup project after realising 40% of its SharePoint sites were duplicates or abandoned, requiring 1,200 IT hours and delaying other strategic initiatives.

2. Inefficient search paralyses technicians

The statistics are damning: knowledge workers need an average of eight searches to locate a single document. For IT teams managing hundreds of technical procedures, this friction accumulates into catastrophic lost time.

20% of IT service desk agents’ time is spent searching for information to share with end users or understanding the correct resolution approach. In a team of 25 support technicians, this represents the equivalent of 5 full-time people searching instead of solving.

3. Critical knowledge isn’t documented

The best resolutions—those that quickly solve complex incidents—often remain in senior experts’ heads. Research from IDC shows that 83% of employees have had to recreate missing documents, wasting precious time and effort.

This tribal knowledge creates a double penalty: experts spend time re-explaining the same solutions, and juniors lack access to the procedures that would accelerate their skill development.

4. Context is missing to apply procedures

Even when procedures exist, they often lack the necessary context to apply them effectively. Which version of the procedure applies to which environment? When was this procedure last validated? Who should be contacted in case of problems?

Without this context, technicians hesitate to apply documented procedures, preferring to ask for confirmation—which brings us back to the initial problem of expert dependency.

5. ITSM systems and knowledge bases don’t communicate

Teams use GLPI, ServiceNow, or Jira Service Management to manage tickets. However, technical documentation lives elsewhere—in SharePoint, Confluence, or separate wikis. Result: technicians juggle between systems, losing context and multiplying interface changes.

This cognitive friction slows resolution and discourages documentation. Why document if it’s complicated to access at the critical moment?

Why IT service desk teams need centralised knowledge management

Whether your organisation calls it a help desk, service desk, or IT support centre, the challenges remain identical. However, IT service desk operations face unique pressures that make effective knowledge management not just beneficial—but essential for survival.

SLA compliance creates constant pressure. Service desk teams operate under strict service level agreements with defined response and resolution times. Every minute spent searching for information pushes teams closer to SLA violations. When a Level 1 technician can’t find the documented procedure for a common issue, the choice becomes: guess the solution (risky) or escalate unnecessarily (inefficient).

24/7 coverage amplifies knowledge fragmentation. Service desks never sleep. Knowledge that exists in one shift’s collective memory disappears when that shift ends. Night shift teams shouldn’t have to rediscover solutions that day shift already knows. Weekend coverage shouldn’t mean degraded service quality because senior experts aren’t available.

Escalation paths break without documented context. Modern IT service desk operations rely on tiered support structures. But escalation only works if Level 2 and Level 3 teams receive complete context. When knowledge isn’t captured during resolution, escalated tickets arrive incomplete, forcing experts to restart investigation from scratch.

Service desk efficiency improvements require more than faster ticket systems. They require knowledge infrastructure that supports every technician, every shift, every day.

How knowledge management transforms IT support: from reaction to infrastructure

A single source of truth that eliminates treasure hunts

The first fundamental transformation consists of centralising all technical knowledge in a single platform with intelligent categorisation. No more hunting between SharePoint, Teams, and Outlook to find the right procedure.

VINCI Energies’ IT department organised its content by service domains with clear tagging for software, hardware, and procedures. 110 spaces created for each key topic. Each team has its own space, and for teams sharing the same manager, spaces are grouped—which facilitates and accelerates search.

Emma Hurteaux, Digitalisation & Service Quality team lead at VINCI Energies, explains:

A knowledge base’s success depends mainly on staff involvement, who must write, correct, and update articles very regularly. So we needed a dynamic, user-friendly, and easy-to-use tool. Elium meets these criteria perfectly!

AI integrated into workflow: contextual assistance without changing systems

The second transformation: integrating artificial intelligence directly into the existing ticketing system rather than forcing technicians to switch between applications.

The Orne Département integrated Elium’s AI assistants directly into their GLPI tickets—their open-source ITSM software for IT service management. No workflow disruption, just instant access to the right answers.

This aligns with Gartner’s prediction that by 2026, one in 10 agent interactions will be automated using AI—a trend already visible in early adopter organisations.

Damien Houllegatte confirms:

All our technical documentation is now centralised. Elium’s AI assistant answers questions instantly – it’s our support technicians’ best ally.

Forrester statistics show that teams using AI-first service desk platforms achieve 60% higher ticket deflection rates and 40% faster response times than traditional help desks. In one case study, a technology company’s virtual agent resolved 65% of initial contacts, whilst chat summarisation delivered over 50% productivity gains.

This approach exemplifies how forward-thinking IT service desks integrate AI without disrupting existing workflows—delivering immediate value whilst preserving operational stability.

The impact on mean time to resolution (MTTR) is spectacular: organisations using AI for ticket resolution commonly see reductions ranging from 40% to 70% within 6 to 18 months, particularly when AI accompanies process changes and data centralisation. Some organisations document 54% improvements, reducing average resolution time from 27.5 hours to 22.5 hours—a gain of nearly 5 hours per ticket.

Automated freshness: procedures that never become obsolete

The third transformation eliminates the risk of outdated documentation through automated obsolescence management.

VINCI Energies implemented expiration dates associated with documents. This functionality ensures that, when updates are necessary, content creators receive timely notifications. This systematic approach not only improves the update process but also guarantees that the knowledge base remains impeccably up to date at all times.

The Orne Département implemented a system of automatic annual reviews—no more outdated instructions lingering and creating security risks.

This automation solves a structural problem: in organisations without an adapted review process, old procedures remain active indefinitely, creating confusion and danger.

Self-deflection: reducing tickets by 40% to 85%

The fourth transformation exploits the knowledge base to deflect tickets before they’re even created.

Industry data shows spectacular gaps in self-deflection effectiveness:
– Average rate: 23% (organisations with basic knowledge bases)
– Good performance: 40-50% (organisations with well-structured bases)
– Best practices: 60-85% (organisations with AI and complete bases)

For an organisation handling 10,000 tickets annually at an average cost of €22 per ticket (Tier 1 cost according to MetricNet 2024 benchmarks), improving the deflection rate from 23% (average) to 60% (best practices) generates savings of approximately €81,400 annually—not counting secondary benefits such as improved user satisfaction, reduced turnover, or operational capacity gains.

Accelerating skill development: from 6 months to 6 weeks

The fifth transformation concerns onboarding new technicians and knowledge transfer.

Statistics show that training time for new recruits has increased by 44% over the past five years, with 57% of organisations citing additional tools or software to learn as the primary reason.

Organisations adopting the Knowledge-Centered Service (KCS) methodology—where documentation creation integrates directly into the resolution process—document 70% faster skill development for new analysts. A technician who required 6 months to reach full productivity now achieves it in 6 weeks.

Architecture of an effective IT knowledge base: the five pillars

Pillar 1: Organisation by service domains, not by tools

Organisations that succeed structure their knowledge by functional domains (business software, hardware, networks, security, administrative procedures) rather than by storage tools. This approach works equally well for customer support and service desk teams and internal IT teams.

VINCI Energies created 110 thematic spaces aligned with the company’s organisation chart. Each team possesses its own space, with personalised access rights: users can contribute and create content in their own space, whilst in others, they’re limited to reading existing knowledge.

This structure preserves content integrity whilst guaranteeing universal information accessibility.

Pillar 2: Template standardisation for consistency

Template standardisation ensures consistent documentation across different service domains whilst retaining the specific context each technical domain needs.

When each procedure follows the same format—context, prerequisites, steps, checkpoints, rollback if necessary, escalation contacts—technicians know where to find critical information without wasting time deciphering heterogeneous formats.

Teams define dedicated tags and templates, which then help administrators automatically configure Smart Tabs—intelligent widgets that group and display knowledge related to a specific topic.

Pillar 3: Native integration with existing ITSM tools

Direct integration with ticketing systems—GLPI, ServiceNow, Jira Service Management—eliminates the need to switch between applications.

The Orne Département uses the Elium browser plugin which delivers contextual AI assistance directly within GLPI tickets. Technicians can query the knowledge base whilst consulting incident details—no system change, no workflow interruption.

Intelligent search enables finding specific solutions among hundreds of technical procedures in seconds, not those frustrating minutes when every second counts.

Pillar 4: Freshness governance to eliminate obsolescence

Automated obsolescence management transforms a manual burden into a continuous, reliable process.

Organisations implement several complementary mechanisms:
– Expiration dates attached to procedures with automatic notifications to creators
– Mandatory periodic reviews (annual for critical procedures, quarterly for security procedures)
– Freshness indicators visible to users (last update, number of consultations)
– Approval workflow for modifications to critical procedures

VINCI Energies maintains 8,000 procedures up to date through this system—an impossible feat with a manual approach.

Pillar 5: Conversational AI that understands technical context

Modern AI assistants don’t just search for keywords. They understand technical context, interpret question intent, and synthesise answers from several related procedures.

When a technician asks “How do I reset MFA for a VPN user working remotely?”, the AI doesn’t simply return three separate articles on MFA, VPN, and remote work. It provides a synthetic answer integrating all three contexts with the specific steps applicable to that precise situation.

This contextual synthesis capability—made possible by RAG (Retrieval-Augmented Generation) architecture that anchors answers in the organisation’s verified knowledge—reduces search time by 46% according to studies.

From theory to practice: how to start with IT knowledge management

Step 1: Identify the highest-value domain (don’t do everything at once)

The natural temptation consists of wanting to document the entire IT infrastructure from the outset. This is a mistake.

Organisations that succeed begin with a single high-value domain—generally the one generating the largest volume of repetitive tickets. For many IT teams, this is access and password management, representing 20-30% of total ticket volume.

Recommended approach:
1. Analyse the past 6 months of tickets to identify the 10 most frequent categories
2. Select 1-2 categories together representing 20-25% of total volume
3. Document only these categories during the first month
4. Measure the impact (ticket reduction, technician satisfaction, resolution time)
5. Progressively extend to following categories

This progressive approach generates visible results quickly—essential for maintaining team engagement and justifying the investment.

Step 2: Create 5-10 reusable templates (not 50)

Standardisation doesn’t mean rigidity. Effective organisations create a limited number of templates covering the most common procedure types:
– Troubleshooting procedure (symptom → diagnosis → resolution → validation)
– System configuration (prerequisites → steps → verification → rollback)
– Access management (request → validation → provisioning → confirmation)
– Security incident (detection → containment → eradication → recovery → lessons)
– Preventive maintenance (planning → execution → verification → documentation)

Five well-designed templates cover 80% of IT documentation needs. The remaining 20% can use a generic template or variants of the five main ones.

Step 3: Integrate documentation into the resolution workflow (KCS)

The Knowledge-Centered Service methodology—recommended by ITIL 4 and adopted by the most mature organisations—transforms documentation from an administrative chore into a natural by-product of resolution. Modern service desk platforms require this integrated approach to maintain knowledge quality at scale.

The KCS principle: document whilst solving, not afterwards.

Organisations adopting KCS report:
– 30-50% increase in first-contact resolution rate
– 20-35% improvement in employee retention
– 20-40% improvement in employee satisfaction

According to HDI research, first-contact resolution correlates strongly with customer satisfaction. Remote control technology adds 10 percentage points to FCR, whilst mature KCS practice adds 12 percentage points.

SPIE ICS—whose IT service desk in Toulouse handles 540,000 annual requests with 140 agents—documented spectacular results after integrating the KCS methodology into their workflow:
– -73% information search time
– -25.5% critical incident processing time
– Turnover almost halved: from 29.6% post-pandemic to 17.17% end 2024
– Accelerated training: from 3 weeks to 2 weeks to reach operational level

These benefits stem from a simple change: making knowledge contribution as natural as ticket resolution itself. For the complete story of how SPIE transformed their IT service desk operations, see their full case study.

Step 4: Deploy AI on a controlled subset before generalising

Don’t activate all AI assistants on day one. Start with a specific, measurable use case.

The Orne Département began with GLPI integration—AI assistants answering technical questions directly in support tickets. Once this use case was mastered and impact measured, they extended usage.

This progressive approach allows:
– Adjusting AI responses based on technician feedback
– Progressively training teams on AI capabilities and limitations
– Measuring real impact before engaging additional resources
– Building trust in AI recommendations

Step 5: Measure, celebrate, iterate

Essential metrics for managing an IT knowledge base:

Usage metrics:
– Number of articles consulted per week
– Top 10 most consulted articles
– Successful search rate vs. searches without results
– Average time spent on articles

Impact metrics:
– Ticket volume reduction in documented categories
– Average resolution time improvement
– First-contact resolution rate increase
– Reduced onboarding time for new technicians

Health metrics:
– Percentage of articles updated in the past 90 days
– Number of active contributors
– Average time between creation and first update

Organisations that publicly celebrate knowledge contributors—for example by recognising technicians who document the best resolutions—maintain long-term engagement.

AI and knowledge management: synergy, not replacement

The common mistake consists of thinking AI will replace human documentation. This is false. AI amplifies the value of existing documentation by making it instantly accessible and contextual.

How RAG architecture exploits your IT knowledge

Retrieval-Augmented Generation (RAG) anchors AI answers in verified organisational knowledge rather than relying solely on the model’s training data.

The process:
1. The technician poses a question in the GLPI ticket
2. The system converts the question into vector embeddings
3. It searches for the most relevant procedures in the knowledge base
4. It augments the language model’s prompt with this retrieved context
5. It generates an answer anchored in your verified procedures

This approach reduces hallucinations by 70%, provides source transparency (the technician sees which procedures were used), enables rapid knowledge updates without retraining, and adapts to exploit unlimited organisational knowledge.

Human governance + AI execution = reliability

AI handles access and synthesis. Humans handle governance and validation.

What AI does well:
– Search instantly among thousands of procedures
– Synthesise information from multiple sources
– Suggest solutions based on similar incidents
– Detect potentially obsolete procedures (not consulted for a long time)

What humans must do:
– Validate new procedures before publication
– Review critical procedures periodically
– Arbitrate ambiguous cases or unprecedented situations
– Decide which knowledge deserves to be documented

Organisations that maintain this balance—access automation, quality human governance—achieve the best results.

The European and French context: GDPR, data sovereignty, and DORA

GDPR/General Data Protection Regulation

IT knowledge management in Europe operates under specific regulatory constraints that influence system architecture.

GDPR imposes strict requirements on:
– Data storage and residency (where are procedures containing personal data hosted?)
– Access rights and audit trails (who can consult which sensitive procedures?)
– Data processing documentation (the procedures themselves must be documented)
– Breach notification (security incidents involving procedures)

Since May 2018, cumulative GDPR penalties have reached €5.88 billion across 2,245 enforcement actions, with Ireland alone issuing €3.5 billion in fines. These figures underscore that GDPR compliance isn’t optional—it’s existential.

IT knowledge bases must integrate granular access controls ensuring that only authorised persons access procedures containing sensitive information. VINCI Energies particularly appreciated Elium’s ability to easily manage access rights for each space, thus ensuring the flexibility to keep certain spaces private or even secret.

DORA: Digital Operational Resilience Act for financial services

The DORA regulation, entering application phase in 2025-2026, establishes a common framework for ICT risk management in the financial sector. Financial entities and their critical ICT service providers must maintain comprehensive risk management frameworks, conduct resilience testing, maintain registers of contracts with third-party ICT suppliers, and establish formal incident notification procedures.

DORA penalties can reach €35 million or 7% of global turnover—making it a major compliance imperative for financial sector organisations and their service providers.

IT documentation in DORA environments must therefore:
– Trace all critical ICT dependencies
– Document resilience testing procedures
– Maintain audit trails of all incidents
– Prove that controls actually work (not just that they exist)

Data sovereignty and French cloud

The French State’s investment in sovereign cloud ecosystems—€1.8 billion allocated specifically to sovereign cloud platforms—reflects a deliberate digital sovereignty strategy.

Organisations using SecNumCloud-certified platforms gain regulatory credibility with authorities whilst demonstrating their commitment to data residency. For IT teams managing sensitive information or critical infrastructure, the choice of sovereign platforms isn’t just a compliance question—it’s a question of trust and operational continuity.

The European AI-driven knowledge management market was projected to reach €3.4 billion in 2025 with growth at a compound annual rate of 42.9% over the forecast period. Cloud deployment mode commands 64.2% of market share, reflecting organisational preference for scalable and flexible architectures supporting real-time access and seamless collaboration among distributed teams.

Tangible results: what organisations actually achieve

Instant resolution support and efficiency improvement

The Orne Département’s AI assistants provide immediate answers to technical questions directly in GLPI tickets. With teams able to access relevant procedures without changing systems, resolution times improve drastically.

All departmental IT infrastructure—from user workstations to critical servers—now has centralised, instantly accessible technical documentation. 100 applications and 3,500 pieces of equipment covered.

850+ active users and 8,000+ living procedures

VINCI Energies has progressively gathered more than 850 users who now interact daily with the platform’s content to effectively accomplish their support tasks.

More than 8,000 procedures have been formalised and maintained up to date, illustrating how the platform successfully centralises the key knowledge necessary for the IT team.

Overall, after several years of use, the team has reported increased efficiency and the confidence of being able to provide excellent support.

Measurable cost reduction and satisfaction improvement

Organisations implementing sophisticated AI self-deflection systems report 30-55% cost reductions through reduced staffing needs, improved response times, and higher customer satisfaction scores.

A Forrester study commissioned by SymphonyAI revealed that modelled customers achieved 204% return on investment over three years with a payback period of less than 6 months. The study documented 35% ticket deflection due to self-service, 75% reduction in handling time, and 60,300 handling hours saved annually.

This rapid payback period explains the acceleration of investment in automation infrastructure: organisations recognise that automation investments recover costs in 6-12 months whilst generating multi-year benefits.

Frequently asked questions

How do you measure the ROI of an IT knowledge base?

The return on investment of an IT knowledge base is measured through several complementary dimensions. Direct metrics include ticket volume reduction in documented categories (deflection rate), average resolution time improvement, and first-contact resolution rate increase. Organisations can calculate savings by multiplying deflected tickets by average cost per ticket (approximately €22 for Tier 1 support according to MetricNet 2024 benchmarks). Indirect benefits—accelerated skill development, improved employee retention, reduced stress—are equally important but require satisfaction surveys to be quantified.

What’s the difference between knowledge management and ITSM?

ITSM (IT Service Management) encompasses all processes, policies, and procedures for managing IT services end-to-end—incident management, change management, problem management, etc. Knowledge management represents a specific practice within ITSM that focuses on capturing, organising, sharing, and using knowledge to improve the efficiency of all other ITSM processes. The ITIL 4 framework elevates knowledge management to the rank of principal management practice that contributes to each component of the service value system, rather than treating it as a peripheral support function.

How do you integrate a knowledge base with GLPI, ServiceNow, or Jira Service Management?

Integration options vary by ITSM platform. For GLPI (open-source), integration typically occurs via browser plugin or REST API enabling display of knowledge base articles directly in the ticket interface. ServiceNow offers deep native integration with built-in knowledge management capabilities, allowing articles to be linked to tickets and contextual recommendations displayed. Jira Service Management integrates naturally with Confluence for knowledge management, enabling referencing and recommending articles directly from service workflows. The Orne Département uses the Elium browser plugin which works with GLPI to deliver contextual AI assistance without modifying the existing ITSM system.

How do you keep IT documentation up to date with overloaded teams?

The key consists of automating freshness governance rather than counting on manual discipline. Effective organisations implement expiration dates attached to procedures with automatic notifications to creators when review becomes necessary. Mandatory periodic reviews (annual for standard procedures, quarterly for security) ensure that no procedure becomes silently obsolete. The Knowledge-Centered Service approach integrates documentation updates directly into the resolution process—when a technician resolves an incident and notices the documented procedure no longer matches, they update it immediately rather than creating a separate task. Visible freshness indicators (last update, number of consultations) create positive social pressure to maintain current procedures.

How do you encourage technicians to document their resolutions?

Resistance to documentation generally stems from three factors: perceived time required, lack of recognition, and difficult-to-use tools. Organisations that succeed address all three simultaneously. They implement templates that reduce documentation time from 10-15 minutes to 3-5 minutes by eliminating format decisions. They publicly recognise knowledge contributors—for example by displaying “top contributors of the month” or integrating knowledge contribution into performance evaluations. They use tools where documentation integrates naturally into the resolution workflow rather than requiring a system change. Finally, they demonstrate the impact: when technicians see that their documented procedures are consulted dozens of times and effectively reduce repetitive ticket volume, the value becomes evident.

How does knowledge management improve IT service desk efficiency?

Knowledge management transforms IT service desk efficiency by reducing ticket resolution times, increasing first-contact resolution rates, and enabling self-service deflection. Organisations with mature knowledge bases report 40-60% ticket deflection, 70% faster technician onboarding, and measurable improvements in mean time to resolution. The key is integrating knowledge directly into service desk workflows rather than maintaining separate systems. When technicians access verified procedures without changing applications, resolution accelerates whilst documentation quality improves.

Can AI replace human IT documentation?

No. AI amplifies existing documentation but doesn’t replace it. Modern AI systems work best when they rely on verified knowledge bases maintained by humans—this is precisely the principle of RAG architecture. AI excels at searching instantly among thousands of procedures, synthesising information from multiple sources, and suggesting solutions based on similar incidents. However, humans remain essential for validating new procedures before publication, reviewing critical procedures periodically, arbitating ambiguous cases, and deciding which knowledge deserves to be documented. Organisations that treat AI as an intelligent assistant rather than a replacement achieve the best results—combining AI’s speed and scale with human judgement and expertise.

About Elium

Elium helps IT teams build a knowledge infrastructure that transforms technical documentation from a burden into a competitive advantage. Our platform adapts to different contexts: customer support, knowledge transfer, or company-wide sharing.

Our platform combines intelligent centralisation, AI assistants integrated with your existing ITSM tools (GLPI, ServiceNow, Jira), and automated freshness governance to guarantee your procedures remain up to date without constant manual effort.

Organisations like VINCI Energies (8,000 procedures for 850 users) and the Orne Département (GLPI integration with contextual AI) trust Elium to unify their IT knowledge and accelerate incident resolution.

Discover our customer stories to see how other IT departments are transforming their technical support.