TL;DR:
- Enterprise IT support faces rapid tool proliferation, requiring careful evaluation of AI-driven solutions for scale, cost, and risk. AI automation and agentic AI are transforming incident management and support decision-making, promising significant efficiency gains. Building a solid operational foundation is essential before layering advanced AI to achieve long-term success and measurable ROI.
Enterprise IT leaders face a paradox: the tools meant to simplify support are multiplying faster than teams can evaluate them. AI promises, vendor claims, and analyst forecasts arrive daily, yet budget pressure and risk aversion demand careful choices. Forrester notes that the shift from traditional reactive ITSM to proactive, AI-driven service management is no longer optional for enterprises that want to stay competitive. This article cuts through the noise, laying out a practical framework and the five most consequential trends shaping enterprise IT support right now, so your team can act with confidence rather than chasing hype.
| Point | Details |
|---|---|
| Evaluate with value metrics | Focus on trends that offer genuine productivity and experience gains for your users. |
| AI delivers real speed advantages | Adopting GenAI and automation can drastically reduce incident handling times. |
| Prepare for autonomous systems | Agentic AI will soon enable self-resolving IT issues, requiring new skills and governance. |
| ESM platforms drive integration | AI-native platforms are breaking silos, blending IT service with HR and finance workflows. |
| Core ITSM remains vital | Successful adoption depends on strong foundations alongside innovation feature sets. |
With the challenge defined, it is vital to use concrete criteria for separating real innovation from industry noise. Not every trend that dominates a conference agenda will deliver value at enterprise scale. Large organisations carry constraints that smaller businesses simply do not face: compliance obligations, complex integrations, distributed workforces, and the reputational risk of a failed rollout.
Before committing budget and people, evaluate any emerging trend against these questions:
Forrester’s research shows a clear shift to value-based metrics such as productivity gains, ticket deflection, and employee experience, rather than purely operational KPIs like uptime percentage. That shift matters enormously for how you benchmark vendor claims.
Understanding the future of IT support requires seeing beyond individual tools towards a broader service model that prioritises employee outcomes at every touchpoint. Trends that score well across all six criteria above are the ones worth prioritising.
Pro Tip: Build a simple scoring matrix using these six criteria and apply it consistently to every vendor shortlist. Weight the criteria by your organisation’s current pain points. If employee experience is your biggest challenge right now, weight that criterion 30% rather than splitting evenly across all six.
Armed with robust evaluation criteria, let us explore how AI automation is transforming the most resource-intensive element of IT support: incident management. For most large enterprises, incident management consumes more support hours than any other ITSM function. It is repetitive, high volume, and historically dependent on skilled engineers doing work that intelligent systems could handle.
The data here is striking. GenAI adoption in ITSM reduces average incident resolution time by 17.8%, but the organisations leading adoption are seeing a 54.3% reduction. That gap between average and best-in-class is enormous, and it represents the difference between a marginal efficiency gain and a structural transformation of your support operations.
What AI-powered incident management typically includes:
The resolution time comparison between adopters and non-adopters is significant:
| Metric | GenAI adopters | Non-adopters |
|---|---|---|
| Average resolution time reduction | 17.8% | 0% |
| Top-tier adopter resolution reduction | 54.3% | 0% |
| Autonomous triage capability | Yes | No |
| Predictive alerting | Yes | Rarely |
| Knowledge article automation | Yes | Manual |
There is an important nuance here. As AI complexity in incident management grows, engineers often find their responsibilities shifting rather than disappearing. Fewer routine alerts reach human hands, but the incidents that do escalate are more complex. This means the skill profile of your support team needs to evolve alongside the tooling.
Reviewing IT process automation trends across global enterprises reveals that organisations succeeding with AI-driven incident management invest equally in people development and technology deployment. The tools are only as good as the governance structures and human expertise surrounding them.
17.8% average reduction in incident resolution time with GenAI adoption, rising to 54.3% for leading adopters. (2025 State of ITSM Report)
For a practical starting point, exploring automation steps for IT support can help teams understand the sequencing required to move from basic workflow automation towards full AI-assisted incident management without disrupting live operations.
Building on automation, the next wave alters not just what is automated but how decisions are made, and by whom. Agentic AI represents a meaningful departure from traditional ITSM automation. Where conventional automation follows predefined rules, agentic AI reasons across multiple data sources, selects actions, executes tasks, and adapts based on outcomes, all without waiting for human instruction at each step.
Forrester identifies agentic AI as one of the defining trends in service management, enabling autonomous decision-making and task execution at a level that rules-based systems simply cannot match. The practical implications are significant for enterprise IT support.
What agentic AI can now do autonomously in IT support:
| Capability | Agentic AI | Traditional ITSM automation |
|---|---|---|
| Decision-making | Dynamic, context-aware | Rule-based, static |
| Human oversight required | Minimal for defined tasks | Required at most steps |
| Adaptability | Learns and adjusts | Manual updates required |
| Cross-platform orchestration | Native | Limited or custom-built |
| ROI timeline | 6 to 18 months | 12 to 36 months |
Gartner projects that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, delivering a 30% reduction in operational costs. For an enterprise spending tens of millions annually on IT support, that figure should anchor every conversation about technology investment priorities.
This matters for how organisations approach automation in IT support logistics as well. Physical device logistics, equipment collection, and hardware replacement workflows are natural candidates for agentic orchestration, where AI manages the end-to-end process from request to fulfilment without manual handoffs.
Pro Tip: Do not wait until agentic AI is fully mature in your environment before upskilling your team. Begin now by identifying which engineers will transition into AI oversight and governance roles. These individuals will need to understand how to audit AI decisions, set confidence thresholds, and manage exceptions. This capability gap is the most common reason agentic AI deployments stall at the pilot stage.
As enterprises move towards agentic AI and automation, the broader vision is service management platforms built natively with AI across all business functions. Enterprise service management (ESM) refers to applying ITSM principles and tooling beyond IT to functions like HR, finance, legal, and facilities. The AI-native iteration of this concept goes much further.
Forrester describes ITSM platforms evolving into AI-native hubs for enterprise service management, integrating across IT, HR, and finance with AI handling workflows and knowledge management at scale. The promise is a unified service experience where an employee raising a query about IT hardware, a payroll discrepancy, or an onboarding task receives consistent, intelligent responses from a single platform.
Integrated workflows enabled by AI-native ESM platforms include:
The challenge, as AI-powered workflow improvements demonstrate, lies in the quality of underlying data and knowledge architecture. AI is only as reliable as the information it reasons from.
“The most common failure mode in enterprise AI service management is not the AI itself. It is the absence of clean, governed, consistently structured knowledge that the AI can actually use. Organisations that invest in data quality before deploying AI see returns that others simply cannot replicate.”
This is a genuine implementation risk that many vendors understate. Skills gaps in managing agentic systems, inconsistent data quality across business units, and the ever-present challenge of AI hallucinations producing confident but incorrect answers all require deliberate governance structures. Organisations that treat ESM AI as a feature to switch on rather than a capability to build will consistently underperform against those taking a more disciplined approach.
The enterprises seeing the strongest results are those prioritising holistic strategy over feature-chasing. They define the business outcomes they need first, then work backwards to the technology required to deliver them.
Understanding what is new matters, yet enterprises must also get the fundamentals right to realise long-term value. Emerging trends can obscure the fact that foundational ITSM capabilities still underpin everything else. Without them, even the most sophisticated AI layer will fail to deliver consistent results.
Gartner’s ITSM market guidance makes clear that core capabilities including automation, transparent reporting, and robust integrations remain essential alongside AI, and that total cost of ownership (TCO) is the metric that most reliably differentiates solutions in practice.
Here are the foundational requirements for successful enterprise adoption:
Understanding the true costs of IT inefficiency is essential context here. Organisations frequently undercount the labour cost embedded in manual processes, the productivity loss when employees wait for equipment or resolution, and the compounding effect of deferred modernisation.
Pro Tip: Before evaluating any new ITSM or AI platform, audit your current knowledge base for accuracy and coverage. Outdated articles, missing documentation, and inconsistent formatting are the primary reasons AI-assisted self-service tools produce wrong or unhelpful answers. A knowledge management sprint before deployment will directly improve your AI outcomes from day one.
The enterprises that achieve the strongest long-term results are not those that adopt the most features. They are the ones that match the right capabilities to clearly defined outcomes, govern their AI implementations rigorously, and treat modernisation as an ongoing discipline rather than a one-time project.
Here is an uncomfortable truth the industry rarely states plainly: most enterprises are not failing at IT support because they lack technology. They are failing because they have not yet resolved the operational model that technology is supposed to support.
We have worked with organisations across financial services, aerospace, government, and healthcare that have invested significantly in ITSM platforms yet still rely on manual processes for basic functions like device collection, hardware replacement, and on-site IT support. The AI layer they are adding on top is, in many cases, being built on an operationally fragile foundation.
The trends covered in this article are real and consequential. But the organisations that will extract the most value from AI-powered incident management and agentic automation are those that have first automated the physical, logistical, and transactional layer of IT support. That means employees collecting devices without queuing at a help desk. It means hardware being exchanged at a smart kiosk without requiring an on-site technician. It means every device transaction recorded automatically in ServiceNow, with no manual data entry and no GDPR risk from a separate platform.
When the operational foundation is sound, AI has clean data to work with, support engineers have capacity to focus on complex problems, and the metrics you report to leadership actually reflect reality. That sequencing matters more than any individual technology trend.
The enterprises that will win this decade are not the ones that adopt AI the fastest. They are the ones that build the right operational architecture first, then layer intelligent automation on top of a genuinely solid foundation.
Velocity Smart Technology helps large enterprises build exactly that kind of foundation. Our platform, including Velocity Smart Collect, smart lockers, smart vending machines, and Smart IT Support Kiosks, automates the physical and logistical layer of IT support across distributed workplaces. Built natively on ServiceNow and certified to the same security standards trusted by 85% of the Fortune 500, our solutions eliminate manual processes, remove data replication risks, and integrate seamlessly with the ITSM workflows your team already uses. From device collection to remote diagnostics and equipment exchange, Velocity gives your IT team the operational clarity needed to make AI investments genuinely productive. Explore how we can help your organisation reduce support costs and deliver measurable ROI.
GenAI adoption reduces average incident resolution times by 17.8%, with leading adopters achieving up to a 54.3% improvement, making it one of the highest-impact investments available in enterprise ITSM today.
Agentic AI allows systems to reason, decide, and act autonomously across multiple platforms. Gartner projects that by 2029 it will resolve 80% of common customer service issues without human intervention, fundamentally reshaping support team structures.
Not replacing, but significantly extending them. Forrester describes ITSM platforms evolving into AI-native enterprise service hubs integrating IT, HR, and finance, while core capabilities like automation, reporting, and integrations remain foundational to success.
Data quality and governance are the primary risks. AI hallucinations and bias require clean, well-structured knowledge bases and clear oversight frameworks before AI tools can be trusted to operate reliably at enterprise scale.