The Future of ITSM: Data-driven insights and AI integration
The service management market, historically shaped by frameworks like ITIL™, has largely evolved to maintain the status quo. It enforces standard procedures and favours incremental change over transformation—to ensure the "lights stay on". This traditional approach often stands in stark contrast to the ethos of digital transformation, which demands agility, innovation, and rapid evolution. The gravity of legacy ITSM thinking can mean that an organization is held back from achieving its potential.
Is IT service management destined to remain a discipline of gradual improvement, or are we on the brink of a true revolution? To answer this, we must look to the future, and more specifically, to data-driven indicators that show how the landscape is evolving.
Hornbill's proprietary market intelligence engine
At Hornbill, we operate a big-data-driven market data model to monitor trends across the service management sphere. It has been running continuously for over five years, collecting 7.5 million data points every week across 20,000 UK organizations. The dataset is structured around a comprehensive taxonomy of 2,500 management and technical terms that are focused on service management.
The output reveals clear patterns that signal how the ITSM market is changing in real time. Wave after wave of repeat activity surfaces across specific ITSM capabilities such as incident, problem, request, collaboration, and remote working. Our top-level taxonomy includes around 30 core terms, and what we’re observing is striking.
An organization (say, a university) may show spikes in intent data related to asset management, collaboration, or major incidents. These spikes recur every 3 to 6 months. But what’s more telling is how the amplitude of each spike increases over time. This indicates that despite attempts at transformation, problems are not being resolved at their root.
Instead, they’re tackled in isolation using incumbent tools, leading to temporary relief. But the underlying inefficiencies persist, and employee frustration grows. Ultimately, repeated unresolved issues culminate in tool substitution: organizations switch vendors in search of better ITSM solutions.
AI's evolving role in ITSM
Two to three years ago, artificial intelligence began gaining serious traction in service management, with steady quarter-on-quarter growth. But in the last 12 months we’ve seen a distinct shift.
AI, machine learning, and AIOps are now appearing in combination - paired or grouped with use cases like organizational transformation, collaboration, or major incident response. These are not General AI implementations; they are contextual, purposeful applications of AI—targeted to specific ITSM use cases.
Examples include:
- ESM transformation + AI
- Collaboration + Major Incident + AI
What this shows is that AI is no longer viewed as an isolated innovation. Instead, it’s being embedded into specific, high-value use cases that materially improve outcomes—whether it's solving a Monday morning backlog, or streamlining midweek escalations.
AI-driven change and the employee experience
Across the board, buyers in the ITSM market are focused on:
- Driving efficiency
- Improving the employee experience
- Automating and deflecting routine interactions
New technologies are enabling frontline ticket deflection, better routing via self-service, and intelligent triage—often directly from collaboration tools like Microsoft Teams. This enables service desk agents to spend less time on repetitive work and more time supporting the organization’s new technology needs (accelerating the pace of business transformation).
A market in transition
Our analysis confirms a broader shift. The industry, through still tethered to ITIL-aligned requirements, is seeing a reclassification of priorities.
Requirements that were previously “should have” or “could have” are becoming “must have”. This shift signals a maturing market that understands the need for a comprehensive, future-ready ecosystem.
This transition has accelerated significantly in Q1 of 2025 and points to a clear trajectory of the next 2-3 years. Operational efficiency boosts capacity. Capacity enables greater agility. Greater agility means faster digital business transformation.
Agent deflection: A critical use case
One of the most vital areas where technology needs to be tightly interwoven is agent deflection.
Organizations are investing in ecosystems that:
- Redirect repetitive tasks to AI-powered systems
- Provide intelligent self-service options
- Free up agents to focus on high-value, complex, or novel issues—instead of well-known routine work.
These solutions aren’t just tech upgrades—they are strategic enablers of scalability, satisfaction, and sustainability in service management.
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