Gartner AI service operations forecast: 2026–2029 guide

What does Gartner’s AI service operations forecast say?

The headline figure is stark. Worldwide AI spending is forecast to rise significantly in 2026 compared to the previous year. Within that, the AI services segment alone is projected to grow substantially from 2025 to 2027. By 2029, the market reaches $516 billion in composite AI spending specifically, with composite AI accounting for 66% of all AI services expenditure, up from just 8% in 2025.

Key figures at a glance:

• AI services market (2025): $436 billion globally
• AI services market (2027): $759 billion projected
• Composite AI share by 2029: 66% of AI services spending
• 2026 inflection point: Enterprise spending begins to overtake hyperscaler and vendor-led investment
• By 2030: 25% of IT work done by AI alone; 75% by humans augmented with AI

What trends are driving AI services market growth?

2026 marks the year enterprise spending takes the wheel. Until now, AI investment has been dominated by technology vendors and hyperscalers building infrastructure. Gartner analyst John-David Lovelock describes 2026 as the inflection point where enterprises begin to flex their own spending potential, shifting from tactical efficiency gains toward broader workflow transformation.

Several forces are converging to accelerate that shift:

• Composite and multi-model AI: Organisations are moving beyond single-model deployments toward architectures that combine multiple AI capabilities across broader business processes, expanding both scope and spend.
• AI-optimised infrastructure: Spending on AI-optimised servers is forecast to triple over five years, as cloud providers expand capacity ahead of agentic workflow demand.
• Agentic AI adoption: Model consumption is rising through multi-step processes and integration into enterprise software suites, with AI model spending forecast to grow 110% in 2026 alone.
• Regulatory pressure: New rules mandating accessible human agents will increase assisted service volume by 30% by 2028, reshaping service channel design.
• Shifting AI goals: Leading organisations are pivoting from pure cost reduction toward customer engagement and personalised experiences, where AI creates competitive advantage rather than just operational savings.

For UK IT leaders tracking enterprise IT support trends, these forces are not abstract. They are already reshaping procurement decisions, vendor relationships, and workforce planning.

Why do so many enterprise AI projects fail?

At least 50% of generative AI projects are abandoned after the proof-of-concept stage. Gartner’s analysis of hundreds of implementations points to five consistent failure patterns:

• Poor use-case selection: Chasing impressive demos without clear success metrics dilutes resources across low-impact initiatives. Without measurable business outcomes, projects become easy targets when budgets tighten.
• Data quality failures: Unreliable data affects every department attempting to use generative AI. It produces failed retrieval-augmented generation implementations and models that cannot be fine-tuned effectively.
• Escalating total cost of ownership: Token costs that appear negligible in a proof of concept become budget problems at production scale. Organisations consistently underestimate how costs compound across thousands of users and hundreds of use cases.
• Inadequate risk controls: Governance gaps around fairness, security, and auditability create compliance exposure that stalls or kills projects.
• Technology-first framing: Treating AI as a technology deployment rather than a business transformation is the root cause behind most of the above. Gartner recommends that enterprises approach AI as business transformation, not a technical upgrade.

Pro Tip: Before committing to production, build a cost model that accounts for token usage at full scale, not just proof-of-concept volumes. Prompt optimisation and intelligent routing can materially reduce runaway spending.

Why human readiness matters as much as AI readiness

Gartner’s position is unambiguous: balancing AI and human readiness is the condition for sustaining AI value. Technology deployment alone does not deliver outcomes. The workforce must be capable, culturally aligned, and structurally prepared to work alongside AI systems.

Critical workforce considerations for enterprise leaders:

• Re-hiring and upskilling: Regulatory mandates requiring human agent access mean organisations may need to maintain or increase headcount, potentially at higher salary bands than previously budgeted.
• Human-in-the-loop design: Production AI systems favour bounded supervisor-worker orchestration with audit trails and escalation paths. Full autonomy on consequential actions remains experimental and cost-prohibitive for most organisations.
• Skills investment: In EMEA, 73% of CIOs report their organisations are breaking even or losing money on AI investments. Hidden costs, including training and change management, are frequently underestimated.
• Cultural alignment: Workforce resistance and unclear role definitions are as likely to derail AI programmes as technical failures. Understanding IT engineer roles in 2026 and how they evolve alongside automation is a practical starting point for workforce planning.

What does Gartner’s forecast mean for UK enterprise IT leaders?

UK organisations face a specific combination of regulatory scrutiny, skills market pressure, and legacy infrastructure that shapes how Gartner’s global forecast translates locally. The UK’s AI regulatory environment is tightening, and the 30% increase in assisted service volume projected by 2028 carries direct staffing and cost implications for service operations teams.

Practical priorities for UK IT and business leaders:

• Align AI investments with measurable business objectives. Lovelock’s warning is direct: CIOs face difficulty proving AI value when initiatives lack strategic alignment. The cost equation for AI in IT services is shifting fast, and incremental efficiency gains will not justify enterprise-scale spend.
• Assess sector-specific risk. Regulated industries, including financial services, pharma, and energy, face the sharpest regulatory exposure around human agent access and AI auditability.
• Build governance foundations early. Data quality, model observability, and audit posture compound over time. Organisations that invest in these foundations now will have a structural advantage as regulatory expectations become more specific.
• Close the physical layer gap. AI agents can automate digital workflows end-to-end, but physical IT support, device handovers, peripheral distribution, and walk-up support, still requires a hardware endpoint. Velocity-smart’s Smart Collect® platform closes that gap natively within ServiceNow, without a parallel database or additional security review. A UK utility customer using Smart Collect cut shared-equipment loss and damage by 90% before agentic AI was driving any part of the workflow.

For UK enterprises building an AI-driven IT operations strategy, the Gartner forecast is a call to act with discipline, not speed.

Key takeaways

Gartner’s AI service operations forecast confirms that 2026 marks the enterprise inflection point, but sustained value depends on governance, workforce alignment, and closing the physical layer gap that digital AI cannot reach.

Velocity-smart’s AI–Physical Bridge is the only ServiceNow-native platform that lets AI agents close physical-handover tickets without dispatching an engineer. As Gartner’s forecast reshapes enterprise IT spending, the physical layer is the one gap that remains. Smart Collect® closes it.

Recommended

• AI agents in enterprise IT: a practical 2026 guide
• AI driven IT operations: a strategic guide for CIOs
• Agentic AI in IT services: a 2026 CIO guide