Benchmark Your
AI Maturity for
the Year Ahead

Unwrap the insights and real-world results that defined service AI and use them to guide your 2026 AI priorities.

A Resolution Pathway created from technical documentation in minutes with 90%+ accuracy

What High-Performing
Teams Did Differently

While MIT claimed 95% of AI projects fail, top service teams saw results
by prioritizing accuracy, validated guidance, and practical integration.

Inside the Report

A comprehensive look at how 125 service leaders are using AI to improve accuracy, speed, and consistency, and what they’re planning next.

AI trends across field service, technical support, and customer care
Top use cases that delivered measurable ROI
Specific KPIs teams used to track AI success
Benchmarks you can use for planning and budgeting

The Measurable Impact
Behind the Hype

3 hours → 3 seconds

Faster resolutions for complex issues

20% deflected escalations

Lower reliance on experts

8,000+ techs

Consistent service across 60 regions

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FAQs

What will service leaders need to plan for in 2026?

Service leaders will need to focus on moving AI from pilots to full operational deployment. This includes improving resolution accuracy, standardizing troubleshooting processes, strengthening technician onboarding, and ensuring AI integrates with existing CRM, field service, and support workflows. The 2025 report highlights where organizations saw real ROI, and what’s now essential for 2026.

What are the top AI priorities for service organizations in 2026?

Based on service leader insights, the top priorities are:
- Improving accuracy and consistency in resolutions
- Guiding technicians and agents with validated, step-by-step support
- Reducing escalations and deflecting repeat issues
- Strengthening knowledge capture and SME expertise
- Using AI to shorten time-to-expertise for new hires
These areas show measurable outcomes across field service, support, and customer care.

How are service teams successfully using AI today?

Organizations are leveraging AI for case deflection, troubleshooting guidance, predictive diagnostics, knowledge capturing, and onboarding acceleration. The 2025 report breaks down the most impactful use cases, including improvements in accuracy, technician confidence, and time to resolution.

What challenges are service organizations facing with AI adoption?

The biggest challenges include outdated knowledge, inconsistent processes, limited access to expert insights, and solutions that don’t integrate with daily workflows. The report outlines how teams overcame these barriers and what frameworks service leaders can use for 2026 planning.

Does the report include real data from other service teams?

Yes. The report reflects insights from 125 service leaders across field service, technical support, customer care, and service operations at mid-market to multi-billion-dollar companies. The report includes metrics based on accuracy improvements, escalation reductions, faster training times, and adoption metrics from enterprise service leaders across industries like medical devices, high-tech equipment, telecom, and industrial manufacturing.

How do I know if our service organization is ready for AI at scale?

Readiness depends on having pristine service data, documented resolution paths, and clear KPIs. The report highlights key metrics to help service leaders assess their AI maturity and priorities for 2026.