Shield Technology Benchmarks AI for IT Support
New data evaluates how leading AI models resolve challenging IT support tickets.
Updated on Oct. 30, 2026 in Artificial Intelligence
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Shield Technology Partners has released a new set of benchmarks comparing how different artificial intelligence models resolve complex IT support tickets. The study tested multiple models against 52 tickets that were previously handled incorrectly by automated systems.
Why it matters
This evaluation provides critical data for managed service providers looking to balance accuracy, processing speed, and costs when selecting AI models for technical support workflows. The findings aim to help developers refine AI capabilities for specific enterprise use cases.
GPT-6 Astra with extra-high reasoning achieved a 61.5% success rate on 52 test tickets, while DeepSeek V4.1 Flash recorded the lowest cost at $0.13 per ticket. Processing times varied significantly, with Astra requiring nearly 20 minutes on its highest reasoning setting compared to under 10 minutes at lower effort.
The players
Shield Technology Partners
An organization that develops and evaluates technology solutions for managed service providers.
The details
The study utilized 52 challenging tickets to measure success rates, cost per ticket, and total processing time. While Forge currently handles over 50% of actionable tickets for ShieldCos, these benchmarks highlight the trade-offs between higher reasoning effort and speed.
Timeline
Shield Technology Partners released the new benchmarks on October 30, 2026.
The Tech Race
This evaluation benchmarks the current capabilities of foundation models against the Forge automated support system. It tracks the rapid shift toward models capable of handling complex technical troubleshooting that legacy systems struggle to resolve.
Managed service providers can use this data to optimize their technical support workflows by selecting models that align with their budget and speed requirements. Users may notice faster, more accurate ticket resolution as providers adopt more efficient model configurations.
The takeaway
Selecting an AI model requires a strategic trade-off between the depth of reasoning required for complex problems and the speed of response. IT managers should analyze their specific ticket volume and cost constraints to determine if higher-reasoning models offer sufficient ROI.
Further reading
For additional context on how generative models are transforming industry workflows, see Artificial Intelligence.
Source note: This article includes information reported by Channel Insider.
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