AI Could Save IT Admins 160 Hours a Year

Fortra is using AI to troubleshoot managed file transfers, claiming it can save IT administrators up to 160 hours per year.

Fortra, a company specializing in cybersecurity and automation software, has announced a new AI-powered feature for its managed file transfer (MFT) platforms. According to reporting from The New Stack, Fortra claims this integration could save system administrators up to 160 hours annually per person. This significant time saving, which breaks down to over three hours per week, would come from automating the often-tedious process of diagnosing and resolving failed data transfers. MFT systems are a critical but often invisible part of modern business infrastructure, responsible for securely and reliably moving sensitive files on a schedule. They underpin essential operations like processing payroll exports, settling financial transactions between banks, and transmitting purchase orders within a supply chain. When these transfers fail, the ripple effects can be immediate and costly, making rapid troubleshooting a high-stakes priority for IT teams. The new AI capability is being introduced to Fortra’s flagship MFT products, GoAnywhere MFT and Globalscape EFT.

The new feature functions as an intelligent diagnostic co-pilot for IT administrators. Traditionally, when a file transfer fails, the system generates a log entry with an error code that an admin must manually investigate. This process can involve cross-referencing documentation, checking system configurations, and analyzing network logs—a time-consuming and often frustrating exercise. Fortra's AI aims to eliminate this manual labor. By analyzing a wide range of data points in real-time—including error codes, system status, network connectivity, and historical transfer patterns—the AI model can pinpoint the root cause of the failure. It then presents its findings in plain English, explaining not just what went wrong but also recommending concrete steps for remediation. This moves the troubleshooting process from manual detective work to a guided, streamlined workflow, directly targeting a reduction in Mean Time to Resolution (MTTR) for common operational incidents.

This move by Fortra is a clear example of the broader industry trend known as AIOps, or AI for IT Operations. As digital infrastructures become more complex, distributed, and dynamic, managing them with purely manual processes is becoming untenable. AIOps seeks to address this challenge by applying machine learning and data analytics to automate and improve IT operations. We are seeing this pattern across the technology stack. Observability platforms like Datadog and Dynatrace use AI to detect anomalies in application performance before they impact users. Cybersecurity tools leverage machine learning to identify novel threats that signature-based systems would miss. Cloud cost management platforms use AI to predict usage and recommend optimizations. Fortra’s announcement shows this trend is now reaching even the most established and foundational areas of enterprise IT, like file transfer management. The goal is consistent: augment human operators, reduce manual toil, and build more resilient systems.

For technical leaders and practitioners, this development offers a glimpse into a more efficient future for IT operations. The promise of reclaiming 160 hours per administrator—the equivalent of a full month of work—is a compelling value proposition. Such efficiency gains allow skilled engineers to shift their focus from reactive firefighting to proactive, high-value work like system architecture improvements, security hardening, and strategic planning. This also has direct implications for business continuity. By accelerating the resolution of failed transfers for critical processes like payroll or financial settlements, the technology helps mitigate the business and financial risks associated with operational downtime. As organizations evaluate their technology roadmaps, they should consider the potential ROI of AIOps features within their existing and future tooling. The key is to critically assess these claims and pilot them to validate their real-world impact on team productivity and system reliability. We can expect competitors in the MFT and broader infrastructure space to follow suit with similar AI-driven diagnostic and self-healing features.

This highlights the shift from manual to AI-assisted troubleshooting in a core, often overlooked, part of IT infrastructure. For engineers managing critical data pipelines, this could mean faster incident resolution and less time spent on tedious log analysis, directly improving system reliability and operational efficiency.

The claimed 160-hour annual time saving per admin translates directly to reduced operational costs and increased productivity. By speeding up fixes for critical processes like payroll and financial settlements, this AI feature can minimize business disruptions and the financial risks associated with delayed data transfers.