Allied Digital reports 65% SOC-alert auto-triage in live use

Allied Digital Services Limited reported that a live cybersecurity-operations deployment auto-triaged 65% of security-operations-centre alerts, while a live managed-services rollout deflected 32% of first-line tickets. Allied Digital also reported a fourfold faster mean time to contain in the cybersecurity case, its largest disclosed automation percentage.

How did Allied Digital apply AI to enterprise support?

Allied Digital applied AI to enterprise support through a unified interaction layer, workflow automation and enterprise-system integration for a major Indian fast-moving consumer goods conglomerate and edible-oil processor. The support interface covered web, mobile and collaboration platforms, while stated automated tasks included password resets, account unlocks and guided troubleshooting.

Allied Digital said the design addressed fragmented support across multiple sites and dependence on manual Level 1, or L1, support for repetitive requests. It described reduced manual-support dependence, faster issue resolution and a standardised support experience, but did not disclose a percentage for ticket reduction, resolution time or cost savings for this client deployment.

For a personal-care and healthcare-products conglomerate, Allied Digital embedded an agentic-AI support layer within Microsoft Teams. The implementation combined multilingual conversational support and workflow automation with Allied Digital's in-house IT service management platform, identity-management systems and enterprise applications.

This second enterprise-support case used an AI-as-a-Service architecture, which Allied Digital said did not require customer-side AI infrastructure. The presentation said the model could scale across functions and geographies, although it gave no measured productivity, ticket-volume or financial result for this case.

What did Allied Digital's industrial vision deployment measure?

Allied Digital's live automotive-manufacturing computer-vision deployment reported an 18% increase in line productivity and 97% detection accuracy. The system was edge-deployed on manual assembly lines for a global European auto-parts manufacturer, using object detection, activity recognition and counting models integrated with the manufacturing execution system, or MES.

The automotive case measured production-line output and detection accuracy, unlike the enterprise-support cases, which described service delivery outcomes without numerical results. Allied Digital said its models were trained for the operating environment and connected vision data to MES workflows, linking line detection with manufacturing operations.

Beyond the automotive example, Allied Digital described a vision platform for industrial operations, smart cities and public infrastructure. Its stated capabilities included production-cycle monitoring, fire and smoke detection, unsafe-behaviour detection, automated licence-plate recognition and video synopsis for event compression and faster search.

The presentation did not provide separate accuracy or productivity figures for smart-city, public-safety or mobility applications. It instead described a move away from manual surveillance and reactive monitoring towards real-time incident detection through a centralised platform intended for factories, cities and transport infrastructure.

What were Allied Digital's service desk and cybersecurity results?

Allied Digital reported 32% L1 ticket deflection in a live managed-services portfolio deployment using a multi-modal chatbot, machine-learning classification and intelligent ticket routing. It also reported a 44% faster mean time to resolution, or MTTR, and a 21% reduction in cost to serve across the managed-services accounts covered by the rollout.

These three results measure different operating effects: ticket deflection measures demand removed from L1 handling, MTTR measures resolution speed, and cost to serve measures delivery cost. Allied Digital did not identify the number of accounts, ticket volumes, baseline period or whether the 32% rate applied consistently to every managed-services account.

In the live cybersecurity-operations case, Allied Digital reported that 65% of security-operations-centre, or SOC, alerts were auto-triaged, mean time to contain was four times faster, and 30% of analyst time was recovered. The deployment integrated threat triage, endpoint detection and response automation, and policy-compliance checks with client tools including CrowdStrike, Palo Alto and Sentinel.

The 65% SOC figure is higher than the 32% service-desk deflection figure, but the two measures are not directly comparable. One covers alerts automatically triaged, while the other covers tickets prevented from reaching L1 support; both cases are labelled LIVE, unlike the insurance case, which is labelled ROADMAP.

Which other live AI deployments produced quantified outcomes?

Allied Digital reported a 27% knowledge-worker productivity increase from governed Microsoft 365 Copilot adoption across Outlook, Word, Excel, PowerPoint and Teams. The digital-workplace programme also reported nine hours saved per user each month and first measured value in under eight weeks, with role-based enablement, data-loss-prevention and responsible-AI controls included.

The workplace case measured user productivity rather than service operations. Its nine-hours-per-user result is therefore not equivalent to the 30% analyst-time recovery in the SOC case, although both report less time required for routine knowledge or operational work.

In finance operations, Allied Digital reported 85% straight-through processing, 99.2% field-level accuracy and 5,400 annual hours saved in document intelligence for invoice and contract review. The implementation used generative-AI extraction, validation and exception routing on Digital Desk Plus, integrated with the financial-services client's existing enterprise resource planning system.

The finance deployment was one of five cases marked LIVE, alongside automotive manufacturing, managed services, digital workplace and cybersecurity operations. The insurance pilot-scaling roadmap states a target automation rate above 70%, an 80% MTTR reduction versus baseline and a three-month timetable to production launch.

Conclusion

Allied Digital's disclosed AI cases span factory lines, enterprise support, digital-workplace tools, cybersecurity operations and finance workflows. The most specific live measures were 65% of SOC alerts auto-triaged, 32% L1 ticket deflection, 97% vision-detection accuracy, 99.2% finance field-level accuracy and 5,400 annual hours saved in document processing.

The next disclosed milestone is the insurance pilot-scaling roadmap, which targets automation above 70% and production launch within three months. The presentation does not disclose baseline volumes, customer counts or measurement periods for most live metrics, leaving those details unresolved alongside the reported outcomes.