A large enterprise with complex operations and an extensive digital footprint. The organisation managed service requests and system incidents across multiple regions through disconnected tools, from call centres and ERP platforms to vendor systems. This fragmented environment led to slower resolution times, alert fatigue among IT teams, and difficulties maintaining consistent service quality and system uptime.
The 8-Step AI Adoption Framework in Action:
- Enterprise AI Adoption Strategy – The organisation set out to transform its IT operations and service management from a reactive “fire-fighting” model into a proactive, data-driven resilience engine. The goal: to improve system reliability, accelerate issue resolution, enhance SLA compliance, and strengthen overall customer experience.
- AI Maturity Assessment & Ladder – An enterprise-wide AI maturity review revealed multiple opportunities to improve through automation and predictive intelligence. The assessment identified fragmented processes, duplicated alerts, and high Mean Time to Detect (MTTD) and Resolve (MTTR). A clear roadmap was developed to evolve from reactive response to predictive and autonomous IT operations.
- AI Technology & Data Foundations – A unified operational data model was built by integrating telemetry, ticketing, customer feedback, and work order data. This enabled the creation of an AI reliability platform that continuously monitors service health, detects anomalies, and correlates incidents in near real time. The same data backbone also powered an AI dashboard for executives and service leads to track performance and trends.
- AI Governance Framework – AI governance was embedded to ensure transparency, accuracy, and data privacy. A human-in-the-loop process validated anomaly classifications, root cause analysis, and escalation handling. This governance layer built trust in the AI engine while ensuring all recommendations met compliance and operational standards.
- AI Prototype-Driven Use Cases – Two high-impact AI prototypes were launched:
- Smart Ticketing Automation – introduces a streamlined approach to IT service management through automated ticket prioritisation and routing. It intelligently balances workloads based on team members’ skills and availability, while predictive assignment and SLA alerts ensure that high-severity issues are addressed promptly.
- AI-Powered IT Operations Engine – enhances system reliability with real-time anomaly detection across telemetry and log data. It clusters incidents to identify common root causes and leverages machine learning for root cause analysis and next-best-action recommendations. Additionally, automated alert deduplication helps reduce noise and alert fatigue.
- Embedding Meaningful AI – To enhance IT service delivery, AI models were deeply integrated into everyday workflows. Incoming requests and incidents were automatically classified and routed, reducing manual effort. Predictive analytics flagged potential issues early, allowing teams to act before customers were affected. AI-recommended actions helped accelerate recovery and minimise downtime. This led to faster resolutions, fewer escalations, and improved visibility across the enterprise IT landscape.
- AI–Human Collaboration – IT operators and service teams transitioned seamlessly to this “AI + human” hybrid model. The model enhanced trust and adoption, allowing teams to see AI as a partner in operational excellence.
- Service desk agents focused on problem-solving rather than triage.
- IT leads leveraged the AI dashboard for proactive monitoring and decision-making.
- Technicians received more accurate incident data and prioritisation cues.
- AI Continuous Innovation – With strong results from the pilots, the enterprise established a centre of excellence for AI-driven operations. The platform now continuously ingests new data, retrains models, and refines recommendations to improve Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR). Continuous learning ensures adaptive resilience and a scalable foundation for future innovations.
Results:
- Faster detection of anomalies and incidents (MTTD).
- Faster ticket resolution across key service areas.
- Significant reduction in recurring incidents and downtime costs.
- Major improvement in SLA compliance through predictive monitoring.
- Improved ROI by accelerating time to market and optimising talent for innovation
- Centralised visibility for leaders through AI-driven dashboards and insights.
By applying the 8-Step AI Adoption Framework, the enterprise transformed its fragmented IT landscape into a unified, intelligent operations engine. AI now drives proactive service management, predictive issue detection, and continuous improvement.
The outcome: faster recovery, fewer disruptions, and a scalable AI foundation powering resilient, always-on operations.