Nissan Cuts IT Operational Costs 40% with AI-Powered Monitoring and Automation

Nissan had a hard problem. Its websites and dealer platforms needed to support faster releases, better personalization and stronger dealer engagement. But the systems behind them were already stretched. IT was still working in a reactive support model, spending too much time chasing issues across the stack.  

Publicis Sapient has partnered with Nissan for over 11 years across managed services. Building on this foundation, we introduced Sapient Sustain, our AI-powered IT operations platform, to change how IT actually runs day to day.

The shift moved teams out of constant firefighting and into a more predictive model—reducing issues before they escalated, improving stability and lowering the cost of keeping systems running.

Nissan’s digital platforms weren’t just support systems—they were directly tied to how the business sold cars, engaged dealers and delivered customer experiences. When performance dipped or issues slowed things down, it showed up quickly in customer journeys and dealer interactions.

But the way IT was operating made that harder to control. 

IT operations were highly reactive, with teams manually identifying and resolving issues across fragmented tools and workflows. This led to rising costs, slower resolution times  and limited visibility into system health. Teams were spending more time responding to problems than preventing them, and that created instability across customer-facing websites. 

At the same time, Nissan needed IT to do more than maintain uptime. It needed to support revenue-driving priorities like personalization, sales enablement and dealer experience without increasing cost or operational overhead.    

We implemented Sustain, our AI-powered IT operations platform, to transform how systems were monitored, managed and optimized—without disrupting the existing technology stack, including AEM, AWS, Spring Boot, SonarQube, Apigee, Chef and Datadog. 

This transformation combined platform capability with a shift in how teams operated:

• AI-powered observability: Centralized monitoring with anomaly detection to identify patterns and flag issues earlier
• Self-healing automation: Automated fixes for recurring issues, reducing manual effort across L1-L3 teams
• Predictive operations model: Early signals used to detect and prevent failures before they impact users
• Embedded Site Reliability Engineering (SRE) practices: Stronger release quality and more resilient systems over time
• Real-time operational visibility: Clear, shared view of performance, SLAs and user experience
• AI-enabled productivity: Reduced manual effort and faster decision-making for operations teams

Together, these changes reduced noise, improved consistency and helped teams focus on preventing issues instead of constantly reacting to them.

The transformation delivered measurable improvements across cost, performance and operational efficiency:

• 40% reduction in operational costs
• 62% same-day issue resolution rate
• 80% shift from reactive to proactive operations
• 99.99% platform uptime maintained

More importantly, IT became more predictable and easier to manage—supporting Nissan without constant disruption or reactive effort.