Real-world lessons in scaling enterprise AI to accelerate outcomes

A global manufacturing company faced challenges with fragmented customer experiences, 400+ disconnected systems, and the lack of a unified data strategy, limiting its ability to scale in response to changing business demands and to serve customers consistently. In partnership with us, the company launched a large-scale transformation with a global CRM rollout, a centralized data lake, and 30+ AI-implemented use cases. These investments improved forecasting accuracy by 98.5%, reduced warranty claim timelines by 95%, and unlocked millions in annual savings through supply chain and fleet optimization.

This is what AI at scale looks like. Moving beyond AI pilots to scalable enterprise AI implementations drives measurable outcomes and reshapes how organizations create business value. Technology leaders are no longer debating if AI will benefit their enterprises, but how quickly they can scale these benefits across geographies and customer journeys.

According to our 2025 Voice of Our Clients research, organizations with holistic data and enterprise-level AI strategies see multiplier effects on maturing AI implementations that range from 1.7X to 6.6X across GenAI, advanced AI, and traditional AI, as compared to enterprises without those strategies in place.

The conversation with clients has shifted from proof of concepts to an expectation of AI delivering real value at scale and not just efficiencies. Clients aim to scale enterprise AI to also become more innovative and competitive, to open new revenue streams, to drive greater resilience, to better manage risks, and more.

Globally, we are seeing the same pattern:

• A surge in demand for holistic AI strategies that extend beyond internal functions
• Increased focus on data quality and governance to ensure reliable AI-driven insights

Scaling AI: What we have learned so far

In our experience in supporting enterprise AI journeys, several common success factors have emerged, including:

• Clear strategic vision, aligned with business priorities and supported by executive leadership
• Future-ready talent, blending domain expertise with AI fluency
• Innovation culture to foster experimentation, continuous learning, and cross-functional collaboration
• Outcome-driven mindset with KPIs linked to growth, efficiency, and customer impact

At our flagship APAC technology event, Envision 2025, our leaders explored these themes with clients and alliance partners. Watch the recording of the panel session: From strategy to practice - The next step in AI for enterprises.

Scaling AI: Real-world lessons and examples from the field

In our recent blog, Building a mature AI ecosystem for scalable impact, we explored the foundational elements that enable successful AI scaling, including data, governance, strategic alignment, and talent readiness.

This blog explores how we are working alongside organizations to put these principles into action. Our real-world examples reflect different facets of transformation—from use cases, to actionable insights, to strategic enablers—offering a practical view of how ecosystem principles are being applied to achieve enterprise AI. These principles include:

Breaking down silos with connected intelligence

Organizations that integrate AI into SDLC (software development life cycle), operations, and customer channels can better learn, adapt, and stay aligned with changing business needs.

Case in point: As mentioned in the opening paragraph, we worked with a global manufacturer to break down silos and improve product development. This improved forecast accuracy by 98.5% and reduced deployment time from 3 months to 5 days.

Strengthening data and governance foundations

AI insights will translate into measurable results only with a trusted data backbone and governance aligned with business context.

Case in point: We embed responsible AI by design through CGI’s Responsible AI Principles and Frameworks to help clients build trust, reduce risk, and align with evolving regulations.

Embedding intelligence into managed services

Clients increasingly expect AI and automation to be integrated into managed services delivery. They want each to be ‘baked in,’ not ‘bolted on.’

Case in point: CGI DigiOps, our AI-powered delivery transformation approach, embeds intelligence across managed services with platforms and tools designed to integrate seamlessly with existing client environments. It enables near real-time decision-making through traditional AI, advanced AI, and, in some scenarios, composite AI, where predictive and preventive insights help teams identify, analyze, and respond to IT, application, and business process scenarios.

With our agentic AI methods, we introduce autonomous workload management and have seen clients achieve significant improvement in system and application performance, SDLC efficiency, customer satisfaction, and productivity gains (between 25–30%).

Prioritizing high-impact use cases

It’s important to start with use cases that deliver the greatest business value to build momentum and confidence.

Case in point: We are working with Helsinki University Hospital in Finland to apply AI in radiology. Our solution reviews CT scans and detects brain hemorrhage with 98% accuracy, improving patient care while helping the system scale capacity.

Focusing on domain-specific accelerators

AI must be tailored to industry context for faster ROI, improved relevance, and compliance with security and data sovereignty regulations.

Case in point: For a global insurer serving more than three million customers, we improved the speed and accuracy of its software delivery ecosystem using CGI NAVI, a multi-layered AI solution. The organization achieved a 45% reduction in manual effort, faster time to market, and stronger compliance in a highly regulated environment. Our solution was deployed with added security and data protection controls specific to the industry, ensuring sensitive customer and business data remain secure.

Measuring business impact with growth in mind

While cost-efficiency is often a starting point, we encourage organizations to prioritize how AI can drive innovation, accelerate time to market, and drive sustainable business value.

Case in point: We encourage organizations to measure AI success through growth-focused and transformation-related KPIs, not just operational savings. In one case, implementing CGI DigiOps for an international convenience retailer enabled predictive maintenance and improved system performance tracking, powered by end-to-end AIOps and human-in-the-loop remediation. This led to higher store availability, reduced service disruption, and, ultimately, what matters most—a seamless and consistent customer experience.

Investing in your people, not just in tools and technology

AI maturity depends on your talent understanding how to responsibly use and apply AI.

Case in point: One of the most important accelerators for AI adoption and innovation is AI literacy and fluency, where employees are empowered to integrate AI confidently and effectively into their roles. With input from both leaders and professionals, we help clients develop tailored and advanced training initiatives. This leads to improved AI adoption, especially when employees are actively involved in shaping the AI strategy.

As we engage with clients across the globe, one message is clear; they are exploring enterprise AI in the search for tangible outcomes at scale. However, doing so requires agility, strong partnerships across the ecosystem, and alignment with business priorities.

This isn’t just about keeping up; it’s about shaping what’s next. If you’re exploring how to scale your AI initiatives or bring intelligence across your business and operations, learn more about our AI success stories here, and let’s connect.