Making AI work: 

A practical view from the field

This article is the third in a three-part series examining the impact of disruption. Read part one and part two.

Many organizations find themselves caught between the promise and practicality of AI implementation. Headlines tout endless potential, while business leaders struggle with real-world deployment challenges.  

Our recent AI research underscores this dynamic: while 93% of executives believe AI will be a permanent part of their organization's strategies, only 30% feel their current AI adoption gives them a competitive advantage.  

Drawing from our experience at Unisys, where we have over 120 AI projects – 50 in production and 40 serving clients directly – we've identified what separates successful AI initiatives from those that fall short. 

Our hands-on experience shows three key factors driving AI success. First, successful AI implementation depends less on the sophistication of AI models and more on foundational elements many organizations overlook. Second, organizations often underestimate the importance of data quality and infrastructure readiness. And third, the path to AI value requires methodical preparation rather than rushing to deploy the latest technology. 

Getting the foundation right  

Effective AI requires excellent data management. Without clean, accessible, well-governed data, even the most sophisticated AI models will struggle to deliver results. We see this play out in our client work, where data preparation often becomes the critical factor in project success. This means ensuring data quality, making the right data available to the right people at the right time, and establishing clear governance policies. 

Cloud infrastructure plays an equally crucial role. The scalability and flexibility of cloud environments make them ideal for AI workloads, but implementation requires careful planning. Organizations need a clear cloud strategy, whether public, private, or hybrid. They need efficient and secure data migration processes. And they need effective cloud management to optimize both performance and cost. 

Putting AI to work  

At Unisys, we've applied these lessons in our work with clients across diverse industries. Take our work with a major higher education institution. When developing an AI-powered education companion, success came from carefully selecting and structuring specific textbook content, ensuring it could handle multiple languages, and designing it to make education more accessible to a diverse student body. This practical approach aligns with our research findings - 83% of organizations report AI positively impacts day-to-day productivity when properly implemented. 

In our digital workplace solutions — our Service Experience Accelerator uses generative AI to analyze operational data within a client's trusted environment. This maintains data sovereignty while improving service efficiency. The solution delivers immediate value while protecting client data. 

Our advanced applications demonstrate similar results. Our Unisys Logistics Optimization solution combines AI with quantum annealing capabilities for cargo capacity warehousing and routing. We're expanding this to include revenue management capabilities and multi-modal routing. These applications solve real business challenges today. 

Making security central  

As AI systems access and process increasing amounts of data, security becomes critical. Organizations need robust data protection for sensitive information used in AI models. They need to protect the AI models themselves from tampering or theft. And they need to ensure their AI systems operate ethically and comply with regulations. 

Effective AI security requires clear policies, ongoing monitoring, and regular audits. Organizations that build security and governance into their AI initiatives from the start avoid costly problems later. 

Focusing on people  

AI success requires both technological and human capabilities. Our research shows this clearly: 71% of employees report improved job satisfaction with AI implementation, and 79% believe gaining AI skills enables faster career progression. Even more telling, 44% of employees who have saved time using AI invest that time in training and professional development.  

Preparing your workforce for AI adoption means developing new skills, helping teams adapt to AI-driven processes, and ensuring everyone understands both the potential and limitations of AI systems. Our internal operations demonstrate this approach. We've engineered specialized AI assistants that help our teams navigate internal systems and policies more efficiently. Success came from helping our people work effectively with these new tools. 

Creating lasting value  

Moving beyond individual projects, organizations need the infrastructure and expertise to manage and optimize AI workloads over time. This includes everything from multi-cloud management to data center operations to the physical infrastructure required for AI operations. 

Client demand for AI-related managed services continues to grow. Companies understand that maintaining AI systems requires specialized skills and infrastructure beyond traditional IT operations. Success depends on having both technical capabilities and operational expertise to keep these systems running effectively. 

Moving forward  

AI technology will continue to evolve rapidly. At Unisys, we see this in our own innovation pipeline, where we continuously evaluate and implement new capabilities. However, the fundamentals of successful AI implementation — solid data management, clear business focus, and strong operational capabilities — will remain constant. 

For technology leaders planning their AI initiatives, the path forward requires prioritizing these foundational elements, taking a pragmatic approach to deployment, and building the operational expertise to sustain these systems over time. This measured approach is the surest way to deliver results that truly matter to the business, now and in the future.