Realizing the value of enterprise-grade AI: From proof-of-concept to real-world problem-solving

Principal Data Scientist at digital engineering leader GlobalLogic, Dr José Albornoz, shares his insights on overcoming challenges when moving artificial intelligence from pilot projects to full-scale deployment

The market for enterprise-grade AI has seen significant growth, reaching a staggering US$10.08 billion last year, signaling the beginning of a promising journey. By 2032, it's projected to soar upwards of US$270 billion—a compound annual growth rate (CAGR) of 44.1 per cent. These statistics not only underscore the bright future of AI integration in enterprises but also ignite a sense of optimism about the potential of artificial intelligence for businesses. 

The maturity gap

As Forbes reported, organizations indicate a strong willingness to embrace AI. Yet, despite the optimism, industry data suggests most enterprises need help to realize its full potential. With an average score of 44 out of 100, a recent survey of 4,500 executives concluded global enterprise AI maturity is low across the board—highlighting a significant gap between enthusiasm and ability to move AI from proof-of-concept models to real-world problem-solving effectively. 

Barriers to going beyond pilot projects

Organizations face a whole host of challenges as they transition from small-scale pilot projects to broader implementations of AI. These obstacles can act as roadblocks, creating friction and hindering the journey toward responsible, reliable, and reusable integrations. 

Navigating volume and complexity: Data is the cornerstone of effective AI implementation, but the sheer volume and computational power needed, coupled with complexities integrating with legacy technologies, mean many businesses struggle to manage the vast amount of data effectively.

Finding the right use case: Misalignment between implementing artificial intelligence and business goals, such as using machine learning for customer service when the objective is to increase sales, leads to wasted resources. Therefore, understanding and pinpointing areas where AI can deliver the most value is an essential but challenging undertaking.

Tailoring AI solutions: Fine-tuning algorithms, training models with proprietary data, and adjusting intelligent solutions to meet specific organizational needs are time-consuming processes that require specialized knowledge and expertise. Many organizations fail to fully exploit such technologies because of this shortage of specialized knowledge and concerns over security when proprietary data is used to improve the performance of models that reside outside of an organization’s IT infrastructure.

Managing the integration process: Transitioning from development to deployment involves rigorous testing, validation, and fine-tuning, all while mitigating the risk of real-world operational disruption. It’s a dynamic process fraught with difficulties that can overwhelm already stretched organizations. To further complicate matters, AI applications must be monitored in production not only to ensure that they provide an answer to a business need but also to guarantee that the outputs are not inappropriate or dangerous.

Ensuring data quality, accessibility, and compliance: Navigating the complex compliance landscape is no easy feat for any organization. The potential legal consequences and the reputational risk can be daunting, making the task of cleaning, standardizing, and safely integrating data from various siloed sources not just crucial but a significant challenge that every enterprise must tackle head-on.

The building blocks of effective integration 

To overcome obstacles and harness the transformative power of AI, organizations can adopt various strategies based on their unique characteristics and specific needs. In my experience, businesses achieve the greatest success when they include certain building blocks. The first of these is a strategic vision and use-case framing supported by a strong foundation.

A strategic vision—Starting a building project without a solid plan is unthinkable. The same goes for integrating AI into your organization. To truly succeed, you need a clear outline that sets out what you want to achieve and the steps needed to get there. Understanding how solutions fit into your overall business strategy and communicating the vision to stakeholders to ensure buy-in is also vital. 

Use case framing—Translating high-level objectives into specific, data-driven tasks that can be addressed using AI is fundamental. Identifying target outcomes using clear success metrics, such as increasing revenue or improving customer satisfaction, and identifying where AI can add value is key. 

Strong foundations—Just like a building needs a solid foundation, creating accurate and reliable AI models relies on a robust data management framework. The entire data estate must be structured, ensuring integrity and quality, meaning enterprises must invest time and money in infrastructure, including collection, storage, versioning, and processing capabilities. 

Deployment models—Testing before integration in controlled environments helps uncover potential issues and refine solutions without disrupting operations. Such models also provide insights into the feasibility and day-to-day impact, allowing your organization to secure support resources and necessary infrastructure at scale. 

Human collaboration—AI is a powerful tool that should amplify human potential rather than replace valued workers. By equipping and empowering your workforce to embrace and adapt to new technologies, you enhance their capabilities and transform potential resistance into a culture of collaboration.

Risk mitigation—When it comes to integrating artificial intelligence, security and compliance aren’t just important—they're absolutely essential. Adhering to regulations and standards, such as data protection and AI transparency laws, is a must. Establishing strong governance policies and reliable processes at every stage of integration creates trust and accountability, mitigating risks such as data breaches and ethical concerns. 

The benefits of working with expert partners 

Just like buildings have architects, engineers, and tradespeople, AI implementation requires a range of technical expertise. Partnering with specialists gives you the necessary insights and capabilities your organization might lack internally. 

Expert partners bring a unique perspective on industry best practices, emerging trends, and regulatory controls, which means they can help you refine strategies, mitigate risks, and avoid common pitfalls. Their input not only accelerates your learning curve but also instill a sense of reassurance and confidence in AI initiatives, accelerating your time-to-value. They also bring a wealth of resources, including specialized tools and technologies that enhance your specific deployments. 

Specialized partners have a deep understanding of the nuances and can guide you through the complexities to ensure a smoother transition from pilot projects to practical applications. Put simply, expert partners engineer impact and play a pivotal role in advancing enterprise-grade AI from proof-of-concept to real-world problem-solving.

About Dr José Albornoz 

Dr José Albornoz heads the Data Science practice at GlobalLogic in the UK&I region. In his prior positions, he was Head of Data Science at Catalyst BI, Data Science Manager at DataRobot, Lead Data Scientist at Unisys and Capgemini, and Principal Data Scientist at Fujitsu. Before moving to the UK, he was an Associate Professor of Electrical and Computer Engineering at Universidad de Los Andes (Venezuela) while running his own data science consulting business.

About GlobalLogic

GlobalLogic is a leader in digital engineering. We help brands across the globe design and build innovative products, platforms, and digital experiences for the modern world. By integrating experience design, complex engineering, and data expertise—we help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. 

Headquartered in Silicon Valley, GlobalLogic operates design studios and engineering centers around the world, extending our deep expertise to customers in the automotive, communications, financial services, healthcare and life sciences, manufacturing, media and entertainment, semiconductor, and technology industries. GlobalLogic is a Hitachi Group Company operating under Hitachi, Ltd. (TSE: 6501), which contributes to a sustainable society with a higher quality of life by driving innovation through data and technology as the Social Innovation Business.