Fuel Innovation with unstructured data and AI

Introduction

As organizations around the globe increasingly turn to AI to drive innovation and efficiency, it’s clear that AI is a vital component of modern business strategy. From healthcare to human resources and from customer service to research and development, companies are deploying AI technologies across various functions to enhance decision-making, automate processes, and improve customer experiences. Yet, achieving success with AI requires more than simply adopting the technology—it demands a thoughtful and strategic approach to data, particularly unstructured data.

Unstructured data, which includes everything from text and images to audio and video, represents one of the largest untapped resources available to organizations. When paired with advanced AI technologies, this data has the potential to unlock powerful insights, automate complex tasks, and revolutionize decision-making processes. But here’s the catch: no matter which AI model you plan to use, your data must be cleaned and prepped to meet the quality, trustworthiness, and accessibility standards AI models need to deliver reliable and optimal results. What’s more, few currently have all these processes and methods in place.

If organizations can overcome these challenges and effectively partner unstructured data with advanced artificial intelligence (AI) models, they can unlock new opportunities for innovation across the enterprise. This partnership enables organizations to transform operations, achieving greater efficiency, fostering creativity, and driving growth.

Current AI adoption

AI adoption has become a pivotal focus across industries, with organizations increasingly leveraging its capabilities to enhance efficiency, customer service, and innovation. As global interest intensifies, our research highlights where within business AI is currently being deployed, demonstrating widespread but varied adoption. This trend underscores how AI is not only shaping core functions like IT and security but is also creating nuanced differences across countries and sectors, driven in part by national strategies and regulatory environments.

AI is transforming business functions

Globally, AI is most used within IT and security (82%), customer service (53%) and research and development (53%) applications. These adoption trends reflect AI’s versatility, with organizations leveraging AI across an average of five operational areas. However, as organizations grow to be more AI mature, they show signs of refining where and how they use AI. AI maturity gives them the confidence to focus on optimizing AI applications in specific functions to drive greater efficiency, innovation, and value across their operations.

What’s more, as organizations move further in their AI journey, they not only gain confidence in where to use AI, but they also develop a clearer understanding of its strategic potential. This confidence can be critical in transitioning from scratching the surface of AI’s capabilities to unlocking its full transformative value.

Strategic AI support drives varying adoption across countries and industries

There are distinct differences by country and sector, in both the number of functions where AI is used, and which functions have adopted the technology. Looking first at the number of operational areas, France leads, with organizations using AI across an average of six areas, surpassing the global average of five. In contrast, the UK lags, using AI across an average of four operational areas.

Government policies and strategies are likely drivers of adoption. For instance, France has taken significant steps to position itself as a leader in AI adoption. The 2018 implementation of a comprehensive National Artificial Intelligence Strategy laid the groundwork, with a further 1.5 billion euros of investment in 2022 to enhance AI development. This also included 700 million euros allocated specifically for research, incentivizing organizations to adopt AI and embed it across multiple functions.

This strategic policy support has created a fertile environment for AI adoption in France, encouraging businesses to integrate AI into diverse areas such as IT, finance and customer service. In contrast, the UK’s approach to adoption has been more cautious and delayed, limiting their ability to scale AI across as many areas. For instance, the UK only started rolling out positive AI initiatives in 2021. The years between France’s strategy implementation and the UK’s initiatives means the UK has delayed adoption, resulting in fewer than average AI use cases being implemented.

The broader implication of these differences is substantial. Countries with comprehensive AI strategies and widespread implementation, such as France, are better positioned to drive innovation and revenue growth (amongst other benefits). In fact, more than half of IT and data decision-makers (54%) feel AI is very important in helping organizations achieve their revenue growth goals over the next two years. Furthermore, importance grows among those with widespread AI adoption such as France (62%) and drops in importance for those without vast implementation (44% in the UK). Organizations in countries with slower AI adoption should proactively address gaps by leveraging global best practices and fostering collaboration with AI- focused research initiatives.

A similar story emerges when analyzing AI adoption across industries. Organizations in the energy, insurance, manufacturing and production sectors report using AI in six areas, more than the global average of five.

In contrast, banking and financial services (excluding insurance) organizations tend to use AI across four operational areas.

This disparity may be attributed to the varying operational demands and technology priorities of these industries. For example, sectors like energy and manufacturing often rely on AI to optimize supply chain management, predictive maintenance and resource allocation; areas where AI can help streamline and save costs. Insurance organizations similarly leverage AI across multiple functions, from automating claims processing to risk analysis and fraud detection. What’s more, these industries often face intense pressure to innovate and continually streamline operations for competitive advantage, driving the need for broader AI adoption.

Moreover, the differences between the banking and insurance industries illustrate the nuanced use of AI within financial services. For instance, our research shows that nearly twice as many insurance decision-makers (61%) use AI for customer service compared to their banking counterparts (37%). This disparity likely stems from the broader range of customer data scenarios insurance organizations handle, such as risk assessment, claims processing, fraud detection, and customer retention.

Understanding these vertical differences highlights the importance of tailoring AI strategies to industry-specific needs and challenges. According to our research, sectors leveraging AI across multiple functions should focus on scaling operations and ensuring the seamless integration of AI technologies via advanced training. Meanwhile, industries earlier in AI adoption may benefit from exploring underutilized opportunities and addressing barriers like regulatory compliance, infrastructure limitations, or skill gaps. By aligning AI adoption with industry-specific goals, organizations can maximize the value of their investments and remain competitive in an evolving technological landscape.

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