A new global survey highlights a big shift in how companies are using artificial intelligence today. While more organizations are now running AI in production, many still struggle with the quality of their document data. This gap can slow down AI projects and limit their potential to deliver insights or automate processes effectively.

Most Companies Have AI in Action, but Data Quality Lags

The survey found that 64.5% of organizations have already implemented AI in their daily operations. They mainly use AI to boost efficiency, enhance customer experiences, and make better data-driven decisions. However, despite this widespread adoption, only 38.1% of these companies rate their document data as “excellent” for AI purposes.

Many companies rely heavily on documents, with 76.6% storing between a quarter and three-quarters of their data in this format. But the quality of that data is often inconsistent, messy, or unstructured. Without good data, AI models struggle to interpret information correctly, making automation and accurate insights much harder to achieve. Manual vetting can help, but it isn’t scalable for most businesses. This highlights a critical need for better data governance and smarter pre-processing tools.

Barriers to Scaling AI and Future Investment Plans

Data security and data quality are the top concerns among companies. About 54% see security as a major barrier, while 49% worry about the overall quality of their data. Despite these challenges, most organizations plan to invest heavily in document automation. Nearly 83% say they will increase their spending within the next year, although many lack confidence in their current data pipelines.

Document quality issues are common, with over 62% experiencing problems occasionally or frequently. This ongoing struggle hampers the ability to scale AI initiatives effectively. Experts say that infrastructure around document data needs to catch up with AI’s rapid growth. Without better visibility, governance, and integrated tools, many companies find it difficult to process documents at scale and extract reliable insights.

Regional Differences and Emerging Leaders in AI Maturity

While North America leads in overall AI deployment at 77.7%, the Asia-Pacific region, especially Australia and New Zealand, shows notable progress in infrastructure maturity. These areas are adopting more advanced AI techniques like generative and predictive AI, and technologies such as optical character recognition (OCR) and hybrid cloud solutions. This marks a shift towards smarter, more efficient document processing worldwide.

Experts note that Oceania has been quick to adapt to new rules around data residency and privacy. Many regulated industries like healthcare, government, and finance push organizations in this region to implement rigorous document workflows. This early adoption creates a robust model for others to follow, combining compliance with high-quality data management and processing capabilities.

Overall, the survey indicates that while AI is now mainstream, the backbone of effective AI — high-quality, well-governed document data — still needs significant improvement. As organizations invest more in automation tools, they must also focus on strengthening their data infrastructure to unlock AI’s full potential and operate at scale more confidently.

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• https://ai-techpark.com/ai-in-production-surges-but-data-quality-lags-behind-apryse/