Why AI is changing the game for data buyers in 2025

Once upon a time, sourcing web data at scale was a complex and expensive endeavor. Companies either built in-house data pipelines—spending months on infrastructure and compliance—or relied on limited, costly external datasets.

But today, AI is rewriting the rules. Businesses that once hesitated to invest in web data due to cost or complexity are now finding that buying data has never been easier. Supply and demand for external data is surging—driven by AI-powered data collection, automation, and scalable marketplaces.

These changes are not just making data easier to obtain—they are redefining how businesses integrate it into their decision-making processes.

Artificial intelligence is significantly improving the efficiency of data extraction.

In the past, companies needed to develop and maintain custom web crawlers, constantly updating them to respond to website layout changes and anti-bot measures. This time-consuming and expensive process required specialized engineering teams to monitor and adjust scrapers as websites evolved.

Now, however, AI-powered web scraping tools can automatically adapt to changes in website structures, reducing the need for costly manual intervention.

These tools intelligently utilize only the necessary technology to unblock websites and avoid bans, optimizing resources for efficient data extraction. Additionally, they can dynamically update their schema when a website layout changes, ensuring that data continues to flow without interruption—without requiring manual adjustments or constant monitoring.

AI also enhances the ability to extract unstructured data, such as text inside PDFs and raw data on webpages, which previously required complex processing pipelines.

This automation is shrinking the traditional cost structure of web data collection, making it possible to complete what previously required several days of development in minutes.

Zyte predicts that AI will continue to lower the barrier to high-quality data acquisition.

This shift makes high-quality web data accessible to companies lacking the resources to collect and process it. For data buyers, this means greater reliability and scalability.

Historically, companies looking to source external data had two choices: build their own web scraping infrastructure or negotiate custom data agreements with vendors.

Both approaches required significant time and financial investment, making high-quality external data a luxury only available to well-funded organizations.

Today, data marketplaces like AWS Data Exchange, Databricks Marketplace, and Datarade have transformed access to web data. Instead of building complex pipelines, companies can now purchase pre-cleaned, structured datasets with just a few clicks.

These platforms offer a wide range of data sources, from real-time financial feeds to e-commerce pricing intelligence, allowing businesses to experiment with external data.

Zyte forecasts that data marketplaces will continue to expand, offering even greater customization and flexibility. The shift from rigid, pre-packaged datasets to modular, API-driven data access will allow companies to tailor their purchases based on evolving needs.

This shift means that, for data buyers, testing and scaling external data usage is faster and easier than ever.

Companies can now explore new data-driven strategies without committing to long-term development efforts. Additionally, marketplace providers handle data extraction and cleaning, ensuring businesses receive high-quality datasets.

This dramatically reduces the operational risks associated with web scraping, making external data acquisition safer and more streamlined.

Large-scale data acquisition providers have optimized their collection and distribution processes to meet the growing demand for data. Companies like Zyte handle massive data extraction operations across multiple industries, allowing them to reduce per-unit costs for data buyers.

Previously, organizations had to build and maintain their own infrastructure, leading to significant upfront and ongoing costs. Now, data providers can spread these expenses across a broad customer base, making high-quality datasets more affordable than ever.

The efficiency of large-scale data operations also means buyers benefit from more frequent updates and improved data accuracy, ensuring they receive the most relevant, real-time insights.

Zyte anticipates that this trend will further accelerate the shift toward hybrid data strategies, where businesses combine vendor-sourced data with in-house capabilities for maximum cost-effectiveness. Instead of choosing between buying and building, companies will increasingly blend both approaches based on their data maturity and use cases.

For data buyers, this trend translates to a lower cost of ownership for external data. Instead of investing in expensive, in-house data collection, businesses can now outsource at a fraction of the cost.

This allows companies to focus their resources on deriving insights and making strategic decisions rather than managing the complexities of data extraction. Additionally, outsourcing eliminates the technical maintenance burden, enabling businesses to scale their data operations without additional infrastructure investment.

The data-buying landscape is evolving, and companies' decisions today will shape their competitive advantage in the years ahead. 

Understanding when to buy, when to build, and how to leverage AI-powered data strategies is now essential for any organization that relies on external data, as AI reduces costs and complexity, data marketplaces expand access, and economies of scale drive affordability.

Explore our 2025 Web Scraping Industry Report to stay ahead of these changes. In it, we explore the trends shaping the future of data acquisition and provide actionable insights for businesses at every stage of the data journey.