Web Scraping Trends for 2025 and 2026

Tariffs, AI, and the Data-Driven Future

As we move through 2025 and into 2026, enterprise web scraping is entering a new era shaped by economic uncertainty and rapid technological advances. Businesses are more data-hungry than ever, using web scraping (automated data collection from websites) to gain an edge in volatile times. According to insights from Scott Vahey, Director of Technology at Ficstar, companies today are laser-focused on monitoring tariffs and prices amid inflation, while also harnessing AI to improve data quality. Looking ahead, AI is set to transform both how data is gathered and how it’s utilized, from smarter scraping algorithms to dynamic pricing strategies. In this article, we explore the key web scraping trends for 2025 and 2026 based on Vahey’s observations, and suggest how enterprises can navigate the road ahead. 

At Ficstar, we’ve built solutions that adapt quickly—tracking real-time changes and delivering structured data back to our clients in a matter of days, not weeks. That gives them the ability to stay responsive without overloading their teams.

— Scott Vahey, Director of Technology at Ficstar

Tariffs and Trade Uncertainty: Real-Time Data Tracking

One striking trend in 2025 is the use of web scraping to track tariff changes in real-time. Geopolitical shifts such as evolving U.S. trade policies have made tariffs a moving target. “We have clients monitoring tariff status on some websites because of the dynamically changing tariff situation in the U.S.,” notes Scott Vahey. Recent events illustrate why: in April 2025, the U.S. imposed sweeping new import tariffs (a 10% baseline on nearly all imports, plus steep country-specific surcharges) only to partially roll them back with temporary reductions in May. Such rapid shifts mean companies can no longer rely on static data or infrequent manual checks. Instead, they are deploying scrapers to continuously pull the latest tariff rates and policy updates from government portals, trade databases, and news sites. By automating tariff monitoring, businesses in manufacturing, retail, and logistics can quickly adjust supply chain strategies or pricing in response to new fees. The ability to scrape up-to-the-minute tariff data ensures they stay agile – hedging against evolving political risks rather than operating on outdated assumptions. In short, real-time tariff intelligence has become a must-have for globally exposed enterprises.

Inflation Drives Price Monitoring Demand

Another priority for enterprises is monitoring competitive prices driven by high inflation and economic uncertainty. In 2025’s volatile market, prices can swing quickly, and consumers are extremely price-conscious. Companies are responding by using web scraping to closely monitor competitors pricing and market rates. Vahey observes that many firms are now more interested in price monitoring than ever as they grapple with inflation and an uncertain economy. Demand for data remains strong, even as the sheer volume of available data explodes. The global supply of data doubles every few years, yet businesses continue to crave timely, relevant data to make informed decisions. This appetite is especially evident in retail and e-commerce, where dynamic pricing and frequent promotions are the norm.

Scraping competitor sites for pricing, stock levels, and promotions enables companies to react swiftly – by lowering certain prices, adjusting inventory, or offering targeted discounts- to stay attractive to price-sensitive customers. Recent consumer research highlights the importance of this. A late 2024 BCG survey found that 44% of consumers are investing more time in comparing prices online (rising to 60% in electronics), and 30% said they would “jump ship” to another retailer for better prices. Price has become “the kingpin of switching behaviour,” far outweighing factors like product selection. To keep these value-focused customers loyal, businesses need dynamic, competitive pricing strategies powered by real-time data. In practice, this means robust price intelligence programs: scrapers that continuously feed pricing data into dashboards or algorithms, alerting decision-makers to market changes. By monitoring the web for price fluctuations and competitor moves, companies can proactively adjust their pricing and avoid being undercut. In uncertain times, staying on top of the market in near-real-time isn’t just beneficial, it’s necessary for survival.

AI Boosts Data Quality and Efficiency

To make the most of all this scraped data, enterprises are increasingly integrating AI into their web scraping pipelines, particularly for data quality assurance. Collecting vast amounts of data is only half the battle; ensuring that data is clean, accurate, and actionable is the other half. 

"We have been implementing more AI into our data quality checking to weed out discrete issues. With AI, we can automatically spot inconsistencies in massive datasets before they cause problems. This has allowed our clients to trust the accuracy of their data pipelines without needing to manually inspect every record."

— Scott Vahey, Director of Technology at Ficstar 

Manual data cleaning and validation can be painfully slow (and error-prone), especially as datasets scale to millions of records. AI offers a powerful remedy. Machine learning algorithms can automatically detect anomalies, duplicates, or outliers in scraped data and even correct them in real-time. For example, AI-powered validation systems utilize techniques such as anomaly detection to identify data points that don’t conform to expected patterns, allowing them to be reviewed or corrected. This is crucial because poor data quality comes at a high cost – on the order of $12.9 million per year for businesses on average. By deploying AI to catch mistakes early (say, a price field that suddenly shows an unrealistic spike due to a website glitch, or a product description parsed incorrectly due to an HTML change), companies can maintain a high level of data integrity without exhaustive human review.

Industries from e-commerce to finance are already leveraging AI for better data quality. One report notes that Shopify was able to cut manual data review time by 60% by using AI tools for data validation. Moreover, AI can enrich scraped data by understanding context through natural language processing (for instance, ensuring a product’s description matches its category. The result is more reliable datasets feeding into business intelligence, pricing models, and decision-making systems. Efficiency is improved as well – AI can work 24/7, scaling effortlessly as scraping jobs expand. This trend aligns with the broader introduction of AI into data analytics; as Splunk’s tech experts point out, we now see AI assisting tasks like auto-detection of outliers in data and even simplifying web scraping itself as part of modern data workflows. In short, AI has become the secret sauce that ensures scraped data is not only abundant but also trustworthy and ready for use. The companies that invest in AI-driven data quality today will be the ones with a competitive edge tomorrow because they can act on data faster and with greater confidence.

The AI-Powered Future of Web Scraping

Looking beyond 2025, what’s on the horizon for web scraping? Scott Vahey predicts that most emerging topics in web scraping will revolve around artificial intelligence. From how bots collect data to how organizations analyze it, AI is poised to redefine the landscape.

Here are three key trends to watch as we approach 2026:

1. AI vs. AI: The eternal battle between scrapers and anti-scraping defences is intensifying, with both sides now wielding AI. On one side, we see scrapers becoming smarter and more human-like. Cybercriminals and aggressive data miners are already deploying AI-powered bots that can dynamically adapt to website changes, mimic human browsing behaviour, and even solve CAPTCHAs to avoid detection. These bots operate with remarkable efficiency and stealth, making them hard for traditional defences to spot. On the other side, website owners and security teams are responding in kind with AI-driven bot detection. Modern anti-bot platforms leverage machine learning to identify subtle patterns or anomalies that betray automated traffic, enabling a more proactive and adaptive defence. In essence, an arms race is underway: AI vs. AI. We can expect blocking and crawling algorithms to leapfrog each other in sophistication, each update trying to outsmart the other. This cat-and-mouse dynamic will likely escalate in 2026, forcing companies that rely on scraping to invest in smarter crawling tech and ethically sound practices while data source owners invest in smarter shields. For enterprises, staying on the right side of this evolution ensuring their scrapers remain effective while respecting terms and laws will be a delicate balancing act. The takeaway is clear: basic scraping scripts might no longer cut it in the age of AI-powered defences.

2. Big Data to Smart Strategies: With datasets growing larger, simply having data isn’t enough; the winners will be those who extract actionable insight fastest. AI will make analyzing large scraped datasets more effective, allowing companies to swiftly inform business strategy. One immediate application is in dynamic pricing. By feeding competitor data and market signals into AI algorithms, companies can adjust their prices in near real-time to optimize revenue and market share. Modern pricing algorithms already ingest real-time data about competitors’ prices and stock levels collected via web scrapers, but AI takes this to the next level. Machine learning models can identify patterns in demand, forecast trends, and recommend price changes far more granularly than any human could. This could lead to pricing models that constantly self-improve based on competitor moves and consumer behaviour. In fact, many retailers are gearing up for this shift – a recent survey showed 55% of European retailers plan to pilot AI-driven dynamic pricing by 2025. The appeal is clear: AI can automate the drudgery of monitoring competitors and markets, react instantly to changes, and even personalize prices for different customer segments. We’re entering an era where pricing is not static or rule-based, but algorithmic and fluid. Companies like Amazon have long used dynamic pricing, but expect the practice to become far more widespread across industries as the tools become more accessible. The strategic impact is huge: businesses will be able to fine-tune prices to balance competitiveness and profitability in real-time, essentially running thousands of micro-experiments to find the sweet spot. Those who master AI-driven analysis of scraped data will enjoy a significant competitive edge in everything from marketing strategy to product development.

3. Price as the Priority: Ultimately, broader economic and societal trends indicate that price transparency and competitiveness will continue to grow in importance. We live in uncertain times – inflation remains a factor, and wealth gaps persist. This means consumers in many sectors are extremely sensitive to price and quick to seek value. Vahey anticipates that these conditions will put even more emphasis on price for the end consumer. By 2026, expect companies to intensify their use of web scraping for market intelligence, ensuring they remain attuned to consumer demand and competitor pricing. When every dollar matters to shoppers, businesses must ensure they’re not caught with uncompetitive prices or missing out on a chance to offer a better deal. Web scraping will be the eyes and ears in the market, feeding data into AI systems that help firms respond to customer needs dynamically. Retailers are already advised to embrace dynamic pricing and targeted promotions to retain cost-conscious customers, and this will become standard practice. The flip side is that if companies fail to leverage data and AI here, they risk losing customers to more savvy competitors. We could also see more public price transparency tools (for example, apps or services that scrape and aggregate prices for consumers) as the culture of deal-hunting intensifies. In short, price intelligence, powered by web scraping and AI, will be at the heart of customer experience and loyalty in 2025 and 2026. Companies that use these technologies ethically to genuinely deliver better value will likely earn trust and business, whereas those that don’t risk appearing out of touch or overpriced.

   
   

Enterprise web scraping is evolving from a behind-the-scenes data-gathering tactic to a front-and-center strategic asset. Tariffs, inflation, and AI are shaping a landscape where having the right data at the right time can mean the difference between thriving and falling behind. As Scott Vahey’s insights highlight, demand for data isn’t slowing down if anything, it’s surging. The tools and techniques for web scraping are becoming more sophisticated, with AI playing a starring role in both extraction and analysis. For enterprise leaders and tech decision-makers, the message is clear: invest in robust web scraping capabilities, leverage AI for enhanced data quality and analytics, and remain vigilant about market changes such as tariffs and price fluctuations. The companies that do so will navigate the choppy waters of 2025–2026 with agility, while those that don’t may find themselves blindsided by faster-moving competitors. In an era of uncertainty, one thing is sure: Web scraping will be more important than ever, and its trends will have a profound impact on how businesses gather intelligence and execute strategy in the years to come.