Have you ever wondered if salespeople know you better than you know yourself? When AI steps in—it is more about digging into the details of who you are. Actually, alternative data alone does not create a competitive advantage in the B2B sector—intelligence does. In standard analytics, decisions on partnerships, commercial credit, and large-scale procurement have always relied on lagging indicators, such as previous quarters, annual balance sheets, or official credit histories.
In an era when economic challenges are so varied, it is imprudent to be bogged down by paper-based reports. A modern approach to data dictates its own way, establishing a digital footprint through hiring trends, server utilization as well as network traffic profiles, among other innovations. You are never left alone at risk anymore with reliable services like TX Express Lend that focus on present events rather than long-forgotten outcomes.
This shift is helping the alternative data market expand. According to Coherent Market Insights (CMI), the global market is estimated to be valued at USD 10.1 billion in 2026 and is projected to grow at a CAGR of 28% during the forecast period, reaching USD 56.7 billion by 2033. The growth is being supported by the need for faster, data-driven decisions and by the wider use of digital technologies.
Several trends are shaping this growth. One is the growing use of AI to process large amounts of alternative data. Businesses may have access to social media activity, web traffic, transaction information, location data, and other sources, but collecting the data is only the first step. AI and machine learning can help find patterns in these large datasets and turn them into information that teams can use.
Another trend is the growing demand for real-time business information. Traditional reports can show what happened months ago, while alternative data can provide more current signals. Hiring activity, website traffic, foot traffic, online reviews as well as supply chain activity can help companies spot changes earlier. This is useful when businesses need to adjust sales plans, assess risks, or respond to changing customer demand.
A third trend is the rising use of ESG and sustainability-related data. Satellite images, supply chain information, social media sentiment as well as other sources can help companies assess environmental and social factors. As businesses pay more attention to sustainability, this type of data is becoming another use case for alternative data providers.
What AI Really Inspects Within the B2B Sector
Within the B2B business domain, a genuine technological breakthrough is occurring at the intersection of alternative data and AI. Artificial intelligence does the heavy lifting in research, turning giant piles of logs into clear explanations of what the future holds. But what does it look like on the ground?
As a matter of fact, scanning alternative data makes it possible to predict supply chain disruptions or financial insolvency months before they interrupt the flow. Unlike the B2C sector, where unconventional information is predominantly limited to browsing history and social media clicks, in the commercial arena, AI takes care of more tangible layers of data:
- Technographics, or Tech Profile of the Company. A deep dive into the enterprise's tech stack can easily reveal what is going on within the organization, uncovering its true game plan.
- Ready to buy. Even if search activity seems flawless, suspicious signals can interrupt the smooth flow. For example, time-tested software might track an engineering department researching cybersecurity frameworks. This way, B2B vendors understand clearly that their major buying window has opened.
- Location metrics. To stay ahead of the curve, algorithms are supposed to follow any slight changes in the supply chain. There are so many ways to estimate how much production is manufactured, dominated by tracking satellite imagery, cargo ships, and IoT sensors.
These examples also show why alternative data is becoming relevant across different parts of the B2B economy. CMI's market analysis covers data types such as credit and debit card transactions, email receipts, geolocation records, mobile application usage, satellite and weather data, social and sentiment data, web-scraped data, and web traffic.
The market is also divided by industry vertical, with BFSI, retail and consumer goods, IT and telecommunications, healthcare, manufacturing, government and public sector, transportation and logistics, and other industries among the areas covered by the report. By end use, hedge fund operators, corporations, private equity firms, venture capital firms, and other users form important parts of the market.
Two segments show particularly well why businesses are investing in alternative data. Hedge fund operators are estimated to account for about 48% of the market in 2026. They use alternative data to follow market sentiment, consumer activity, company performance as well as other signals that may not appear in traditional financial reports. This can support investment research and help fund managers build and test investment strategies. Their early use of unconventional data has also encouraged providers to develop new datasets and analytics tools.
The BFSI industry is another major area of adoption, with an estimated 28% share in 2026. Banks, financial institutions, and insurers can use alternative data to understand customers, assess risk, detect unusual activity, and improve financial products. Social media activity, online behavior, transaction patterns, and other external signals can add information to traditional customer and financial records. This makes alternative data useful not only for investment decisions but also for credit, risk management, customer engagement, and fraud-related activities. CMI identifies BFSI as the leading industry vertical in its market analysis.
Unlocking the Invisible B2B Edge
Case 1: Reading the Room Before the Meeting
Picture this: a financial institution receives a high-stakes loan application from a regional supply node. At first glance, every single number is in place—yet an AI model repeatedly flags anomalies while uncovering alternative sources:
- The number of job openings has dropped by 40% over the past month;
- When searching through professional forums, negative feedback appears here and there regarding missed salary payments;
- Based on truck geolocation data, inbound traffic at the warehouse terminal dropped by a third;
In this scenario, the algorithm instantly lowers the counterparty’s reliability score, preventing a credit or contract default.
Case 2: Striking When the Iron is Hot
Instead of making thousands of scattershot cold calls to a database of 10,000 businesses, the sales department of a major SaaS developer has developed an AI platform. The algorithm aggregates data on registered patents, open positions, and management changes at target enterprises. As a result, managers receive a list of 20 companies that need the product as soon as possible, exponentially increasing deal conversion rates.
Why is the approach described above so effective? First, it reduces some of the guesswork in the discovery phase. Second, reps can walk straight into the meeting with useful information about what is happening in the company right now. Alternative data does not guarantee a sale, but it can help sales teams decide where to spend their time.
North America is an important part of this growing market. The region, spearheaded by the U.S., is slated to account for a global market share of 33% in 2026, supported by widespread digital adoption, online platforms, and e-commerce activity.
Businesses across financial services, technology, retail, and other industries generate large volumes of digital information that can be used alongside traditional datasets. U.S. companies are using alternative data for investment research, customer analysis, risk assessment as well as sales intelligence.
Strong presence of technology and financial companies also supports demand for new data products and analytics tools. At the same time, companies have to pay close attention to privacy, data quality, and how information is collected as well as used. This balance between the value of new data sources and responsible data use will remain important as adoption grows.
Case 3. Turning Friction into Fuel
What is the biggest challenge that you observe in the synergy of AI and alternative data when it comes to B2B projects? Let's check what the facts say.
- Data Noise. Today, the primary objective of Machine Learning (ML) models is to separate a temporary data collection hiccup from an actual market trajectory, which is not the easiest task to perform.
- The "Black Box" Problem. Nobody is ready to lose thousands of dollars just because there was a mistake in the neural network verdict. To establish trust, Explainable AI (XAI) is critical to auditing specific signals that drove that conclusion.
- Compliance. A lot has been said and done in terms of compliance; responsible data practices must always be a cornerstone of any web scraping or alternative data collection. Privacy and compliance remain important challenges for the industry, along with data bias and questions about whether a dataset accurately represents the wider market.
The alternative data market includes a mix of established providers and newer companies. Advan Research Corporation, Dataminr, Eagle Alpha, M Science, and UBS Evidence Lab are among the major companies identified in CMI's market analysis. Their offerings cover areas such as business intelligence, financial research, location information, and other specialized datasets. The market remains competitive, with providers working to improve the quality, speed, and usefulness of their data.
Summary
Alternative data and AI do intersect, changing how the B2B market shifts corporate strategy. In the coming years, the ability to identify patterns within the chaos of external data will define business resilience and competitiveness—spanning everything from risk assessment to global B2B sales.
For a sales team, that could mean identifying a company that is getting ready to buy. For a financial institution, it could mean having more information to review before making a decision. For an investor, it could mean seeing changes in a company or market before they appear in traditional reports.
The market is moving in this direction as businesses look for faster and more useful information. AI will continue to make large datasets easier to process, while alternative data providers will continue to develop new sources. At the same time, data quality, privacy, compliance as well as transparency will remain important. For B2B companies, the real opportunity is to use these new sources alongside traditional information rather than treating them as a replacement for it.
