Navigating global trade data is often a dual challenge for policymakers and researchers: finding the right numbers is hard, but understanding exactly what those numbers mean for policy is even harder. Users need both the data itself and the context to interpret it correctly.

We are constantly looking for ways to make global trade statistics more accessible and actionable. Today, we are taking a major step forward by bringing data discovery and methodological guidance together in a single experience, allowing users to move more easily from numbers to understanding.

The evolution of trade data accessibility

Long before conversational AI became an everyday tool, we recognized its potential to make complex data more accessible. In 2019, we introduced α-ßΩτ (pronounced Alpha Bot), our first intelligent data search assistant for the World Integrated Trade Solution (WITS). Its mission was simple but transformative: when a user asked, “Give me China’s imports of fuel in 2022,” Alpha Bot generated the exact WITS URL containing that data and provided indicator and country definitions to help users interpret the results.

Building on this foundation, we launched WITS@AI in 2025 to address an even broader challenge. Beyond locating data, users also needed clear guidance on how to use the WITS platform and understand the methodologies behind the numbers. WITS@AI was designed to synthesize answers from technical manuals and documentation across the WITS website and partner agency platforms, providing users with timely guidance on trade policies, indicators, and tariffs.

While both tools were highly effective on their own, our users faced a fragmented journey. You had to use one tool to find the data, and another to understand the context.

Not anymore.

The breakthrough: a unified WITS@AI

To address this gap, we have now integrated Alpha Bot’s data discovery capabilities directly into WITS@AI.

The result is one interface that brings together data access and methodological guidance in a more seamless user journey.

Users no longer have to move between different tools. Whether they are querying raw datasets or seeking methodological context, the newly unified WITS@AI supports both in a single experience.

WITS@AI now also supports data queries down to the HS 6-digit level. This highly granular search capability makes detailed trade data easier to access and can save researchers significant time that would otherwise be spent on manual filtering.

What can you ask WITS@AI now?

With both capabilities now combined, trade policy analysis becomes faster and more intuitive. Try asking the new WITS@AI:

• “What is the total export value of coffee from Colombia to the United States in 2024?” (Data Discovery)
• “Can you explain how the Global Simulation Analysis of Industry-Level Trade Policy (GSIM) calculates consumer surplus?” (Methodology)
• “What is country growth, how is it used , and what does it show for Singapore in recent years?” (Combined Data & Knowledge)
   

From raw data to actionable knowledge

For impactful policy decisions in developing nations, access to data is only half the battle.

This integration helps policymakers and researchers move more quickly from complex data to usable insight — an important step in making development information easier to access, understand, and apply. By lowering barriers for non-experts and streamlining workflows for seasoned economists, the unified WITS@AI reduces the steps needed to find data and supports faster, evidence-based policy decisions.

This matters not only for trade specialists. More broadly, it shows the value of linking datasets more closely with the knowledge that gives them meaning. In that sense, WITS@AI is more than a feature enhancement. It points toward a practical model for connecting structured data with the documentation, concepts, and institutional knowledge users need to interpret it confidently.

Try it today

Explore the newly unified WITS@AI on the WITS platform and see how it can support your trade research and policy analysis. We welcome your feedback on how this more integrated experience can help users move from data to insight more efficiently.

Source : World Bank