Using AI for International B2B Prospecting: What It Can and Cannot Do

The hard part of prospecting abroad is translation

When you look for customers at home, you already know the vocabulary: which industries buy from you, what the companies call themselves, which ones are serious. In another country you lose that. The company register is in a foreign language, the industry codes are national versions of international standards, company names tell you little, and a list of ten thousand companies in "your" industry is no easier to work with than none.

This is where AI language models are genuinely useful. Not because they know which companies will buy, but because they are good at translating between how you describe your business and how a foreign register describes its companies.

Three jobs AI does well

1. Turning your offer into the local search

Describe what you sell in your own words: "We make hygienic stainless-steel conveyor systems for fish processing" or "We build mobile apps for logistics companies". A model can propose which industries in the target country are likely buyers, which words those companies use about themselves in their registered purpose or on their websites, and which places to include. A good tool then checks the proposals against reality, for example against the industry codes the register actually uses, rather than inventing codes.

2. Ranking real companies against your offer

Once candidates come from an official register, AI can read the facts about each one (industry, size, accounts, website text, recent changes) and judge how well each fits what you described. This replaces the first hours of manual reading, and it scales to hundreds of companies.

3. Explaining and summarising

A useful assessment shows which facts it is based on, what is uncertain, and a suggested next step, so you can check it quickly. AI is also good at summarising a company's own website in your language.

What AI cannot do

• It cannot see intent. No public data tells you that a company is about to buy. Hiring, public tenders, growth or a new location are signals that make a conversation more likely to be relevant, not evidence of a need. Treat any tool that claims to "predict who will buy" with caution.
• It can be wrong. Models can misread a company's activity, especially when the description is short or ambiguous, or confuse companies with similar names. Good tools limit this by only using facts they actually have and by showing them; you should still check.
• It does not replace the register. An AI answer about a company is only as good as the data behind it. Always confirm status, legal form and accounts in the official register. For Norway, see how to verify a Norwegian company.
• It does not know local rules. Marketing law, data protection and customs rules differ by country. In Norway, for example, e-mail marketing to individuals requires prior consent under the Marketing Control Act.

A responsible workflow

1. Write a clear description of what you offer, to whom, at what price level and where. The more specific, the better the translation.
2. Let the AI propose industries and words, then look at them critically. Remove what does not fit, add what is missing.
3. Search the official register, not a scraped list, so every company is real and current.
4. Read the AI's assessments as a first filter. Open the companies at the top and check the facts behind each judgement.
5. Verify before you contact: status, accounts, website, who runs the company.
6. Write personally. Use what you learned to explain in a few sentences why you contact this company. A message written entirely by AI usually reads like one.
7. Learn from the replies. Which kinds of companies answered? Refine your description and repeat.

An example of the translation step

Suppose a company abroad writes: "We make hygienic stainless-steel conveyor systems for fish processing." A human new to Norway might search for "conveyor" and find other equipment makers, that is, competitors. A good AI-assisted search instead reasons about who buys such equipment: fish and seafood processing plants, aquaculture companies with their own processing, and food producers more generally, plus wholesalers of food-processing equipment who could resell it. It also notices that companies describing themselves with the user's own words ("conveyors", "stainless steel fabrication") are more likely to be competitors or partners than customers. The result is a list of real processing companies, each with a short explanation such as "seafood processing, filed accounts show growth, hiring production staff". That explanation is where your own judgement starts, not where it ends.

Costs, privacy and data

• Costs. AI calls cost money. Well-built tools reuse results and assess only the most promising companies in depth, which keeps prices predictable.
• Personal data. Company data is mostly not personal data, but sole proprietorships and named contact persons are. Choose tools that do not build profiles of people and that respect the rules where the company is registered.
• Your own input. Do not paste confidential customer lists or contracts into general AI tools without checking how the data is used.

How Propler uses AI

For Norway, Propler's AI search (free account) does the three jobs above: it reads your description, proposes industries and checks them against the codes Norway's register actually uses, retrieves real companies from the register, and assesses the best matches using the facts we hold, such as accounts, job ads, public tenders, register changes and the company's own website. Each assessment shows the facts it rests on and what is uncertain. It is decision support, not proof of a buying need, and it covers Norwegian companies only. The free company search works without login. Our comparison of AI prospecting and traditional lead generation goes deeper into when to combine the approaches.