Legal AI isn't sexy. No viral consumer apps, no impressive demos on YouTube. Just lawyers who want to get through documents faster. And that's exactly where the big money is flowing right now.
Legora, a Swedish startup that helps lawyers with contract analysis, is now worth $5.6 billion. Nvidia co-invested, Atlassian too. A month earlier, Legora had already raised $550 million in a Series D. The US competitor Harvey is at $11 billion, after Sequoia tripled its investment. This isn't a normal startup race anymore.
The question is: Why here, of all places?
#The job is clear, the pain is real
Lawyers are expensive. An hour at a large firm easily costs 300 to 600 euros. And a significant portion of that time goes toward tasks that are essentially high-level text processing: reading contracts, comparing clauses, marking risks, writing summaries.
AI can do that. Not flawlessly, but well enough that firms like Bird & Bird, Cleary Gottlieb, and Linklaters are paying for it. Legora reports that after just 18 months, the platform is being used by more than 1,000 firms and legal teams in 50 markets. Harvey counts 100,000 lawyers across 1,300 organizations as customers.
These aren't pilot projects anymore. This is production.
The reason for the success isn't that Legal AI is particularly intelligent. The reason is that the job it takes over is clearly definable. You put in a contract, you get out a structured analysis. Input defined, output defined, value immediately measurable.
#The pattern behind the hype
What's happening with Legal AI is happening simultaneously across many industries. The spectacular use cases don't win first. Not the chatbots that can do everything and nothing properly. But the tools that handle a specific, expensive, time-intensive job.
Accounting: Capturing receipts, categorizing them, tax preparation. Repetitive, rule-based, document-heavy. AI tools like Dext or integrated functions in DATEV environments are increasingly taking this over.
Code review: Developers spend hours looking for errors and checking quality. GitHub Copilot and similar tools measurably shorten that.
Customer inquiries: When 70 percent of all inquiries are the same ten questions, that's not a communication problem. That's an automation problem.
The pattern is always the same: a clearly definable job, high repetition rate, high time investment per cycle, measurable result. Where these four factors converge, AI pays off.
#Why this matters for SMBs
You're not an investor in Legora. The $5.6 billion valuation rightly doesn't interest you directly. But the pattern behind it does.
Because what large law firms are doing with Legal AI, you can think through for your own business. Not with a billion-dollar budget, but with an honest look at your own processes.
Which tasks in your business are repetitive and document-heavy? Where do you or your employees spend time on tasks that essentially always work the same way? Writing proposals following the same template, checking invoices, recording orders, typing minutes.
These aren't tasks where you need to be an AI expert. These are tasks for which tools exist today that you can try without hiring a consultant.
#Concretely: Where to start
Three areas I recommend SMB decision-makers look at first:
First area: Texts that get rewritten over and over. Proposals, order confirmations, standard emails. Tools like ChatGPT or Claude can function as draft helpers if you give them a good template. Not for creative texts, but for the 80-percent tasks that you then briefly adjust afterward.
Second area: Documents you read to extract information. Delivery terms, supplier terms and conditions, contract clauses. You don't have to become a Legora customer, but a well-formulated prompt in ChatGPT can give you a summary of a long document in two minutes that would have taken you 20 minutes to read.
Third area: Data you regularly prepare. If you build the same Excel table every month, gather the same numbers, write the same report, it's worth looking at automation tools like Make or Zapier combined with AI functions.
#What doesn't work
A mistake I often see: SMB decision-makers look at AI tools and ask what the tool can do. Wrong question. The right question is: What specific job should the tool do for me?
Legora isn't successful because it's a general AI tool for lawyers. It's successful because it takes over a specific job that lawyers previously spent a lot of time and money on. This clarity is what many SMBs lack when getting started with AI.
No tool solves a problem you can't clearly name. If you can't say which task you want to automate and how you measure whether it works, you won't be using the tool after three weeks.
#The actual takeaway
The billion-dollar valuations of Legora and Harvey are a signal, not a benchmark. The signal reads: AI wins first where the job is clear and the previous effort is high. Not with the big, vague transformation promises.
For you as an SMB decision-maker, that means: Don't start with the question of what AI can do. Start with the question of which task in your business is repetitive, document-heavy, and expensive in time or money. Then see if there's a tool for it.
Usually there is one.
