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It can seem that hardly a day goes by that a new technology startup hasn’t raised investor cash on the hope that it uses artificial intelligence, or AI, as a key part of its business. Now however, a new report makes the surprising claim that 40% of European firms that are classified as an “AI startup” don’t exploit the field of study in any material way for their business.
Out of 2,830 startups in Europe that were classified as being AI companies, only 1,580 accurately fit that description, according to the eye-opening stat on page 99 of a new report from MMC, a London-based venture capital firm. In many cases the label, which refers to computer systems that can perform tasks normally requiring human intelligence, was simply wrong.
“We looked at every company, their materials, their product, the website and product documents,” says David Kelnar, head of research for MMC, which has £300 million ($400 million) under management and a portfolio of 34 companies. “In 40% of cases we could find no mention of evidence of AI.” In such cases, he added, “companies that people assume and think are AI companies are probably not.”
Kelnar added that these startups were not necessarily promoting themselves as being AI firms. They were rather being classified this way by certain by third-party analytics websites (and they were not correcting them).
Kelnar declined to name examples of analytics websites and startups; an MMC spokesperson said doing so would risk sending the wrong message to potential investments from the venture capital firm.
Firms that invest in startups tend to use several well-known analytics websites for tracking potential investments, sites with names like Pitchbook, Crunchbase and CB Insights.
“I think in most cases [startups] will be aware of how they’re being classified,” Kelnar added. But there is little incentive to correct a listing, since it spells potentially less investment down the line and it can pay to brand yourself as being an AI company.
Startups that are labelled as being in the field of artificial intelligence attract 15% to 50% more in their funding rounds than other technology startups, Kelnar added.
It can make sense for AI startups to raise more money than normal since, for instance, they’ll probably need to pay higher salaries for specialist engineers. But the figures are “also a reflection of the dynamics of supply and demand,” Kelnar says.
MMC’s research, pulled together over several months and sponsored by the British bank Barclays, found that the use of artificial-intelligence-powered software like text and vision recognition or predictive analysis was also growing in startups and large companies.
One in 12 startups use AI as part of their products or services, up from one in 50 about six years ago, according to the survey. Meanwhile some 12% of large companies are using AI applications in their business, up from 4% in just the past year.
The most popular uses of AI were chatbots, followed by process automation tools that replace simple administrative tasks like processing an insurance claim and fraud detection.
When businesses apply AI-powered software, they’ll often use it for things like vision recognition, tech-to-speech synthesis, or making predictions and decisions. While that poses tantalizing prospects for business and has put AI in vogue among investors, many are still unsure how AI works or is defined. For some startups, that may be an advantage.