As the use of artificial intelligence is expands, more small firms say they’re harnessing AI to help their businesses.

In a survey by The U.S. Chamber of Commerce and Teneo, nearly every small business—98%—said they are utilizing a tool that is enabled by AI. Forty percent said they’re using generative AI tools like chatbots and image creation, nearly double from last year’s survey.

Small business owners say finding the right AI tools helps them save on personnel costs and frees up time. But they also stress that human oversight is still a necessity.

“AI allows small businesses—who many times do not have the staff or resources of their competitors—to punch above their weight,” said Jordan Crenshaw, senior vice president of the U.S. Chamber’s Technology Engagement Center. “It’s encouraging to see small businesses embrace technology and express optimism for the future as these platforms serve as a catalyst for innovation and resilience.”

Randy Speckman, who owns San Diego-based web design agency Randy Speckman Design with seven staffers, found the right AI tool for his business by trial and error. He tried a few tools that didn’t provide high enough quality copy before settling on tools including Conversion.ai and Copy.ai to generate blog posts, email newsletters and social media content.

The tools save Speckman’s staff a lot of time while allowing for a consistent volume of higher quality content. The improvement in production means he hasn’t had to hire more writers.

“The only downside is needing to review and tweak the AI’s initial drafts,” Speckman said.

The survey also found that 91% of small businesses using AI say it will help their business grow in the future. Seventy-seven percent of small business owners said they plan to adopt emerging technologies, including AI and metaverse.

Amanda Reineke owns Notice Ninja, a digital compliance company that helps tax professionals automate their incoming tax notices in Phoenix, Arizona, with 15 employees. Her company built an AI-powered platform that scans and captures data from tax notices, then automatically routes each notice to the right department and person to handle it.

“When implemented thoughtfully by domain experts, (AI) can drive major efficiencies,” she said. “AI won’t replace human work, but will augment and lift it.”

The survey found more businesses are using technology platforms in general. Forty-seven percent of business owners surveyed said they use four or more technology platforms—up from 39% last year—and more than a quarter said they use six or more technology platforms.

Jan Watermann, owner of marketing agency Waterman Consulting in St. Petersburg, Florida, uses AI tools such as Jasper AI and SurferSEO.

“Jasper helps us quickly generate blog posts, ad copy, and other written content, while SurferSEO ensures it’s optimized for search engines,” Watermann said.

Watermann says that for all its promise, AI still needs human oversight. “It’s great for efficiency but still requires human creativity and strategy to get the best results,” he said.

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Modern crowdfunding has grown from relatively modest beginnings in the late 1990s to a multi-billion-dollar financing market for all kinds of early-stage innovations. The platform Kickstarter alone went from $276 million pledged in 2012 to $7.8 billion in 2024. There are even professional project designers to help craft that winning proposal.

With stakes like those, getting the pitch right is everything.

Enter machine learning to assist. Researchers from the University of Toronto’s Rotman School of Management put four different types of this artificial intelligence application to the test, including Deep Learning. Machine learning proved not only superior to conventional statistical methods for predicting whether a crowdfunding campaign would reach its goal, it also identified which elements helped most, and how.

The findings are published in the Journal of Business Venturing Design.

“Running a crowdfunding campaign is costly and could fail,” says Ramy Elitzur, a professor of accounting at the Rotman School. “Our analysis shows project creators ways to improve their chances of success, or, alternatively, whether they should pursue a different project funding strategy.”

Kickstarter is an all-or-nothing platform, meaning that project creators don’t receive any money unless they meet their fundraising goal. Analyzing more than 100,000 Kickstarter projects, Prof. Elitzur, Prof. David Soberman, who is the Canadian National Chair of Strategic Marketing, and other researchers found that the size of the campaign’s monetary goal accounted for more than half of a project’s success. The creator’s social capital, the number of reward options offered, and the campaign’s duration were also top factors.

Machine learning also got into the nitty-gritty of how much and how long. A project’s chances of success remained pretty much the same up to a fundraising goal of $100,000, then began to drop beyond that, with a sharper drop-off over $133,300. Conventional standard regression models, however, predict that the chance of success consistently drops as the monetary goal increases. That’s because these models lean towards identifying “linear” relationships where influencing factors and outcomes move in only one direction.

Crowdfunding, like many things, is more complex, with multiple variables acting on each other as well as the outcome.

“One of the things machine learning does is model all possible interactions among variables,” says Prof. Elitzur. “It gives us the direct effect on the outcome of each variable and the total effect of the interaction with other variables.”

While standard regression showed success increased with greater social capital—measured through the number of comments a project earned—machine learning revealed that success actually increased up to about 750 comments, then leveled off.

Its results also suggested the sweet spot for campaign duration was 10 to 15 days and that the number of project reward options has a moderate positive effect on success up to about 15 reward options, then slightly negative effects between 15 and 20 options, followed by a positive effect in waves between 20 and 50 reward options, finally plateauing after 50 options.

When machine learning’s text analysis capabilities were deployed—something numerically based standard methods can’t do—they could reach beyond Kickstarter’s 15 main project classifications to identify “gadgets” as the least successful project type.

It turns out that creators looking for that winning proposal should steer clear of flyable Second World War aircraft. In that case, “the deck is stacked against you and you would have a lower likelihood of success than any other domain,” says Prof. Elitzur, who is currently applying the same methods to predicting high-tech start-up success.

In addition, the text analysis capabilities of the models illustrate that as with real estate, the location of the project counts.

The research was also co-written with Noam Katz of Ben Gurion University in Israel and Perri Mutath of The Israel Innovation Authority.