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Artificial intelligence for small business: 5 lessons from an early adopter


trends affecting business

Many CEOs want to invest in artificial intelligence (AI) to enhance the performance of their business. But few know exactly how to do it.

That’s understandable — we’re still in the early days of automation and analytics, and the business applications of AI aren’t fully defined or foolproof. What’s more, no one has figured out a process for integrating AI-powered applications that work for everyone.

In spite of these uncertainties, a recent report from Vistage and Salesforce indicates that many CEOs from small and midsize businesses are eager to venture into AI. In fact, 13.6% of the 1,467 CEOs surveyed by Vistage said they are already using AI to improve their businesses in the following areas:

  • business operations (51%)
  • customer engagement (45.5%)
  • talent management/hiring (20.2%)
  • and financial reporting (16.2%)

For these early adopters, it’s a good idea to study — and learn from — the successes and failures of other trailblazers in the AI space. One of these trailblazers, Ali Moshfeghian, participated in a panel that I moderated at the most recent Salesforce Dreamforce conference, “Customer Insights: How AI is Revolutionizing Small and Midsize Businesses Today.” Moshfeghian, the chief operating officer for Xtreme Lashes, a global leader in eyelash extensions, described his company’s journey with AI and offered these five lessons to leaders on a similar path:

Report: Artificial Intelligence for small and midsize businesses
Download report: Artificial Intelligence for small and midsize businesses

1. Clean up your data.

When Xtreme Lashes decided to invest in an AI application, it initially made the mistake of feeding the application bad data. “We didn’t read the fine print, and the system didn’t work,” Moshfeghian says. “That was a setback because we had a lot of excitement and buy-in to the system. But we learned from that experience.” To recover, the company used the application to build models that classified, categorized, and cleaned up its historical data so that it could be analyzed for insight going forward.

2. Don’t be afraid to explore and experiment.

If you’re running a small business, you might feel intimidated by AI. If that’s the case, start by simply exploring an application, says Moshfeghian. “Get in there and just poke around,” he advises. “Start with a small use case where you have decent data.” He also recommends using online resources and communities to educate yourself on the AI basics — and then experiment. “I think trying things and failing is better than just waiting three months to try it,” he says. “I’d rather just throw stuff at the wall and see what sticks.”

3. Implement good practices for data governance.

Moshfeghian recommends appointing a gatekeeper in your company who can oversee data governance. They should be able to ask questions such as: Is this field needed? Are we creating this field for the right reason? Do we need a validation rule? Do we need to take a step back?

4. Remember that artificial intelligence can analyze audio, video and images like data.

Many AI-powered applications are capable of processing images, audio and video as data — just like numbers. If your company works with these assets, then AI may be able to streamline and improve some of your business processes. Xtreme Lashes, for example, is training AI-powered models to classify images of eyelash extensions from its eyelash extension training programs. The goal is for the model to do the classification so that trainers no longer need to perform this task manually. “We want our technicians, trainers and stylists to do the actual work, rather than the administrative task of classifying images,” says Moshfeghian. “That’s just a waste of human capital.”

5. Don’t assume you need to hire a data scientist.

The majority of small businesses don’t need data scientists to get started with AI. They just need someone from their team who is interested enough in AI to give it a try. “If you don’t have the passion to get in there and do it, find someone on your team that does,” says Moshfeghian. “Give them some freedom and carve out enough time for them to explore. With analytics, you don’t know if you’re going to be successful. You just have to try.”

This post originally appeared on Inc.com

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