Humans and patience needed to make AI work for small business

Symrise, in partnership with IBM Research, is developing a method of using artificial intelligence (AI) to create perfumes based on digital fragrance models. Photo credit:  Symrise .

Symrise, in partnership with IBM Research, is developing a method of using artificial intelligence (AI) to create perfumes based on digital fragrance models. Photo credit: Symrise.

Frustration and “inflated expectations” confront many business managers who try to adopt artificial intelligence (AI) into their operations, according to MIT Technology Review.

“Despite what you might hear about AI sweeping the world, people in a wide range of industries say the technology is tricky to deploy. It can be costly. And the initial payoff is often modest,” reports the Review, a media company with 2.8 million followers on social networks.

“It’s one thing to see breakthroughs in artificial intelligence that can outplay grandmasters of Go, or even to have devices that turn on music at your command. It’s another thing to use AI to make more than incremental changes in businesses that aren’t inherently digital,” the Review notes.

An example is the experience of Achim Daub, an executive at fragrance manufacturing giant Symrise. According to the Review, “Daub hired IBM to design a computer system that would pore over massive amounts of information—the formulas of existing fragrances, consumer data, regulatory information, on and on—and then suggest new formulations for particular markets. The system is called Philyra, after the Greek goddess of fragrance. Evocative name aside, it can’t smell a thing, so it can’t replace human perfumers. But it gives them a head start on creating something novel.”

So how did Philyra do?

Daub described using AI in the perfume-development process a “steep learning curve.”

Some of the challenges included the high amount of training needed to get Symrise’s perfumers used to having AI as part of the process, as well as the costly IT upgrades that were needed to provide disparate data to Philyra while keeping some of the information confidential from the perfumers themselves.

MIT Technology Review cautions, “AI might eventually transform the economy — by making new products and new business models possible, by predicting things humans couldn’t have foreseen, and by relieving employees of drudgery. But that could take longer than hoped or feared, depending on where you sit… All this requires not just money but also patience, meticulousness, and other quintessentially human skills that too often are in short supply.”

A slightly more optimistic view is offered by Manufacturing.net.

Citing data from Accenture, ManufacturingNet.com reports that 85 percent of executives plan to invest in AI technologies over the next three years.

“AI can increase productivity by 40 percent, many times without having to grow the workforce,” the site reports.

Advice offered by Manufacturing.net includes finding “the low-hanging fruit where AI capabilities can make an immediate impact.”

Processes such as billing, invoicing, procurement and expense are examples of the “low-hanging fruit” that can become automated with AI and machine learning.

However, ManufacturingNet.com urges executives to remain realistic about the uses of AI, noting “that AI-based solutions can provide insights, patterns and recommendations from the massive amounts of data they process and analyze, but in the end the decisions must be made by humans.”

So while we hear how AI is creating great efficiencies for companies like Google, Netflix, Amazon, and Facebook, we must remember that those companies exist to capture and use digital data and they are staffed with data and computer science experts. Most organizations do not have those resources.

MIT Technology Review concludes, “This doesn’t necessarily mean that AI is overhyped. It’s just that when it comes to reshaping how business gets done, pattern-recognition algorithms are a small part of what matters. Far more important are organizational elements that ripple from the IT department all the way to the front lines of a business.”

In other words, people matter in the AI process.