Business Applications for Artificial Intelligence: What to Know in 2019
Discussion of artificial intelligence (AI) elicits a wide range of feelings. On one end of the spectrum is fear of job loss spurred by a bot revolution. On the opposite is excitement about the overblown prospects of what people can achieve with machine augmentation.
But Dr. Mark Esposito wants to root the conversation in reality. Esposito is the co-founder of Nexus Frontier Tech and instructor of Harvard’s Artificial Intelligence in Business: Creating Value with Machine Learning, a two-day intensive program.
Rather than thinking about what could be, he says businesses looking to adopt AI should look at what already exists.
AI has become the latest tech buzzword everywhere from Silicon Valley to China. But the first piece of AI, the artificial neuron, was developed in 1943 by scientist William McCulloch and logician Walter Pitts. Since then, we’ve come a long way in our understanding and development of models capable of comprehension, prediction, and analysis.
How Businesses Use A.I. Today
Artificial intelligence is already widely used in business applications, including automation, data analytics, and natural language processing. Across industries, these three fields of AI are streamlining operations and improving efficiencies.
Automation alleviates repetitive or even dangerous tasks. Data analytics provides businesses with insights never before possible. Natural language processing allows for intelligent search engines, helpful chatbots, and better accessibility for people who are visually impaired.
Other common uses for AI in business include:
- Transferring and cross-referencing data; updating files
- Consumer behavior forecasting and product recommendations
- Fraud detection
- Personalized advertising and marketing messaging
- Customer service via telephone or chatbots
Harvard Business Review reports that AI stands to make the greatest impact in marketing services, supply chain management, and manufacturing.
Esposito noted that there’s also opportunity to use AI in finance and banking, two sectors still reliant on antiquated processes. But applications of AI can be used across industries.
In particular, health care, education, transportation, and waste management can all be made more efficient and effective with solutions that automate, predict, and respond when humans can’t.
Demystifying Artificial Intelligence for Business Owners
According to Esposito, there’s a lot of misunderstanding in the business world about AI’s current capabilities and future potential. At Nexus, he and his partners work with startups and small businesses to adopt AI solutions that can streamline operations or solve problems.
Esposito discovered early on that many business owners assume AI can do everything a person can do, and more. A better approach involves identifying specific use cases.
“The more you learn about the technology, the more you understand that AI is very powerful,” Esposito says. “But it needs to be very narrowly defined. If you don’t have a narrow scope, it doesn’t work.”
For companies looking to leverage AI, Esposito says the first step is to look at which parts of your current operations can be digitized. Rather than dreaming up a magic-bullet solution, businesses should consider existing tech that can free up resources or provide new insights.
“The low-hanging fruit is recognizing where in the value chain they can improve operations,” Esposito says. “AI doesn’t start with AI. It starts at the company level.”
For instance, companies that have already digitized payroll will find that they’re collecting a lot of data that could help forecast future costs. This allows businesses to hire and operate with more predictability, as well as streamline tasks for accounting.
Businesses That Have Transformed Operations With A.I.
One company that’s successfully integrated AI tech into multiple aspects of its business is Unilever, a consumer goods corporation. In addition to streamlining hiring and onboarding, AI is helping Unilever get the most out of its vast amounts of data.
Data informs much of what Unilever does, from demand forecasts to marketing analytics. The company observed that their data sources were coming from varying interfaces and APIs, according to Diginomica. This both hindered access and made the data unreliable.
In response, Unilever developed its own platforms to store the data and make it easily accessible for its employees. Augmented with Microsoft’s Power BI tool, Unilever’s platform collects data from both internal and external sources. It stores the data in a universal data lake where it’s preserved—to be used indefinitely for anything from business logistics to product development.
Amazon is another early adopter. Even before its virtual assistant Alexa was in every other home in America, Amazon was an innovator in using machine learning to optimize inventory management and delivery.
With a fully robust, AI-empowered system in place, Amazon was able to make a successful foray into the food industry via its acquisition of Whole Foods, which now uses Amazon delivery services.
Esposito says this kind of scalability is key for companies looking to develop new AI products. They can then apply the tech to new markets or acquired businesses, which is essential for the tech to gain traction.
Both Unilever and Amazon are exemplary because they’re solving current problems with technology that’s already available. And they’re predicting industry disruption so they can stay ahead of the pack.
Of course, these two examples are large corporations with deep pockets. But Esposito believes that most businesses thinking about AI realistically and strategically can achieve their goals.
Looking to the Not-So-Distant Future
Part of Esposito’s optimism stems from the widespread application of AI across industries, from health care to transportation to finance.
“Every major place where we have multiple dynamics happening can really be improved by these technologies,” Esposito says. “And I want to reinforce the fact that we want these technologies to improve society, not displace workers.”
To ease fears over job loss, Esposito says business owners can frame the conversation around creating new, more functional jobs. As technologies improve efficiencies and create new insights, new jobs that build on those improvements are sure to arise.
“Jobs are created by understanding what we do and what we can do better,” Esposito says.
Additionally, developers should focus on creating tech that is probabilistic, as opposed to deterministic. In a probabilistic scenario, AI could predict how likely a person is to pay back a loan based on their history, then give the lender a recommendation. Deterministic AI would simply make that decision, ignoring any uncertainty.
“There needs to be cooperation between machines and people,” Esposito says. “But we will never invite machines to make a decision on behalf of people.”