Business Applications for Artificial Intelligence: An …

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 Harvards 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, weve come a long way in our understanding and development of models capable of comprehension, prediction, and analysis.

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:

Indeed, many experts note that the business applications of AI have advanced to such an extent that we live and work alongside it every day without even realizing it.

In 2018, Harvard Business Review predicted that AI stands to make the greatest impact in marketing services, supply chain management, and manufacturing.

Two years on, we are watching these predictions play out in real time. The rapid growth of AI-powered social media marketing, for instance, makes it easier than ever for brands to personalize the customer experience, connect with their customers, and track the success of their marketing efforts.

Supply chain management is also poised to make major AI-based advances in the next several years. Increasingly, process intelligence technologies will provide companies with accurate and comprehensive insight to monitor and improve operations in real-time.

Other areas where we can expect to see significant AI-based advancements include the healthcare industry and data transparency and security.

On the patient side of the healthcare business, we are likely to see AI help with everything from early detection and immediate diagnoses. On the physician side, AI is likely to play a larger role in streamlining scheduling processes and helping to secure patient records.

Data transparency and security is another area where AI is expected to make a significant difference in the coming years. As customers become aware of just how much data companies are collecting, the demand for greater transparency into what data is collected, how it is used, and how it is secured will only grow.

Additionally, as Esposito notes, there continues to be significant opportunity to grow the use of AI in finance and banking, two sectors with vast quantities of data and tremendous potential for AI-based modernization, but which still rely heavily on antiquated processes.

For some industries, the widespread rollout of AI hinges on ethical considerations to ensure public safety.

While cybersecurity has long been a concern in the tech world, some businesses must now also consider physical threats to the public. In transportation, this is a particularly pressing concern.

For instance, how autonomous vehicles should respond in a scenario in which an accident is imminent is a big topic of debate. Tools like MITs Moral Machine have been designed to gauge public opinion on how self-driving cars should operate when human harm cannot be avoided.

But the ethics question goes well beyond how to mitigate damage. It leads developers to question if its moral to place one humans life above another, to ask whether factors like age, occupation, and criminal history should determine when a person is spared in an accident.

Problems like these are why Esposito is calling for a global response to ethics in AI.

Given the need for specificity in designing decision-making algorithms, it stands to reason that an international body will be needed to set the standards according to which moral and ethical dilemmas are resolved, Esposito says in his World Economic Forum post.

Its important to stress the global aspect of these standards. Countries around the world are engaging in an AI arms race, quickly developing powerful systems. Perhaps too quickly.

If the race to develop artificial intelligence results in negligence to create ethical algorithms, the damage could be great. International standards can give developers guidelines and parameters that ensure machine systems mitigate risk and damage as well as a human, if not better.

According to Esposito, theres a lot of misunderstanding in the business world about AIs 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 dont have a narrow scope, it doesnt 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 doesnt start with AI. It starts at the company level.

For instance, companies that have already digitized payroll will find that theyre 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.

One company thats 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 Microsofts Power BI tool, Unilevers platform collects data from both internal and external sources. It stores the data in a universal data lake where its preservedto 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 theyre solving current problems with technology thats already available. And theyre 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 ahead from 2020, it is increasingly clear that AI will only work in conjunction with people, not instead of people.

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.

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