Artificial intelligence (AI) has become mainstream in the world of business to business (B2B) technology with 86 percent of CEOs saying that AI is common in their workplace. But what actually is AI? Until you know exactly what AI encompasses, you’ll be limited in the ways you can use it in your business.
- What is artificial intelligence?
- Artificial intelligence vs. machine learning
- Business use cases for artificial intelligence
- Deciding if AI is right for your organization
In this article...
What is artificial intelligence?
Artificial intelligence is a type of software that gathers information appropriate to its use case (e.g. street signs for self-driving cars) and analyzes it to make decisions without relying on human input. The goal of AI, especially in business, is to complete tasks without human intervention to speed up processes, avoid unconscious bias, and gather better insights from data.
In order to determine whether a model falls into the category of artificial intelligence rather than automation, many developers employ the Turing Test. Proposed by Alan Turing in 1951, the test relies on the machine being able to pass as a human at least 50% of the time. For example, AI customer service chatbots that use natural language processing (NLP) to simulate human conversation are often indistinguishable from live agents, therefore passing the Turing Test.
There are four types of artificial intelligence:
- Reactive machines
- Limited memory (Example: Generative AI)
- Theory of mind
Only the first two have actually been achieved so far, with chess-playing robots like Deep Blue falling into the category of reactive machines, which don’t learn and can’t improve over time. They look at the information in front of them and make the best decision they can, regardless of historical data. Self-driving cars belong to the limited memory category, which uses past information to influence decisions, either through the AI’s own experiences or data it’s received.
Theory of Mind relies on the ability of machines to interpret human emotions and base their own decisions on them. Once scientists have achieved Theory of Mind, the next step would be self-awareness in which the AI machine would have its own thoughts and emotions and be able to understand how it affects the world around it.
Artificial intelligence vs. machine learning
Artificial intelligence and machine learning (ML) are often used interchangeably, but there are slight variations between the concepts. ML is actually a subset of AI in which the program learns from past data without additional programming from a human developer. With AI, developers are concerned with providing the greatest chance of success (creating a chatbot that provides helpful answers to customers), while ML is focused on producing the highest levels of accuracy (using facial recognition to identify someone).
Additionally, AI can process unstructured (qualitative) data, while ML can only deal with structured (quantitative) and semi-structured data. For example, an artificial intelligence model can comb through qualitative customer reviews using sentiment analysis and NLP to provide quantitative insights that help organizations determine what their next steps should be. Because ML can only be used with structured data, it also has a more limited scope of use than AI as a whole.
Business use cases for artificial intelligence
Artificial intelligence is part of a large number of enterprise software solutions that organizations use every day. Here are a few places where AI would be helpful.
Supply chain management
Supply chains sometimes struggle with fragility due to stock shortages or sudden spikes in demand, but AI can help organizations prepare for both of these scenarios. With predictive analytics, artificial intelligence models examine historical sales data and current trends in the market to more accurately predict future sales. Then, the company will know how many staff they need on hand and what their inventory levels should look like.
Artificial intelligence can also help supply chains avoid inventory shortages. Let’s say there’s a shortage of lumber. A construction company would obviously need lumber to build houses, but it can only store so much in the warehouse. So, it uses the lumber it has, and once its stock drops below a certain level, its inventory management system automatically makes another order from its regular supplier.
The only problem is, that supplier is completely out of stock. If artificial intelligence is part of the company’s supply chain management system, the AI can then quickly identify a vendor that does have the stock they need and place a new order, preventing them from running out completely.
AI can also help organizations reduce unconscious bias during their hiring process. Some recruiting tools like XOR and HireVue hide information that may cause hiring managers to favor or oppose the candidate, like name, gender, or even the college they attended. Then, it scrapes the resume for past experience and potential and compares it to the job description.
Based on the number of similarities and the likelihood that a candidate will succeed using data about current successful employees, the AI assigns the candidate a score and passes the pared-down version of their application onto the hiring manager. This way, a hiring manager makes decisions on who to interview based on skills and experience rather than subjective factors.
Customer service departments often use AI in their customer service software to free up the time of their human representatives to handle more complex issues. AI-powered chatbots can handle common requests from customers, direct them to relevant articles in a self-service knowledge base, and route them to the correct representative if they need to escalate the issue. These chatbots use NLP and sentiment analysis to determine what customers are looking for and point them in the right direction. This speeds up support workflows, prevents multiple reps from working on the same ticket by accident, and reduces the workload of each representative.
Companies that incorporate AI into their business intelligence platforms get better insights from their data and can become more agile. Artificial intelligence helps organizations sort through the large amounts of data they generate. While analyzing that data, it provides insights that help them make more informed decisions for the business. For example, AI might tell them which products are most likely to sell well in the upcoming quarter or how many people they’ll need to hire to meet forecasted demand.
Additionally, companies can use artificial intelligence to make faster decisions. AI can draw conclusions faster than humans, and it doesn’t need to rest. Once AI has connected the data, analysts can use the insights to make any necessary changes. The company becomes more agile and makes more data-driven decisions.
Deciding if AI is right for your organization
AI is helpful for a variety of industries, and you don’t even need technical expertise to use it efficiently. Many enterprise software vendors are now building it into their platform and making it easy to use and accessible for businesses of all sizes. AI works well for businesses that need to become more agile, back their decisions with data, and reduce the workload on their human employees.