Artificial Intelligence in businesses: how it works

Artificial Intelligence in businesses: how it works

The adoption of Artificial Intelligence in businesses continues down the path of growth. Here is a quick look at some successful examples and case studies that have implemented AI in a smart way. 

The adoption of Artificial Intelligence in businesses continues down the path of growth, and many around the world have understood that this technology is here to stay. Integrating AI into business is currently helping several companies and organizations to identify trends, to make insights into huge sets of data, to make faster decisions, and to work more efficiently in many aspects.

Robot figure and Artificial Intelligence reference

The latest McKinsey Global Survey shows a nearly 25% year-after-year increase in the use of AI in standard business processes. The paper also reveals that 58% of organizations have adopted at least one AI capability within a process or product in at least one function or business unit, up from 47% in 2018.

So, how are they using and adopting AI within their businesses and becoming more productive and intelligent? To answer this question, it is necessary to take into account that AI usage can be classified into three types of applications:

  1. The ones related to automating processes
  2. The ones that gain insights through data analysis
  3. Those that engage with customers and employees

Deciding which one to use will depend on what the company is aiming for, the technology available, and the professionals’ skills that build the software product, among many other factors. In the following lines, I will take a quick look at some successful examples and case studies that have implemented AI in a smart way. Let’s take a look at them.

1.  The ones related to automating processes

These are the most common uses of Artificial Intelligence in businesses: digital and physical tasks automation, back-office administrative and financial activities, and when code on a server acts like a human handling information from and to multiple systems (commonly known as robots), just to name a few examples.

Because these types of AI applications aren’t programmed to improve and learn, they are the easiest to implement, and for the same reason, they are the least expensive. Despite this, it is the least “smart” AI application among the three mentioned, but it typically brings a quick and high return on investment.

One success case to highlight is at NASA, where due to the cost pressures, they launched four AI applications to manage human resources, accounts payable and receivable, and IT costs in a centralized way. These projects work as part of the Human Resource application, and a very high percentage of all the organization’s transactions are completed without human intervention. Currently, NASA is implementing more of those processes to continue down the same path.

2.  The ones that gain insights through data analysis

The second type of AI application is using algorithms to detect patterns in huge sets of data and then understanding their meaning. Some typical applications may be able to identify credit card fraud in real-time, predict what a customer is likely to buy or when a part could fail or break, automatize personalized ads, or identify an illness from images, etc.

Artificial Intelligence and code

All these models improve over time. They are very well-trained, are able to put things into categories, use new data to make predictions, and are usually much more data-intensive than the automation application.

As an example of this type of AI, at Amazon, the recommendation system is a transactional artificial platform that is continually learning. The shoppers teach the system to better display items likely for sale, match one item with another that in the past has been sold, and link items with semi-related concepts. There is no random items display, and all is shown in order to maximize sales and improve the capacity in each new sale.

Another example we can find is in two giants of marketing, sales, and customer relationships management. We are talking about HubSpot and Salesforce, where the latter, for example, uses its so-called technology “Einstein” to analyze each aspect of customer relationships with companies. This allows Salesforce to create more detailed profiles of their customers. For its part, HubSpot uses a Machine Learning system (“DeepGraph”) to analyze prospects and clients.

3.  Those that engage with customers and employees

This last type of Artificial Intelligence in business application is becoming more adept as time goes by. Here, projects process the natural language and use chatbots to hire employees or deal with customers, automatize agents for 24/7 customer service, or answer employees’ questions on internal sites, among others.

Hand holding a cellphone with a conversation on it

As an example of this last case, most of the social media networks use every aspect of AI. The algorithm defines how it will be displayed, decides whether or not to show some information, and learns from anything people do and don’t do (e.g., who they interact with and who they normally ignore, so that way, they can see a more personalized session).

Another example is Nike. This company uses prediction algorithms and augmented reality apps to provide futuristic, customer-centric service to their customers, letting them customize their shoes according to their preferences. Another one of Nike’s applications allows consumers to scan their feet when choosing the size of their shoes to get the most precise measurement.

ARTIFICIAL INTELLIGENCE in businesses — a huge opportunity for companies

Organizations are facing significant obstacles in implementing and developing applications with AI, but as they become more and more familiar with these kinds of tools, they are experimenting with projects that combine elements from all three categories to get the full benefits from all of them.

The future is coming quickly, and AI will not only be a part of it but will be an essential part of the digital transformation that companies are looking for.

We will see more user cases, more startups, higher numbers of business applications, and entirely brand new jobs associated to this growing technology.

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