How to implement AI in your business [16 real-life examples included]

Nowadays, we hear a lot about these new opportunities that artificial intelligence and machine learning open. Most of it is predictions and futuristic plans, but what is really going on with AI today?

In this article, we have collected 3 ways and 16 examples of how machine learning and artificial intelligence can be implemented in business today.

What is artificial intelligence (AI)?

Artificial intelligence (AI) is an intelligent machine that behaves and reacts like a human. This term usually applies to applications that can do specific tasks in human ways, by replicating such brain functions as:

• Learning
• Problem-solving
• Language-understanding
• Visual perception
• Reasoning

What is machine learning?

Machine learning (ML) is a ‘brain’ of artificial intelligence. Its work is based on data patterns detection and reacting in a certain way according to these patterns. For example, machine learning based applications can:

• do real-time text translation
• understand the mood and reason of the text
• find look-a-like music or images
• classify images or text based on the elements they contain
• accurately recognize speech, objects, and faces

In a nutshell, ML enables applications to learn by themselves and act in the ways they were not specially programmed to.

How AI and machine learning can be used in business?

For the past years, AI was rapidly developing and now is used for:

• predictive analytics
• virtual agents and chatbots
• targeted advertising
• suggestive web searches
• automatic scheduling
• advanced translation
• auto-pilot driving
• voice, face and speech recognition

Most of these functions are commonly used in real-world to solve business and human problems. To see a more detailed picture of how AI can be implemented in business lets take a look at AI use cases.

Business process automation

Machine learning gives more opportunities to automation in existing business processes due to its pattern recognition ability.

An existing data in such business department as finance, logistics, and sales is already stored, sorted and structured, which is ideal for creating predictive models for machine learning and automatization of these processes.

It is easier to implement machine learning in existing business processes, rather than to create new processes or business changes as it requires a lot of data, process modeling, testing, and correction.

Examples of business process automation:

• Analyze a document, or email, make a decision about what type of document it is and extract relevant data
• Predict when contracts based on usage will need to be renewed
• Automated warehouse inventory management systems

These use cases of machine learning are the real value for businesses today. According to McKinsey, around 43% of financial processes can be automated using AI, and Gartner says that processes automation will save us 2 hours of work per day. This is a huge business opportunity, by investing into AI, you save time, reduce the number of employees and expenses.

More intuitive interfaces

Recently, machine learning dramatically enhanced its ability to understand human writing and speech.

Virtual assistants made it easier for customers to research products, consult and pay via voice or chat applications. Machine learning algorithms can scan a huge amount of data in seconds and give a relevant answer almost immediately, this speeds up customer conversations, reduces support cost and increases customer satisfaction.

Instead of spending hours on researching and clicking through websites people now can just simply ask: “Book available hotel for a reasonable price in Manhattan” or “Show me the data from stock market”.

Reveal and optimize processes in new ways

Machine learning opens an opportunity to analyze and process vast amounts of data that human just can’t do physically. This helps to analyze processes in new ways:

Examples include:

• Predictive maintenance. ML can gather information from sensors and make a prediction of when the parts of the production line will break. This can save a huge amount of money because companies don’t have to wait until the parts break and stop production. This also can be used in sports. For example, a coach can analyze the data of physical activity of the athletes and see when they should stop training to avoid injuries.
• Image analysis and tracking. New “deep learning” algorithms now can understand and analyze complex images. And of course, it opened a wide range of new opportunities for business. For example, a manufacturer can scan its products to ensure everything is labeled right and in the right place. Or an agriculture company can analyze footage of the fields from drone to check if the whole area is planted and everything is alright with plants.
• Text analysis and classification. Now you don’t need human to read hundreds of pages of documents to detect issues or classify docs by categories. Machine learning can scan thousands of pages in seconds and is sure-footed if trained well.

16 Examples of Artificial Intelligence Across 6 Industries

A recent McKinsey Global Institute research shows that there’s a strongly marked difference in AI adoption among business industries.

Businesses should understand where artificial intelligence can improve their effectiveness, speed up processes and lead to revenue growth, and where it can’t.

Finance & Banking

As more people use online transactions on a daily basis, it becomes harder for finance and banking institutions identify theft and fraud issues. AI lifts financial cybersecurity to the next level, as machine learning can detect fraud patterns and block a transaction before it happens.

In addition, AI can make financial predictions based on statistics and patterns that are not visible to a human. And of course, it can automate and speed up customer interactions.

Healthcare

In the healthcare industry, AI brings a huge help for doctors and large service improvement for patients by providing immense help in analyzing complex medical data such as medical tests, CT scans, screenings, X-rays, etc. Based on the patient’s data and medical database, ML can help doctors to create a personalized treatment for each case. Moreover, AI can also provide real-time medical advice to patients.

Real-world examples:

• The Babylon AI doctor app consult with patients by speech recognition function, it establishes a diagnosis based on symptoms and offers them appropriate treatments.
• ML helps to create virtual nurses, such as Molly by Sense.ly, that listens to the patient’s request, redirect them to an appropriate medical institution/service or gives an instant reply.

Retail/E-commerce

Artificial intelligence plays a huge role in the retail industry. First of all, chatbots and virtual customer assistants allow e-commerce companies to have a 24/7 high-quality customer service by answering frequently asked questions and consulting customers without any human involvement.
Secondly, machine learning is used to provide personalized recommendations to each customer with strong recommendation engines based on data analysis. Basically, big e-commerce players such as Amazon knows what we like and what we are looking for. Implementation of this technology increased Amazon’s revenue up to 30%.
Thirdly, AI enabled such a thing as geo-targeted sales campaigns and price optimization. For instance, Darwin Pricing, dynamic prices software, uses neural networks to give merchants an opportunity to offer geo-targeted discounts based on price expectations modeling at different locations.

Technology

Technology companies don’t just develop AI solutions, they also use them in their products to provide better customer experience. Moreover, such companies are trend-setters and early adopters of their own technologies.

When talking about real-world examples, let’s start with intelligent voice assistants such as Apple’s Siri, Google Home, and Microsoft’s Cortana. They analyze human speech and return appropriate answers or execute some task, for example, schedule a meeting or switch off the lights in a room.
AI-powered translation engines become a common thing. Skype uses this technology to provide a real-time translation, and Google Translate uses a deep machine learning translation to ensure the most accurate translation. Yes, it’s still not perfect, but it automatically learns on a daily basis and consistently becomes better.
Facebook uses face-recognition systems to find help us find ourselves on photos or suggest to mentions a certain person.

Actually, there are limitless ways to implement AI and its abundance continues to spread.

Higher Education

Artificial intelligence has left its footprint on higher education field too. The main benefit of it is that it enables personalized learning by adjusting educational content to the demands of each student. In addition, machine learning helps educators to collect and analyze data about the progress and learning manner of each student and adjust learning program according to their performance.

Oregon State University already uses such new technologies in their learning program to personalize their hardest courses. And the University of Michigan had implemented automated text analysis (ATA) application that reviews works of students, analyzes mistakes, and gives recommendations.

Energy & Utilities

The energy & utility industry is on the early stage of artificial intelligence adoption, but companies are more than interested to invest into this technology. The foresee AI to make energy systems more stable, safe, cleaner and more affordable.

AI is going to be mostly used to analyze energy data, create self-healing digital grids and intelligent energy forecasts.
Moreover, machine learning algorithms will be used for patterns analysis to recognize the vulnerabilities of power grids and prevent or minimize electric grid failures by real-time monitoring and quick response to issues.

Bottom line

Nowadays, we are at the rise of the Fourth Industrial Revolution. Such technologies as machine learning, artificial intelligence, and data analytics open a wide range of opportunities for businesses. Leaders of industries already use the benefits of it. Those companies, who will first implement AI or ML in their business will have a huge competitive advantage and can easily become a niche leader.