Artificial Intelligence can help your brand out-innovate and out-compete your industry peers. A subset of AI, machine learning is about enabling machines to learn from data so they can perform their tasks more intelligently. Are you wondering if machine learning can solve your business problems?
The global market for machine learning is growing (it accounted for USD 6.9 billion in 2018 and reached USD 8,43 billion in 2019), and more companies are applying ML technology in their business solutions every day. As many large enterprises have already recognized machine learning’s potential and embraced ML-based business solutions, smaller companies should also take note of what AI-powered systems can help them accomplish.
This article will show you
- What machine learning and its business value are
- Why machine learning is important in today’s business environment
- What problems machine learning can solve – some examples
What is machine learning and what is its business value?
A main subfield of AI, machine learning (ML) enables machines to analyze information better – that is, more intelligently. ML enables computers to learn and interpret without being explicitly programmed to do so. This helps AI-based systems, for example, to detect patterns in structured data, and computers to find hidden insights, a task they are, as validation confirms, quite excellent at.
Beyond analytics, there are countless other applications in business, leading the makers of more and more brands to invest in ML-based solutions and make their business more data-driven and automated. 50% of enterprises plan to spend more on AI and ML this year, according to Forbes.
A Refinitiv study shows that today machine learning is most commonly used in risk management (82%) and performance analysis and reporting (74%), but it is also very important for trading (63%) and automation (61%). There are plenty of ways to gain a competitive advantage from machine learning. It can improve the performance of your company’s internal processes or ensure the best user experience for your customers.
For example, deep learning paved the way for making voice assistants (almost half of the world’s online population – 42% – use voice-activated search and assistants) and many other applications we all use every day. Meanwhile, according to The state of AI in 2020 McKinsey report, 50% of business respondents said that their companies had already adopted some AI-based solutions last year.
The main benefits of adopting ML-based solutions include:
- Great UX and increased sales – many companies offer their customers virtual agents, or software programs that use scripted rules and AI to provide automated service or guidance. Such customer service is more efficient than traditional service, and AI enables companies to offer more human-like experience to the customer. Machine learning also powers recommendation systems and makes product searches easier. It is widely used in marketing for content personalization. And that means it has a big influence on UX. Customer service can also be improved with natural language processing, a technology hotly anticipated for adoption (Dataversity article, 2019) in a huge number of applications.
- Reduced risks – whether it is about data safety or overall business safety, machine learning will help your company significantly reduce risk. A Cyber Threat Actor, or malicious actor, is a person (or group) performing online actions characterized by malice or hostile intentions. According to IBM, hostile activity can last approximately 280 days, during which a victim may have data or money stolen or systems running inefficiently. Advanced analytics are used in cybersecurity systems to spot suspicious activity and prevent data leaks or theft, and risk management is one of the main applications of ML.
- Increased performance – business automation helps you complete tasks faster while reducing the risk of “human error”. According to Gartner predictions, the Robotic Process Automation (RPA) market is expected to grow by 20% in 2021.
Learn more about machine learning and the fantastic applications of this technology.
Why is machine learning important in today’s business environment?
As machine learning can really increase competitiveness, modern companies are keen to adopt it. But they often lack raw data, and ML is all about data – huge data sets are essential in the training of machine learning algorithms. A lack of experience or ability to deal with data once you have it is yet another stumbling block. Processing information, creating data models and data security are among the thorniest challenges companies must solve if they want to benefit from ML-based solutions.
The lack of proper infrastructure may also be problematic. You will need the right environment to test different tools. Using ML-based solutions requires access to advanced tools, sufficient infrastructure and experienced staff, all of which comes at a tremendous cost. Data scientists are specialists skilled in developing and deploying ML models, and happen also to have the second most sought-after (and well-compensated) job in the US in 2021, outperformed only by Java developers.
Yet access to staff and computing power is not enough. Getting through digital transformation is a main condition for those companies looking to get a start with machine learning. But all those challenges can be overcome. With good providers of ML-based solutions, adopting this modern technology for your business should go smoothly. For a bit of inspiration on where such solutions could take you, check out the interesting examples of ML below.
Problems machine learning can solve – some examples
3D vision technology is still emerging, but it has already proved to have huge potential in business. It recognizes a three-dimensional shape or the volume of a part. 3D image sensors can detect an object from a distance. One of the most common applications of this technology is bin-picking, or the automated process of detecting, classifying and sorting objects by moving them from one place to another. This can improve efficiency in production and many other industries. It can also raise the speed of palletization and depalletization processes. 3D vision can also be useful in other tasks besides just picking up and moving objects.
Using computer vision eliminates the need for manual entry for the machines to perform their tasks. To see how, let’s take one fascinating project we worked on – the June Intelligent Oven. It used 2D vision. Our team helped improve the oven’s food recognition model and also built tools for training algorithms. The end-user doesn’t need to type the name of the dish into the oven’s interface – the oven simply recognizes the kind of food and current weights to choose the right program to prepare your meal.
Predicting Customer Lifetime Value
Companies today have access to huge amounts of data, which can be effectively leveraged to produce useful business insights. A large portion of the information that every business gathers every day is customer information. By analysing it, you can learn more about customers, including their shopping behaviours, needs, requirements and complaints. A Customer Lifetime Value prediction can help you create an effective strategy for sending personalised offers to all groups of your clients.
Optimizing marketing campaigns and detecting spam
Optimizing marketing campaigns is possible thanks to customer segmentation and content personalization. Machine learning provides brands with analytics that can be used for better ad targeting and marketing automation. Another great use case for ML technology is detecting SPAM. Such solutions have actually been in use for some time already. Before machine and deep learning, email service providers needed to create specific rules to qualify a message as SPAM. Today, SPAM filters use neural networks to come up with new rules by themselves.
Machine learning is often used in e-commerce systems. Advanced ML-based solutions are capable of analysing the current and past customer activity on the various platforms. ML systems identify patterns and produce recommendations for users, showing them products they’re likely eager to buy or offers they’ll possibly answer to. We followed a similar approach when working for the recruiting specialist Pracuj group. The company now has a powerful machine learning algorithm matching candidates with job postings based on their resumes.
Improved cyber security (analytics)
Since 2018, McAfee recently estimated that since 2018 the cost of global cybercrime has passed $1 trillion. But cyber crimes can cost you more than your money. Data leak incidents can be really damaging to your brand image and both employees’ and customers’ privacy. Machine learning powers analytics systems that ensure data safety and overall cybersecurity. ML-based solutions analyze activity and try to spot suspicious user behaviors, unauthorized access, breaches, fraud, system weakness, and any number of other issues to keep administrators awake at night. This capability makes ML very important, especially for financial institutions.
Become more competitive using machine learning
Adopting machine learning can make your company more competitive no matter what industry you work in. Of course, implementing ML-based technologies is no piece of cake, so you’ll need an experienced ML solution provider if you are considering taking the AI plunge. Contact us if you want to learn more about machine learning and its importance to your industry.