Machine learning technology, a key cog in the broader concept of artificial intelligence, offers benefits to many industries, from helping retailers upsell to customers to assisting fintech companies in fighting fraudsters. For example, look at these statistics concerning the explosive growth of machine learning development for optimization in the manufacturing industry:
30-50% reduction in machine downtime;
15-30% boost in labor productivity;
10-30% gain in throughput; and
10-20% decline in quality-related costs.
If you want to improve the results of repetitive, routine, and standard tasks requiring many people, look at machine learning applications.
What are machine learning and its applications?
Machine learning (ML) is a significant sub-field of artificial intelligence (AI). It helps computers learn to solve tasks without explicitly being programmed by identifying historical and current data patterns and then generating predictions with minimal human interference.
ML models can process very different data types to produce valuable output, such as:
images or videos used in computer vision models, including image segmentation and object detection
textual data used in NLP applications, including text classification and sentiment analysis
audio data used in speech recognition.
Machine learning applications can become a part of the routines of everyday business: customer service automation, factory floor control, safety enhancement, sales prediction, price optimization, quality inspection, etc.
Let’s take a closer look at the most potent machine-learning business applications.
Customer service automation
End-user customer support automation is a flourishing field for NLP applications, such as the conversational AI of chatbots or callbots. Human speech recognition via NLP enables machines to answer the users’ questions or direct them to the most qualified person, thus reducing inefficiencies and saving time both for a client and an agent. In addition, with the help of natural language generation, you can create virtual assistants.
Similar technology is used for advanced customer support bots.
These use cases can reduce customer service costs and increase the interactions handled, thus growing customer loyalty.
For example, the leading Northern European telecommunications company Elisa implemented the chatbot, Annika.
It handles 70% of all the inbound contacts and fully resolves customer issues with 42% FCR (first contact resolution).
Machine learning-based solutions can monitor equipment and identify necessary maintenance and repairs. AI alerts a human supervisor, who can watch the video to validate or fix the operation. As the faults are detected early, the maintenance costs and downtime decrease. For further details, read about the Hepta Airborne and MindTitan collaboration on power grid maintenance, described in this computer vision case study.
Moreover, some algorithms can predict faults in advance, reducing unexpected problems and unplanned work disruptions.
Decision assistance system
Machine learning can support decision-making by helping management foresee trends, detect problems and enhance decisions. ML models analyze the avalanche of data businesses have and offer actionable insights that lead to benefits. First, historical data or other relevant dataset is fed to machine learning algorithms to train them to extract valuable insights from the data. Then the machine can process new input data and forecast the possible results of multiple scenarios to suggest the best course of action. Usually, a well-trained model can do this at superhuman speed and scale. Afterward, a cycle reinforces itself over time thanks to a human in the ML training loop.
However, ML doesn’t replace humans; instead, it helps people become much more effective.
Here are some examples of decision support systems in various industry sectors:
Clinical decision support tools contain machine learning elements. They suggest diagnoses and treatment options to clinicians, improving treatment efficiency and patient outcomes.
Machine learning-enabled decision support systems analyze data on climate, resources (energy, water, et cetera) and other factors. Therefore, they provide farmers with insights, helping to make crop-management decisions.
Sentiment analysis for better decision-making
As is common knowledge, illegal expenditure of public money for political promotion happens; thus, there is a need to supervise financial processes to detect any instances of suspicious spending.
MindTitan created anAI system that helps flag signs of public money misuse. Furthermore, to keep a clear and honest depiction of the political field for Estonian taxpayers, it is necessary to halt insults or excessive unreasonable praise if there are any.
The sentiment analysis model allows auditors to check the emotional background and context of the mentions of politicians or parties across media.
Dynamic pricing tactics
With the help of machine learning, companies (e.g., retail and transport) can analyze their historical pricing data and datasets of additional variables to find connections. AI can suggest how certain factors — from time of day to weather to the seasons — impact demand for goods and services. Machine learning algorithms can learn from that data, processing vast and numerous variables. By combining insights from that processing with additional market and consumer information, machine learning algorithms help companies dynamically price their goods. This strategy ultimately helps companies maximize revenue and profit.
For example, machine learning solutions can analyze customer reactions to changes in price: how much demand increases as the price falls and vice versa. These ML models process many data sources, from customers’ tweets to online product reviews. As a result, it allows a company to react quickly and change pricing accordingly, effectively increasing profits by expanding sales while maintaining margins.
The clearest example of dynamic pricing (also known as demand pricing) occurs in the transportation industry. Just remember surge pricing at Uber when rain increases the number of people looking for rides simultaneously or sky-high prices for airline tickets during school vacation weeks.
Recommendation engine systems
Today businesses utilize recommendation engines to provide the most attractive offer/product to their clients at any given time. This standard technology for online shops and many other services considers browsing history and proposes new customized offers accordingly for each user.
Customer recommendation engines powered by machine learning provide personalized experiences that enhance customer satisfaction. In this use case, algorithms process data from an individual customer, such as past purchases or recently viewed items, as well as other data sets, such as demographic trends, a company’s current inventory, and other customers’ purchase histories, to select what products and services to suggest to each customer.
Examples of recommendation engine systems:
Fuzu, a Helsinki-based company, aims to provide ambitious young East African professionals with job opportunities, career advice, and new skills. With MindTitan’s help, Fuzu implemented a technology that matches job seekers with potential employers to enhance client satisfaction. It increased the job applications’ click-through rate by 30%, the recommendations engine case study shows.
Amazon, Walmart, and other big e-commerce companies utilize recommendation engines to personalize and enhance the shopping experience.
Netflix is another well-known example of this machine learning business application. The entertainment service uses a client’s viewing history, the streaming history of viewers with similar interests, personal information, and other data points to deliver personalized recommendations.
YouTube uses recommendation engine technology as well, enabling the online video platform to help users quickly find videos that fit their tastes.
As more financial transactions move electronically, the risk of fraud rises, necessitating fraud detection software. The capabilities of machine learning algorithms to detect patterns and identify anomalies that stand out from those trends make it an excellent tool for recognizing fraudulent activities.
Businesses in the financial sector have successfully been utilizing ML in this capacity for years. The use of machine business applications in fraud detection can be seen in retail, gaming, travel, financial services, and the public sector. For example, MindTitan created an AI that detects tax fraud by analyzing and recognizing patterns in tax return information and many other sources. As a result, the system flags the companies that should be more closely inspected.
Image and video processing
Mining images and videos for business value can also be a machine learning task. Specifically, deep learning models aimed at image and video data are increasingly common among ML applications. AI image recognition, object tracking, and object detection in video are widely used; you can find these technologies everywhere, from tagging photos posted on Facebook to identifying illegal behavior in real-time to automated cars’ need to see the road to computer vision in manufacturing.
Robots enabled with computer vision and machine learning can monitor shelves to detect misplaced, low-in-, or out-of-stock items.
Machine learning video analysis detects suspicious activities, such as shoplifting.
Furthermore, it identifies workplace safety policy violations, such as the lack of personal protective equipment.
By detecting objects in video, AI can find production line issues such as conveyor malfunction or product defects, such as cracks.
As machine learning helps to process repetitive tasks, it can drive efficiency in many business departments; for instance:
Machine learning speeds up work in finance departments and firms and reduces human error rates.
Machine learning as part of software testing automation can significantly speed up and enhance the process, resulting in better and faster-developed software at lower costs.
Machine learning technology with natural language processing automatically identifies critical pieces of information from documents, even if the needed data is held in unstructured or semi-structured formats, such as letters or customer reviews.
Thus, companies can instantly process massive amounts of documents, from tax forms to invoices to legal contracts. Using machine learning and NLP increases efficiency, improves the accuracy of such processes, and frees human talent for more creative work.
Self-driving transport and robot couriers
Machine learning can enhance and speed up delivery. For example, robots enabled with computer vision minimize human physical involvement (a helpful feature during pandemics) and provide uninterrupted last-mile deliveries. An example from Estonia is Starship. Their advanced autonomous devices use mobile technology and computer vision for short-distances delivery.
The Starship platform accepts mobile phone requests to transport parcels, groceries, and other purchases. Afterward, the client can monitor the robot’s journey and location after placing the order via smartphone.
Manufacturers can significantly improve their logistic planning and forecast by AI that collects real-time data. For example, manufacturers can stock up their warehouses before a high-demand peak using forecasting AI solutions. Researching demand fluctuations and then predicting them with AI algorithms allow companies to foresee the demand for specific goods in each outlet depending on the seasonal conditions, the demographics of that area, and other data points – such as trending news on social media. Thus, AI algorithms help reduce transportation costs while keeping up with customer demand.
Moreover, AI can automate all heavy duties, such as tracking, lifting, moving, and sorting items, while leaving strategic and other more complex tasks to humans. In addition to productivity improvements and increased sales and profit margins, such automation will reduce workplace injuries.
Automating product quality control, managing inventory, upgrading warehouse management, and reducing costs are among AI applications’ many advantages across industries, from retail to the oil and gas industry.
Benefits of machine learning business applications
Machine learning will be helpful for your customers (i.e., your business). For example, chatbots, virtual assistants, or callbots can eliminate annoying lag that could happen between consumers’ needs and business responses.
A good ML algorithm frees employees from repetitive and dull tasks. The machine precisely follows the given instructions without becoming distracted or tired. Thus, it eliminates “human factor” errors from your automated processes.
AI-powered automation can augment almost every business process, from onboarding and support to communications and marketing to distribution: it speeds up repetitive tasks and reduces errors. For example, in manufacturing processes, machine learning automation can decrease scrap rates and testing costs and improve yield by up to 30%.
Operational efficiency growth
Efficiency increase is another benefit of machine learning, as a logical consequence of automation. Moreover, a machine can tirelessly and efficiently work 24/7; algorithms can process enormous amounts of data without burning out. Hence, the estimated improvement in business productivity after implementing AI reaches 54%.
Tackling complex problems
Some tasks require an analysis of multiple connections within data, which can be challenging and time-consuming for humans. For example, going through thousands of sources to look for the mentions of politicians and parties and then analyzing each message’s emotional context take hours. In contrast, the AI solution, made by MindTitan, performs the same task in minutes, detecting and flagging suspicious cases for auditors to determine if there was any public money misuse.
Since humans cannot process and coordinate the avalanche of data as quickly and well as machines do, AI and machine learning technology provide businesses with value. A machine learning algorithm analyzes raw data and translates them into an objective decision.
As described above, machine learning applications are broad. They make a difference in almost every task, from customer service to manufacturing. Machine learning algorithms can improve chatbots, detect product faults, and predict equipment malfunction. Computer vision machine learning can improve the quality of products and goods distribution. Machine learning and AI in manufacturing improve production through assembly line automation and help maintain workplace safety. This list could be very long, demonstrating that those business applications of machine learning are worth a more profound analysis in terms of application for your particular case.
Since there are a lot of machine learning business applications, choosing the most beneficial one could be challenging. Having implemented 90+ AI projects, MindTitan experts suggest using gap analysis first. This handy tool to discover what process to enable with machine learning will help in process analysis and provide insights on what to change. Seven clear steps lead to a transparent and complete picture of the consequences of machine learning applications. Thus you can make an educated and the most beneficial decision.
However, no one has to struggle with this difficult task alone. The helping hand of a like-minded team will speed up the journey to the successful application of machine learning. The best way to find a suitable machine learning application for your business is to find an experienced machine learning team and to consult experts in machine learning development services. Having such experts onboard, MindTitan will help you figure out the suitable applications for machine learning and make an AI strategy for further steps and enhancements.