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22 Apr, 2024

Unlocking Competitive Edge: Harnessing the Potential of Machine Learning Across Various Industries [Includes Infographic]

The tide is turning. ML in business is no longer a futuristic fantasy; it’s a powerful reality reshaping industries at an unprecedented pace. From streamlining factory operations to revolutionizing healthcare diagnostics, ML’s ability to learn and adapt is unlocking a treasure trove of competitive advantages for businesses of all sizes.

This article, accompanied by an insightful infographic, delves into the transformative power of ML. We’ll explore how various industries are harnessing this technology to gain a strategic edge, enhance efficiency, and deliver unparalleled customer experiences.

Using machine learning applications in different industries | LITSLINK Blog

Machine Learning Market Analysis

ML in business continues to gather momentum among large enterprises and startups alike. In 2020, the total ML market value was estimated at $22.59 billion, and this figure is projected to increase up to $126 billion in 2025 with a compound annual growth rate of 42.08%.

Risk management, trading, automation, and performance analysis remain the top ML applications businesses use to boost their results, drive sales, and increase market share. With more than $28 billion of total funding allocated to AI, this technology has the potential to substitute a great deal of manual work and drive greater automation. Thus, companies can significantly increase performance and gain a competitive edge.

What do all these figures mean for your business? It is already clear that machine learning is not a flop as some experts projected. ML in business has become an inseparable part of industries and brought such meaningful advancements as telemedicine, self-driving cars, hyper-targeted ads, and tons of other useful solutions that already benefit entrepreneurs, making their businesses more profitable and customer-centric.

What is more, ML in business is insanely accessible and affordable today. You don’t have to invest lots of funds in research or building an innovative solution. Instead, you validate your idea with PoC within 3-7 weeks and build an MVP in 6 months. With a reliable vendor, chances are you’ll get the first version of your product quickly and at an affordable price!

Find below an infographic on the major machine learning figures and trends.

Machine learning applications in different industries - infographic / LITSLINK blog

Machine Learning Applications in Healthcare

Machine learning provides healthcare professionals with a variety of tools and techniques that significantly affect health outcomes. Equipped with smart predictive algorithms, ML in business can provide immediate benefits to disciplines that possess big datasets. In this perspective, machine learning models can be trained to analyze medical records to spot abnormalities or areas that require attention, thus providing new insights into patients’ well-being.

If you think ML in business is a breakthrough in healthcare, you’re probably wrong. Medical organizations have already been using ML models for a long time now to identify risks and predict disease progression.

In 2017, Google trained the software to find cell patterns in tissue to detect breast cancer at an early stage. The results are impressive — the findings have revealed 89% accuracy compared to the doctors’ 79%. Numerous healthcare organizations have been using this solution to build their own products.

Machine Learning Applications in Healthcare | LITSLINK Blog

Machine Learning Use Cases in Healthcare:

  • improved medical diagnosis;
  • accurate prediction algorithms;
  • drug discovery and manufacturing;
  • NLP for administrative tasks;
  • visual data processing;
  • patient risk identification;
  • higher level of process automation.

Where to Start?

The world of ML in business might seem complex, but the first steps are surprisingly achievable. Here’s how to bridge the gap between your initial concept and a real-world ML solution:

  • Identify the Need:  Every successful product addresses a specific problem.  Start by thoroughly researching your target audience or organization.  What challenges do they face?  What unmet needs exist in the market?  By clearly defining the problem you’re trying to solve, you lay the foundation for an impactful ML in business.
  • Data Acquisition is Key:  Machine learning thrives on data.  This data serves as the fuel that allows your ML model to learn and make intelligent decisions.  Ideally, healthcare providers already possess a wealth of data from patient records and other sources.  This data can be leveraged to train your ML model and integrate it seamlessly into your product.
  • Don’t Fear Data Scarcity:  Even if you don’t have a vast amount of data readily available, there are ways to bridge the gap.  Techniques like data crawling or sourcing information from external resources can help enrich your data set.  However, in the healthcare domain, leveraging existing patient data is often sufficient to get started.
  • Validate Your Idea & Build Your MVP:  Have a brilliant concept but unsure how to proceed?  Many vendors offer affordable Proof-of-Concept (PoC) or Minimum Viable Product (MVP) development services specifically for ML solutions.  These PoCs and MVPs allow you to test your idea in a real-world setting, gather valuable feedback, and refine your approach before investing in a full-fledged solution.  This iterative process ensures you’re building an ML product that truly meets the needs of your target audience.

By following these steps, you can transform your initial ML idea into a tangible solution with the potential to revolutionize the healthcare landscape.  Remember, the journey to successful ML implementation in business starts with a clear understanding of the problem you’re solving and a strategic approach to data acquisition and validation.

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Machine Learning Applications in Retail

AI technologies, along with machine learning and deep learning, have already penetrated the world of retail, providing companies with more opportunities to market their products and achieve greater results.

First-movers, such as eBay, Amazon, and Alibaba, have long integrated AI products into their sales cycle from logistics to customer service, which now are excellent practical applications of ML in business. And their solutions are being used by millions of retailers throughout the world, which use machine learning to streamline their own operations, automate processes, and improve decision-making.

The possibilities of ML in business and in the field of retail are full of promise. The companies leverage the power of AI to build personalized recommendation systems, websites, define their pricing strategy, source analytics, and encourage visual search. Below we listed the top machine learning use cases you can apply to boost your retail business.

Machine Learning Applications in Retail | LITSLINK Blog

What is ML Capable of in Retail?

  • predictive customer behavior;
  • providing personalized recommendations to users;
  • brand monitoring in social media;
  • improving customer service with virtual assistants and chatbots;
  • providing intelligent search.

Where to Start?

The first step is identifying opportunities in the market. Recommendation engines, pricing strategy tools, and visual search are among popular machine learning mobile applications and desktop apps used by companies in the retail industry. To build such software, ML algorithms must be trained with information obtained from products, customers’ portfolios, or clients’ reviews.

If you do not have relevant data, you can still build a machine-learning application. Much information can be sourced by crawling the web or by using special services.

Also, you might encourage your clients to share their data by providing them with a personalized experience. For instance, you might offer them valuable content if they fill out the form with information about their background, preferences, etc.

Machine Learning in Fintech

Only a few industries can boast of having as much structured data as financial and banking have, which makes it a perfect field for the application of AI and machine learning. Investment banks were the first to use AI innovation in the late 1980s. Since then, the industry has progressed to an unprecedented level, improving the experience of practitioners and their clients.

Today machine learning advancements are used to develop accurate prediction, effective asset management, and in-depth market analysis. Enterprises also apply AI to build smart chatbots that help them manage responses timely and enhance customer service. According to the studies published by Mordo Intelligence, artificial intelligence is expected to reach $7 billion and become one of the most promising techs in the financial sector.

Core Applications of ML in Fintech:

  • asset management systems;
  • fraud detection;
  • trading algorithms;
  • security software;
  • stock market predictions;
  • chatbots.

Where to Start?

FinTech and machine learning are an unbeatable duo. As a rule, companies operating in this industry have enough data to build a full-fledged solution that will work properly and benefit their business. So, if you’re wondering what you need to start, we’d say it highly depends on the machine learning use case you’re going to implement.

For instance, for predictive models to forecast your customers’ behavior, you will require data from clients, such as the number of purchases, demographics, navigation data, etc. You should also remember that the retailer’s experience helps to fine-tune your ML models. Thus, it would be beneficial if you could engage in the process and contribute to building your solution.

Machine Learning Applications in Marketing

Marketing and machine learning is a perfect mix when it comes to boosting sales and accelerating your business growth. When used together, these two have the potential to provide you with meaningful insights, which can be used to define your positioning or develop a strategic vision.

Being able to anticipate customer behavior is the core factor affecting the company’s success today and artificial intelligence has become a key to optimizing marketing campaigns and making them more efficient. Here we listed core ML applications that can be integrated today to strengthen your marketing efforts.

Machine Learning Use Cases in Marketing:

  • improved segmentation & targeting;
  • recommendation engines;
  • hyper-targeted ads;
  • content generation;
  • marketing attribution.

Where to Start?

ML for business might become a perfect match for companies operating in the field of marketing. As a rule, they are pretty aware of their competitors, and in case they have a strong marketing team in place, they possess some knowledge about clients and their buying habits. The more data about your target audience you provide, the better you’ll be able to train a model.

There are already many ready-made and open-source solutions on the market, which you can freely use to boost your marketing. When integrated into your product, they can provide real value to your target audience and equip you with insights into the customers’ buying habits and future trends.

How Big Companies Contribute to the Development of ML

While you’re only searching for ways to apply ML in business, startups and big behemoths have made their names by leveraging the power of AI to create smart solutions that disrupt their industries at some point in their development.

The good news is that most of these solutions are accessible to a broad audience. You can pay an affordable fee and use them to build your own models that could be further integrated into your product. This approach reduces the cost of ML development by several times and enables anyone from any industry to leverage the power of AI. Thus, you can develop a simple ML model starting at $4k! But keep in mind that the final cost of development is highly dependent on your needs, requirements, and type of implementation.

Also, it is worth mentioning that there is an ever-growing community of AI enthusiasts, who constantly contribute with their software, products, and other developments to the AI marketplace. Thus, if you need an open-source ML solution for your product, chances are you’ll find it here.

Below we listed some of the services released by large corporations that are frequently used to build and train ML models:


IBM Watson Machine Learning is a product that enables developers and data scientists to launch their data science and ML projects smoothly. Its scalable and open-source model operation software provides companies with an opportunity to harness the power of AI for their purposes.


TensorFlow is an open-source machine learning platform that helps software engineers build and train ML models. It equips practitioners with a flexible and comprehensive ecosystem of libraries, tools, and other community resources that significantly contribute to AI development.


This IT giant could not but benefit from the growing potential of ML and launched a series of AI-based products. Azure Machine Learning is one of the services catered by Microsoft that helps companies to build and deploy ML ML in business faster.

Apart from Azure, Microsoft has put much effort into developing the AI direction. The company has recently launched a deepfake tool. Video Authenticator provides the user with the “confidence score” that the picture, video or any other media has been manipulated, helping the audience to differentiate between authentic and synthetic content.


Intel is one of the leading adopters of machine learning on the global market. The company applies ML in business to decide on its pricing strategy, optimize expenses, and apply large-scale data analysis. It also implemented machine learning to analyze IoT data streams.

The company also offers a set of tools and platforms to accelerate ML deployment. For instance, Intel’s Deep Learning SDK is widely used for ML model training and deployment, while AI Analytics Toolkit is applied to accelerate machine learning and data science pipelines.

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