AI & Machine Learning Solutions: Real-World Applications & Case Studies

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Publish date:

December 18, 2023

Updated on:

March 7, 2024

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AI & Machine Learning Solutions: Real-World Applications & Case Studies


Few things are transforming the tech world today as much as AI & ML. Over the last year alone these technologies have had a revolutionary impact as generative models have launched to widespread use and appreciation amongst the general public. Yet, we're only just scratching the surface of what technologies powered by machine learning solutions can deliver for businesses.

Over the next four years, the global ML market is expected to continue to grow ten times in size from $15 billion in 2021 to 150 billion in 2028. The source of this growth largely comes from organizations learning to lean on machine learning service providers & solutions to overcome old business challenges and find innovative new solutions.

Precisely what vendors do to power an organization forward varies significantly from one business to the next. What remains consistent, however, is the processes, skills, and knowledge used to deploy cutting-edge technologies with revolutionary impact.

Here, we outline machine learning and AI solutions currently being used to transform businesses and detail how you can access them now.

What Are AI & ML?

Today's highly popular AI technologies might feel almost sci-fi in their capabilities and functionality but they remain a far cry from Hollywood depictions of the same name. At the heart of modern AI is machine learning software capable of building its own internal algorithms for specific narrow domains by simulating the way an organic brain might learn a task.

More precisely, ML (machine learning) is a subset of AI that details how these algorithms are developed and trained. Whether describing previous eras of AI such as chess computer Deep Blue or cutting-edge generative AI such as ChatGPT — both rely on methods of machine learning at their foundation.

Today's AIs go far beyond what Deep Blue was capable of in the 1990s, however. The availability of big data, exponential leaps in processing power, and progress made in machine learning solutions development have made it possible for current AI technologies to begin learning and adapting within their environment in real time and do so much more.

Types of Machine Learning

There are three key types of machine learning relevant to modern AI technologies.

1. Supervised Learning

The simplest mode of machine learning relies on a vast set of training data and consistent feedback. When provided with both the input training data and the desired output as a goal, a supervised learning system transforms one into the other using iterative feedback rounds to measure how close it's getting to its goal at each attempt.

Gradually, the system learns which transformations to apply to which input data to successfully perform any given task. After enough rounds of feedback and a large enough set of training data, supervised learning produces a system capable of taking new and unseen input data and transforming it correctly with a high degree of accuracy.

Practical applications of this technology are vast and varied from image recognition capable of 'reading' pages of text to predictive analytics and spam detection algorithms.

2. Unsupervised Learning

In contrast, unsupervised learning removes the need for feedback during the training phase, relying instead on a more autonomous process. In this mode of learning algorithms are given another vast amount of training data and tasked with picking out and labeling any patterns they find independently.

The advantage of this approach comes from the machine's ability to identify patterns and trends that would be otherwise invisible to a human observer. As a trade-off, it can be more difficult for engineers and data scientists to identify why the algorithm has categorized some sets of data, making the resulting algorithms even more of a black box solution than a supervised learning algorithm would produce.

Key uses for unsupervised learning are in streaming recommendations on media platforms, data analysis in all industries, and fault prevention systems for hardware and industrial machinery.

3. Reinforcement Learning

The most complex ML algorithms are trained exclusively from their environment. At its early stages, a reinforcement learning algorithm stumbles around with little purpose or structure until it begins to complete activities that earn a positive reward. It then attempts to retrace and replicate some of those steps to maximize its positive rewards while minimizing negative 'reprimands'.

Repeated over time, and a vast number of iterations, the system learns to meet its goals in the fastest and most efficient way it can. This model of learning is well suited to domains without vast amounts of training data to work from. Industrial machinery, robotics, and natural language processing systems use reinforcement learning often.

What Can AI & ML Deliver for Your Business?

So far, these examples have been abstract, and their use cases and benefits only theoretical. Yet, their real-world applications are well worth taking a closer look at. AI & ML technologies are now routinely used to improve decision-making, analyze internal data to find new trends, enhance customer experiences, and reduce costs.

Let's take a look at the fields that are doing just that in areas where machine learning solutions are having the biggest impact today.

4 Most Profitable Applications of AI & ML in 2023

1. Sales Tech

AI agents are the best sales staff you'll never meet. Able to dive deep into customer data, algorithms trained by machine learning work continuously to identify unseen trends in purchasing, predict customer behavior, and make more accurate forecasts from existing data.

Three productive use cases for machine learning software in sales include:

  • Lead scoring tools. AI recommendations that use historic internal data and external sources to help teams prioritize high-quality sales leads — making better use of staff time and delivering better outcomes.
  • Tailored product recommendations. An AI agent is ideally placed to suggest relevant products to customers based on historical data and powerful recommendation algorithms.
  • Forecasting tools. Using broader market trends external to the organization and combining them with in-house data sources can allow organizations to make accurate predictions on future sales volumes.

2. Fintech

The rapidly evolving world of Fintech makes it ideally suited to the strengths and advantages of machine learning and AI solutions. Modern financial service providers can richly benefit from personalized customer service, advanced fraud detection, and automation made possible by machine learning software.

3. Health Tech

One of the most exciting and innovative fields for machine learning software is Health Tech Development. Today, the technology is already being deployed to diagnose patients faster and more accurately than doctors working without AI assistance. Future uses of the technology include creating better training processes, generating faster research outcomes, and providing better data collection and analysis for patients.

By putting healthcare decisions into a patient’s own hands, the potential for new technologies in this field is off the charts.

4. Logistics

The logistics industry is a field where machine learning solutions are having some of the largest impacts today. The abilities of the technology to improve high-level strategy and reporting, optimize supply chains, and enhance predictive forecasts are proving especially powerful in this industry. Some of the benefits firms are making use of today include:

  • Optimizing inventory levels to enhance just-in-time operations.
  • Increased oversight through automated reporting and management.
  • Real-time insights and alerts for supply chains.
  • Continuously improving demand forecasts and insights.

Biggest Challenges of Adopting Machine Learning and AI Solutions Today

Engineers working on machine learning algorithms on pen and paper before coding.

When it comes to these transformational tools and resources the tech industry is like any other — there's no free lunch. The trade-off for machine learning software comes from the technical demands AI/machine learning engineers overcome to put solutions into practice.

Leaning on time spent in the field, a background knowledge of engineering, and experience with specific technologies allows engineers to overcome the most pressing challenges in the field. For businesses, these often include:

Machine Learning Infrastructure Requirements

The most pressing challenge for organizations entering the AI/ML field is the tools and infrastructure necessary to develop, test, and deploy effective data models within their domain. The time needed to build the same infrastructure and expertise is often seen as taking mission-critical resources from operations businesses need to automate and improve.

How to Meet the Infrastructure Requirements for AI/ML Solutions

Companies lacking the necessary infrastructure to build AI models typically find success by partnering with a machine learning software development agency that can deploy extensive technical resources. Once trained and tested, these same models can be deployed to systems as small and simple as an IoT note to run autonomously at the edge of a network collecting data or managing operations.

Sourcing the Necessary Data

One of the biggest challenges of modern machine learning is the vast amount of data necessary to build effective models. Training algorithms need data that has been processed and refined so that they can work effectively once they're unleashed into the 'real world'. Gathering and processing this vast volume of data itself is a significant engineering challenge.

How To Source the Data Necessary for Machine Learning Applications

There are many cutting-edge solutions to this historic problem of machine learning. Synthetic data is one emerging field that creates new data free of security or privacy concerns by generating it from real-world sources. Additionally, there are data repositories available ranging from open-access to industry-specific paid resources for AI/Machine Learning engineers developing new algorithms.

Sourcing the Relevant Skills and Talent

The biggest practical stumbling block referenced when envisioning AI/ML-powered services is gaining the skills, experience, and talent necessary to build them. Machine learning solutions development is a fiercely competitive market with engineers and data scientists being in higher demand now than ever before. Yet, this is a hurdle easily overcome.

How To Find the Relevant Skills and Talent for AI & ML Projects

Partnering with a team of experts to bring in machine learning service providers and solutions is an ideal way to recruit skills and practical experience instantly. Better yet, choosing a machine learning software development agency with skills and experience matching those within your industry can enhance your services even further and speed up development projects.

Is Now a Good Time to Invest in AI/ML?

Machine learning solutions are currently having a transformational effect on every industry around the world. The only drawback to their emergence is the challenges listed above creating a barrier of entry into accessing the field.

You don't have to reinvent the wheel to take advantage of AI/ML technologies. You can start small with just one part of a single service, find out about the advantages and capabilities it brings, and build from there.

We can help. Simply submit a brief to tell us where you want to begin and we'll connect you to machine-learning service providers and solutions to get you there. Within a short amount of time, you'll get to find out the shape of future technologies the world is just beginning to wake up to.


What Can You Solve with Machine Learning?

Machine learning is one of the tech world's broadest tools and one that can be applied to almost any problem a business has today. Over recent months new companies have emerged on a near weekly basis applying AI and machine learning to problems in a unique way to astonishing success.

Some of the key areas that machine learning has advanced in leaps and bounds over this time include:

  • Product recommendations and sales.
  • Image recognition for still frames and video.
  • Fraud identification in the Fintech industry.
  • Demand forecasting for sales and services.
  • Virtual personal assistants in every field.
  • Sentiment analysis in social media and marketing.
  • Customer service automation within all businesses.

What Are AI/ML Solutions?

AI/ML is short for artificial intelligence and machine learning. These two technologies are closely linked, with machine learning being a subset of AI technologies. Today, the AI systems we talk about in real-world use are built using machine learning technologies — algorithms that adapt (or learn) from input data to reach a pre-defined goal.

These aren't truly intelligent in the Hollywood sense of AI. They don't think or reason about tasks but they do 'learn' to perform specific functions astonishingly well and with a higher degree of flexibility and reliability than conventional automation software.

What Are the 4 Types of Machine Learning Algorithms?

Currently, machine learning algorithms break down into 4 types of learning that are each better suited to different types of problem-solving and completing different tasks. These are:

  • Supervised learning. The algorithm is given examples of input data and examples of acceptable output data to learn from. From there, the machine transforms the input data and measures how close to the output data it has reached before deciding to evolve its current approach or disregard it for something new.
  • Semi-supervised learning. This approach mixes both categorized and un-categorized data to train the algorithm to apply categorizations gained from one group to the other appropriately
  • Unsupervised learning. In this model of learning the algorithm has no goal to hit but is simply given a vast set of input data to pick out patterns and categorizations in on its own.
  • Reinforcement Learning. Here, the algorithm is provided with positive and negative reinforcements according to the task it's designed to do. The machine begins operating with little instruction but to seek positive reinforcements. From there it can begin to learn new tasks and gradually gain efficiency and speed as time goes on.


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Ian Deed

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Software developer, mobile application engineer, and writer helping companies to enhance their tech branding and improve the way they communicate with technical and non-technical audiences.

Leaning on years of experience and knowledge to understand technical communication that works from wordy jargon that doesn't.

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