June 9, 2023
ML technologies are fundamentally transforming the way businesses work. With the advancement of ML algorithms, computers can process data sets quickly and efficiently, enabling organizations to better process large amounts of information faster. In addition, the advancements in machine learning solutions and expertise have allowed for more accurate predictions of customer behavior and market trends. So it is only natural that the global ML spending is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%, according to Fortune Business Insights.
The growth in ML usage has resulted in various ways organizations can implement these technologies. From healthcare to retail to finance, machine learning unlocks new insights and improves operational performance. With so much potential and opportunities to leverage ML, let’s explore some of the most common examples of its use cases and applications across industries.
Scheme title: Worldwide AI software by category value, 2020-2021
Data source: idc.com — Artificial Intelligence Spending Grew 20.7% Worldwide in 2021, According to IDC
Table of contents
Computer vision is a subfield of ML that focuses on enabling computers to understand the content of digital images, videos, and other visual data. Computer vision use cases include facial recognition, object detection/classification, image segmentation, etc. and is relied upon across retail, healthcare, cybersecurity, and other sectors.
Natural Language Processing (NLP) is a subset of machine learning that allows computers to comprehend and interpret spoken and written human language. It is increasingly implemented in various sectors, including customer service, healthcare, and marketing automation, for tasks such as sentiment analysis, text classification, document summarization, and more.
Data mining uncovers patterns and discovers correlations, trends, and outliers in large data sets. Data mining use cases can be seen in various industries such as finance, healthcare, and retail for tasks like fraud detection, medical diagnostics, customer segmentation, and more.
To successfully implement ML, companies need to carefully consider a number of factors and have an in-depth understanding of the available options. To help with that, we have developed a step-by-step guide:
Pinpoint the improvement area
The idea to implement ML usually stems from problems or inefficiencies in established business processes or the desire to improve workflow speed or outcomes. So companies that have their sights on machine learning adoption should begin by answering a seemingly basic question “What are we adopting ML for?”. For this, they need to review the process intended for ML-based improvement and try to identify exact business pain points and areas for improvement.
Decide whether ML is the best solution
Machine learning can be a very effective solution, but its implementation isn’t reasonable in every case. ML algorithms require a lot of time, money and effort to build and train, as well as a significant revamp of the company’s IT infrastructure and ongoing maintenance post-deployment. So businesses need to make sure that no regular software solution is already able to deliver the expected results or solve the existing problem.
Review the existing ideas
If you’ve decided in favor of ML adoption, you next need to evaluate your rough ML solution ideas more closely in terms of implementation complexity, data availability and quality, and the value they can bring to your business. Moreover, companies should assess whether they can scale up the ML-powered application in the future and what resources they would need for its maintenance. Next, they should assess the regulatory, risk, and transparency aspects of the potential use cases.
Assess your ML maturity
To identify suitable use cases for machine learning, one should also assess the organization’s current level of maturity with respect to ML implementation. Companies need to consider factors like the established degree of automation in processes and systems, the quantity and quality of data available for training ML models, and the availability of relevant talent. Assessing your ML readiness is critical to understand whether you can realistically implement complex use cases.
Prioritize use cases
At this final stage, companies single out specific tasks or issues the ML solution will be built for. More often than not, this stage can take multiple iterations due to the discovery of hidden implementation bottlenecks. For example, companies can come to the conclusion they should implement several smaller, more flexible ML tools than a single complex solution. This is why effective use case prioritization should be approached gradually, with each potential ML application re-evaluated at every step.
Successfully implemented machine learning use cases can bring a range of benefits to organizations, including cost savings, improved efficiency, and better decision-making:
Businesses worldwide have already unlocked the benefits of machine learning and proved its infinite potential. From enhanced customer experiences and improved efficiency to better decision-making and reduced costs, the potential of ML is unparalleled compared to other technologies currently on the market. As such, it is wise for businesses to start taking advantage of this technology sooner rather than later to gain a competitive edge. However, companies must remember that the success of an ML project heavily depends on the unique business case of each organization, its goals, and available data. By taking all these factors into account before launching an ML project, businesses can ensure they get the most out of their investment.