It seems likely that 2023 will be a decisive year for artificial intelligence (AI) and machine learning (ML). Some industry observers predict that recent advances in AI could spark a new revolution in society similar to the industrial revolution, the invention of the Internet or the arrival of the smartphone. However, 2023 does not mark the invention of AI, but rather the year it went viral thanks to OpenAI's ChatGPT technology.

A variety of industries have been using AI for decades, which begs the question: What are some of the common use cases for AI/ML? What use cases should business and software application development company consider when designing a comprehensive process automation strategy ?

Why is AI/ML important?

Before we go further, let's define some of these terms. Artificial intelligence refers to computer systems that mimic human thought and decision making. While generative AI tool ChatGPT has received a lot of buzz this year, it's far from the first popular AI use case. Early adopters of AI/ML skills included banking and investment trading firms, which used the technology to determine when to purchase or sell assets on exchanges and the stock market (a practice known as high-frequency trading). AI is also widely used in other industries, like as manufacturing, utilities, and the healthcare sector, and we anticipate that this trend will only increase.

7 AI/ML use cases to consider.

Let's examine some compelling use cases that continue to develop.

1. Document processing.

Most organizations are drowning in documents. Whether it's paper checks, electronic invoices, or barcode scans, companies often spend a lot of time processing documents. Intelligent document processing ( IDP) allows companies to extract data from documents at scale without requiring a lot of manual work. This saves companies huge amounts of time, effort and money. 

2. Supervision of financial fraud.

One of the most widespread AI/ML concepts is the detection of anomalies in data sets. When AI is trained on a data set, it can develop a baseline of behaviors. When something out of the norm occurs—an anomaly—the system can flag this anomaly for further analysis. Financial institutions use anomaly detection during the crucial Know Your Customer (KYC) process to detect fraudulent or identity theft transactions. For example, if someone's credit card shows a large purchase made in a different country at the same time as a purchase in her hometown, the company could block the card and verify the holder's purchase.

3. Credit and loan risk approval.

Institutions in the financial sector can make extensive use of AI/ML capabilities to better understand the creditworthiness and risks of money lending. Software development solutions like AI can analyze multiple data points, such as credit histories, credit utilization, and financial statements, to understand whether it is a safe bet to extend credit to a potential borrower for a credit card, mortgage, or business loan. Additionally, over time, AI can analyze the data to detect possible patterns among default risks that human analysts would not otherwise see. This not only reduces risk, but also makes application processing much more efficient for loan officers and underwriters.

4. Diagnosis and medical images.

An interesting area where we will see greater use of AI/ML is the healthcare sector. For example, medical imaging professionals such as radiographers and sonographers will make more use of artificial intelligence to detect potential problems in patients' medical scans. Using machines to detect potential problems and having a technician interpret the results can be more accurate than relying on the human eye. On the other hand, doctors will use patient data to detect symptoms that will allow custom enterprise software development like AI to diagnose diseases and even recommend treatments.

5. Customer service.

One of the most interesting AI/ML use cases is customer service. Chatbots can use natural language processing (NLP) to respond to customer requests and launch other workflows on the backend to help resolve a customer's issues. This saves service agents a lot of time in their daily operations and improves customer experience, allowing companies to improve customer satisfaction in the long term.

6. Energy prediction.

For decades, utilities have worked on smart grids. AI and machine learning can analyze historical data such as energy usage, weather patterns, and other variables to better predict demand. Utility companies can better forecast energy consumption and boost supply in response to growing demand without overwhelming the system thanks to this (or it helps them plan for interruptions).

Additionally, this new software development solutions plays an important role in sustainability efforts, as AI can help optimize energy efficiency, usage and distribution patterns and prevent waste. This also helps reduce costs for both utility companies and industrial and residential customers.

7. Supply chain management.

Recent years have highlighted the fragility of global supply chains. The COVID-19 pandemic disrupted typical supply and demand, causing spikes in some areas and troughs in others. It is difficult to predict demand based solely on historical data: modern supply chain managers need to use more sophisticated forecasting methods for supply chain management.

AI/ML capabilities enable supply chain professionals to better predict demand with real-time data across multiple data points to avoid shortfalls. They can also use AI to help with tasks such as setting prices, predicting weather patterns and routes for ships and transport, and creating more agile supply chain networks with their suppliers and partners.