This blog is an attempt to shed light on the best way businesses use enterprise data effectively using machine learning and artificial intelligence. Implement these business use cases and make your organization smarter, more efficient, and more profitable.
Having a huge database and being able to pull a relevant keyword has existed since the 1970s. As a search company, you wouldn't be surprised to see Google doing this. But what makes the tech giant unique is that it shows the most relevant result- and it does it through machine learning.
There is a lot of conversation happening around rule-based and machine learning systems, especially among enterprise businesses. The noise surrounding the technology will only increase, thanks to the real-world applications that it brings about. You might be thinking of artificial intelligence and machine learning as happening in a far-off land, but that's far from the case.
Amazon wants to show you related products so that the size and value of your cart increase. Airbnb wants to show you listings that are relevant to your requirements. IKEA wants you to choose the right furniture items for your 2-bedroom apartment. The New York Times wants to build a flexible paywall personalized to individual readers using 100s of criteria.
We all are subject to incredible applications in our lives. From the movie recommendations on Netflix to booking the nearest cab on Uber, so many of our day-to-day lives are driven by AI and ML. The same is the case with enterprises as well.
Large enterprises implement these technologies to bring higher levels of innovation to make organizations intelligent, more efficient, and more profitable.
This article will look at how businesses leverage AI and ML to use their enterprise data to scale revenues.
Popular Machine Learning & Artificial Intelligence Use Cases for Enterprise Data
Companies in all industries collect, store, and manage much more information than they ever needed before. The problem, however, is that businesses often don't know how to leverage their data effectively. Machine learning (ML) and artificial intelligence (AI) are becoming critical tools in the enterprise data management process — here are some of the most popular use cases for these technologies:
- Advanced Automation
- Simple & Seamless AI-enabled Business Products
- Smooth Financial Management
- Improved Security
- Increased Customer Satisfaction
- Sales Optimization
- Market Forecasting and Planning
- Natural Language Processing for Text Data
- Monitoring Brand Sentiments
- Recommendation Engine
Let's get started.
AI & ML Business Use Case #1: Advanced Automation
Automation is generally considered to make repetitive tasks done using a rule-based system. But by adding machine learning into the mix, you can create automation that keeps improving with time.
Machine Learning automation or Intelligent Automation can be used in different industries. Machine learning algorithms have been applied to various automation tasks in the last few years.
This has resulted in a number of successful use cases such as:
Ad Tech - Automatically detecting and blocking ads malicious ads in real-time.
Insurance - Automatically detecting fraud in real-time.
Financial Services - Intraday risk monitoring, Trade Surveillance, Customer Segmentation, Lead Scoring, Pricing Optimization, Trade Execution, Credit Risk Analysis.
Healthcare - Patient risk prediction and treatment recommendation, fraud detection.
Retail - Product Recommendation (customer segmentation), Inventory Optimization, Pricing Optimization.
Manufacturing - Predictive Maintenance / Anomaly Detection (e.g., detect when a particular part is going to fail)
AI & ML Business Use Case #2: Simple & Seamless AI-enabled Business products
While the algorithms behind AI and machine learning in data science might be complex, some of the biggest successes in AI are centered around providing a simple product or service that makes life easier for users.
We've seen this with consumer-facing apps like Uber and Lyft, which use self-learning algorithms to route drivers and passengers in real-time, creating a seamless experience for both parties.
There is a lot of proprietary data and an existing network of customers that you can use to deploy AI products for an enterprise. You will have to look at customer data and their varied requirements and scout for pattern recognition tasks that can be done at scale using MLOps methodology rather than manually.
Here are some of the most exciting ways that businesses are using artificial intelligence and machine learning to increase efficiency, reduce costs, and improve customer service with simple AI-enable products:
Spam filters - Machine learning is used to identify spam emails by considering patterns in email subject lines, the contents of the email, and even sender domain names.
Face ID - The face recognition technology used in Apple's iPhone X is an example of ML being used for security purposes. It uses a neural network trained using thousands of images of people's faces.
Chatbots - Chatbots have been around for some time already but have become even more popular recently, thanks to advancements in machine learning technologies. They can be used for customer support automation and lead generation by having human-like conversations with customers via text messaging or voice interaction.
AI & ML Business Use Case #3: Smooth Financial Management
For one, financial management is becoming more and more automated. The days of back-office data entry are quickly disappearing. This creates a need for new processes and technology to handle today's massive amounts of data in the finance industry.
Some examples of machine learning applications in financial management include:
Risk Management - Machine learning models can be used to identify patterns that are not visible to humans. This allows you to develop models that can identify fraudulent transactions or investments.
Portfolio Optimization - Investing is not always as easy as it sounds. The market is highly volatile, and even the best minds sometimes make irrational decisions. By applying machine learning algorithms, you can better understand what business portfolio is ideal for your company's risk tolerance level.
Credit scoring - Instead of assigning a credit score based on limited information — such as FICO scores — banks and FIs are moving to machine learning to predict creditworthiness. They run algorithms to analyze thousands of consumer attributes and assign a risk score to determine creditworthiness.
Loan decisions in minutes - Machine learning can help institutions review loan applications and make decisions quickly, often in just minutes. This enables banks to approve loans outside regular working hours (evenings or weekends) and provide responsive customer service that traditional systems cannot match.
Fraud detection - Machine learning allows online banking platforms to use behavioral analytics to detect fraud accurately in real-time, impacting customers' experiences positively by blocking fraudulent transactions instead of declining legitimate ones.
AI & ML Business Use Case #4: Improved Security
Thanks to web-based technologies, there is a lot of interconnectedness between systems, posing a security threat. From data breaches to phishing attacks, ransomware, and other privacy concerns, there are so many things that a business should be careful about. To ensure the security of your customers and business, specific mechanisms must be followed.
Machine Learning can help with monitoring and vulnerability assessment tasks in this instance and even complement the existing security team. ML & AI can also help predict threats and point out the glitches in the environment so that future attacks are predicted with the help of the past attack data.
The following are use cases for machine learning in cybersecurity:
Logging and Monitoring - Machine learning can sift through the massive amount of log files created on enterprise networks every day to find anomalies in user behavior, access rights violations, malware infections, etc., helping IT teams identify security threats faster.
Detecting Fraudulent Activities - Businesses can detect fraudulent activities through machine learning techniques like clustering based on IP addresses, location, OS type, or device type. It can also classify legitimate vs. illegitimate behavior based on historic characteristics.
Detecting Spam and Phishing Emails - Machine learning algorithms can analyze past emails to determine whether a new email is a spam or phishing based on its contents. Phishing emails can be detected by comparing the sender's domain name with the domain name mentioned in the message body or other characteristics of the sender's identity.
Security Analytics - Most organizations generate large volumes of data that are often difficult to monitor manually. Machine learning helps organizations monitor and analyze data for unusual activities that may indicate a security threat.
AI & ML Business Use Case #5: Increased Customer Satisfaction
As a business owner, you're always looking for ways to increase customer satisfaction. With AI and machine learning, you can now predict your customers' needs before they even know it themselves. Amazon uses a machine learning approach to recommend products to its customers based on their previous purchases. In fact, 35% of the items sold on Amazon are through these recommendations!
For the past several years, businesses have been using data analytics to gain insight into their customers' behavior and preferences. However, traditional data analytics methods are based on rule-based systems. This means that a human analyst will determine which factors are relevant to making a prediction and then program that information into the system. This can be time-consuming, costly, and prone to errors since humans are involved.
Machine learning systems can replace rule-based with intelligent systems that won't run on autonomous data models. Netflix's ML-powered recommendation engine saves the streaming service more than a billion dollars a year.
Some of the most common use cases for ML and AI in customer service include:
- Automated customer support via chatbots
- Social media sentiment analysis
- Customer behavior/intent prediction
AI & ML Business Use Case #6: Sales Optimization
Which area do you think will have an immediate impact on the company's bottom line? Sales. ML recognizes patterns, so it is easy to find which type of customers are ready for an upsell or a cross-sell. It can even tell you which kind of leads are more likely to close and recommend the right kind of products based on customer profiles and previous sales data. By using all of this, the business will be able to increase its conversion rates.
Here are the use cases of Machine learning & Artificial Intelligence in Sales:
Predictive Lead Scoring - Machine learning predicts the likelihood of a lead turning into an opportunity, which is then ranked and prioritized for sales teams.
Intelligent Lead Routing - AI is used to understand the customer context, personality, and tone of the conversation, which is then routed to the right agent at the right time.
Automatic Email Personalization - Machine learning techniques are used for automated email personalization, using data about customer behavior and interest to personalize email content for each customer.
Sales Forecasting - AI tools can analyze market conditions, historical data, and other factors that affect sales performance. When a business has a detailed understanding of these factors, it can make more accurate forecasts about its future sales. A company that understands how its sales are likely to perform in the next three months will be better equipped to plan its operations and investments accordingly.
AI & ML Business Use Case #7: Market Forecasting and Planning
Forecasting is the use of historical data to determine the direction of future trends. Businesses employ forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time.
If a business wants to know how many customers will purchase a product in a given month, it may use forecasting to predict that number. Forecasting can be used for any variable, such as sales, unit costs, revenue, or company profits.
Companies often use forecasting to plan future expenditures. If a company wants to increase marketing spending in a given month, it may forecast the number of sales that additional marketing spending may produce.
Here are some other key ways that businesses are using machine learning and AI for market forecasting and planning:
Inventory Optimization - The ability to predict the most effective levels of inventory to maintain will be a boon to businesses everywhere. Companies can move from traditional approaches of carrying large stocks to just-in-time ordering based on demand forecasts, reducing their costs and improving customer service.
Demand Forecasting - As with inventory optimization, demand forecasting is an area where AI will significantly allow companies to improve their performance. Businesses will have much better information about the products and services customers want, when they want them, how much they want, and at what price, allowing them to respond effectively to changing market conditions. There will be a greater ability to predict sales volumes over time and spikes in demand due to holidays or special offers.
Price Optimization - With AI-powered price optimization, companies can determine the optimal pricing for each product or service individually based on market dynamics. This is particularly valuable for online retailers who have access to massive amounts of data about competitors' prices that AI systems can analyze.
Predictive Maintenance - The idea here is to predict when an asset will fail before it actually happens. To do this, you need to create a model that will learn the patterns in your data and use those patterns to predict failures. You can use data modeling and machine learning algorithms to do this.
AI & ML Business Use Case #8: Natural Language Processing for Text Data
Natural Language Processing (NLP) is a branch of artificial intelligence with the ability to interpret human language. NLP can help businesses gain valuable insights from large amounts of text data.
Understanding or interpreting the human language requires understanding its linguistic elements, such as grammar and semantics, and its communicative function. A human can easily understand a phrase like "Are you coming with us?" but this sentence would be difficult for a machine to interpret.
The following are some NLP use cases that can be applied to text data:
Customer Service Automation - When someone sends an email or social media post about a problem with a service or product, companies can use NLP to automatically respond with either a solution or a request for more details. For example, if you post that you're having trouble logging into your bank's mobile app, the bank could use NLP to send you instructions on resetting your password.
Speech Recognition - Speech recognition technology helps convert audio data into text format. It's used in many applications such as virtual assistants like Siri and Google Assistant, speech-to-text conversion apps, and call center automation. It can also be used in business meetings to make MoM and easily share it with other stakeholders.
Smart Search - Online businesses can leverage NLP to enhance their search functionality so that customers get better results that match what they're looking for. Consider Amazon's smart search feature, which suggests product categories as you type in your product search.
Summarization: Summarization condenses long paragraphs of text into a few important sentences using NLP techniques. This can be used for extracting important topics for news articles, blogs, etc.
AI & ML Business Use Case #9: Monitoring Brand Sentiments
Organizations use the machine learning technology to monitor internal and external brand sentiments. The system can be trained on what constitutes a positive or negative message about the brand. The system then scans all the social media channels, blogs, and websites for messages related to the brand. When such messages are posted, they are quickly identified and flagged depending on whether they are positive or negative messages. The management can then review this information and use it to improve their relationship with customers and employees and their branding strategies.
AI & ML Business Use Case #10: Recommendation Engine
Amazon uses machine learning algorithms such as collaborative filtering to recommend products based on customer ratings and purchases. Netflix also uses similar algorithms to recommend movies based on what a customer has watched before. The more a user watches or buys products, the more accurate these recommendation engines get over time.
Recommendation engines have become an essential component of e-commerce businesses since they help improve conversion rates by making relevant recommendations to customers.
Machine Learning and Artificial Intelligence are fast becoming an important cog in the wheels of enterprises. Using one's enterprise data effectively is only possible when you leverage these advanced technologies. It can help solve complex problems that allow businesses to scale operations quickly.
The effects of AI and ML will be seen more in the coming decade as every industry you can think of will transform its core processes to take advantage of them and become market leaders.
If you are looking to implement these technologies in your business and are looking for a partner, then the folks at Zuci would be more than happy to help you. Get on a call with us to understand how we can help you.