In today’s uncertain world, digital transformation is no longer an option for organizations that want to flourish—it’s a necessity. According to Accelerate Your Data-Driven Transformation, a commissioned study conducted by Forrester Consulting on behalf of RapidMiner, those surveyed estimate that the ROI for machine learning projects will grow from 4.4 times today to 6.7 times in the next 2-3 years. When done successfully, businesses gain a serious competitive advantage, increase productivity, and find ways to become more effective and efficient than ever before. On the other hand, those that overlook the need for digital transformations will fall behind their competitors.
A key step on the path to digitization is discovering ways to leverage the power of artificial intelligence and machine learning. Oftentimes, the hardest part about executing on AI is simply knowing where to start.
At RapidMiner, we’ve been lucky enough to have a front-row seat in the world of AI and have seen it applied to just about every problem or inefficiency imaginable. In fact, we’ve recently compiled a comprehensive library of high-impact uses cases across all industries to help get you inspired.
In this post, we’ll go over some of the most exciting applications of how AI and machine learning are being used to help companies thrive.
15 Remarkable Applications of AI in Business
Below, we’ve outlined 15 ways that AI can be used to impact businesses across industries. While some of these applications are about a very specific industry, each still provides general insights and guidelines that can be applied to your business.
1. Optimize energy cost
An unavoidable fact is that every business will consume energy. In manufacturing, this is a particularly difficult challenge to overcome, as a single asset can consume millions of dollars a year in energy costs.
With artificial intelligence, models can be developed to monitor the variables that drive a manufacturing plant’s efficiency, and dynamically adjust parameters based on real-time sensor data. This helps significantly reduce wasted energy and extend the amount of time between scheduled maintenance on equipment.
Of course, not all companies have one costly energy resource like in manufacturing. But even in these cases, energy consumption adds up between different assets and sectors of the business to create a total energy usage that is much higher than expected. AI helps aggregate and monitor these costs, enabling organizations to reduce energy usage, save money, and become greener.
2. Predict delays to improve profitability
Delays are one of the costliest problems an organization can face. This is especially true in the travel industry, where a delay of only an hour can snowball into days wasted and lost customers.
AI helps dramatically reduce these costs by:
- Easily identifying the most predictive variables and factors that create delays
- Using them to build useful widgets for flight operations
- Automating flight scheduling to maximize efficiency
This doesn’t solely apply to the travel industry, as every company experiences some type of delay that can cost time, money and effort. Through AI powered predictive analytics, organizations can anticipate delays before they occur to give organizations time to prepare and, ultimately, use these predictions to become more profitable.
3. Create a data-driven product portfolio
With businesses expanding operations globally, many retailers and large consumer goods providers have to properly manage thousands (if not millions) of unique product SKUs and ensure each is performing well.
Monitoring these products for user satisfaction, customer perception, and the specific factors that influence reviews and ratings can be a massive undertaking. With text analytics and topic mining, companies can streamline these processes by aggregating data from many sources, including competitors, and building easy-to-use dashboards.
For businesses that don’t deal with thousands of SKUs, this technology is still incredibly powerful. Using text analytics and topic mining allows you to better understand what people are saying about your company and products, no matter the scale.
4. Streamline customer service
It’s no secret that customer service is a vital factor to maintaining and growing a business. For organizations that are prone to high volumes of requests (like utility companies), serving each customer appropriately can be a massive challenge.
Utility companies can make excellent use of text mining to sort and prioritize customer requests based on region, urgency and other factors. These methods help reduce the rate of customer callback as well, resolving problems quickly and efficiently.
Every organization needs quality customer service, and text analytics can help make your service more efficient and more effective – no matter how small or large the customer base. Leverage data to turn service requests into a competitive advantage, rather than a burden!
5. Predict market share threats
Organizations need to stay conscious about what their competition is doing. In pharmaceuticals, there is an ongoing threat of competitors releasing a new drug that will cut into a company’s market share.
The best way to prevent losing any of the market is to target the doctors most likely to switch to a competitor’s product and provide them with extra engagement. Using clustering and classification models, companies can segment physicians by how likely they are to adopt another product and target them appropriately.
These tools are powerful in other industries as well, allowing marketing teams to segment their audiences based on the likelihood that they take a specific action. This helps teams identify threats and opportunities in their respective market.
6. Increase yields
Scaling up production often results in a decrease in output quality, a trend that is especially costly in the energy industry, where oil and gas refinement requires a high level of mixture precision.
By incorporating a prescriptive optimization model into production, adjustments can be made in real-time to optimize product quality. Additionally, machines can be set to maximize performance and time between maintenance, and even increase the net yield while reducing waste.
In any industry, increasing output is not where the challenge lies. Doing so without disproportionately increasing input or sacrificing quality is where things often get tricky. This is where AI comes into play, enabling you to accurately tune production to maximize efficiency.
7. Prevent fraud
Fraud is a common problem in the healthcare industry, and successful identification and prevention can save an organization valuable time and resources. For organizations that deal with thousands or even millions of customers a day, using manual processes to catch fraud becomes impractical, if not impossible.
Machine learning enables healthcare industries to automatically scan and flag high-risk fraud cases and integrate a variety of new data sources that weren’t possible with manual flagging.
Detecting and preventing fraud is a time-consuming process in any industry and is often done manually rather than with automated systems. AI presents an opportunity for organizations of any size to implement automated anomaly detection and reduce the amount of time and resources that need to be spent dealing with fraud.
8. Identify repeat customers
One of the most wasteful problems faced in healthcare is readmissions. Patients often return for an issue that should have been resolved during their first visit or go to the ER when a simple follow-up visit would suffice.
These unnecessary readmissions create a huge strain on hospitals—taking up staff time, hospital beds, and more when resources would better be spent on other patients. Using classification models, hospitals can accurately flag patients with a high risk of readmittance and take preemptive measures before a second visit is even necessary.
For the rest of the business world, repeat customers often aren’t a bad thing. Knowing what attributes and behavioral patterns are most likely to indicate that a customer is going to return can help appropriately target customers and give them the nudge that they often need to make a repeat purchase.
9. Create powerful cross-sell and up-sell
The acquisition of brand-new customers is always a costly process for retailers. Tapping into the existing customer base through cross-selling and up-selling is a great way to avoid these extra costs while strengthening existing relationships.
Leverage AI to analyze the purchasing habits of your customers and predict what they’re most likely to purchase next. These real-time predictions allow for targeted up-sell or cross-sell upon a customer’s very first purchase.
These are powerful tactics in virtually any industry, not just retail. Analyzing shopping carts and creating targeted recommendations has proven successful for some of the largest ecommerce titans.
10. Automate daily tasks
Data is a central component of any business in financial services. Personnel must deal with vast swaths of data daily—a repetitious and tedious task that is not only inefficient and time consuming, but is also prone to errors due to information fatigue.
AI can be used to automate these daily data integration tasks. In just a few clicks, predictive models can complete what would be a day’s work of data sorting and integration, allowing workers to focus on more important tasks and reducing error rates.
Most businesses have data that needs to be integrated from disparate sources in order to generate useful analytics. With AI, organizations can create a streamlined system to shorten time-to-insights and free up workers for more important tasks.
11. Optimize the supply chain
As a consumer goods business grows, supply chains and number of SKUs will inevitably become more complicated and difficult—if not impossible—for a human to manage.
CPG and retailers can use text analytics and clustering to better manage large inventories and understand the profitability and revenue generated by different SKUs. Analytics generated from this data can help reduce holding costs of SKUs, avoid complications downstream in the supply chain, and increase overall savings.
For any business that deals with supply chains or SKUs, using AI for portfolio optimization is a can’t-miss opportunity. Simple volume metrics aren’t enough to understand your supply chain and understanding the profitability of individual SKUs can be greatly simplified by using predictive analytics.
12. Generate real-time public sentiment analysis
Knowledge is power, and in the pharmaceutical industry, knowing what your customers think of your business, the medication you produce, and your competitors can help increase customer satisfaction and avoid costly mistakes. Combining sentiment and text analysis with data from social media, forums, and user reviews will help do just that.
Public opinion is often seen only anecdotally, but it still has a dramatic impact on the success of business strategies, and not just in the pharmaceutical industry. Using AI, you can easily understand and quantify public sentiment across the globe.
13. Improve resource allocation
Knowing when and where to place resources is vital to college admissions agencies, which match prospective students with the best possible universities. Different students have different needs and understanding those needs before they present themselves can be the difference between acceptance and rejection.
Admissions agencies can use classification models to predict whether a student might need visa assistance or work to strengthen their profile and decide the next best action in allocating resources to assist the student.
Using AI to predict how to best allocate your resources is a powerful tool in any context. AI classification algorithms make this possible in any industry, allowing companies to more efficiently allocate valuable resources, and improve the customer journey and revenue conversion.
14. Empowering citizen data scientists
Very rarely does an organization have a full team of data scientists ready to implement an AI strategy. There might only be a handful of dedicated data scientists available to an entire organization, which makes predictive models nearly impossible to implement at scale.
However, with a collaborative platform like RapidMiner, engineers, domain experts, and other non-data scientist team members become a part of the process. Time spent resolving data science issues will be drastically reduced, and models can be implemented without the direct oversight of a data scientist.
By providing any subject matter expert with the tools and ability to explore machine learning and AI without the help of a dedicated data scientist, businesses can implement AI more quickly and easily.
15. Better forecast demand
Statistical modeling has been commonplace in the energy sector for many years, so most businesses have some form of legacy models integrated into the production cycle. The challenge with these integrated models is that they are typically out of date, lacking the exploration features and advanced analytics found in modern AI solutions.
The energy industry isn’t the only one suffering from outdated models. Many companies use basic statistical forecasting models that are outdated and inefficient. These models require more maintenance and are inflexible, but AI solves these problems through flexibility and optimization.
What’s the bottom line? AI and machine learning can be applied to any business—no matter the industry—to drive real impact. These are only a few tangible examples of how we’ve helped customers do this in the past.
We hope this post has given you some inspiration to apply to your organization. Looking for even more applications? Here are 50 ways to impact your business with AI.
Did we miss something remarkable? We’d love to hear some of the ways that your business is using AI to improve productivity, uncover efficiencies, and gain a competitive advantage, so please share with us on Twitter!