Artificial intelligence. Is it a technology that will end civilization as we know it? Is it a tool that will make our lives easier? Is it something between the two? Only time will tell.
Overlords, Apocalypse-Bringers, or Intelligent Assistants?
Regardless of whether it becomes our overlord, destroys the world by finding the most efficient way to make paperclips, or simply helps us close our books faster, what we do know is that it’s going to be a big deal as we enter this decade.
In fact, in the last year, AI was considered one of the buzziest buzzwords, listed as a key strategic technology in the next five years, and became an important addition to a variety of business applications. But there are many different ways that AI exists and many different applications that it serves.
Today, we’re looking to break down AI, looking at current applications that may exist and explore how these may be used to help businesses. To help ease your fears about AI and explore this in a rational way, we’re limiting our terms to the intelligent assistant category and exploring some of the use cases that can be or already have been applied.
AI in Business: Real-World Uses of AI Today
Compared to its theoretical uses in the next few decades, today’s AI is still reasonably immature. Perfect for repetitive tasks, in-depth analysis, and automation, AI can learn, adapt, and evolve with the input given. However, it will still require training. Can it benefit your business? Of course. Will ignoring it leave your business at a disadvantage? Yes.
From chatbots to analysis, AI is best used where people aren’t. In the words of 7-Eleven EVP and Chief Digital Officer Gurmeet Singh, “If you have a lot of data, and you are making lots of daily decisions with that data, then the impact of AI is huge.”
Essentially, if the tasks are repetitive, error-prone, and task-based, today’s AI shines. That said, it won’t just be a task-based automation platform forever. So, what types of AI can you expect in your organization? According to Harvard Business Review, these seven use cases have already become a reality.
Robotic Process Automation: The Early Stages
Likely the most introductory AI you will use (though some may doubt if this constitutes AI), robotic process automation represents the automation of basic tasks using business logic and rules. As the entirety of RPA is based on strict rules and there is no learning, the jury is out on whether it actually constitutes AI. According to the Enterpriser’s Project:
RPA can do a great job of handling repetitive, rules-based tasks that would previously have required human effort, but it doesn’t learn as it goes like, say, a deep neural network. If something changes in the automated task – a field in a web form moves, for example – the RPA bot typically won’t be able to figure that out on its own.
So where can you use RPA? Nearly anywhere you need repeatable tasks automated. In fact, many ERP products have incorporated RPA into their products long before announcing they added an artificial intelligence layer. Things like invoice processing, claims processing, and so much more have benefitted from the introduction of RPA, and its use will only continue to grow. Check out this article from AI Multiple for 61 use cases of RPA.
Machine Learning: AI That Evolves
The conventional view of machines is they do what they’re programmed to do. ML disrupts this notion, taking data analysis to a new level. A branch of artificial intelligence (AI), it automates analytical model building so that machine systems can learn from data they receive.
In Machine Learning, data—often in very large amounts—is fed into an algorithm so the algorithm can train itself and learn.
There are many use cases for ML technology. We’ve discussed how you can pair ML with data generated by Internet of Things devices, but with its ability to learn, this represents one of the most important ways to use AI to understand inputs and adapt.
Deep Learning: The Next Level of ML
While ML technology is often based on the algorithms provided, deep learning is the next iteration. In deep learning, there are more layers than limited algorithm-based improvement. Deep learning relies on artificial neural networks.
The learning process is deep because the structure of artificial neural networks consists of multiple input, output, and hidden layers. Each layer contains units that transform the input data into information that the next layer can use for a certain predictive task. Thanks to this structure, a machine can learn through its own data processing.
This will rely on much more data, much more processing power, and requires much more training. After training, the outputs will be much more complex. Learn how Deep Learning differs from ML here.
Computer Vision: Teaching AI to Understand Visuals
Computer Vision is defined by HBR as “A field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images and deep learning models, machines can accurately identify and classify objects—and then react to what they “see.””
The use cases for this are among the most mature, yet also present reasonably intriguing long-term uses. Computer vision is already successful for simple things. Google Lens has been evolving for the last decade to identify items you snap with a camera.
The business cases are already established. It could be something as simple as automating the processing of optical character recognition—teaching AI to understand different receipts or invoices and route it to the right person. As it evolves, computer vision presents incredible uses in manufacturing, healthcare, and retail. Amazon Go is already seeing traction.
Long-term, and with the right inputs, computer vision will deliver unmatched QA testing that’s more reliable and detailed than a human. Healthcare serves to benefit, and of course, autonomous cars. Learn more about the growth of Computer Vision from InData Labs.
Natural Language Processing: Robot Support
Natural Language Processing (NLP) is the study and application of techniques and tools that enable computers to process, analyze, interpret, and reason about human language.
Another reasonably mature AI category with a huge amount of room for growth, NLP still needs to derive meaning and context from human language. Think of it like this. Your smart speaker can add something to your shopping list, but the conversations you’d have with Alexa are limited. You could likely find the right document with the help of a chatbot or get assistance finding the right item of clothing, but more detailed requests often need clarification.
According to Unite.ai, Natural Language Processing involves the application of various algorithms capable of taking unstructured data and converting it into structured data. If these algorithms are applied in the wrong manner, the computer will often fail to derive the correct meaning from the text.
As this evolves, chatbots will be able to detect sentiment and semantics, using the inputs to create truly unique and personalized dialog. Learn about some of the underlying terms that go into NLP here.
One of the biggest drivers of fear in AI is its lack of empathy. In the same sense as our paperclip making robot that causes the apocalypse because ‘making paperclips’ was its job, it’s vital to understand how AI came to any conclusion it made so that corrective action can be taken before it’s too late.
AI regularly operates in a black box of existence. While an AI black box is fine in above-mentioned workflows like a chatbot finding the right department or analyzing the sentiment of a social feed, when AI is in charge of life and death, it needs to explain how it came up with the decision. Not only will this help fix errors in the learning, it will help us to understand the most efficient path to a decision.
One of the biggest places explainable AI will come into place is autonomous vehicles. What inputs caused the car to swerve? As defined by HBR, explainable AI is Machine learning techniques that make it possible for human users to understand, appropriately trust, and effectively manage AI.
In simple terms, explainable AI is akin to showing your work in a math problem. What inputs resulted in what outputs? Why did the AI come to the conclusion? This is the simple way of looking at explainable AI.
AI Operations (AIOps)
IT operations has evolved. But what’s next? Will IT experience a renaissance in the wake of digital transformation? Yes. IT departments, while often facilitating transformations of their own, have an opportunity to do more. Enter AIOps, Artificial Intelligence for IT Operations.
Artificial Intelligence for IT Operations describes the paradigm shift required to handle digital transformation in IT Operations. According to Gartner and BMC, AIOps refers to multi-layered technology platforms that automate and enhance IT operations by
- using analytics and machine learning to analyze big data collected from various IT operations tools and devices, in order to
- automatically spot and react to issues in real time.
As IT management becomes more complicated, laborious, and time consuming, AIOps will fit into the equation to make everyone’s job easier. Though many examples exist, one area would be cybersecurity. How will a company stand up to smarter threats? Humans can only do so much, but with AIOps, threat vectors can be scanned more efficiently. Learn more about AIOps here.
Business Is Moving Faster, Working Smarter, and Becoming More Competitive
As AI improves in maturity and grows, you can expect it to have a much larger footprint. Regardless of where you start, it pays to get started. We invite you to learn more about evolving competitively by reading our most recent guides