Whether you’re thumbs up or down on artificial intelligence, it’s here to stay, and it’s here to change how we do business. At FortressIQ we are big advocates of using AI for what it’s good at, and alternatively, having humans focus on what they’re good at. Implementing AI effectively gives workers the time to spend on those job functions where AI cannot add value and can increase employee productivity and satisfaction.
Using AI technology to enable better, more effective business outcomes all sounds great but where do you start? This AI mega trend means that business executives (and other non-technical roles) are expected to evaluate and make decisions on where to implement AI in the workplace. For many employees it’s a task just to decipher the jargon, what it all means, and how it can be used to address digital transformation initiatives.
AI technology addresses 2 key areas in the enterprise:
- How to make sense of the mountains of data collected
- How to make better decisions based on that data collection
Your current systems – as well as your people – have a lot of knowledge on current processes, customers, suppliers, etc. As businesses expand, the data explosion continues. To enable better decision making through data-driven insights, a few different AI technologies can be deployed, each with a different attribute to address these challenges.
- Computer Vision
- Machine Learning
- Deep Learning
- Natural Language Processing
Computer vision provides the ability for a computer to gain a high-level understanding of digital images and videos so that machine can then recognize and make decisions based on the set of images produced. The technology has grown to include facial recognition and the identification of objects such as traffic signals, stop signs, and pedestrians.
Computer vision is used in the automotive industry to create anti-collision detection technology for better vehicle safety. It’s also very popular in healthcare to improve patient diagnoses through enhanced detection on MRI, X-ray and other scanned images. In finance departments, it can quickly identify and process invoices, improve cash flow, and build better relationships with vendors and suppliers.
While machine learning focuses more on making sense of a large amount of data, computer vision and deep learning technologies are focused on training a computer to be able to understand its environment and make decisions similar to a human brain.
Machine learning is the ability to create meaning from mountains of data. In business, this is often referred to as data mining. Machine learning technology can rapidly make inferences from a large amount of data, whereas if a human performed the same task it could take them thousands of hours. This field of computer science gives the computer the ability to learn without being explicitly programmed.
Companies can use machine learning to accomplish anything from targeted marketing to revenue forecasting. For example, online advertising companies use aggregate user data collected from companies like Google, Twitter, and Facebook to serve up targeted ads to people identified as more likely to purchase. Credit card companies can use machine learning to quickly process thousands of applications and monitor user purchase and payment history to serve up offers such as a credit limit increase.
Deep learning technology, a subset of machine learning, uses algorithms to learn in a supervised or unsupervised manner; the algorithm does not need to be task-specific. For example, it can be used to classify a large data set or identify and analyze patterns within that data. It can then use those patterns to predict possible outcomes. In business this is often referred to as predictive analytics. In short, deep learning replaces the traditional intuitive aspect of decision making with more data-driven decision making.
In a supply chain scenario, deep learning can be used to reduce the number of product modeling scenarios, and laser-focus on those models that will drive the most revenue. In finance departments a scanned invoice with an abnormally high dollar amount listed will be flagged as an error and automatically sent for review.
A system that can process data faster than a human, while simultaneously learning and applying that knowledge, can increase the overall productivity of an organization and reduce risk. And in the example above, when the task is finance related it could result in quicker revenue recognition.
Natural Language Processing
Natural language processing is the ability of a computer program to understand language as it is spoken. Natural language processing can be used when the text is provided. When text is produced, the computer will use algorithms designed to extract meaning associated with phrases and sentences and then collect essential data from them.
Although very intuitive to humans, aspects of natural language processing can be difficult to implement properly and haven’t been fully resolved. Sarcasm is a good example here – most humans can identify sarcasm immediately, but a computer or chat bot has a difficult time.
When big social media campaigns are launched, natural language processing can be used to track trends and customers’ pulse in real time, and campaign interactions can be addressed directly and be personalized, a critical element to successful brand marketing.
Until very recently, these more sophisticated embodiments of artificial intelligence have mainly been used for academic and scholarly research. Organizational efforts to stay competitive and remain a market leader (such as the race to build the best self-driving car) has forged a quantum leap in AI technologies, making them tangible and cost effective for the enterprise.
Even knowing the basic differences is a good starting point for researching where these technologies might be applicable for your organization. For additional information, check out our on-demand webinar “AI in Business: When and Where to use Artificial Intelligence in Your Organization.”