3 Key Ways AI is Powering the Modern Enterprise
Artificial Intelligence (AI) has had a massive impact on the way companies do business, and it goes without saying that the technology is here to stay. An article from Forbes stated that Gartner predicts the business value created by AI will reach $3.9 trillion dollars in 2022. Meanwhile, IDC forecasts worldwide spending on cognitive and AI systems will reach $77.6 billion dollars in 2022.
AI technologies serve an integral role in helping businesses collect and analyze the extremely high volumes of data they’re producing. And the quantity of data being generated is only growing, with IDC predicting a ten-fold increase by 2025. This data provides companies with a unique opportunity to act on the insights they produce, but only if they adopt AI strategies within the enterprise as part of their digital transformation journey.
Here are three key ways Machine Learning (ML), Computer Vision (CV), Deep Learning (DL), and Natural Language Processing (NLP) are powering the modern enterprise and giving adopters a competitive edge.
Machine Learning at Scale
ML is a vital technology for helping organizations derive meaning from their mountains of data. Companies are leveraging ML to decode their data and effectively execute on everything from targeted marketing to revenue forecasting.
For example, online advertising companies are using aggregate user data collected from companies like Google, Twitter, and Facebook to serve up targeted ads to people who are identified as more likely to purchase their products. By the same token, credit card companies are employing ML to quickly process thousands of applications and then monitor user purchase and payment history to spin up offers such as a credit limit increase.
As the amount of data and the use of ML increase, the infrastructure is forced to keep up. Companies are no longer able to rely on a small scale machine learning operation and a handful of data scientists to satiate the appetite for actionable insights and predictions. For example, rideshare giant, Uber, built Michelangelo to manage its AI and ML pipelines as the infrastructure needed to keep up with the demand for insights.
These systems offer unprecedented value by processing data much faster than a human could, while simultaneously learning and applying knowledge. AI is giving companies the ability to quickly understand their data, which in turn enhances the overall productivity of an organization, and reduces risk.
Computer Vision in Production
CV, a subset of DL and AI, is finally making its way into modern business workflows. CV lets computers gain a high-level understanding of digital images and videos so that they can recognize and make decisions based on a given set of images produced. This technology has grown to include facial recognition and the identification of objects such as traffic signals, stop signs, and pedestrians.
CV is being used in the automotive industry to create anti-collision detection technology for better vehicle safety. It’s also proven valuable in healthcare for improving 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.
Intelligence comes from learning and for AI and CV, that “learning” comes from example images that have been annotated to provide ground truths. Just as children are shown and taught what a dog is and what a cat is, AI needs to have that same “experience” as part of its learning process.
The availability of data is what has changed -- we didn’t have labelled data until the mid to late 2000s and then many more came in the 2010s thanks to major common datasets such as Pascal VOC, ImageNet and COCO. All of that learnable data was aggressively consumed and learned by DL neural networks; it’s what is making technologies like autonomous driving a reality.
Automated data-driven decisions
NLP lets computer programs understand language as it is spoken. While processing this spoken language, it uses algorithms designed to extract meaning associated with phrases and sentences, and then collects essential data from them to aid in decision making.
For example, in the context of social media campaigns, NLP may be used to track trends and keep a pulse on customers in real time. This allows businesses to address campaign interactions directly, and even personalize responses at scale—a critical element to successful brand marketing.
NLP can be used in chat bot models, so “smarter” bots can automate the mundane customer service tasks, and free up customer service representatives for more focused, valuable work. The Google Duplex project is a perfect example, with lead engineers stating that, “In particular, automated phone systems are still struggling to recognize simple words and commands. They don’t engage in a conversation flow and force the caller to adjust to the system instead of the system adjusting to the caller.”
Freeing up human capital
Every organization’s most valuable assets are its employees. Keeping them happy, and giving them the tools they need to do their job in the most efficient way possible is key to business success.
Implementing the various technologies discussed above lets employees reallocate their time to strategic tasks where AI cannot add value. Focusing AI technology where it can be the most productive and focusing employees where they can be most impactful often results in an increase of both employee productivity and overall job satisfaction.
Until very recently, these more sophisticated embodiments of AI 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, making it tangible and cost-effective for the enterprise.
As companies continue to acquire more and more data, it’s critical that they reevaluate their digital transformation goals and determine where AI can best support strategic initiatives and surface data-driven insights. AI, coupled with an emphasis on boosting employee productivity and satisfaction, is a recipe for staying on top in today’s highly competitive business landscape.
Ryan Scolnik is the Head of Data Science at FortressIQ where he leads the company's data science, computer vision and AI strategy. Prior to FortressIQ, Ryan was a lead data scientist at 6sense, a predictive analytics company, and senior marketing analyst at JP Morgan Chase. Ryan holds a PhD in Statistics from Florida State University and enjoys Kaggle competitions and long bike rides in his spare time.