The Three Components of Real-Time Artificial Intelligence Decision-Making
Data by itself doesn’t have any real business value. A growing number of innovative companies are turning to artificial intelligence to unlock the full potential of big data.
The advent of big data has unlocked a vast library of new business insights for enterprises. In the past, companies had only their own performance record to inform major business decisions. Now, they can collect virtually limitless customer and user data from a number of sources.
But data by itself doesn’t provide significant business value — that’s why so many companies are turning to artificial intelligence tools to process, organize, and analyze mountains of collected information. By attaching operational machine learning to large data sets, companies are empowered to optimize their data resources, solve complex business issues, and inform ongoing business decisions. Data Informed recently unpacked the three core AI capabilities that support real-time decision making: the decision service, the learning service, and the decision management interface.
Three Core Artificial Intelligence Capabilities
First, every AI decision-making platform must contain a decision service — or rules engine — that determines an array of possible outcomes. Decision services isolate the logic behind business decisions, separating it from business processes. It accepts decision requests, applies business filters to decision sets, determines outcomes through predictive analytics, and returns optimized results back to the business process.
Learning services, by contrast, improve the AI’s statistical predictions and categorizations through machine or statistical learning. Essentially, the learning service allows the AI to analyze its past decisions and further optimize future moves based on previous results.
Lastly, the platform also requires a decision management interface. According to Data Informed, this interface “allows [the] business to define and update a decision set and/or decision set metadata, define business rules, and define a segmented decision-making strategy that includes rules, predictive analytics, and other key decision metrics.” The goal of the interface is to improve business outcomes by increasing precision, consistency, and agility of decisions.
Artificial Intelligence in Practice
Many financial firms already employ AI systems to handle a number of complex decision-making processes — from trading to loan underwriting. Since the late 70s, investors have utilized algorithmic trading, also called automated trading systems, to inform their decisions on when to buy and sell stocks. Much has changed in the last four decades, but these systems still combine predictive analytics and machine learning to handle thousands of trades every day in a process called high-frequency trading.
AI has also found its way into medical facilities worldwide. As more healthcare providers transition to digital record-keeping, the amount of medical data available for analysis has grown exponentially. Doctors now leverage data and artificial intelligence to inform patient diagnosis, prognosis, and treatment options. Memorial Sloan Kettering’s oncology department recently used AI to merge their data on cancer patients in order to narrow down a limitless list of potential treatments to a few good options for clinicians everywhere.
Artificial Intelligence in Marketing
Marketers have also found ways to leverage predictive analytics and AI-based decision-making platforms to craft high-impact advertising campaigns across dozens of digital channels. Albert™, the world’s first AI-driven marketing platform, uses AI principles to autonomously purchase media, target specific audiences, and optimize campaigns across both paid and unpaid channels in real-time. Rather than crunching numbers — or even worse, outright guessing — advertisers simply set rules and parameters for Albert to follow, and he does the rest.
No human could ever parse through the large amounts of information modern companies are able to collect. The growth of big data across industries will continue to necessitate advanced artificial intelligence applications to drive valuable results.