- November 29, 2018
- Posted by: admin
- Category: Uncategorized
AI Embed- How might your everyday working life change if you have artificial intelligence and machine learning? Here’s one very simple example.
Consider an employee who normally fills out his weekly time card on Thursday afternoon, because he doesn’t work most Fridays. Machine learning that’s built into a payroll application could help the app learn the individual working habits of each employee. Having learned this specific pattern, the app could ask him if he meant to fill out the time card when he goes to log out of the system Thursday. There’s no policy there: It’s a behavior pattern that machine learning can pick up on.
In fact, modern-day AI might be able to fill in the time card automatically, and present it to the employee for review and approval, saving even more time, and potentially eliminating errors. This capability, known as “auto defaulting,” could have applications for nearly every form-based application, from accounting to inventory to sales reporting, says Clive Swan, Oracle Adaptive Intelligent Apps senior vice president of product development,
Executives wrestle with how to take advantage of artificial intelligence capabilities, now that cloud computing resources have made the technology accessible to companies of all sizes. One of the fastest roads to AI payoff comes from using AI capabilities embedded in applications that your employees use every day—like that time card app. Below is a framework to think about your AI strategy, plus examples of how AI embedded in your apps can help your teams in supply chain, customer service, HR, and more.
Three AI Capability Buckets
Smart classification, smart recognition, and smart predictions—those are three big buckets that encompass many cutting-edge AI and machine learning capabilities, explains Swan.
Smart classification involves studying both structured and unstructured data to take action based on what it means, such as to automatically identity unreliable suppliers, properly interpret complex invoices, and categorize consumers based on their current activities and past history.
Smart recognition looks to find anomalies in the data to find innocent errors—not-so-innocent errors. Smart recognition can help stop fraud, enforce corporate and compliance policies, and even speed financial reconciliations.
Smart predictions go farther, such as offering proactive advice to sales reps, making recommendations in e-commerce, or providing suggestions for service reps on how to direct a customer. Pattern-matching can come into play here, such as predicting which add-on product recommendation a customer’s most likely to buy.
A key element of application-centric AI: Context. Say a sales executive wants to call on important customers in several cities. AI can review the accounts and predict which customers might increase business after a sales call, based on past history, and suggest an itinerary that would maximize ROI from the trip.
One common factor in all those buckets is that integrating AI and machine learning into applications lets the app take some type of action automatically. Automation allows many tasks to be performed without human intervention—and without human error, says Swan.
AI systems can execute relatively straightforward actions, such as booking a rental car for that sales trip. They can also tackle harder tasks that normally require not only time, but also some level of expertise, such as optimizing business workflows, reviewing financials for anomalies, or finding expense report violations. Often there’s still a human review, but that review can often be done faster, and more accurately, with the AI’s assistance in laying all the groundwork, presenting recommendations, and providing the background, documentation, and reasoning behind those recommendations.
Read More Here
Article Credit: Forbes
The post Want A Bigger Bang From AI? Embed It Into Your Apps appeared first on erpinnews.