Chances are, a bot is managing your investment portfolio right now. Investment companies develop proprietary algorithms to analyze the behavior of markets, and make predictions for future gains and losses. Computers can store and analyze millions of data inputs, using a process called data mining. With a massive amount of data points, a neural network seeks to find patterns and correlations, grouping, storing, and classifying the data, and making predictions about the future. Finally, as more data is gathered that reflects the accuracy of those predictions, machine learning applies the new data to update and revise those predictions, becoming more accurate over time. These combined, recursive processes: gathering large data sets, analyzing them for patterns and making predictions, and revising those predictions to make them more accurate over time, is collectively called artificial intelligence, and investment firms use it every day, to make trades executed by bots (software program(s) that execute various functions with differing degrees of autonomy).
The stock market loves AI and bots for several reasons:
- the amount of data that can be processed is vaster than any human brain or group of brains could store, recall, and manage.
- once the initial rules and algorithms are in place, the system can operate with little human management or time investment
- bots can execute tasks in milliseconds, much faster than a human could do them
- these systems can operate 24/7 and never need rest
Other industries have been slower to adopt AI, but in recent years these technologies are becoming more useful in an ever-wider array of applications. Today, the average person can see AI applications in digital assistants, image and voice recognition, and automated vehicles. On a larger scale, AI is being used in weather prediction, healthcare and epidemic mapping, and manufacturing and transportation of goods around the world.
In recruiting and HR, AI is becoming an important aspect of talent management. Many companies have to contend with large data sets of existing and former employees, as well as applicants for current and past positions, and tracking events and communication throughout the talent ecosystem. Most companies manage this work with a combination of intensive human time and effort (it can take up to 23 hours to screen resumes for a single position), as well as technological investment in maintaining different sets of lists, records, and datapoints.
Not only is HR and recruiting incredibly data-intensive work, but it poses special human challenges:
- Employees and candidates need to be treated respectfully throughout their contact with the company
- All inquiries should receive a fast, accurate response
- Employees and candidates should be treated fairly and impartially
- But employees and candidates also want a warm, human touch in their dealings with the company
Intelligent data systems help companies deliver on all the softer, human aspects of talent management, by automating what can be automated and executing tasks swiftly, reliably, and impartially. This frees up HR staff, creating the necessary time for relationship building and problem-solving on an individual level.
Today, companies are using AI in a variety of ways to augment their recruitment processes, from chatbots that interactively guide candidates through the application process, software that analyzes job listings against keywords on resumes or LinkedIn profiles to suggest matches, and shortening the hiring window.
Finally, everyone wants to know if artificial intelligence will take our jobs. The answer is that AI will transform our jobs, just as social media, mobile devices, and the internet itself transformed work as we know it today. Some jobs will become obsolete, while at the same time we will discover whole new industries and opportunities. What we know right now is that we can use AI to automate work that is rule-based and repetitive, and free up human intelligence to do work that is more important, impactful, and meaningful. Contact us today to find out how.
No Comments Yet
Let us know what you think