Artificial intelligence and the computer automation it can enable are driving powerful changes in business. From fraud detection and social media filters to wearable health sensors and personalized shopping recommendations, artificial intelligence is transforming everyday life and business from the most trivial details to the most impactful. But what is artificial intelligence? And why is it such an effective tool for managing business needs?
What Is Artificial Intelligence?
Artificial intelligence refers to a type of programming that powers most of the "smart" technologies with which people tend to interact today. Standard computer programs are built around set rules, filters and exceptions that allow them to appear intelligent. But none of these programs rely on true intelligence; each of them is just following the rules.
In contrast, artificial intelligence enables computer systems to crunch large sets of data and gives them the ability to learn and change their own behavior based on past experience. Because of its reliance on large sets of data, artificial intelligence is especially useful for pattern-recognition tasks like categorization and prediction.
How Does It Work?
At the heart of artificial intelligence is the algorithm, which can be defined as a set of instructions that tell a computer what to do. A main difference between simple computational tasks (such as the kind performed in a spreadsheet) and artificial intelligence is that artificial intelligence algorithms can rewrite their own instructions. Instead of mindlessly crunching the same computations over and over again, an artificial intelligence algorithm can learn, and refine its predictive accuracy, by feeding exceptions to its predictions through a feedback loop. Also, artificial intelligence most often is deployed on data sets that are much larger than the capacity of a spreadsheet, which can hold barely more than one million rows of data.
What Can Artificial Intelligence Do?
The implications of artificial intelligence are broad. Perhaps you've heard of the concepts of "neural networks" and "deep learning," which are algorithms created to mimic the responses of neurons in the human brain. So, in the same way that the human brain is made up of about 100 billion neuron "switches" that fire in response to certain stimuli, data scientists create synthetic neurons in computer code that respond likewise, creating visual recognition systems that mimic the way human beings actually use their sense of vision to navigate the world of objects.
Technology this advanced starts to sound like science fiction. Computers that can learn are capable of performing some very advanced functions, the kind that currently require human workers. The increasingly ubiquitous "cloud"—a series of networked computers and databases—is such a large set of data that, with advanced enough processing capacity, computers could develop capabilities (on their own) that we can now only dream of. But how can this technology be harnessed in a practical way?
How Artificial Intelligence Impacts Workplace Efficiency
Artificial intelligence isn't just theoretical. It's also a deeply practical way to manage data, and to relieve the burden of automatic tasks from human workers. Here are three ways that artificial intelligence can help a team achieve efficiency and success in less time, with machines picking up some of the labor.
- Visualization and KPI Tracking: Right now, when decisions have to be made in business, human workers can access a database of information and run various computations to help them determine, say, the best way to optimize shipping, or the right time to launch a product. A lot of this work depends on human beings having the ability to access the right data and ask the right questions. Artificial intelligence will help first by turning vast amounts of data into visualizations that can be easily understood by everyone on a team. After that, artificial intelligence algorithms can monitor key metrics to alert managers when those metrics are being threatened by forces that might slip past even the most savvy business thinker.
- Deeper Insights Into Consumer Behavior: Since the dawn of e-commerce, retail giants have been capturing vast amounts of data about their customers, from what they buy, to when they buy it, to what they look at before they click "purchase." Before artificial intelligence, the work of generating sales leads was done by individuals and delivered with little sophistication. With this method, over-intervention and customer churn are significant risks. But with the insight developed by artificial intelligence, customer intervention can be delivered at optimal times and customer churn reduced.
- Maximize Human Labor: Perhaps the most impactful effect of artificial intelligence is freeing up labor hours from the tasks of creating reports and making simple decisions. Though this can have frightening implications for a potential loss of jobs, it's important to remember that for every job lost for someone who used to churn data mindlessly to produce reports, there is a job gained for a data scientist who creates, manages and runs the processes that enable other human workers to focus on higher-level tasks.
The Bottom Line on Artificial Intelligence
Artificial intelligence isn't just good for workers, it's good for the bottom line. It helps streamline inventory management and reduces overhead. Artificial intelligence enables true, real-time measurement of the return on investment in marketing strategies, replacing human guesswork. Logistics can be streamlined by fleet management systems, lowering costs to ship and receive goods. Customer service can be offloaded to chatbots (50 percent of consumers prefer to interact this way, anyway1), freeing employees from phone banks and putting them to work on more complex, intensive tasks. The bottom line on artificial intelligence is that it saves money by eliminating the need for workers to monitor simple minutiae.
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1 Retrieved on February 28, 2018, from forbes.com/sites/forbestechcouncil/2017/05/16/the-economics-of-fashion-retail-improving-the-bottom-line-with-machine-learning/2/#44bb893b5baa