Consumers have embraced artificial intelligence (AI) as part of everyday life. They’ve asked virtual assistant Siri to remind them to buy milk or chatted with an AI-powered bot on a customer service line.
Now, AI is moving into the back office.
Corporations are expected to triple their investment in AI to $100 billion by 20251. Many are exploring and implementing applications to save time, cut costs, and improve operations. From identifying fraud to generating reports for investors, many day-to-day operations could be automated with AI technologies.
Big data powers new applications of AI
AI technology has been around for decades. However, the growing availability of extremely large data sets, also known as “big data,” is now enabling AI capabilities that could make business processes more efficient2.
The term “AI” is often used to describe several technologies that could improve operational efficiencies and other business needs3. These technologies include:
- Robotic process automation, which can automate repetitive, high-volume tasks.
- Machine learning, which uses large quantities of data to train systems. Deep learning is a subset of machine learning that uses layers of algorithms to draw conclusions from data similar to the way that humans do4.
- Natural language processing, which relies on machine learning algorithms to help computers understand human languages5.
Four ways businesses are using AI to boost efficiency
Businesses are experimenting with a variety of AI technologies to improve internal business functions, from finance to human resources. Here are a few ways companies are using AI to save time and cut costs:
- Assisting customers and employees. Customer service is one of the best-known applications of AI, with companies using technologies to make it easier for employees to find information and do their jobs. Pinterest, for example, uses a Zendesk application that relies on machine learning and deep learning to predict potentially unsatisfied customers and escalate their cases6. A few companies are developing chatbots to help HR departments answer questions employees frequently ask or help generate interview questions and survey job candidates7.
- Automating repetitive financial tasks. According to the consulting firm PwC, “micro” robotic process automation (RPA) can handle defined, brief, repetitive tasks completed by humans8. Some tasks that micro-RPA could automate include sourcing and formatting data for pricing research, processing intermittent transactions like account-opening requests, and performing simple reconciliations. Automating these small tasks could add up to significant time-savings for businesses.
- Generating reports. Natural language processing could help financial businesses save time assembling relatively complex reports, such as reports for the board or investor relations9.
- Identifying fraud and misconduct. Organizations are harnessing the power of AI to recognize patterns in data to detect unusual activity and fraud. The U.S. Securities and Exchange Commission (SEC) uses machine learning methods to identify patterns, trends, or language that could indicate fraud or misconduct10. PayPal’s fraud detection program relies on a combination of AI technologies and human detectives11.
What’s next for businesses and workers?
Businesses still face a number of challenges to integrating AI into operational processes. For AI to succeed, companies must have a large volume of high-quality data to train algorithms1. They’ll also need to ensure technology aligns with business priorities.
Additionally, companies will need to consider the cultural implications of AI. Although technology has the potential to eliminate some monotonous tasks for employees, it can also create anxiety about the future of their roles. Addressing the human factors and encouraging employees to contribute to the success of AI initiatives will be critical.
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2. “How Big Data Is Empowering AI and Machine Learning at Scale,” by Randy Bean, MIT Sloan Management Review, May 8, 2017.
3. “Artificial Intelligence in Capital Markets: The Next Operational Revolution,” by Axel Pierron, Opimas Research, March 1, 2017.
4. “A Simple Way to Understand Machine Learning vs. Deep Learning,” by Brett Gosfield, Zendesk, July 18, 2017.
5. “Introduction to Natural Language Processing (NLP),” Matt Kiser, Algorithmia, August 11, 2016.
6. “Satisfaction Prediction,” Zendesk: https://www.zendesk.com/explore/satisfaction-prediction/.
7. “The Future Of Work: The Intersection Of Artificial Intelligence And Human Resources,” by Jane Meister, Forbes, March 1, 2017.
8. “Automating the small stuff: How micro-RPA is helping to redefine financial services work,” Kevin Kroen, PwC, April 3, 2017.
9. “Emerging Technologies and the Finance Function,” AFP Mindshift, October 13, 2017.
10. “The Role of Big Data, Machine Learning, and AI in Assessing Risks: a Regulatory Perspective,” Scott W. Bauguess, OpRisk North America Keynote, June 21, 2017.
11. “How PayPal Is Taking a Chance on AI to Fight Fraud,” by Penny Crosman, American Banker, September 21, 2016.