A few years ago, when AI was a more novel concept, the conversation often centered around whether or not this technology would cause members of the workforce to become redundant en masse. In other words, AI was seen somewhat fearfully as a potential way to automate and replace humans, rather than a technology capable of improving existing workflows — often by handling tedious or repetitive tasks, which actually frees up employees to focus on more strategic and creative endeavors.
It’s now become clear there’s room for both, and that effectively implementing AI can help companies streamline costs, optimize performance and enhance customer interactions. There’s still a lot of potential here, though. A McKinsey & Co. study found that as of late 2018, a little under half of organizations say they’ve embedded at least one usage of AI into their standard business practices, but only 21 percent have embedded it into multiple functions.
There are many ways enterprises can harness artificial intelligence to improve business outcomes. Here are three to consider.
To Optimize & Personalize Customer Service
As you can imagine, the most resource-heavy way to interface with customers is requiring human customer service teams to personally address every inquiry. While this approach may work for small businesses, enterprises need a more effective strategy — something that allows them to address a high volume of customer service needs in the most responsive and cost-effective manner.
Many enterprises have found success with virtual, AI-powered customer service assistants in the form of online messaging chatbots and/or extensions for smart home speakers. Chatbots are able to address first-wave customer inquiries with no queue time and direct customers toward human customer service teams when needed.
As Chatbots Journal outlines, five banks recently implemented chatbots for customer service:
Swedbank: Aiming to address decades-low customer satisfaction rates with Swedish banks, Swedbank introduced “Nina” to handle service calls — freeing up their agents to spend time on more valuable calls.
Bank of America: “Erica” helps customers with simple transactions like paying down debt and checking the status of their accounts. Erica also offers advice via text message like, “Based on your typical monthly spending, you have an additional $150 you can be putting towards your cash rewards Visa. This can save you up to $300 per year.”
Capital One: A gender neutral, automated chatbot named “Eno” provides account information and helps customers make credit card payments from mobile devices.
SEB: SEB uses two chatbots, “Amelia” for employees and “Aida” for customers. Amelia held 4,000 conversations with 700 employees within just three weeks, many of which were IT requests.
Wells Fargo: A nameless chatbot uses AI and natural language processing to address customer inquiries, like how much money they have in their accounts or where they can find the nearest ATM. It can also respond to simple questions about transactions.
As customers appreciate — and increasingly expect — immediate answers, AI-powered chatbots are a great way to avoid delays and free up human specialists for more complex inquiries.
To Automate Data Analytics Insights
The degree to which enterprises are data-driven in their decision making is increasingly a competitive differentiator. While self-service search analytics are a fundamental part of any strategy, data-forward companies are deploying AI-driven advanced analytics.
These powerful AI engines, like SpotIQ from ThoughtSpot, can mine billions of data points for insights previously invisible to the naked eye — trends, anomalies, business drivers and similarities/differences between data sets. Far from replacing human analysts, this approach frees up specialists to work on higher-order tasks because it can deep dive into data automatically and consistently, then push its findings to humans.
Here’s one example of AI data analytics powered by machine learning, as outlined by Business News Daily: Manufacturing machinery is connected to the network through Internet of Things-powered sensors, feeding “a constant stream of data about functionality, production and more to a central location.” But it’s too much data for humans to sift through manually. So, an AI analytics platform with machine learning can quickly analyze the data, picking out patterns and anomalies as they appear. If a machine is working below capacity or otherwise malfunctioning, the algorithm will catch this fact and push it to human decision-makers who can address the issue.
To Minimize Fraud & Cybersecurity Breaches
AI and machine learning are also coming in handy for cybersecurity, with their abilities to analyze vast collections of data quickly to identify potential threats — like malware or phishing links clicked from employee accounts. Given the potential damage of data breaches and malicious cyberattacks, businesses are looking for technology that will help them identify possible intrusions or instances of fraud and conquer them as soon as possible.
Enterprises can, and should, set their sights on harnessing artificial intelligence for tasks like improving customer service, automating data analysis and addressing cybersecurity concerns.