Recent advances in artificial intelligence (AI) have made the technology more accessible to everyday use, pushing AI to the forefront in nearly every industry. Executives want to know how they can use AI to optimize and streamline their operations, grow their business, and increase their bottom line. Employees want to know how AI can make their jobs easier.
But as AI grows in popularity, so do the misconceptions about what it is and what it is.
This is a common topic for organizational leaders. They want to be able to articulate the key differences between AI, Machine Learning (ML), and Data Science (DS). However, sometimes they don’t understand the nuances of each, and they struggle to strategize their approach to salaries, departments, where to allocate resources, etc.
Software-as-a-Service (SaaS) and e-commerce companies have been specifically advised to focus on their AI strategy, but have not been told why or exactly what that means.
Understanding the complexity of the task you are trying to accomplish will determine where your company needs to invest. It would be helpful to briefly outline the main differences in each of these areas and provide better context on how they are best utilized.
So let’s look at all three through the lens of customer service and customer experience.
artificial intelligence
AI enables machines to perform tasks, perform problem-solving activities, and find creative solutions that humans would otherwise do.
Humans were once expected to create reports and analyze funnels and metrics, but now AI extracts the most important information that drives the business forward. Instead of analyzing high-level metrics about channels, AI analyzes billions of data points to identify key customer profiles and channels in which businesses should invest.
[ Also read How artificial intelligence can inform decision-making. ]
AI can not only synthesize consumer patterns to identify small problems before they become big ones, but also predict customer needs and wants. For example, AI could warn a fitness product company that his WiFi on the treadmill went out while running uphill, or that in a particular city his shop closed an hour too early and lost significant revenue. You can notify your grocery store chain that
AI provides a level of detail that enables product managers and customer service executives to react and resolve issues 6-7 times faster. This provides a more efficient customer experience, significantly improves customer loyalty, and has an immediate and significant positive impact on a company’s bottom line. And in the future, AI will inform supply chain and revenue decisions based on customer behavior.
These insights apply to all customer-facing teams. Sales use them. Revenue operations use them. Everyone involved with a customer relies on AI predictive modeling.
machine learning
Machine learning is the tuning of AI methods to create, solve, and do what humans used to do. ML enables computer systems and machines to learn patterns and classifications from data without human input. There is human supervision, but no human involvement in the process or perception. ML can listen to the voice of the customer, eliminating the need for humans to manually synthesize, tag, and label the voice of customer data.
Machine learning helps businesses differentiate customer segments based on purchasing behavior, demographics, and other factors. This technology can capture this information and target customers more effectively through personalized experiences. Product recommendations are one of the most common uses he has.
Ecommerce companies use machine learning to push accounting algorithms to help customers shop faster and more efficiently. Evaluate machine learning when an online store offers you an item you didn’t know you needed but suddenly had to buy. The machine analyzed previous purchase decisions and “know” that you were likely to add another item to your cart, increasing the value of your visit.
Machine learning will eventually move away from the customer experience perspective as AI eliminates static data for actionable recommendations.
Machine learning also helps detect fraud, recognizing patterns and stopping breaches before they occur, improving security for businesses and customers.
data science
Data science is an interdisciplinary field that uses statistical and computational techniques to extract insights and knowledge from data. This includes tasks such as cleaning, transforming, visualizing, and analyzing data to uncover patterns, relationships, and trends.
Data science includes both descriptive statistics, such as summarizing data and computing probabilities, and inferential statistics, making predictions and inferences about populations based on samples.
Should you use AI, ML, or data science?
To decide whether your company should rely on AI, ML, or data science, focus on one principle first. It identifies the most important tasks that need to be solved and lets it guide you.
For simple, straightforward tasks, data science is often the answer. If you’re looking for a more sophisticated approach to improving the customer experience, explore machine learning options. But if the goal is to truly anticipate customer needs and future consumer behavior, or automate customer service operations, AI will do the best for your company.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
