Recent advancements in artificial intelligence (AI) have made the technology more accessible in everyday use, thrusting AI to the forefront in nearly every industry. Executives want to know how they can use AI to optimize and streamline operations, grow their businesses, and increase revenue. Employees want to know how AI can make their jobs easier.
But as AI rises in popularity, so does misunderstanding of what AI is and what it is not.
It's a common topic for organizational leaders—they want to be able to articulate the core differences between AI, machine learning (ML), and data science (DS). However, sometimes they do not understand the nuances of each and thus struggle to strategize their approach to things such as salaries, departments, and where they should allocate their resources.
Software-as-a-Service (SaaS) and e-commerce companies specifically are being advised to focus on an AI strategy without being told why or what that means exactly.
Understanding the complexity of the tasks you aim to accomplish will determine where your company needs to invest. It is helpful to quickly outline the core differences between each of these areas and give better context to how they are best utilized.
So let’s look at all three through the lens of customer service and customer experience—because at the end of the day, a satisfied customer drives all our businesses.
AI enables machines to carry out tasks, perform problem-solving activities, and find creative solutions that a human would otherwise be tasked with doing.
In the past, humans were expected to build reports and analyze funnels and metrics, but now AI is extracting the most critical information that drives businesses forward. Instead of analyzing high-level metrics regarding channels, AI analyzes billions of data points and identifies the core customer profiles and channels a business should invest in.
[ Also read How artificial intelligence can inform decision-making. ]
AI can synthesize consumer patterns to not only identify small issues before they become big problems but it can also predict customer needs and wants. AI can alert a fitness product company that its treadmill's WiFi disconnects during uphill runs, for example, or it could inform a grocery chain that it is losing significant revenue in a particular city by closing an hour too early.
AI delivers a level of detail that allows product managers and customer service executives to react 6 to 7 times faster to fix problems. This provides a more efficient customer experience and generates significant customer loyalty, which 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.
Those insights apply to every customer-facing team: Sales will use them. Revenue operations will use them. Everyone who has anything to do with the customer will rely on the AI’s prediction modeling.
Machine learning is the coordination of AI methods to create, solve, and do things humans used to do. ML enables computer systems and machines to learn patterns and classifications from the data without any human input. While there is human supervision, a person is not involved in the process or recognition. ML can listen to the customer, so humans do not need to manually synthesize, tag, and label the voice of customer data.
Machine learning can help companies differentiate customer segments based on their purchasing behavior, demographics, and other factors. The technology can take this information and target customers more effectively through personalized experiences, with product recommendations being one of the most common utilizations.
E-commerce companies use machine learning to push fiscal algorithms that help customers shop more quickly and efficiently. Credit machine learning when an online shop serves you a product you did not know you needed but that you suddenly have to buy. The machine analyzed your previous purchasing decisions and “knew” you were likely to add another item to your cart, increasing the value of your visit.
Machine learning will eventually step aside from a customer experience point of view as AI eliminates static data for actionable recommendations.
Machine learning is also useful in fraud detection, recognizing patterns to stop breaches before they happen and thus increasing company and customer security.
Data science is a multidisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. It encompasses tasks such as data cleaning, transformation, visualization, and analysis to uncover patterns, relationships, and trends.
Data science can involve both descriptive statistics, such as summarizing data and calculating probabilities, as well as inferential statistics, which involve making predictions or inferences about a population based on a sample.
Which should you use: AI, ML, or data science?
To decide whether your company needs to rely on AI, ML, or data science, focus on one principle to begin: Identify the most important tasks you need to solve and let that be your guide.
For simple, straightforward tasks, data science is often the answer. If you are looking for a more sophisticated approach to improve customer experience, investigate machine learning options. But if your goal is to truly predict customer needs and future consumer behavior or to automate your customer service operations, then AI will deliver the best results for your company.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
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