Oct 04, 2023
8 min to read
Discover the inner workings, historical context, and retail implications of Artificial intelligence (AI). Explore the world of hyper-personalized product experiences across digital and physical touchpoints, and learn why starting with top-notch product data is essential for unlocking AI's full potential.
Artificial intelligence (AI)
Artificial Intelligence (AI) has become a buzzword in the world of technology, business, and beyond. From improving customer experiences in retail to transforming the way we interact with machines, AI is making waves across various industries.
But what even is AI? It feels as if we’ve reached a point where businesses are just slapping the phrase “Powered By AI” on anything and everything without any consideration as to what that actually means.
So let’s take a step back from all the noise and hype, and go back to basics by diving into the fundamental aspects of AI, exploring its definition, workings, historical context, and its specific implications for the retail sector.
At its core, Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, understanding natural language, and even recognizing patterns. AI systems are designed to simulate human cognitive functions, making them incredibly versatile.
AI operates on the principle of machine learning (more on this later), where algorithms and models are trained on vast datasets to improve their performance over time. Machine learning involves the following key components:
Data Collection: To begin, AI systems require substantial amounts of enriched and cleansed data, which serve as the foundation for learning. In the retail industry, data can include customer preferences, purchase history, browsing patterns, inventory levels, product information, and more.
Training Models: After collecting and consolidating all of that data, AI models are fed the thousands, or potentially millions, of data points and trained to recognize patterns, make predictions, and optimize processes in the data set through iterative training. This is when your team should be closely monitoring the data ingested by the AI model to minimize biases, inconsistencies, or inaccuracies.
Inference: After the training phase, AI systems can make decisions, predictions, and recommendations based on existing and new data. For instance, eCommerce platforms employ recommendation engines that use AI models to suggest products to customers based on their browsing and purchase history, or based on the purchase patterns of similar customer profiles.
While AI is no magic wand, the significance of AI lies in its ability to revolutionize various aspects of business and society based on enriched, accurate data. Here are some key reasons why AI is important:
Efficiency: AI can automate repetitive tasks, improving efficiency and reducing the risk of errors. AI will not entirely replace human jobs; it’s a collaborator, not a competitor. This technology can help to optimize supply chains or help customer service teams intelligently route inquiries to the right person, but it can’t entirely replace the need for skilled engineers to design and create innovative products or service reps to answer complex problems.
Personalization: Personalization with AI is all about delivering tailored experiences to individual customers based on their preferences, behavior, and historical interactions at scale. It’s a game-changer because it shifts the shopping experience from a one-size-fits-all approach to a highly customized one.
Customer Retention: AI doesn’t just help organizations provide personalized shopping experiences at initial purchase; AI can also be used to keep customers engaged over time by suggesting complementary products, sending relevant updates, and recognizing when a customer is due for a replenishment or replacement.
Predictive Analytics: By analyzing vast amounts of historical data and considering factors such as seasonality, economic indicators, and consumer preferences, retailers can utilize AI to accurately forecast demand for their products. This insight allows them to optimize inventory levels, ensuring that they have the right products in stock at the right times. As a result, retailers can reduce the costs associated with overstocking or understocking, minimize the risk of product shortages, and ultimately enhance their profitability by aligning supply with demand more effectively.
AI has a rich history that dates back to ancient times, but its modern development can be divided into several key phases:
Birth of AI (1950s-1960s): The term “Artificial Intelligence” was coined in the 1950s. During this period, early AI pioneers like Alan Turing, John Von Neumann, and John McCarthy laid the groundwork for AI as a scientific discipline. But during this inception phase, AI’s credibility was already under scrutiny due to its groundbreaking nature.
AI Winter (1970s-1980s): Progress in AI faced setbacks during this period due to high expectations and limited computing power. Funding and interest in AI dwindled, leading to what is known as the “AI winter.”
AI Resurgence (Late 20th Century): The late 20th century saw a resurgence in AI research, driven by advancements in machine learning and neural networks. During this period, artificial intelligence witnessed significant advancements attributed to powerful new computers capable of accelerating algorithmic computations, alongside the advent of the internet and widespread data sharing.
Modern AI (21st Century): AI has transcended the realm of theoretical research and has firmly established itself in mainstream use across a myriad of applications. Autonomous vehicles, one of the most prominent examples, rely heavily on AI technologies like computer vision and machine learning to navigate and make real-time driving decisions. Similarly, virtual assistants such as Siri, Alexa, and Google Assistant have become integral parts of our daily lives, utilizing natural language processing and speech recognition to provide information, perform tasks, and control smart devices.
Beyond these well-known applications, AI is at the heart of numerous other innovations, spanning from healthcare diagnostics and recommendation systems in e-commerce to fraud detection in financial institutions. The 21st-century AI landscape reflects a dynamic and rapidly evolving field that continues to push the boundaries of what is possible in technology, profoundly impacting how we live, work, and interact with the world around us.
Simply put, no.
The slightly more complex answer is that machine learning (ML) is a subset of AI. AI encompasses a broader spectrum of capabilities, including natural language processing, robotics, and expert systems.
Machine learning, on the other hand, focuses on developing algorithms that enable machines to learn from data and improve their performance over time. This means that machine learning systems can adapt and improve their performance as they encounter new information, making them well-suited for tasks like image recognition, speech processing, and data analysis.
In essence, while AI defines the overarching goal of creating intelligent machines, machine learning serves as a pivotal component, enabling these machines to acquire knowledge, make predictions, and solve complex problems by leveraging the power of data-driven learning.
Generative AI is a subset of AI that focuses on creating new content or data, often in the form of images, text, or audio. It uses techniques such as generative adversarial networks (GANs) and reinforcement learning to produce creative outputs.
A prominent real-life example of generative AI is in the field of art and content generation. For instance, AI-powered systems can create artwork, compose music, or even write articles, showcasing the potential for AI to augment creative processes.
As with any powerful technology, it’s risky to view AI through rose-colored glasses. Let’s take a look at a few of the potential risks that come with implementing AI technology.
Differentiation: A strong brand identity creates an emotional connection with customers. But as AI continues to grow in popularity, we run the risk of creating a sea of template-based, algorithm-generated content. Maintaining a distinctive brand identity and voice becomes an even more crucial factor as content generation becomes more automated.
Biases & Liability: Artificial intelligence relies on large datasets. If these training datasets contain biases or inaccuracies, the model can learn and perpetuate those biases. In the context of global brands and retailers, this can result in inaccurate, discriminatory, or unfair outcomes in various aspects of operations, from product recommendations to legal compliance and more.
Privacy & Security: Collecting and analyzing customer data raises privacy and security concerns. Brands must implement robust cybersecurity measures to protect sensitive customer information from data breaches or cyberattacks, and be transparent with customers about the types of data they collect, why it’s collected, and how it will be used.
Technical Challenges: There’s no denying that AI is a complex technology, and implementing an AI solution into your tech stack is no small feat. You need to ensure that you have the right folks on your team to prepare your internal teams for adoption, and you need to ensure that you have the right technology in place to integrate smoothly with the new solution and seamlessly communicate product information.
Customer Resistance: Some customers may perceive AI-driven interactions as impersonal and devoid of the human touch, or they can sometimes feel like they’re losing control over their interactions with others. It’s important to acknowledge this hesitation and alway consider the customer’s perspective when implementing new technology, ensuring that your technology is enhancing your team’s work and not overpowering.
For a full breakdown of all of the risks and rewards associated with implementing AI technology, you can download our in-depth infographic on the subject here.
AI has already infiltrated the retail market, and will continue to do so over the next several decades. Let’s take a look at a few of the ways AI can be harnessed by brands to improve the customer experience and impact the retail industry.
Data Cleansing & Enrichment: AI algorithms can standardize product data by enforcing consistent formats, categories, and naming conventions. This ensures that all product information is structured uniformly, making it easier for customers to navigate and compare items on your eCommerce or retail site.
Data Analysis & Personalization: As we’ve mentioned, AI algorithms can ingest vast amounts of customer data to produce data-driven insights into customer preferences and behavior.
Market Expansion: AI can allow brands to reach global markets and channels by providing the opportunity to create tailored, localized content at scale. While AI-generated translations still require human oversight, this technology democratizes the ability to translate titles, descriptions, shipping sizes or timelines, and units of measure, all while considering cultural nuances or local regulations.
Efficient Customer Service: Chatbots and virtual assistants powered by AI can provide round-the-clock support regardless of time zone or language. While a robot often can’t, and shouldn’t, be used to solve complex problems or troubleshoot intricate issues, this technology can be used to triage common technical questions or at least intelligently route particular questions to the right humans for answer.
Inventory Management: Utilizing AI-driven demand forecasting enables retailers to fine-tune their inventory management. By doing so, retailers can make precise predictions for order quantities, effectively mitigating costly overstock or understock situations.
In a world where AI is all the buzz, it can be hard to truly understand how to utilize artificial intelligence (AI) for product experiences (PX) and the benefits this can have for your business. AI for PX is all about harnessing some of the most innovative AI technologies out there today to create efficient, hyper-personalized product experiences across an array of digital and physical touchpoints.
If you take one thing away from this article, let it be this: bad product data in = bad product data out. No matter how advanced AI and technology has become you should always start by looking at the quality of your product data first.
Regardless of how advanced technology becomes, it’s the foundation of reliable, high-quality product data that truly unlocks the transformative potential of AI. As businesses embark on this journey, the wisdom of prioritizing data quality serves as the guiding star, ensuring that AI-driven product experiences reach their full potential, elevating customer satisfaction, and ultimately propelling businesses toward greater success in the digital age.
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