The total amount of data created, captured, copied, and consumed globally is expected to skyrocket from 64.2 zettabytes in 2020 to over 180 zettabytes by 2025. Driving this exponential growth is Artificial Intelligence (AI), reshaping the very foundation of data management processes. As data requirements evolve, AI-driven solutions are becoming indispensable. From optimizing operations to ensuring relevance and personalization, AI is driving efficiency and effectiveness like never before.
If you use the internet nowadays, you're likely engaging with some form of AI. Whether through AI-generated content, personalized recommendations, or chatbots, AI is transforming how businesses engage with consumers and present their products or services — and it’s only the beginning. Recently, ChatGPT, Bard, and generative AI at large have taken the internet by storm, raising questions about the future of human-AI collaboration and the evolution of online engagement.
However, while content creation garners much attention, it’s only the tip of the iceberg when creating a successful content marketing strategy. The real depth of AI's impact lies in the strategic management and enhancement of product information, particularly within the realm of Product Information Management (PIM). Here, AI assumes a key role in refining data organization, ensuring accuracy, and optimizing presentation to deliver consistent, high-quality product content across diverse channels.
Before we dive in, let’s start with the basics. AI, or Artificial Intelligence, refers to the simulation of human intelligence processes by computer systems. It encompasses a wide range of techniques and approaches enabling machines to perform tasks such as learning from data, reasoning, problem-solving, perception, and natural language understanding. A recent IBM study estimates that 35% of businesses have already adopted AI — and even more (44%) are working to integrate it into their current applications and processes.
Within the vast landscape of AI lies an especially captivating subset: generative AI. This facet of AI epitomizes innovation in content creation. Deep learning, an integral component of generative AI, allows machines to learn from data and produce highly realistic outputs. Platforms like ChatGPT, Bard, and DALL-E demonstrate generative AI’s proficiency in producing lifelike text, realistic speech, and even photorealistic images. According to Goldman Sachs, generative AI could drive a 7% (or almost $7 trillion) increase in global GDP.
Navigating the complexities of data management in today's digital world is no small feat. The sheer volume of data generated daily strains storage, processing, and analysis capabilities. Adding to the challenge is the need to craft compelling product content, particularly when 85% of shoppers prioritize product information and pictures when deciding which brand to buy from.
One of the most pressing issues in many companies is data silos — information stored in various places and often duplicated. Compiling and maintaining product information manually across different systems poses a considerable risk of errors. For businesses with extensive inventories, lacking the right technology exacerbates the problem, making the process both costly and inefficient.
Despite the growing adoption of AI tools by marketers, with over 60% leveraging AI and 25% specifically using it to create product descriptions, AI introduces its own unique set of challenges. Overreliance on AI can lead to lower-quality product listings, as sellers may inadvertently overlook errors, resulting in peculiar product titles like “I'm sorry, but I cannot fulfill this request,” as evidenced by ChatGPT error messages on platforms like Amazon.
Striking the right balance between human creativity and AI automation is key to overcoming these challenges and delivering compelling product content that resonates with customers.