With increasing online data consumption, capturing and retaining consumer attention has become more challenging. Customers frequently switch between platforms, stopping only when content is visually engaging or directly relevant to their interests. Artificial Intelligence (AI) in e-commerce is a viable solution in reshaping content creation, consumption, and monetisation in digital retail.
AI content creation enables retailers to automate and scale their content strategies to gain a competitive edge through hyper-personalised customer experience. Content-generation AI tools personalise marketing copies, images, and videos. They deliver customised product suggestions and provide instant support, maintaining brand tone and improving content delivery efficiency.
Infosys BPM retail digital transformation solutions help retailers leverage artificial intelligence in e-commerce and navigate AI content creation challenges through consulting and transformation-as-a-service (TaaS). Stay ahead of recent trends in AI content creation to deliver personalised customer experiences by enhancing operational efficiency and reducing costs with end-to-end approaches in automation and advanced analytics.
Recent trends in AI content creation
Recent trends in AI content creation reveal a significant shift towards automation and personalisation. A recent survey found that 88% of marketers incorporate AI into their daily activities. A substantial portion of it utilises AI for content generation. The growing demand for highly personalised customer experiences is evident in:
Generative AI for text creation
Generative AI creates engaging website content, product descriptions, blogs, and social media posts. Content-generation AI tools optimise content for SEO, adapting to dynamic search algorithms to improve visibility, drive organic traffic, and boost customer engagement.
Automated image and video creation
Artificial intelligence in e-commerce creates high-quality visuals, supporting retailers in reducing costs and reliance on expensive photoshoots and production teams. AI generates photorealistic product images in multiple settings without traditional photography. AI analyses marketing visuals and tailors them to customer demographics and needs, ensuring relevant content delivery to the target audience. AI content engines create high-quality videos, reducing time, cost, and dependence on experts.
AI-driven interactive shopping experiences
Content-generation AI tools use Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) to generate interactive 3D product models, helping customers to visualise products in real-world settings and enhance confidence in purchasing decisions. AI-powered virtual sales assistants further enhance the shopping experience by walking customers through virtual stores and extending support as the customer requires. Virtual dressing rooms simulate in-store try-on experiences for clothes, accessories, or makeup, easing online purchasing decisions.
Personalised advertising through AI in retail marketing
Artificial intelligence in e-commerce tailors website banners, marketing campaigns, and product recommendations based on customer interactions, past purchases, interests, demography, and trends. These tools optimise landing pages and personalise social media ads and email marketing, creating a unified content strategy to enhance customer engagement and purchase possibility.
AI-driven personalised customer support
AI chatbots enhance customer services by providing real-time interactive support through text, visuals, and voice commands. AI content creation is based on Natural Language Processing (NLP), making the chatbots sophisticated enough to mimic human conversations.
AI in influencer marketing
AI generates digital personalities as virtual influencers, aligning with the brand identity for specific marketing campaigns. These influencers generate cost-effective, scalable promotional content for brands to engage with audiences innovatively while maintaining authenticity.
Key areas where retailers can leverage AI in content creation
Artificial intelligence in e-commerce provides opportunities to produce high-quality, scalable, personalised content to enhance content strategy. However, to maximise the benefits of AI in content creation, retailers need to ensure the strategic alignment of brand identity, customer expectations, and overall business goals.
AI content generation tools for scalability
Selecting AI solutions that suitably convey the brand identity is non-negotiable. The right AI content creation tools enable quick content delivery across multiple platforms in no time while consistently meeting marketing objectives.
Content optimisation strategies
AI in retail marketing supports performance marketing tools to analyse engagement data and customer feedback to identify KPIs and bottlenecks for refining content strategies. Retailers should assess AI-generated and human-created content performance to optimise resources effectively.
Hyper-personalised content for expanding the market
Content-generation AI tools analyse and create content by analysing the culture, traditions, and laws of a particular region, enabling businesses to expand into new markets while maintaining relevance. Content localisation ensures compliance with regional standards and stronger customer engagement.
With innovations in technology and its growing adoption, artificial intelligence in e-commerce would focus on hyper-personalisation, consistent and quality content, and operational efficiency. AI in retail marketing will continue to grow, requiring businesses to prioritise ethical practices of transparency, data security, IPRs and copyrights, and other regulatory compliances.
Frequently asked questions
From 2 August 2026, Article 50 of the EU AI Act requires that AI-generated or manipulated images, audio and video that could be mistaken for authentic be disclosed, that chatbots identify themselves as AI, and that providers apply machine-readable marking to synthetic content, with fines of up to EUR 15 million or 3% of global turnover. In the US, the Federal Trade Commission's 2024 rule bans fake and AI-generated consumer reviews and testimonials. Retailers typically need a content provenance policy covering product imagery, virtual influencers and reviews.
Brand governance for generative AI requires a centrally managed brand model covering tone, terminology and visual rules, a human review tier for customer-facing content, and automated checks on the factual accuracy of product claims. Product descriptions generated without structured attribute data are prone to fabricated specifications, and under Section 5 of the FTC Act the retailer remains liable for false product claims regardless of whether a model generated them. Enterprises typically ground generation in the product information management system and audit a sample of published content monthly.
In the US, the Copyright Office has stated that works generated wholly by AI without meaningful human authorship are not eligible for copyright protection, so fully automated content may not be defensible against reuse by competitors. Retailers also carry risk if generation tools reproduce protected material from training data. Enterprises typically use tools with indemnification provisions, retain human creative direction, and document the human contribution to each asset.
McKinsey estimates generative AI could add USD 240 billion to USD 390 billion of value to the retail industry, equivalent to 1.2 to 1.9 percentage points of margin, with marketing and content among the earliest value pools. Reported gains include up to 40% improvement in advertising click-through rates from AI-generated imagery. Most retailers remain in pilot: around 90% of executives report experimentation while very few report organisation-wide scaling.
Content should be measured on the same commercial metrics regardless of origin: conversion rate, return rate, organic search visibility, engagement and cost per asset. Enterprises typically run holdout tests, powered to at least 95% statistical confidence, in which AI-generated and human-created variants are served to comparable audiences before reallocating budget. Quality metrics such as factual error rate and brand-compliance score should be tracked alongside commercial results, because a lower cost per asset does not offset consumer-protection exposure.
