2 AI trends that are transforming customer engagement

by Dino Bernicchi: AI strategy consultant
As AI rapidly advances, an increasing number of companies are learning to leverage this technology to not only enhance their customer experience, but also to generate significant business value. Having developed AI systems for 14 years across various organisations and use cases, I would like to highlight two trends where we are using AI to transform the way in which companies engage with their customers.

Leveraging AI for more relevant product recommendations in fashion retail

Product recommendations are not novel, but specific sectors like fashion demand more sophisticated, journey-integrated applications. Traditional ecommerce recommender systems, such as “Customers Also Bought” or “Related Products,” often fall short. They usually rely on static, historical data or rules requiring constant oversight by ecommerce teams. Ever wondered why you were recommended a green bikini while browsing a red evening dress (or insert your bizarre experience here)?

Deploying AI, particularly Computer Vision, to visually analyse all the products in your ecommerce store, would enable you to offer automatically generated, relevant recommendations like “Visually Similar Products” (e.g., similar evening dresses), “Complete-The-Outfit” (e.g., matching shoes and handbag for that dress), and “Products With Similar Colours” (e.g., other red dresses or accessories). This approach, particularly relevant for fashion, is more akin to an in-store experience. This closely resembles how a sales assistant would interact with a customer, firstly by understanding the customer’s intent, and finding a fitting item (i.e. conversion), then completing the purchase with complimentary products (i.e. up-sell).

Real-time, hyper-personalised customer engagement experiences

AI has enabled personalised experiences for some time, but many companies are just beginning to tap into its full potential. This is partly because AI development and integration are complex, and mastering even a single AI technology is a significant achievement. However, by combining multiple AI technologies, one can achieve extraordinary results. A few businesses have already started to merge various AI technologies to transform how they interact with their customers.

Through Machine Learning at scale, one can predict hundreds of future scenarios for each customer. Generative AI allows content teams to dynamically create personalised content. Recommender Systems can then use these predictions to suggest the most relevant generated content, delivered via the most optimal channel, at the perfect time, and with the best offer or proposition. Reinforcement Learning is an addition which could then enable you to continuously learn and adjust your engagement strategies in real-time, on an individual customer level.

Imagine providing each of your customers with a highly intelligent personal assistant, constantly updated with forecasted probabilities of future events, and capable of generating any required content instantaneously. This is possible when various AI technologies are used in sophisticated orchestration systems like the one detailed above.

AI is not just enhancing ecommerce - it's redefining it. As these technologies continue to evolve, they promise even more innovative solutions, creating a future where AI and ecommerce are forever linked. The businesses that embrace this AI-driven future will be the ones leading the charge in the new era of digital retail.

Dino has designed recommender systems for large retailers including PEP, Ackermans, Woolworths, TFG, and Homechoice. He is currently building visual-based recommender systems for fashion retailers at Keks (, and real-time, hyper personalised customer engagement systems at Decollo (

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