Intrepid CEO Jasper Knoben
As generative AI becomes more popular, various industries begin to explore different use cases for the technology including e-commerce—an area where it has many potentials.
For e-commerce and digital solutions provider Intrepid, one example of how it leverages AI includes individualised product recommendations feature in chat, based on a consumer’s personal inputs, according to CEO Jasper Knoben.
“Other examples include optimising written content on marketplaces based on search queries, and generating very appealing key visuals like campaign banners or product images at scale. In general, it is an amazing productivity driver if harnessed properly,” he writes in an email interview to e27.
“You could argue that the personalised homepage content and product recommendations that you encounter on e-commerce marketplace apps is also a form of AI, as it is powered by algorithms that take large quantities of data into account, like your demographics, your previous browsing and shopping history and many other factors.”
Examples of brands that have utilised this greatly include beauty brands which offers features to enable instant, personalised and professional skin analyses and matching product recommendations without the customer having to visit a physical store.
There are certainly many untapped opportunities here.
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“The more data an AI model has at its disposal and the more advanced the model, the more sophisticated it’s outputs can be. We have only scratched the surface of how far AI can go, which is an exciting and a bit scary prospect at the same time,” Knoben says.
“With the anticipated advances in AI, could an e-commerce marketplace run semi-autonomously in a few decades from now? With AI powering marketing, demand forecasting, inventory planning, pricing, content optimisation and personalisation of content and recommendations? How will the role and contribution of humans evolve? The changes will be profound and much wider than most people consider today, but what that future will look like exactly and how quickly the advances will be remains to be seen.”
How we are using AI today
In the SEA e-commerce industry, there are are already various case studies on how AI is being implemented for optimisation and personalisation by leading e-commerce platforms.
Knoben sees that some markets are ahead from the rest in this matter.
“For new innovations in e-commerce, Thailand and Singapore are typically leading markets. Thailand because of the size of the e-commerce market and the curious nature of shoppers who love new innovations, and Singapore because it is a small but advanced market with sophisticated shoppers and therefore also a good testbed for innovations before expanding across SEA.”
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But this does not mean that AI implementation in SEA is not without challenges. According to Knoben, there are three main barriers of entry for brands in SEA to implement AI in their e-commerce front:
Data quality and availability
“AI models require large quantities of high-quality data to train effectively. Brands need to ensure that they have access to reliable and relevant data sources. Data cleaning, integration, and management can be complex and time-consuming tasks,” the CEO says.
Infrastructure and scalability
“AI implementations require robust infrastructure and scalable systems to handle the computational demands of training and deploying AI models.”
Talent and expertise
“Building and maintaining an AI team of AI specialists, data scientists, and machine learning engineers with the right talent and expertise can be a significant challenge, and expensive.”
Knoben also predicts that in five to 10 years from now, every global brand will have an in-house AI team.
“It is crucial that they know how to leverage AI as it will be a significant driver of efficiency, consumer experience and commercial performance in the future,” he says.
“The question is what is the most efficient set up to harness AI, where – like in e-commerce – I think a hybrid approach will thrive of having in-house experts that interface with the brands wider organisation and drive adoption and brand-specific innovations to maintain a competitive edge, while working with external partners to develop tailored models for specific use cases (like specific markets or platforms) as these players will have the scale to build the required expertise for each use case across many brands, and therefore should be able to leverage those economies of scale to offer more advanced capabilities at competitive costs.”
While the first wave of AI is still “very much” focused on generative AI, automation, and efficiency, Knoben believes that mass personalisation in marketing and e-commerce across the entire funnel to improve recommendations, user experience, and commercial performance will be the next frontier.
“Brands will want to have their own bespoke AI models, and agencies will increasingly shift from designing and executing campaigns, to developing the AI models that do this for brands. Whatever activities are performed by people at brands, enablers or agencies today to enable e-commerce, people will be developing and training AI models to do those activities for them in the future.,” he closes.
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Image Credit: Intrepid
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