Artificial Intelligence (AI) is a hot thing in technology.
From text to audio to art, AI is fundamentally changing how we approach content. It’s still a fairly nascent — or at least evolving — technology, but has great potential to disrupt several industries.
This includes ecommerce retailers. Of most immediate interest is the potential of AI technology, which is used to create various kinds of content. Its potential for producing content spans across industries and opens the kind of automation that IT enjoys and expands it to other disciplines.
Recent studies say 75% of generative AI users are looking to automate common work tasks and streamline communications.
Integrating AI has the potential to greatly increase the volume of what can be done, although quality still needs to be considered.
Generative AI vs. Traditional AI
What is the difference between traditional AI and generative AI? Are there any benefits that come with one over the other? Let’s dive into the technicalities of these two types of AI and explore their strengths and limitations.
Traditional AI.
Traditional AI, also known as rule-based AI, is a system designed on pre-existing rules. These rules are coded in the machine for it to follow when operating tasks and is limited in its performance and can only function within its programmed rule-set.
Traditional AI is better suited for platforms requiring less cognitive effort, such as chatbots, recommendations engines, and simpler decision-making processes.
Generative AI.
Generative AI, on the other hand, can self-learn its rules from the provided data, making it less dependant on pre-programmed rules.
The system is trained on data sets, and through repetition and analysis, it builds up knowledge and creates algorithms that guide its decision-making.
The critical differences in ecommerce.
Traditional AI in the ecommerce industry is most often used to enhance customer experiences, including chatbots, personalised product recommendations, and voice assistants. Think about how Amazon has evolved its platform.
Generative AI models in ecommerce are typically used for content creation, product design, and marketing campaigns.
Types of Generative AI
While the field of generative AI is relatively new, it is already being used in a wide range of applications, including creative fields. Here, we'll cover the different types of generative AI and how they work.
Generative Adversarial Networks (GANs).
GANs consist of two neural networks — a generator and a discriminator. The generator network takes random noise as input and generates new data. The discriminator network is then trained to differentiate between the generated data and real data.
The process continues until the generator produces realistic data, and the discriminator can no longer tell the difference. GANs are often used for creating realistic images and videos.
Transformer-based models.
Transformer-based models are architecture models that transform sequence-to-sequence. Transformer models generally enable faster processing and a more efficient way of training sophisticated deep learning models.
They use bidirectional attention mechanisms to process sequential data, such as natural language processing tasks like chatbots, text generation, and automatic captioning.
Examples of transformer models include BERT, GPT-3, and T5.
Variational Autoencoders (VAEs).
VAEs are deep learning models that use an encoder-decoder architecture to compress an input data into a latent representation. They perform probabilistic estimations of latent variables, allowing for greater flexibility and accuracy in the distribution of data samples.
VAEs are mostly used for image generation, data representation, and anomaly detection.
Incorporating Generative AI into enterprise ecommerce efforts.
While AI has been used to improve customer experiences, the incorporation of generative AI can take things up a notch. This type of AI can revolutionise how ecommerce businesses create content, optimise product design, and personalise customer interactions.
Content creation.
Constantly producing new content is a common part of any in-bound marketing plan. Generative AI can be used to create SEO-optimised blogs, social media posts, or even product images to attract customers.
It can also be used for product descriptions and can be especially helpful if an online store is selling a large amount of SKUs.
Personalisation.
Personalisation creates user experiences unique to the user, giving them what they want and how they want it. That means higher customer satisfaction.
Generative AI can help by producing experiences tailored to an individual. Personalised shopping experiences provide an inherent advantage for online shopping versus brick-and-mortar stores.
New product ideation.
Generative AI can be useful for research and development by using data to create product ideas or versions of an existing product. This is a newer use of the technology, but the possibilities for a more refined development process are endless.
Product recommendations.
This is an off-shoot of personalisation. Creating better product recommendations based on customer data and purchase history has the potential to increase sales and retention.
Supply chain management.
Supply chain managers need to be able to forecast demand and plan the procurement of new products. Inventory management requires managing a significant amount of data.
Generative AI can process past sales data, consumer trends, market trends, and supply chain delivery patterns, among other factors, to assist in making informed decisions.
Better operations post-purchase.
The post-purchase experience includes shipping, delivery, returns, and follow-ups. Generative AI can help through personalisation, chatbots, and virtual assistants focused on customer support and automated shipping.
Generative AI ethical considerations
The development of generative AI is not without ethical considerations. The advancement and use of generative AI technology can be a game-changer and impact people's lives in profound ways, which raises significant questions about whether some concerns are being addressed.
For ecommerce platforms, this will mean fine-tuning how it is used and harnessing generative AI tools in very specific ways.
OpenAI, a research lab focused on using AI to benefit humanity, has spoken openly about the need to approach generative AI algorithms in a safe way.
Trust and transparency.
Unlike traditional programming, these generative AI algorithms learn from inputs and adjust themselves accordingly. This can make it challenging to comprehend how they make decisions.
Developers must prioritise using techniques such as explainable AI that will enable them to interpret their algorithms' inner workings.
Data security and privacy.
Generative AI requires vast amounts of data to produce results. This data can often be personal or sensitive, leading to privacy concerns. The knowledge generated by the use of generative AI can quickly become exploitative without regulation.
Accountability.
Some applications of generative AI can make decisions without human intervention. As an example, healthcare diagnoses and treatment options generated by generative AI can potentially have a lasting impact on a patient's life.
Therefore, developers must ensure that their algorithms are set up to provide clear accountability and traceability so the consequences of autonomous decisions can be fully evaluated.
Hallucinations.
With the ability to create realistic experiences, there are concerns that it could be used to manipulate people's perceptions and beliefs, leading to false information and propaganda.
This could blur the lines between what's real and what's not, and make it difficult for individuals to distinguish the difference.
Generative AI ecommerce constraints
As with any technology, there are certain constraints that need to be taken into consideration when implementing generative AI in ecommerce. It’s still an evolving field, which should be noted before fully implementing a solution.
Reliability.
AI requires some level of human oversight to ensure outputs are accurate and free of bias. Though generative AI is becoming more reliable, it still requires monitoring to avoid inaccurate information being produced.
Data quality at scale.
The accuracy and effectiveness of AI models depend on the quality and relevance of the data used to train them. For example, if the product descriptions used to train the AI system are incomplete or inaccurate, the AI will produce similarly inaccurate results.
To mitigate this, it is vital that ecommerce businesses focus on obtaining and maintaining high-quality data sets.
Evaluation and feedback.
Much like humans, AI is constantly learning and evolving. However, it doesn’t know if it’s evolving “correctly” or not. Humans are responsible to look at how the AI is growing and course-correct if necessary.
On-premise.
In this case, this isn’t cloud versus on-premise, but keeping an AI focused on the subject it is learning. Left to its own devices, an AI can stray far from what it was intended to do. It requires human guidance to shepard it to where it needs to go.
Generative AI in the future
Tools such as ChatGPT and Bard are just scratching the surface of what AI can do. The future will bring with it use cases we haven’t thought of yet, with ecommerce in a position to benefit greatly. The optimisation of the purchase experience only means good things.
Better product discovery.
With AI algorithms that automatically analyse customer data, ecommerce companies will have the power to create personalised and relevant content, advertising, and products to match individual customer's preferences and interests. This will result in a more positive customer experience and a better chance at higher conversion rates.
Improved customer engagement.
Generative AI has the power to create highly creative experiences based on customer preferences, ultimately resulting in a personalised approach that fosters loyalty and repeat business. Real-time information and optimised customer experiences means more sales.
Prevent fraud.
The best defence for fraud is catching it before it happens. Leveraging AI to identify potentially fraudulent transactions — and evolve as the methods used by bad actors changes — could produce even more secure shopping environments.
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The final word
It’s difficult to say what the ultimate impact of generative AI initiatives on the ecommerce landscape will be, but it’s not difficult to understand the potential it has to positively impact the industry.
Regardless of industry, however, the possibilities the technology offers are tantalising and, if implemented and monitored well, could create highly engaging user experiences.