FeatureWhile many industries were affected with the onset of the pandemic, e-commerce was one among very few vertical markets that experienced accelerated growth. As consumers increasingly expect more tailored experiences, hyper-personalisation is crucial for the continued growth of e-commerce.
Algorithmic e-commerce, that is smart, systemic digitisation of business functions often handled manually will herald the extensive adoption and implementation of artificial intelligence and machine learning by enterprises in the e-commerce sector.
For instance, artificial intelligence-powered natural language generation technology will deliver bespoke experiences to online shoppers through customised product and category descriptions that turns structured data into a natural-sounding sales pitch.
Root cause of algorithm bias lies in training datasets that machine learning models are exposed to
Eventually, this trend will lead to a market shift that offers more value to consumers – where e-commerce retailers adopt a more product-type approach to personalisation and customer experience, compared to a consulting-product approach.
Artificial intelligence techniques such as machine learning and deep learning need a lot of processing power. Many enterprises do not have enough power to implement such techniques.
Another key challenge faced by enterprises in the implementation of artificial intelligence and machine learning is algorithm bias. Bias can infiltrate algorithms in many ways. The root cause of algorithm bias lies in the training datasets that machine learning models and artificial intelligence systems are exposed to.
The training data can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are eliminated. Hence, it is important to take a step back and observe the training data.
A key challenge faced by enterprises in implementation of artificial intelligence is algorithm bias
It is obvious that artificial intelligence will likely contain built-in biases as it a tool developed by humans. The most effective way to prevent bias is to carefully examine the data one selects to train their artificial intelligence models. It is helpful to test multiple hypotheses, validate models, and monitor them over time for bias and, when applicable, objectivity. By doing so, the resulting models will almost certainly be less biased than human-led decisions. Otherwise, it is possible to face the risk of reinforcing and proliferating bias.
Measuring the ROI of artificial intelligence requires the consideration of several factors. For instance, it is important to evaluate if the artificial intelligence initiative can result in savings by reducing operational costs. Before investing in artificial intelligence projects, it is also key to define the goals and KPIs from the outset which helps in measuring the ROI later.
Training data can include biased human decisions or reflect historical or social inequities
Artificial intelligence and machine learning have made their way into many diverse industries, changing the way enterprises manage everything from supply chains to customer relations. However, there are specific vertical markets that can derive exceptional benefits from artificial intelligence.
Logistics is one such vertical market that can save time, reduce costs, improve productivity and accuracy with artificial intelligence and machine learning. For instance, artificial intelligence can improve logistics route optimisation, which helps reduce the shipping cost, which in turn increases profitability.
Artificial intelligence is a gamechanger for banking and financial services institutions, when it comes to fraud risk management. As BFSIs are inherently prone to risks, they can leverage artificial intelligence to identify patterns over large data sets to detect fraud before it negatively impacts their organisation and customers. This ultimately has a positive impact on reducing the overall cost of frauds.
It is obvious AI will contain biases as it a tool developed by humans and most effective way to prevent bias is to carefully examine the data selected.
