With the rapid rate in which technology is already influencing the way our society works, it is no surprise that Artificial Intelligence will have an increasing presence in our lives. As a result, many of our sectors must increase their commercial awareness of this evolution in order to make the way we live easier and more convenient with the use of AI. Take a look below to see the key AI applications that are already taking effect.
AI’s impact on ecommerce can be seen through the experience of the customer when purchasing items. Many ecommerce companies use AI to provide recommendations to their customers based on their own activity such as their browsing history, preference and interests.
Another example would be the increased use of ‘chat-bots’ when browsing web pages. An AI-generated virtual ‘helper’ can pop on screen and simulate a messaging conversation with the browser in order to guide them through the webpage directly to what they need without waiting for a human employee to come online. This use of AI creates a more user-friendly experience and will allow customers to browse more relevant products for their needs.
Security and protection is also a vital component of ecommerce that can benefit greatly from AI integration. In an industry filled with ‘replicas’ or ‘duplicated’ products, the risk of a consumer purchasing a counterfeit product disguised as an authentic one is higher than ever. Using AI, we can now adopt machine-learning algorithms that can distinguish authentic products from fake one, providing an extra level of protection for its users.
AI-powered recommendation algorithms are one of the most common methods to allow companies to connect with their users and enhance their experience to strengthen their loyalty. A recommendation system collects consumer data and analysis to generate tailored recommendations for the user. These engines use AI for quicker analysis and relies on data points such as the browsing history, interactions, and ratings of the user.
One of the best examples of this in the digital age is the video-hosting app TikTok, which boasts one of the best recommendation engines in the world. With millions of active users uploading content daily, their algorithm can analyse the stored data of what the user is viewing and then use that to recommend new content and videos to watch. This not only increases usage of the app and the level of interaction, but also boosts the user experience by recommending relevant content that would further increase their usage and loyalty to the app.
This end result is why the majority of major companies and networks have utilised these AI algorithms to help improve sales and performance.
When recruiting a new employee for a role, many companies now use an applicant tracking system (ATS) to help make the process faster and more efficient. An APS helps organise the list of potential candidates by allowing recruiters to collect information of the applicants and arrange them based on their experience, skills and qualification. This helps to filter out candidates who are less qualified and focuses on those that are more suited to the role.
A key benefit of this would be that it saves the recruiters time from selecting a shortlist of candidates, which then allows more time for connect with the applicants on a more ‘human’ level. It will also result in a higher quality candidate being selected in terms of relevance.
However, a present concern would be potential bias during the process. AI is still dependent on data used in previous cases, where human bias may have been present. This would therefore lead to the AI copying those same patterns and could ultimately enforce these same issues going forward despite this automation. It is therefore crucial that any companies adopting these systems have a neutral data set that the Ai can rely on to prevent any ‘human bias’ becoming a factor.
The banking sector can benefit greatly from AI integration, with the credit scoring system being the main example. With many banks using an approval service for loans based on financial data and other lifestyle information, using AI could help speed its process by taking the information of these applicants and using AI to quickly analyse this information and automatically determine both their credit score and their likelihood to secure a loan based on their data.
Banking security is also of big interest to many, and AI’s ability to identify patterns could help neutralise and prevent future threats. By having previous cases of fraud or breach as reference points, AI could help locate instances of protection failure at a much faster and more accurate rate through its pattern analysis.
Whilst farming and agriculture may not seem connected to artificial intelligence at first, AI will greatly shape the way we grow our crops moving forward. Every day of the year requires farmers to monitor multiple sets of data in relation to the crops, including measurements for temperature, water, soil, and seed type. AI will allow farmers to monitor these variables in a faster, more accurate way to help maximise the quality of each crop throughout the year.
AI’s ability to detect patterns and abnormalities can be used to treat external irritants such as weeds or infection which can be treated with herbicide spray. Disease diagnosis, as already seen in other sectors such as healthcare, can also be used in agriculture to identify any infected crops and alert farmers quickly to allow them to neutralise the threat as soon as possible. Tending to this quickly will increase the chances of preventing damage to the crop which will in term help protect the finances of the industry as well.
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