Implications of AI for Retail

 

These days, AI has so many applications within retail business, it is hard for a successful business to ignore it. It is a harsh reality for many retailers that competitors who are using AI to perform repetitive tasks have a towering advantage over anyone operating manually. This is because AI is freeing up more time for more complex tasks that require human planning and insights. As such, businesses equipped with AI are at a distinct advantage over peers still relying on manual functioning.

 

However, is there a downside to AI for retail, and what does the future hold for retail businesses using more AI functionality within their businesses? This article will explore the full range of implications that adding AI capabilities and technology within retail businesses has for the retail business owner.

 

 

 

AI is said to turn data into actionable intelligence. This allows retail businesses to plan their operations, spending and day-to-day marketing activities more effectively. However, AI only brings so much to a retail business. Executives using AI need to take the right steps to ensure that the data collated and derived from AI is being acted upon in the right way.

 

An example is the interception of market trends. If AI alerts a retailer that new markets for certain products are opening up, retailers need to act fast and decisively to reap the rewards of the data that has highlighted the opportunity.

 

Walmart’s reliance on predictive technologies provides the perfect example. Known for its fast and savvy adaption to AI driven predictive technologies, as of 2019, Walmart has enjoyed healthy growth over the last consecutive 11 quarters. This has been helped by a hefty increase in online sales, which has produced a plethora of data that has been used by Walmart to predict future market trends. Walmart stores and analyses point of sale data which is gathered immediately after every sale and used to predict which products are likely to sell out and which aren’t performing as well as expected.

 

Making accurate predictions surrounding product demand enables Walmart to assign advertising budget to products that are likely to sell out, and cut costly advertising invested in products that are underperforming. This type of forecasting also allows for greater efficiency surrounding stock handling and the management of the Walmart supply chain.

 

Looking at the success enjoyed by Walmart, it is easier to see how a key word in terms of using AI to benefit a retail business is actionable. All data is intelligence of some form or another, however human executives need to use their business acumen to discern what actions to take following analysis of data, including what data to prioritise and what data to discard. Walmart is doing this incredibly successfully, with their AI driven sales forecasting strategies.

 

This evolution in the availability of actionable intelligence is one of the biggest implications of the rise of AI within retail business – the data reveals opportunities that may be realised if certain actions are taken. In the case of Walmart this action has been an investment in forecasting analysis and data storage, and so for Walmart the risk of spending money on this has paid off, since it has resulted in an explosion of growth and demand for their products.

 

A similar example is the success enjoyed by Amazon, which also stores and analyses point of sale data. The data is used slightly differently in the case of Amazon, however. Amazon uses the data to make predictions about what additional products customers may wish to purchase and then these are displayed and advertised directly to the customer during the check out process. The result is that Amazon has been able to identify a unique audience for their products, and this is the audience that is targeted with product suggestions and examples of complementary products. This has helped position Amazon as one of the giants of online retail marketing in 2019.

 

However, there is no doubt that AI reliance and use exposes retailers to new risks.

 

 

 

The potential for security breaches and data losses through scams and theft increases with an ever-increasing reliance on data to direct operations within retail business. Consequently, the average retail business is exposed to a lot of risk merely as a result of storing information and data.

 

Holding and processing large amounts of data begs the question of what happens if the data is mishandled or used for nefarious purposes. Many countries, especially the UK are introducing heavy regulation, which forces businesses to handle data carefully and face large fines if data regulations are breached.

 

Aligning a retail business to modern day trends can create huge exposure to the risks of data loss and theft of data. An example is the proliferation of apps and facilities that store and make suggestions for future or complementary purchases for customers. To make the predictions necessary to perform these actions, retailers need to store much larger swathes of information than ever before, like a customer’s purchase history, delivery addresses and login information.

 

Equally, because customers value convenience when it comes to online shopping, the data storage required to create convenience for customers will create a risk to the retail business owner. A customer, for example, may expect to be able to check out their baskets using payment information they have already entered. Retailers can lose customers if they constantly ask them to resubmit information like delivery addresses and credit card information. However, providing this level of convenience for consumers necessitates that retailers take risks, one of which is storing large amounts of sensitive data belonging to customers.

 

Many customers using a retailer’s apps will assume that data safeguarding is in place and retail businesses may face mass exodus of customers in the event that a security breach is exposed. Adidas faced a lot of criticism in 2018 for a failure to safeguard data that probably affected millions of customers. Email addresses and other personal information including login information belonging to customers were accessed by third parties and Adidas was forced to apologise to millions of customers. Eventually, their approach to data encryption was overhauled.

 

In light of these risks, retail businesses need to employ data security and handling strategies to keep the data they are using safe and ensure that all risks associated with security breaches are reduced, controlled and managed effectively. For some retail businesses, this means hiring and training new staff whose role is specifically to ringfence and safeguard data, and analyse the various ways that data held by the business can be made more secure.

 

Retail businesses need to consider using things like encryption to safeguard data, but this requires expenditure on security consultancy and cyber strategy, all of which imposes further costs on retailers. Additionally, training and investment in technology capable of storing and analysing data is required.

 

These requirements highlight another of the growing implications of AI-assisted businesses – the cost of using AI.

 

 

 

One of the key implications of increased reliance and demand for AI in business is the cost. Incorporating AI into a business model can be costly, so any decision to invest in AI technology needs to be planned methodically. Investment in the wrong type of AI can be catastrophic for the retail business owner, mainly due to the cost. Many businesses will never be able to absorb the costs of a failed AI implementation.

 

An example is the fast food industry and the possibility of switching to driverless delivery systems. This move has the potential to reduce delivery driver costs and make deliveries more effective and convenient for consumers.

 

The obvious risk is the cost of the initial infrastructure needed to make the driverless system possible. Other risks include the damage to the business that would be caused by a serious systems failure, or litigation associated with the operation of a non-human controlled delivery system.

 

 

 

One way to reduce the risks that AI imposes in terms of infrastructure development costs, is for retailer businesses to partner up with third party businesses or organisations, and in this way, spread the costs and the risks associated with data storage and reliance on data. Nevertheless, whereas this may reduce certain risks, it creates others like the organisational disruption that may ensue from a partnership or association.

 

 

 

As we have see discussed, AI has major implications for the day-to-day operation of the retail business. The cashless business model is a perfect example.

 

A cashless business model means that a retail business uses technology like cameras and sensors to enable customers to make payment. However, this development is a double-edged sword in terms of its implications for the retail business.

 

On one hand it reduces the risk of theft because it makes stock easier to track. Errors, and anomalies are easier to identify as a result and this makes a retailer less vulnerable to losses like theft. Since there is no longer a need to store large sums of cash on site, retailers enjoy an added benefit in terms of security.

 

On the other hand the whole premise of cashless technology creates a whole new set of risks for retailers. Sudden and unexpected data loss might lead to large profit losses. Furthermore, “downtime” associated with technical glitches and technical problems can reduce efficiency and lead to large-scale organisational problems.

 

 

 

Businesses are using AI to help replenish stock levels and assist in logistics like assisting customers to retrieve items (particularly heavy, bulky items) in stores. AI can be used to monitor stock levels and ensure that supply chains operate optimally, reducing the possibility that customers will have to experience problems related to items being “out of stock”.

 

Overall the implications of these changes are positive, because human assistants are freed up to carry out more complex tasks like dealing with complaints and using creativity to solve problems – something AI can’t do.

 

In most cases the need for human assistants will be reduced, as a result of AI, thus saving additional costs. Costs associated with manual operation of repetitive systems like stock monitoring are reduced, too.

 

Other implications of AI assisted retail businesses relate to the ways in which brands interact with their customers, as in the case of fitting on and trying out products and services.

 

 

 

AI advances enables retailers to enjoy more seamless processes when it comes to customers trying on, and testing items. The traditional method of a customer needing to find a product and then physically try it on is dispensed with through the use of systems that analyse customer measurements and show an exact image of what the product looks like while being worn by the customer. This approach has been applied to trying on glasses, trying on shoes and trying on clothes. Makeup retailer Charlotte Tilbury uses a specially designed AI system to enable customers to “try on” makeup without having to physically apply it, and remove it to try on alternatives.

 

While the beneficial implications are obvious, with these systems, retailers are still exposed to risks in terms of technical problems and systems failure, and these risks need to be managed effectively.

 

 

 

The implications of increased reliance on AI within retail business are vast. Overall, reliance on AI leads to greater potential for efficiency savings for retailers. Retailers save money from a lesser need to rely on human assistants and the potential to eliminate human error from repetitive systems like stock replenishing, stock monitoring and stock retrieval in stores. 

 

On the other hand, to initially switch from traditional processes to those supported and directed by AI, requires the creation of expensive infrastructure and technology.

 

Furthermore, retail businesses are exposed to greater risks when they are required to store and utilise large volumes of data that contains personal information belonging to customers. Cyber security is costly and great risks can arise from data losses or unauthorised access to data that a retailer is responsible for.