4 Misinterpretations about AI in Retail and how to get it Right!

4 Misinterpretations about AI in Retail and how to get it Right!

by Dan

The retail industry is changing at a rapid pace. 

For most retailers, the ability to stay ahead of the curve, while also remaining profitable, has come down to them reassessing their current retail planning methods and the technology required to support them. The increase in demand for upgraded and more capable technology has resulted in the terms “artificial intelligence” and “machine learning” quickly becoming buzzwords within the retail industry. 

As some of the world’s leading retail giants have already purchased and implemented retail AI software with great success, many other retail businesses are coordinating future budgets to include AI.

In addition, it’s imperative that retailers conduct preliminary research to help build and support an implementation plan. This plan must take into consideration any misconceptions around AI in retail. Establishing an understanding of the misconceptions upfront can help to avoid a potentially catastrophic loss of time and money. 

Every retail AI project is unique and therefore can take several months to launch. Retailers who have developed a detailed budget and implementation plan are more likely to achieve their desired outcome from their anticipated new technology.  

To assist in your preliminary research and provide some additional guidance, this article will clarify some misinterpretations most commonly associated around AI technology within the retail industry. 

#1 – AI is not a magic pill for all your problems

AI is not a magic pill for all your problems

How would you define AI?

Unless you are a software engineer or data analyst, chances are most answers to this question – with minor variations – will be quite similar.

The definition of the term “artificial intelligence” is quite often subjective and based upon its specific use cases. But, despite its subjective nature, most definitions of artificial intelligence are high level resulting in a unified, quite simple perception of what it is.

This perception and lack of deep understanding can be quite detrimental for retailers interested in implementing AI into their business. To add to the confusion, AI has also become synonymous with the term machine learning and the two are often used interchangeably.

But machine learning and artificial intelligence are not the same things. In fact, machine learning is actually a subset of artificial intelligence, and it’s not the only one. Machine learning is the most common subset of AI, but to provide context, here are a few others:

  • Neural Network 
  • Predictive Analytics 
  • Deep Learning 
  • Robotics 
  • Data Analysis 

Also referred to as segments of AI, each one has unique capabilities that can aid in solving specific problems – depending on the industry.

For you to understand what segment(s) of artificial intelligence will accelerate your retail business you must first establish and outline – in detail – the specific problems you are working to resolve and the specific goals you are trying to achieve. 

Understanding your problems and goals will enable you to dig deeper and generate a more granular understanding of what segment(s) of AI align with and can solve your retail problems while simultaneously aiding in achieving your desired goals. 

#2 – General AI tools are not ready for your unique business 

General AI tools are not ready for your unique business 

All the buzz around AI has created the perception that it is some “magical” solution. It is often perceived as a standalone solution that can solve all the problems and challenges retailers face.

Instead, AI solutions must incorporate and account for multiple retail processes and the attritbutes that can influence them. To achieve this, retailers must apply AI that is part of a software designed specifically for retail processes.

Retailers are unique and each one faces its own set of distinct and specific constraints, challenges, opportunities, and attributes that influence their business operations. For example, these platforms must have the capacity to allow retailers to incorporate unique retail business rules and exceptions that will automate these processes.

Complimenting a retail analytics software platform with AI ensures retailers are accounting for all the factors that influence their specific businesses to generate informed and actionable retail predictions.

So, a standardized AI solution simply doesn’t exist.

#3 – Not all AI tools and vendors are the same

Not all AI tools and vendors are the same

A quick Google search will reveal several potential AI vendors that can assist businesses with their AI initiatives. Unfortunately, not all vendors are created equal.

Retailers must be cautious when selecting an AI vendor. Many claims suggest that companies “do AI”. As mentioned above, AI is not a magical solution, it requires additional processes and platforms to be successful. So, what does “do AI” actually mean?

It’s easy to develop a perception of a successful AI vendor when it could be just some data mining containing undifferentiated statistical analytics and data displayed on an eye-catching dashboard. But don’t let that fool you.

Furthermore, AI software companies that cover a very general scope while working across multiple industries will not have enough focus on the complex retail landscape. A general AI vendor may work with a combination of banks, healthcare, government, and retail organizations.  

Without this narrow focus, AI vendors can potentially discount the significance or be unaware of the critical processes and factors that affect retailers. For large-scale and sophisticated retailers, this generalized approach will not provide the accuracy required to achieve their retail goals.

Unfortunately, many retailers have learned this lesson the hard way. There have been millions of dollars invested and dozens of data analysts hired without having the desired results achieved.

When looking to implement an AI solution into your retail business, it’s important to research vendors with specific and extensive experience and knowledge of the retail industry that will have already faced and dealt with similar challenges you are facing.

#4 – Rolling out AI Requires Preparation

Rolling out AI Requires Preparation

Another common misconception around AI and retail is that no preparation is required before adopting AI. Also known as “AI readiness”, this is the largest and quite often the most tedious obstacle faced when successfully adopting an AI solution.

To achieve the desired benefits, and meet anticipated goals and KPI’s, it’s important that you are prepared well in advance of your AI implementation.

As previously mentioned, retailers must first establish the specific problem or challenge they are hoping to solve with an AI solution. With a problem top of mind, it’s important to establish achievable goals and KPI’s that correlate to the problem.

From there, it’s important to make sure you are able to answer these questions:

  • How can I prepare my data so that AI is able to accomplish my specific and selected tasks?
  • How will the AI solution I have chosen affect my current retail business processes?
  • What specific business roles within my current infrastructure are going to change and to what extent?
  • How will my new AI solution integrate into my current technology environment?
  • What are the KPIs that I will need to measure to assess the success and return on investment for this initiative?

Furthermore, this will position retailers to conduct more in-depth vendor research ensuring alignment with potential vendors.

So how can you get started with AI?

AI solutions can be an extremely valuable asset for any retail business. A proper AI solution that is tailored for the retail industry can fundamentally transform your business. Retailers using AI experience significantly lower operation costs, a boost in sales, and increased customer service levels.

No one retail business is the same and therefore it’s imperative to do the research, understand the myths and clearly define your goals so you can clearly interpret what specific AI tools and segments will provide the greatest return on investment and how each one will align with your anticipated retail infrastructure. 

It’s important to not rush into purchasing a retail AI solution without first having a clearly defined plan that takes into consideration your problems and goals.

Author Biography

Trevor Griffith graduated from St.Lawrence College and is currently the Digital Marketing Specialist at Retalon, the world’s leading provider of predictive analytics and AI for retail. Retalon solutions solve complex challenges in Planning, Inventory, Pricing, and Promotions, and all solutions stem from one unified analytics platform. – Retalon’s platform seamlessly integrates with any ERP or OMS, and our 100% in-house professional services, technical support, and analytics teams ensure you hit your goals on time.

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