Demystifying AI in retail: practical use cases and implementation tips
Author: Jack Fitzgerald, Business Development Director, Zühlke
In recent years, artificial intelligence (AI) has become more than just a buzzword; it’s a strategic imperative that retailers are actively exploring. In fact, the market for AI in retail is expected to grow significantly every year, reaching $31.18 billion by 2028.
As retailers increasingly acknowledge the benefits of AI—from cost reduction and process optimisation to gaining deeper insights into customer behaviour—excitement for its implementation is gaining momentum.
Despite the promise of AI, there is a confusion among retailers regarding its practical uses. While everyone agrees that this technology can be transformative in theory, the path to effectively incorporate AI into retail operations is not always straightforward.
In today’s article, we’ll demystify the complexities surrounding AI in retail and share a few practical use cases which you can get started with quickly, based on our experience.
5 key use cases of artificial intelligence in retail
In the retail sector, AI can be a game-changer when used effectively and for the right use cases. It can help reshape traditional approaches and transform various aspects of operations by leveraging advanced algorithms, data analytics, and machine learning technologies to enhance decision-making, automate repetitive processes, and improve customer experiences.
While there are multiple popular use cases of AI in retail, many business leaders still find the prospect of implementing AI overwhelming. The challenge lies in identifying practical starting points that will demonstrate AI’s benefits without significant upfront investments.
Let’s take a look at the top 5 AI use cases our team has identified as optimal for retailers to start with.
1. Market research
Staying attuned to the ever-changing customer behaviour, market opportunities, and user needs can be a big challenge for retailers. Traditional market analysis often requires significant time and resource investments, making it difficult to stay agile in response to continuously evolving consumer trends. This is precisely where AI can step in, speeding up and streamlining the market research process.
Generative AI and large language models (LLMs) have skyrocketed in popularity over the last year and with good reason. They help increase efficiency and automation capabilities to enhance creativity, communication, and decision support.
In the context of market research, sophisticated LLMs can process and analyse vast volumes of text sourced from the internet or research transcripts. Thus, helping accelerate the extraction of insights, categorising them systematically by theme and persona while discerning your product’s competitive strengths and weaknesses.
Beyond mere analysis, AI can go a step further and generate actionable insights derived from its assessment, providing retailers with immediately applicable information for strategic decision-making.
2. Automated product content generation
Another impactful and highly popular AI use case that also relies on LLMs involves content generation. Retailers can use LLMs to create tailored product descriptions that cater to the specific needs of each target audience, yielding greater results from marketing campaigns.
On a similar note, LLMs can create targeted copy for newsletters, socials, and advertisements, allowing your team to focus less on repetitive tasks and instead prioritise more value-generating activities.
3. Customer analytics
Often, customer feedback is scattered across various channels and there’s no centralised data source that truly indicates how a product was received in the market. This leads to a fragmented understanding of customer opinions.
Once again, AI can come to the rescue. With it, you can centralise access to product-specific customer insights in a single platform, gaining a 360° view of customer needs, behaviours, and sentiments.
With the help of AI and machine learning algorithms, data can be automatically fetched from multiple sources like review sites, social media, the CRM, and other platforms, allowing for real-time access to the latest customer behaviour insights.
4. Inventory management
Artificial intelligence is also proving to be of great value in retail inventory management. Thanks to its ability to analyse vast amounts of data, AI algorithms can quickly go through historical sales data, customer behaviour insights, market trends, and external events to accurately forecast future demand. As a result, you can significantly reduce overstock and understock situations, helping minimise costs and improve operational efficiencies.
Another area of inventory management that AI can enhance is product segmentation. Retailers that often deal with perishable goods can benefit from categorising products based on demand, shelf life, and profitability. By using this AI-powered categorisation, you can manage products with a short shelf life more effectively, optimising resource allocation and saving time.
5. Supply chain
In the modern, fast-paced world of retail, a robust supply chain is imperative. Customers expect to receive their deliveries faster and faster every year, with the expected delivery speed being 2.15 days in 2023. So, for supply chain operations, efficiency is everything.
Thanks to AI, retailers can now forecast the quickest and most energy-efficient routes by relying on historical data and real-time insights. Thus, improving delivery times while staying focused on environmental commitments.
AI can also help predict equipment failures by analysing data from sensors and IoT devices. This proactive approach to maintenance helps minimise downtime, ensuring that machinery and vehicles crucial to the supply chain operate at peak efficiency.
How can you start implementing AI into your retail operations?
Now that we’ve covered a couple of practical AI in retail use cases, you might be wondering how you can actually begin the implementation process.
It’s a good idea to look inwards with your first AI initiatives. The keen-eyed reader may have noticed that none of the use cases we outlined above are customer-facing. There are two reasons for this:
- Value: AI and automation can make the most positive impact in solving operational inefficiencies, the vast majority of which are internal.
- Risk: AI is still a relatively new in terms of development at scale and customer trust in engaging with AI remains low. While experimentation should be encouraged, investing heavily in customer-facing, AI-led services poses significant brand risk. For the time being, customer-facing retail operations are still best put in the hands of a human.
So, in order to maximise the value of AI and minimise the associated risks, we recommend starting with internal implementation projects.
In our experience delivering over 100 AI proofs-of-concept (PoCs) over the last few years, with many transitioning into the production phase, there are three main tips that we can share:
- Use non-proprietary data in the ‘vision and scope’ phase. Non-proprietary data enables swift prototyping, helping you achieve business value efficiently and resolve legal and IP concerns once you are ready for full-scale implementation.
- Resolve assumptions early. Use prototyping in the ‘vision and scope’ phase to understand legal, IP, security, bias, performance, and governance implications.
- Focus on productivity, not just the product. Standalone AI solutions can produce some short-term value, but to derive maximum long-term benefits, you’ve got to integrate AI into your infrastructure.
