Chapter 2: The AI Advantage – How to Stay Ahead of the Curve
Summary
Retailers are increasingly focused on leveraging AI, with inventory optimisation (48%) and improvements to the checkout process (46%) topping the list of relevant use cases. Fraud detection (42%) and product recommendations (40%) also scored highly, while loyalty programmes (11%) and warehouse management (13%) were considered less of a priority.
Integrating AI presents several challenges. High implementation costs (48%) and insufficient data (45%) are the most significant hurdles, followed by difficulties in finding appropriate tools and technologies (43%). Organisational culture (8%) and questions around algorithmic bias and fairness (17%) are less of a concern. Of the 22% who expressed concerns about potential negative impacts, many emphasised the importance of ethical AI implementation. This included establishing clear risk management protocols, ensuring responsible system use and promoting ethical practices.
Looking at how these challenges have evolved over the past year, understanding customer sentiment and consumer shifts has become a key focus. Other emerging trends include improved decision-making and the complexities of integrating AI with legacy systems. Transparency and reliability are also important considerations, alongside difficulties in using AI effectively for customising product categories.
When asked about exciting AI technologies, generative AI (70%) was the clear frontrunner. Computer vision (26%), including real-time object detection, image and video analysis, and augmented/virtual reality (AR/VR), also attracted considerable interest. Natural language processing (3%) and reinforcement learning (1%) received minimal attention, while robotics received no responses.
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Question 1: Which AI use cases are most relevant to your organisation?
Question 2: What are the biggest challenges you face when integrating AI into your organisation?
Question 3: How have the challenges you face changed over the last 12 months when it comes to integrating AI?
Question 4: Which AI technologies are you most excited about?
Which of the following AI use cases are most relevant to your organisation? (respondents were asked to select all that apply)
Inventory optimisation
Improving the checkout process
Fraud detection
Product recommendations
Content creation
Personalised marketing
Chatbots
Fulfilment/delivery planning
Demand forecasting
Price optimisation
Visual search
Warehouse management
Loyalty/loyalty programmes
"I’m not surprised to see chatbots scoring low, because consumers still want to speak to humans. All of the most popular use cases ring true. Inventory optimisation is the biggest P&L driver. You can do lots of clever things to drive sales, but inventory optimisation is the most important thing when it comes to profitable retailing
Mark McKenzie, Technical Director, guitarguitar
What are the biggest challenges you face when integrating AI into your organisation? (respondents were asked to select all that apply)
High costs for implementation
Insufficient data
Difficulty finding the right tools and technologies
Lack of data privacy and security measures
Shortage of skilled AI talent
Poor data quality
Internal resistance to change
A lack of understanding and trust
Potential negative societal impacts
Bias and fairness in algorithms
Our organisation culture
Hallucinations
"Security and privacy obstacles are never good, but it’s at least good to see retailers acknowledging the problem. AI Data locality in particular is a privacy issue I expect to see more conversation around in the near future. As retailers expand their AI usage, they should consider how their chosen AI provider lets them control where data is processed."
Martin Sanchez, Product Marketing manager, Cloudflare
If you answered Potential negative societal impacts, how do you ensure the ethical use of AI in your organisation?
How have the challenges you face changed over the last 12 months when it comes to integrating AI into your operations?
Which of the following AI technologies are you most excited about?
Generative AI (e.g. LLMs, text-to-image creation, AI-content)
Computer visions (e.g. real-time object detection, image and video analysis, AR & VR)
Natural Language Processing (e.g. sentiment analysis, chatbots, voice assistants)
Reinforcement learning (e.g. AI agents, optimising complex systems)
Robotics (e.g for warehouse automation)
"I understand excitement around Gen AI, text to image and content because that’s what people have easy access to. However, there is a risk of overlooking other valuable AI solutions. The sudden widespread of tools like ChatGPT can lead to their use as a "silver bullet" without fully considering the results, impact, or costs."
Mark McKenize, Technical Director, guitarguitar
