Retail is always looking for ways to run things better and faster. AI is changing the game, offering new ways to automate these tasks and saving time and money. This case study dives into how AI can take over these tasks, making operations smoother by cutting labour costs and improving accuracy.

Business scenario

Retail companies often face the headache of identifying and pricing items efficiently. This process is time-consuming and prone to errors, leading to lost revenue and inefficiencies. The need for a better way is clear, and that's where AI comes in, offering a smarter, faster, and more accurate approach.

Technical approach

In this section, we’ll break down the simple yet powerful AI tools and methods that were used to handle item identification and pricing:

Image recognition and title generation

• OpenAI’s Vision Model and Gemini’s Vision Model: These AI models helped create item titles from images.
• Google Lens and Bing Visual Search: These tools helped identify items from images, with Google Lens proving especially reliable.

Retrieval-augmented generation (RAG) approach

• The top ten search image titles were used to help the AI generate accurate item titles.
• Users could then select and tweak these AI-generated titles before using them to search eBay for pricing info.

User interaction

• The AI system allowed users to pick the most relevant titles and eBay search results, enhancing the accuracy of item identification and pricing.
• This interactive approach ensured that AI-generated titles and prices were accurate.

Pricing analysis

• The AI tools provided low, median, and high pricing values from eBay search results, helping users make informed pricing decisions.

How AI tools can benefit retail companies

Efficiency and accuracy in item identification

• AI tools significantly reduce the time and effort needed to identify and label items by automating the title generation from images.
• Better accuracy in item identification means fewer errors and correctly priced items.

Enhanced pricing strategies

• AI-driven pricing analysis provides a full picture of market prices, helping retail companies set competitive prices and maximise profits.
• Seeing low, median, and high pricing values helps set the best prices for different items.

Scalability and cost savings

• Automating item identification and pricing processes allows retail companies to handle large volumes of items more efficiently, saving money.
• AI tools scale easily to keep up with growing inventories without a jump in labour costs.

Improved decision-making

• AI provides data-driven insights that back up human expertise, making decision-making processes stronger.
• Reducing human bias and errors leads to more consistent and fair item valuations.

Handling obscure items

• AI’s advanced image recognition can spot obscure or unique items that might stump humans.
• Setting up business rules for items that AI can’t easily identify ensures no valuable items slip through the cracks.

Tailored solutions for different user needs

• Creating specialised AI applications for different user roles, like store staff and listing teams, meets various needs and priorities.
• Advanced research tools help identify high-value or obscure items, fine-tune item titles for eBay, and boost efficiency.

Lessons Learned

Balancing specificity and relevance

• Initial AI models struggled with creating titles that were either too broad or too specific, impacting pricing accuracy. Enhanced user selection improved the balance between specificity and relevance.

Image quality matters

• The effectiveness of AI in identifying items is significantly impacted by image quality and orientation. High-quality, well-oriented images are crucial for accurate results.

Human-AI collaboration

• While AI can provide significant insights, human judgment remains invaluable. Combining AI-generated data with human expertise results in better outcomes, especially for obscure or unique items.

Iterative development

• Ongoing refinement and testing are essential. Addressing initial limitations demonstrated the importance of continuous improvement.

Context sensitivity

• AI systems need to be context-aware to handle the diversity and complexity of real-world data effectively. This includes addressing issues like background noise and lighting variations.

Cost-effectiveness and business alignment

• It's crucial to evaluate the cost of deploying and maintaining AI solutions, ensuring they align with business goals and provide tangible value.

Conclusion

AI is a game-changer for retail, especially when it comes to item identification and pricing. By automating these processes, AI makes everything run smoother and faster, cutting down on labour costs and improving accuracy. This means more efficient operations and a boost in profits.

As AI continues to evolve, its tools will only get better, offering even more value to retailers. Embracing these technologies now sets you up for long-term success, making your business more agile and ready to meet any challenge. So, if you’re looking to streamline your operations and maximise profits, AI is the way forward.

Strategic Insights in Future AI Solutions - Roberto Ferro

Aligning technology with business needs

• There's often a gap between tech and business goals. Building an app or using AI might not directly solve the real problem.
• Effective AI means really understanding the business issue and continuously improving the solution to fit the need.

Cost-effective AI development

• Setting up cost-effective AI product development pods can help businesses test the waters with AI solutions.
• It's crucial to know the cost of deploying and running the AI, including API usage and other expenses.

Proving the business case

• Before going big, it's essential to show that AI adds monetary value and solves the problem.
• Data from test runs can help make a solid case to stakeholders, securing funding and support.

Balancing innovation and practicality

• AI solutions should balance risks, compliance, and security to be practical for real-world use.
• The goal should be to add real value and meet business needs, not just chase the latest tech trend.