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Enhancing Retail with AI: A First-Hand Practical Guide for Entrepreneurs
Artificial Intelligence (AI) has rapidly evolved from a futuristic concept into a critical business tool. In the retail industry, this shift is happening faster than ever. As businesses seek new ways to optimise operations, engage customers, and boost profitability, AI is proving to be the key that unlocks these opportunities.
This white paper provides a first-hand account of how I, Oded Kishik, co-founder and COO of Cubee, transitioned from being cautious about AI to fully integrating it into our retail operations. Through practical examples and step-by-step guidance, I share insights into how any retail business—big or small—can leverage AI to drive growth and efficiency.
In the recorded presentation, you can watch a few examples of using different tools to achieve common business objectives:
My AI Journey—From Scepticism to Adoption
Like many entrepreneurs, I was initially sceptical about AI. It seemed complex, distant, and more suited for tech giants with endless resources. However, as the market grew more competitive and our operations became more data-driven, I realised that ignoring AI would only put us at a disadvantage. What began as cautious experimentation quickly turned into a full embrace of AI as I saw the tangible benefits it brought to our business.
This paper is not just about the theoretical applications of AI; it’s a personal journey that reveals how AI became an indispensable part of our growth strategy. Through trial and error, I learned how to start small, build on early successes, and integrate AI in ways that align with our brand and values.
Why AI is Essential for Modern Retail: An Entrepreneur’s Perspective
The retail industry is undergoing a transformation. With digitalisation and data-driven strategies becoming the norm, the pressure to innovate is higher than ever. AI is no longer a luxury or an advanced option—it’s a necessity. Retailers today must manage vast amounts of data, from tracking customer behaviours to optimising inventory. AI offers a way to automate these processes, freeing up time and resources for more strategic initiatives. Research by Deloitte found that retailers using AI-driven automation have reported a 20% increase in operational efficiency and up to a 30% reduction in costs.
For me, the turning point came when I realised how much time we were spending on tasks that could be easily automated. Whether it was writing product descriptions, analysing sales trends, or managing customer interactions, the manual workload was holding us back. AI didn’t just make these processes faster; it made them smarter. By leveraging machine learning and automation, we were able to refine our operations in ways that would have been impossible with human effort alone. This shift allowed us to focus more on growth and less on day-to-day operational headaches.
Starting Small: Automating High-Impact Retail Tasks with AI
One of the biggest misconceptions about AI is that you need to go all-in from the start. In reality, the most effective AI strategies begin with small, manageable steps. For retailers, the first step often involves automating repetitive yet crucial tasks, like generating product descriptions.
Product descriptions are more than just text—they’re a critical touchpoint that influences both customer engagement and search engine rankings. When I first explored AI for this purpose, I discovered that it could generate consistent, SEO-friendly descriptions at scale, far outperforming the manual process we had in place.
To empower ourselves and our content licensees (our customers), Cubee has developed custom AI tools that pulls data from our catalogue, understands individual brand voices, and produces content that’s aligned with our marketing goals. The result? A significant reduction in time spent on content creation and a noticeable improvement in how our products perform online.
For retailers just starting their AI journey, this kind of application is ideal. It’s low-risk, easy to implement, and delivers quick wins. More importantly, it builds confidence in AI’s capabilities, laying the groundwork for more ambitious projects down the line.
Scaling AI Applications: Visual Content and Multimedia Integration
As we grew more comfortable with AI, we began exploring its potential beyond text generation. The next logical step was visual content—an area where AI has made tremendous strides. In today’s digital retail environment, visuals are everything. A 2024 report by HubSpot revealed that posts with visual content receive 2.3 times more engagement than those without. Whether it’s product images, promotional videos, or 3D models, having high-quality visual content is crucial for engaging customers.
AI tools like DALL-E and Adobe’s generative fill opened new possibilities for us. Suddenly, we could generate visuals on demand, customised to fit specific campaigns or product launches. This not only sped up our content creation process but also gave us the flexibility to experiment with different creative approaches without relying heavily on external design resources. While these tools still require human input to fine-tune results, they’ve become an integral part of our marketing strategy.
For those working in niche sectors like 3D printing, AI’s capabilities in generating 3D models are particularly exciting. We’ve begun experimenting with AI to create basic models based on text prompts or images. While the technology isn’t yet refined enough for complex designs, it’s incredibly useful for prototyping and early-stage visualisation. As these tools improve, they’ll become even more valuable in helping retailers quickly iterate on product concepts.
Customising AI for Your Brand: Building Tailored GPTs and Bots
The more we integrated AI into our work, the more we realised the importance of customisation. Off-the-shelf AI tools are powerful, but to fully align them with our business needs, we needed to build solutions tailored specifically for us. Enter GPTs—Generative Pre-trained Transformers—that can be customised to handle specific tasks.
At Cubee, we developed a custom GPT for our content licensees (our customers) that manages everything from catalogue descriptions to generating content for different sales channels. By feeding it brand-specific data, we’ve been able to ensure that the AI’s output is always aligned with our voice and values. Internally AI has been particularly effective in automating customer interactions, where the AI handles routine queries while maintaining the tone we want to convey. According to Gartner, companies using custom GPT models for content generation and customer service have seen a 40% improvement in response times and a 25% boost in customer satisfaction.
Building a custom AI solution might sound complex, but it’s more accessible than you might think. The key is to start with a clear understanding of the problems you want the AI to solve. Once you’ve identified those, you can gradually train the system to adapt to your brand’s unique requirements. This process has not only streamlined our operations but also allowed us to offer a more consistent and personalised experience to our customers.
Adapting to the Rapid Pace of AI Innovation: Staying Agile and Informed
One of the most important lessons I’ve learned is that the AI landscape is constantly evolving. New tools, platforms, and updates are released all the time, each offering different advantages. To stay competitive, it’s essential to remain agile and open to change. Relying on a single AI tool might work for a while, but as the market evolves, so should your approach. In fact, over 200 new AI tools have been introduced specifically for retail in the past two years alone, according to CB Insights.
In our business, we regularly test new AI solutions to see how they stack up against the tools we’re currently using. While OpenAI’s GPT models have been our go-to for text generation, we’ve also explored alternatives like Claude for specific use cases. By staying flexible and willing to experiment, we’ve been able to continuously refine our AI strategy, ensuring that we’re always leveraging the best tools available.
For retailers, this adaptability is key. The pace of AI development means that what works today might be outdated tomorrow. By fostering a culture of continuous learning and experimentation, you can stay ahead of the curve and avoid getting left behind in an industry that’s rapidly embracing digital transformation.
Finding the Right Balance: Automation vs. Human Creativity
As AI becomes more embedded in retail, it’s easy to fall into the trap of over-automating. While AI excels at handling repetitive and data-driven tasks, there’s still a vital role for human creativity and strategic thinking. The real power of AI lies in how it complements our skills, rather than replacing them.
AI can handle about 80% of the work in certain areas, but that final 20% still requires human input. - Oded Kishik
In our experience, AI can handle about 80% of the work in certain areas, but that final 20% still requires human input. Whether it’s tweaking a product description to capture the right tone or making a creative decision that resonates with our audience, there are aspects of retail that machines simply can’t replicate. By leveraging AI to handle the heavy lifting, we’re able to focus more on those uniquely human elements that drive brand loyalty and customer satisfaction.
Looking ahead, the businesses that succeed will be those that strike the right balance between automation and creativity. AI can enhance what we do, but it’s up to us to steer the ship and make the decisions that ultimately define our brand’s identity.
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This article was written based on the webinar we held for members of the Hive 3D product sellers community.
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