Zitec, Google Cloud, and 16 other retail industry leaders convened for a closed-door event to delve into the practical implications of AI in retail and go beyond the hype to explore its true business impact.
At the heart of the discussion, we explored how AI is reshaping the retail landscape. Retailers opened up about their journeys, facing challenges like data management, upgrading legacy systems, improving hyper-personalization, and more.
Let's dive into the top five findings uncovered during this informative conversation and tap into the real-life solutions to existing and future challenges in retail.
Event Overview: Objectives, Goals, and Outcomes
The exclusive Zitec and Google Cloud event gathered retail industry leaders to assess and discuss AI's transformative potential in retail. The objective was clear: to shift focus from AI as a buzzword to its tangible business impacts through real-world applicability scenarios.
Two crucial aspects set this gathering apart:
- The closed-door event served as a starting point for some of the greatest minds in Romanian retail to brainstorm, share insights, and come up with practical solutions free from the usual constraints of public forums.
- Featuring insights from pioneers at Zitec and Google Cloud, the curated selection of only 16 participants ensured a diverse crowd spanning retail sectors such as FMCG, cosmetics, fashion & apparel, consumer electronics, and DIY.
More importantly, the event convened these executives under a common commitment: to find new and improved ways of adopting digital transformations using AI that prioritize ethical practices while ensuring technology benefits all stakeholders—partners, customers, and employees, while improving business margins.
Top Challenges of Using AI in Retail – Industry-Shaping Perspectives
Retail leaders across various industries, such as FMCG, cosmetics, fashion & apparel, consumer electronics, or DIY, are working to overcome AI's complexities – and it’s not an easy feat.
From discovering and interpreting large volumes of data to updating outdated systems, the road to AI fluency is bumpy. However, retail leaders agreed on one clear priority: the importance of having the right business scenarios and getting the stakeholders (boards, partners, and employees) on board to navigate these challenges.
Zitec, Google Cloud, and top decision-makers such as our event speakers – AdoreMe, Leroy Merlin, or Dr. Max – uncovered the following challenges of using AI in retail:
Organizing data
Effective AI applications require organized, accessible, and high-quality data to uncover business opportunities. Yet, retail companies often deal with large amounts of unstructured and siloed data.
The solution is to develop methods to organize this data, but the lack of procedures and processes for these activities is a major impediment. This takes us to the next two challenges identified by retail executives.
The talent shortage
There is a significant gap in AI-specific skills in the retail industry. Finding and retaining the right talent who can bridge the gap between technical AI capabilities and strategic business applications is a growing pain.
Lack of existing procedures and systems
Many retail businesses operate on legacy systems that are not optimized for AI integration. But developing or updating procedures to incorporate AI technology demands time and investment.
Cybersecurity risks
Source
More data to handle means more security risks, and with ever-evolving cybersecurity threats, this challenge remains hard to keep up with. Ensuring the protection of customer and business data against cyber risks is a major focus area in 2024 and beyond.
How Retailers Conquer AI Integration
Digging deeper, we explored the immediate opportunities of integrating AI into existing systems. We learned that organizing data and getting systems up to speed are major stumbling blocks. Plus, finding the right talent to lead the charge is key to success in the AI field.
Still, upon a fruitful conversation with the bright minds that participated in this exclusive roundtable event, some insights emerged:
- In adopting new technologies, the right solutions should encompass diverse scenarios rather than solely relying on off-the-shelf applications. While big retailers prefer bespoke solutions from partners like Zitec over off-the-shelf for digital transformation, with AI, things are more complicated than that.
- Pre-trained models like RecommendationsAI, semi-custom options with market testing, and fully custom models should all be leveraged to deliver optimal business outcomes. However, if you’re taking the fully customized model route, we advise you to run a POC before the full-scale implementation to test the solution.
- The knowledge and experience of seasoned experts are a sure way to tackle the complexities of AI. That’s why competitive organizations partner with ready-to-operate teams to get strategic insights and custom-made AI integrations rather than operating in-house at larger costs.
What’s your take on partnering with top IT and digital transformation leaders to help accelerate AI integration in your retail business? If improving business outcomes is a priority, find the right technology solution for your retail company here.
Zitec, Google, and Top Retail Decision-Makers Discussed 5 Business Opportunities for AI in Retail
Venturing into the world of AI is a new and complex feat, which is why retail speakers at the event stressed the need for a solid data foundation and a culture that embraces innovation. Eventually, they uncovered five main findings around AI for retail:
1. AI in data management
During the exclusive roundtable, the focus fell on data management with AI, as participants discussed how retailers handle and use vast datasets in their business.
Retailers discussed the necessity of solid data systems such as data warehouses and data lakes capable of handling both structured and unstructured data to improve decision-making and contribute to more personalized customer experiences.
- One such example is Google's pre-built product, RecommendationsAI (offering personalized product recommendations). With access to extensive volumes of data from retail customers that continuously enrich the AI algorithms, it has been extensively developed to deliver outcomes such as engagement, revenue, or conversions.
- Another business application that stood out was the use of AI for inventory predictions, which already improved supply chain efficiency for one of our participant’s business. By analyzing historical sales data and market trends, their AI systems could better predict stock levels, reduce overstock and stockouts, and ensure customer product availability.
However, there’s growing concern about the compatibility of new AI technologies with old databases and applications. The need for efficient data governance is also a great hurdle to overcome.
2. AI for hyper-personalization
The implementation of machine learning models, like Google's Recommendations AI, allows you to analyze customer data and predict buying behaviors with high accuracy during the online shopping experience. This leads us to our next point discussed: hyper-personalization.
- During the roundtable, one fashion retailer revealed that by integrating AI-driven personalization tools, they could offer outfit recommendations based on customer's past purchases and browsing behaviors. This boosted customer satisfaction and increased repeat purchases, as well as average order values for their brand.
- Another tangible example of AI's impact in retail is through personalized marketing campaigns. For instance, a retailer can implement machine learning algorithms to analyze customer purchase history and browsing behavior. This data can be used to craft personalized email marketing campaigns recommending products that align with past purchases.
- Dynamic personalization is also a hot topic among retailers, who are starting to understand the power of analyzing live user behavior to recommend products, upsell, and help users navigate through products or services with ease.
But AI and personalization is a much more complex topic than that. Moreover, ensuring that personalization algorithms remain transparent and ethical is crucial to maintaining customer trust.
3. Pragmatic AI adoption in retail
The first step in AI adoption is identifying the business scenarios that would benefit most from AI intervention (e.g., low customer retention, cart abandonment, or low cart value). Then, begin with a proof-of-concept that can potentially turn into a scalable project, demonstrating quick wins and tangible benefits.
For instance, in a Zitec study conducted on over 150 CIOs in retail, we uncovered that 57% of these leaders will use AI to improve the checkout process. Many also see the value in using AI for fraud detection (53%), demand forecasting (51%) and chatbots that interact with their customers (49%).
What’s more interesting is that these market findings align with what our retail participants have in plans for their organizations. As AI strategies are still being defined, every organization has the opportunity to develop unique use cases and secure a competitive advantage.
Another aspect we discussed was the alignment of AI technology with the existing digital infrastructure. Here, the role of digital transformation partners becomes invaluable – which takes us to our next topic.
4. Digital transformation in retail with AI
The conversation extended beyond general digital transformation to focus specifically on integrating AI to reshape retail operations.
But the integration of AI affects various stakeholders within the retail ecosystem:
- Partners must engage with more sophisticated data-sharing and analytics platforms.
- Customers benefit from more personalized experiences but also require assurances regarding the security of their data.
- Employees face shifts in their roles and responsibilities.
Our advice? Tackle digital transformation strategically. Start with small projects that delight customers. Focus on key areas to gain distinctive advantages:
- Loyalty programs to build lasting relationships
- Search functionality to simplify product finding
- Data-driven recommendations to improve product discovery
- AI-powered conversational commerce to provide personalized support
Central to digital transformation with AI is cloud technology. Google Cloud solutions offer the efficient, flexible, and scalable infrastructure that e-commerce platforms need to thrive long-term.
With the adoption of AI in retail, new roles such as AI specialists, data scientists, and user experience designers become crucial. However, the talent shortage crisis is a problem that directly affects the pace of AI adoption.
With the lack of skills or talent to develop an in-house solution, experienced tech partners like Google and Zitec can save retailers significant time and money.
5. Drive perpetual experimentation
In the face of evolving consumer behaviors and technological advancements, perpetual innovation is necessary.
A winning strategy is to encourage a company culture that celebrates experimentation and accepts its risks, including potential failures, as part of the learning process.
One of the participants already established a structure for innovation through innovation labs that encourage team members to experiment with GenAI applications.
AI is the Next Big Thing in Retail – But What’s Next?
The journey into AI and digital transformation is just beginning. With each innovation and partnership, retailers are discovering new ways to improve customer experiences and operations. The potential for growth and innovation is limitless, and Zitec is committed to leading this change.
We know – the world of retail AI is moving fast, and you want to keep up with it. The good news is that staying engaged with new issues and participating in these discussions will give you a front-row seat to the latest innovations shaping the market.
Keep engaging with our content, and stay tuned for our next events. With Zitec, you're always one step ahead.
Final takeaways: what can you do now?
- Strategic partnerships are essential for leveraging AI to its full potential in retail and overcoming the talent shortage.
- Start small with AI projects that can scale. Identify a specific area within your retail operations where AI can have immediate benefits, and pilot a project to test and refine your approach via a proof of concept (POC) approach, and asses the anticipated ROI, feasibility, and usability of the project.
- Ensure your data is clean, organized, and accessible. Effective data management is critical for successful AI implementation.
|
The Retail CIO Playbook for 2024Explore the AI trends shaping the future of retail Access exclusive insights from 150 leading IT decision-makers in retail across Europe who shared their strategies to harness the power of AI for enhanced customer experience and retail operations.  Dive into expert digital innovation strategies to respond to tech-first consumers, create personalized, omnichannel shopping experiences, and craft your winning strategy for delivering on time and on target to keep your retail strategy ahead of the curve in 2024 and beyond. |
Share via:
