Artificial intelligence integration is coming of age in the retail industry. Eighty percent of retailers are expected to adopt AI within the next three years, and according to a recent IDC Europe study, 40 percent of retailers are already in the experimentation phase with generative AI. While generative AI (think AI assistants and ChatGPT) is the current focus for an industry built on barcodes, the true transformative potential comes from predictive AI.
Utilizing predictive AI to reimagine the retail experience will help retailers reach key goals like advanced personalization, omnichannel engagement, and sustainable, long-term brand loyalty. However, achieving economies of scale, cost optimization, and other AI-related benefits requires overcoming several significant hurdles, including data utilization, incorporating AI results, and achieving continuous improvement.
In a new AI guide for retailers, AI & the Current State of Retail Marketing, we explore, in detail, the three primary challenges retailers face in maximizing the transformative potential of predictive AI.
1. Driving Advanced Personalization With AI
Personalization is something that consumers want and that retailers have long struggled to deliver at scale. This is an area in which AI can have a real impact — 64 percent of consumers said they would be open to purchasing a product recommended by AI, for example. However, it’s often a challenge for retailers to use their data to generate the insights necessary to fuel personalization at scale. They’re also not sure how to leverage AI to improve this process; five in 10 retail executives lacked confidence in their company’s ability to use AI effectively, according to Deloitte’s 2024 US Retail Industry Outlook.
This is where predictive AI comes in. These tools not only improve the accuracy and effectiveness of data utilization, allowing retailers to pre-process data efficiently to gain more detailed customer insights, but they also generate timelier, more relevant offers based on those insights to enhance the customer experience. The right data — and using it in the right way — allows retailers to determine which offers to provide and when, and predictive AI helps them achieve both.
Companies that learn to use predictive AI effectively will become more adept at personalization at scale than their competitors. Companies incorporating intensive data analytics are 23 times likelier to outperform those without. Yet, selecting the correct tools is only the beginning. This leads us to our next challenge.
2. Integrated AI Generates Data-Based Insights
Data science is a valuable tool for retailers, but it must be integrated into existing workflows to exploit it. Acquiring, organizing and inputting data provides insights that improve the customer experience, but fitting these processes into a retailer’s day-to-day operations is another challenge. Many retailers have comprehensive marketing and customer relationship management systems with well-defined guardrails — especially around consumer data — so finding areas where predictive AI tools can seamlessly slot in with minimal disruption requires striking a balance.
According to the CompTIA IT Industry Outlook 2024 report, 22 percent of firms report aggressively pursuing AI-powered integration into their workflows. Why is the number so low? In short, companies recognize the value of AI but lack a culture of data-driven decision making that would facilitate the introduction of these tools into their established ways of doing business. Moreover, decision-makers may struggle to adapt existing systems to accommodate new AI solutions and make them actionable.
This demonstrates a significant growth area for retail and AI, and businesses that can move forward in this area stand to gain a substantial competitive edge.
3. Building and Iterating for Continuous Improvement
Predictive AI and generative AI tools work together to harness data and create content that encourages action. While the initial investment in predictive AI may give some retailers pause, the beauty of deploying AI-driven predictive and decision capabilities is that constant iterations create constant improvement, which in turn generates significant return on investment.
As higher-quality data flows into predictive AI tools, they become better at making accurate and reliable predictions, which retailers can act upon. However, building this virtuous circle of better data, better predictions, and repeat requires buy-in from retail executives and a willingness to commit to the journey.
The substantial investment in time and resources is why this will likely serve as the most significant hurdle retailers must overcome in generating increased ROI in 2024. Predictive AI is a process, not a plug-and-play solution.
In 2023, many retailers began exploring AI’s potential. This year, that exploration phase has moved to implementation and delivery. However, predictive AI technology is still advancing, and challenges related to data utilization, system integration, and long-term ROI remain.
However, for retailers aiming to offer personalized experiences and seamless shopping across all channels, the potential of predictive AI far outweighs any challenges they might face in implementing it. Incorporating AI into their operations is the first step toward the future of retail marketing.
The faster retailers can implement these solutions, the faster they can take advantage of them. But the clock is ticking.
Jean-Matthieu Schertzer is the chief AI officer of Eagle Eye Group, a SaaS technology company powering the personalized marketing revolution through innovative AI-powered products and solutions.
View Original Article
