How AI Can Improve Consumer Product and Retail Experiences

This can enable organizations to develop the products consumers want and develop them faster, improve current products and get the right products onto the right shelves.

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We’ve heard a lot in the past year about how artificial intelligence (AI) will revolutionize the supply chain in terms of resilience and efficiency, but less about how AI can optimize the supply chain for customer convenience. Especially in the consumer product and retail (CPR) space, there are major opportunities to leverage vast amounts of existing supply chain data through AI and machine learning (ML). This can enable organizations to develop the products consumers want and develop them faster, improve current products and get the right products onto the right shelves.

Taking this opportunity is a way of closing the gap between existing AI-driven supply chain improvements and what’s still possible. While 54% of companies say their supply chains have “changed significantly” since 2020, just 27% are working on the kind of end-to-end supply chain transformation that’s required to connect back-office data and operations to front-office customer experiences.

Elevating CX Through the Power of Data

For many organizations, AI has already made supply chain operations more responsive and agile by improving demand forecasting and inventory optimization. AI can also provide real-time supply chain insights for data-driven decisions that enhance customer experience. Rather than thinking of AI solely as an efficiency tool, it’s time to see AI as the catalyst that can transform the retail success value chain by making it easier to accurately prioritize customer convenience.

This transformation starts with customer demand data. In an ideal supply chain system, companies would only create products that customers demand, but until now it’s been difficult for CPR companies to do that. Forecasting the future based on real-time data is easier and more accurate with predictive analytics and AI, which can reduce operating costs by optimizing stock and minimizing returns.

This approach can also lead to higher sales. With a wealth of data and AI analytics, CPR companies can provide highly personalized customer experiences, enhance their loyalty program offerings, and develop more effective promotional strategies. Tapping into the power of data enables companies to adopt a customer-centric approach that meets individual expectations for holistic customer experience improvements.

Building a More Sustainable, Customer-Centric Supply Chain

Sustainability matters to many consumers, but price is increasingly important to them as well. In a recent consumer survey, 59% said that sustainable products don’t need to cost more than comparable products that aren't sustainable. AI can help CPRs meet both sustainability and price expectations in several ways.

They can use AI and their data to make accurate product and distribution decisions that reduce waste and optimize transportation cost and carbon footprint. CPRs can also use digital twins and AI-driven accelerators to flag potential supply chain issues so companies can address them before they become problems that squander limited resources. AI can also identify the optimal suppliers to buy from and the optimal time to buy, to improve resilience and minimize waste across the supply chain.

Enhancing the Employee Experience

In fall 2023, the part-time retail turnover rate reached 95%. The retail employee experience has been notoriously challenging since the pandemic began, in part because customer frustration with stockouts and shortages drove an increase in negative engagements. AI-driven, data-fueled improvements to the supply chain that improve customer experience can also improve employee experience, potentially reducing employee turnover.

For example, if a CPR sells only the products people want to buy, then their employees’ work becomes more efficient and less stressful. If the CPR can automate tasks like forecasting, inventory planning, and order processing, that frees employees to focus on higher-value tasks that allow for more creative thinking and less stress. Because employees also get direct feedback from customers, they should also have a role in planning products. 

End-to-End Supply Chain Optimization

What does AI for better CX look like in the supply chain? An example company applied AI and data strategies to reduce forecast inaccuracies that were causing costly product overruns and shortages, storage and distribution problems, and stock issues that impacted consumers.

By adding data streams to the existing forecast models, the company has improved its planning capabilities, resulting in less waste, lower operating costs, and a better experience for customers and retail employees when the products customers want are in stock.

Preparing for a Data-Fueled Supply Chain Transformation

Starting the journey to an optimized supply chain that delivers the benefits described above requires several key elements:

  • A detailed assessment of existing data infrastructure. This assessment can serve as a roadmap for planning strategic investments in cloud-friendly platforms and AI technologies to lay the foundation for significant transformation.
  • A comprehensive AI strategy that prioritizes sustainability and responsible practices. Governance and defined procedures provide important guardrails for this fast-evolving technology. Those guardrails and the roadmap can help identify strategic investments and appropriate tools to support the overarching plan.
  • A culture of data-driven decision making and customer centricity. Whether you’re building this from scratch or expanding existing data-driven thinking across the organization, it’s important to cultivate and elevate a mindset that prioritizes the effective use of data for customer-centric improvements.
  • A pilot program. Starting your initiative with a small, controlled test phase will allow you to gauge the effectiveness of your strategies and assess the potential return on your AI investments. A test run also allows you to gather feedback from your customers, which you can use to co-create new and better experiences.

What these elements will look like in practice, and how long it will take to implement them, varies from one organization to another. That’s because the most effective AI supply chain initiatives are highly customized to reach specific goals, and because each organization will have its own scope and budget. The timeline could be measured in months or in years. Regardless of the timeline, it’s critical to begin the journey soon, to meet customers’ expectations for convenience, price and sustainability.