The retail sector is growing and increasingly competitive as companies vie for consumers’ attention and wallets. According to the National Retail Federation, core sales rose 3.2% year-over-year in the first half of 2024, and total sales are forecast to eclipse 2023 by between 2.5% and 3.5%. In a tight market, retailers are looking for a competitive advantage, and many are turning to artificial intelligence (AI).
AI has been positioned as a disruptive capability that can reimagine offerings, expand choice, and drive new business models. Retailers have made significant investments in AI, but they need to better understand how to use the technology to create value for customers and capture value for themselves.
While the technology has been around in some form for years, algorithms have grown better and faster, computing capabilities have improved, and price points have become more affordable. NVIDIA graphics processing units (GPUs) can make what once was a seven-day compute into a seven-minute compute, and Snowflake has added flexibility to its AI cost structure by also charging per compute. These factors have unlocked more AI use cases for retailers and made the technology fit better into IT budgets.
However, many retailers are still struggling to see tangible returns on their AI investments. They’re experimenting within months, not years, and can’t afford to take a spray-and-pray approach with those trials. Retailers must approach AI strategically so they can meet their ROI goals, especially as the industry faces changing consumer behaviors.
Let’s dig in and examine the three steps to unlocking value creation and value capture.
Mature data into a strategic asset
For retailers to successfully leverage AI, they must first ensure their data is mature, clean, and harmonized. Without high-quality data, even the most sophisticated AI algorithms will fall short, leading to the adage “garbage in, garbage out.”
In retail, data comes from various sources: point-of-sale systems, e-commerce platforms, inventory management systems, customer relationship management (CRM) tools, and even external sources like social media and weather forecasts. To create a strategic asset, retailers must integrate data from all those sources, cleanse and standardize it, ensure its accuracy and completeness, and implement robust data governance practices.
One area where high-quality data can significantly impact both value creation and capture is forecast planning. Accurate forecasting is crucial for retailers to optimize inventory levels, reduce waste, and meet customer demand. Consider the fashion industry, where planning cycles can stretch up to 18 to 24 months. Retailers must predict trends, consumer preferences, and demand levels far in advance, often with limited data.
By leveraging AI with a solid data foundation, retailers can incorporate an unprecedented number of variables into their forecasting models, like historical sales figures, demographic information, weather patterns, economic indicators, and social media trends.
Encourage a culture of experimentation
This approach is essential for value creation, as it allows retailers to test and refine AI-driven initiatives that directly benefit customers. By running targeted experiments, retailers can identify which AI applications truly resonate with their customers and drive loyalty without committing to large-scale implementations prematurely.
A critical aspect in driving a culture of experimentation is the creation of concise use cases and deriving KPI measurements to determine its eventual success. Collaboration among business and technology stakeholders, which includes engineers, analysts and data scientists, is necessary as the experiment evolves from concept to reality. Equally imperative, is the mindset to pull back an experiment when the realized value does not meet expectations.
This culture encourages innovation and helps retailers stay agile as market conditions change. It allows them to test new ideas quickly and cost-effectively, reducing the risk associated with large-scale AI implementations.
Build out the ecosystem
While the previous steps focus primarily on creating value for customers, this step is crucial for value capture — ensuring that retailers can effectively monetize their AI initiatives.
A retailer’s ecosystem can include technology providers, brands, influencers, content creators, and even other retailers. By constructing such an ecosystem, retailers can create new revenue streams, enhance their offerings, and strengthen their market position.
For instance, a retailer might collaborate with a computer vision company to create an AI-powered visual search tool, allowing customers to find products by uploading images. This enhances the shopping experience and opens up opportunities for targeted advertising and product recommendations.
Influencer marketing is another area where AI and ecosystem building intersect. Retailers can use AI to identify and analyze the most effective influencers for their brand based on factors like audience demographics, engagement rates, and content relevance. By integrating influencers into their AI-driven marketing strategies, retailers can extend their reach and create more authentic connections with potential customers.
Retailers must carefully navigate issues of data privacy, competitive dynamics, and brand alignment. However, when done successfully, it can create a cycle in which the value created for customers through AI initiatives is effectively captured and monetized by the retailer and its ecosystem partners.
This strategic approach to AI implementation allows retailers to move beyond the hype and toward practical, results-driven applications. As AI continues to evolve, those who master these steps will be well-positioned to thrive in the retail landscape. Skillfully balancing value creation and value capture in AI initiatives turns technological potential into a competitive advantage.
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