Why high-quality, integrated data is critical to innovation with AI in retail.

Innovating with AI tomorrow means fixing your data today.

Recently, I had the opportunity to attend Big Data LDN – one of the UK’s largest data and analytics conferences. While it was inspiring to see the rapid developments in technologies like Generative AI (Gen AI) and the countless ways it can transform the Consumer Products and Retail Industry, there was a clear, resounding message that echoed throughout the event.

Data is everything.

Retailers and brands around the globe are fired up when it comes to harnessing the power of Gen AI to revolutionise every aspect of their operations. According to the latest report from Capgemini Research Institute, 40% of organisations in the retail sector have implemented generative AI across some or most functions/locations, more than doubling from 17% in 2023.

However, there is an important prerequisite that cannot be ignored: you need to get your data right first. 

If you leap forward into innovations without high-quality, well-managed data, even the most cutting-edge AI systems won’t deliver the expected value and may even introduce greater risks.  

There are three key fundamental data management practices that retailers must get right to enable more innovation:

1. The importance of data quality and representation 

The concept of “garbage in, garbage out” has never been more relevant.

One of the most pressing concerns with data in the age of AI is the risk of bias. As GenAI becomes more prevalent in decision-making processes, particularly in consumer products and retail, ensuring that data is unbiased and representative of all customer segments is crucial.

A flawed or skewed dataset can lead to algorithms that reinforce stereotypes, exclude certain groups, or produce discriminatory outcomes.

Take, for example, the Apple Card controversy in 2019, where women were reportedly given lower credit limits than men, even when they had similar or better financial profiles. The algorithm behind the credit decisions may have been trained on historical data that reflected gender biases, which were then perpetuated in the AI-driven system. This incident highlights the significant risks that arise when AI tools are trained on narrow or biased data sets. It underscores the importance of ensuring that data is unbiased and representative to avoid alienating customer segments and fostering inequality.

Unbiased data is the key to ensuring fair and equitable AI outputs. Organisations must critically assess their datasets to identify and eliminate biases before they can leverage AI to its full potential.

2. Data privacy and compliance in an increasingly regulated environment

In today’s regulatory environment, ensuring data privacy and compliance has never been more crucial. With regulations like GDPR in Europe, businesses need to treat consumer data with the utmost care and transparency.

In consumer products and retail, companies often gather vast amounts of personal data, from purchase history to online browsing habits. Yet, with growing consumer awareness of data privacy, organisations must tread carefully to maintain trust.

In today’s environment, a brand’s mishandling of customer data will not only lead to substantial fines, but a significant loss of consumer confidence.

The lesson here is clear: collecting data ethically and respecting consumer privacy is essential. Failing to do so could result in regulatory penalties and a damaged brand reputation that’s hard to repair.

3. Data integration must occur across the value chain 

The true power of data lies in its ability to be integrated across the entire value chain.  

In many organisations, data exists in silos—whether it’s locked within the supply chain, marketing, or customer service departments. This fragmentation prevents businesses from seeing the full picture and hinders their ability to make informed, data-driven decisions. 

Imagine a scenario where supply chain data is not synchronised with marketing and customer experience data. Businesses would miss out on the opportunity to provide seamless, personalised experiences, and operations would become inefficient.  

Integrating data from across the value chain enables businesses to derive actionable insights that benefit the entire ecosystem

Dig into the data to drive innovation. 

By now it should be clear that while the innovations around Gen AI are exciting, the true value of these technologies can only be realised when organisations prioritise their data.

To ensure you’re ready to fully embrace AI’s possibilities, focus on these key areas:

1. Ensure your data unbiased and representative
2. Prioritise ethical data use and adhere to regulations
3. Break down data silos and integrate information across all departments for a holistic view and more informed decision-making

By getting these fundamentals right, any organisation will be in a strong position to unlock the full potential of AI-driven innovations.

But how do you go about it?

With market-leading capabilities in data & AI, Capgemini is your trusted business and technology transformation partner to help you wade through your data to tackle the above challenges.

Armed with 30,000+ data & AI consultants and engineers, deep retail industry expertise, an industrialised assets, we’ll help you cut through the buzz and leverage the most relevant use cases for your specific business needs, to create concrete business impact. We’ll help you define your Gen AI strategy, select priority use cases, and develop and deploy tailored solutions at scale in a responsible, reliable, and trusted manner.

Get in touch with our retail experts to explore your AI opportunities today.

Hear what senior data executives at top consumer products and retail brands have to say on how data and AI are transforming their businesses here.