Data Integration and AI Adoption in Retail

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Drawing together some of the most cutting-edge insights from The Hive community, with a sprinkle of benchmarking and a healthy dose of practical tips.

Whether it’s a fear of being left behind, or wanting to stay ahead of the curve – AI is at the fore. From achieving a single view of the customer (SVOC) to adopting AI solutions, retailers face significant challenges coupled with substantial opportunity!

This guide brings together insights from members of The Retail Hive community who are navigating their digital transformation journeys. This isn’t just theory – it’s practical guidance from retailers who are actively solving these problems right now.

“We see around 4% of searches now come from GPT. We know that Google’s algorithm promotes authenticity. We experimented with some AI enhanced advert images, only to find our customers were disappointed at the lack of believability and authenticity.” – Clothing Brand

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An actionable 8-step roadmap for moving from AI pilots to production

Real retailer case studies from luxury brands, fashion retailers, and clothing companies

Expert recommendations from Rackspace Technology’s Director of Data and AI

Build vs. Buy guidance – when to purchase solutions vs. develop in-house

The leap from ‘cool’ to corporate is tough. Many companies get stuck in pilots and proofs of concept. AI often starts as an exciting experiment, but translating that into something the wider business adopts is a different game altogether.
The tools are often there, even the strategy. But you have to go bottom-up–work directly with teams, help them see what’s possible, and crucially, build something they feel ownership over. Independence in those early phases is really important.”
Shwetank Steel, Director of Data and AI, Rackspace Technology

Data Integration and AI Adoption in Retail

Drawing together some of the most cutting-edge insights from The Hive community, with a sprinkle of benchmarking and a healthy dose of practical tips.

The leap from ‘cool’ to corporate is tough. Many companies get stuck in pilots and proofs of concept. AI often starts as an exciting experiment, but translating that into something the wider business adopts is a different game altogether.

The tools are often there, even the strategy. But you have to go bottom-up–work directly with teams, help them see what’s possible, and crucially, build something they feel ownership over. Independence in those early phases is really important.”

Shwetank Steel, Director of Data and AI, Rackspace Technology