Last month, I had the opportunity to attend Google’s Retail Event, Transforming the Retail Customer Experience with Google Cloud. The event provided a fascinating deep dive into the role of Generative AI (GenAI) in retail, highlighting both the tremendous opportunities and the challenges that companies face.

Here are some key takeaways:

• 89% of business leaders are prioritising AI adoption as their top technology focus.

• GenAI leaders report 33% higher chances of seeing revenue growth of 10% or more.

• 75% of companies are already seeing ROI from their GenAI investments.

Despite these impressive stats, many companies still feel like they're lagging behind the curve. So, what’s holding GenAI back from being adopted more quickly, and how can businesses overcome these hurdles? Rather than an exhaustive list of challenges, I’d like to focus on a few common barriers and how they can be tackled.

I hear this a lot and I heard it there - data quality is a barrier with complaints over the existing data that’s available in retail. It's a barrier we hear about often, especially in retail. Data gaps, data variability at a product level and no single source of the truth for product and customer data alike. The common response? Focus on fixing the data first, then move on to AI.

Flipping it around - I also heard companies that are using GenAI to actually enhance and enrich their data. Instead of viewing data as an obstacle, these organisations see it as a powerful enabler. By leveraging AI, they’re tackling data quality issues head-on and unlocking greater value from the data they already have. This perspective reframes GenAI not as a bottleneck but as a catalyst for improvement.

The culture of resistance to change is not new and it's not unique to GenAI. I heard senior leaders complain of board members stopping AI initiatives in their tracks. The word “risk” seemed to be on everyone’s lips- concerns about the risks of implementing AI, the temptation to dismiss it as just another “shiny new object” and fears about the potential downsides of doing AI for AI’s sake. Yet, this focus on perceived risk often ignores the far greater risk: the cost of doing nothing. In a previous business I worked in, we used to call this “fade”. With such technological progress and dynamism, standing still is an option - it’s a dangerous choice - it leads to lost revenue, diminished market share, and a shrinking customer base.

Start, Think Big, & Go Quickly 

The successful are starting and thinking big. They're setting clear objectives, defining specific use cases, and establishing measurable KPIs. And going quickly. This approach allows companies to experiment, learn quickly, and scale GenAI.