Why AI innovation in Retail demands greater business agility

The retail landscape is in constant flux, but recent announcements from tech giants Google and OpenAI signal a seismic shift driven by advancements in AI and Large Language Models (LLMs). This isn't just about adding a new channel, it's fundamentally reshaping how consumers discover, evaluate, and purchase products.

For technology leaders in retail, the message is clear: build adaptable technology platforms and resilient processes now. This will prepare your business for deep and changing integration with multiple partners and help you prepare for future business growth.

Turning traditional shopping behaviours on their head

A couple of months ago, Google unveiled Shop in AI Mode at its 2025 I/O conference. By integrating 50 billion product listings from Google's Shopping Graph with its Gemini AI chat tool, Google is offering a hyper-personalised shopping experience. Users can try on clothes virtually, get real-time price drop notifications, and have Google complete purchases on merchant websites via Google Pay on their behalf, all without ever leaving Google's ecosystem. This dramatically alters the traditional shopping journey, shifting from the original Google Shopping capability of merely recommending products to facilitating the entire transaction.

Two weeks prior, OpenAI revealed its own Shopping Feature. This largely similar proposition includes trials of a native Shopify checkout within ChatGPT and testing intent from brands to build their own product graph. Their new Operator product acts as a smart assistant, completing online shopping journeys on behalf of customers, much like a dedicated personal shopper. And it's not just these two; other players like Perplexity, with its "Buy with Pro" feature, are also entering this space.

This isn't simply an addition to the omnichannel experience; it has the potential to become a new layer on top of existing architectures. For retailers, this means rethinking their entire digital experience and where they focus their investments. Traditional aspects of the buying journey like search, product listing pages, and checkout on brand-owned channels may become less relevant as AI-driven platforms take over the discovery and transaction process.

Instead, I think the focus will shift dramatically to: 

1. Continuing the drive to decouple systems
   Breaking dependencies in systems through a headless, API-first, microservices-driven architecture and domain-driven design, to surface more granular, accessible and structured data, with richer business context and meaning.
2. Optimising the quality of product data 
   Improving the quality of product, pricing and promotion data becomes paramount to feed the chat engines and leverage user transactions.
3. Creating high-quality, informative content 
   Content will be crucial for visibility in AI-driven searches and featured snippets. Focus and investment moves from Search Engine Optimisation to Answer Engine Optimisation to ensure relevancy to typical consumer searches and questions.
4. Investing in Generative Engine Optimisation 
   Developing tokenisation strategies to enhance Model Context Protocol adoption, adding semantic meaning to product data, and building knowledge graphs to connect product data with other relevant entities. Combined, this will enhance visibility to consumers in this new mode of shopping.

Without this new focus, retailers risk being matched to customer queries based on chance rather than relevancy.

Accelerating innovation and shifting consumer behaviours

It's not just the buying experience that faces disruption. A multitude of new technology providers and enablers will emerge from this AI-supercharged innovation, creating even more opportunities for business differentiation across the entire retail operation - from customer acquisition to supply chain management.

We're already seeing this with ML and AI-driven solutions launching regularly in areas like CRM, inventory management, demand forecasting, customer service, warehousing, and logistics.

• ASOS launched their ‘Test & React’ model towards the end of 2024, using AI to analyse real-time sales data and social media trends to quickly identify emerging styles. It allowed them to place smaller initial orders and then rapidly scale up production on items that prove popular, minimising the risk of overstocking and subsequent markdowns.
• Ocado has been testing and deploying machine learning in their Customer Fulfilment Centres for some years now, implementing robotic pick and pack solutions to automate fulfilment and reduce wastage for their brand partners and end customers. I was lucky enough to see these fulfilment centres in action first hand in my previous work with Ocado.
• Tesco and Sainsbury's have partnered with technology platforms to implement AI-powered route optimisation for their home delivery fleets. Systems analyse a multitude of factors in real-time, including traffic conditions, delivery time windows, and vehicle capacity, to calculate the most efficient routes for their drivers. This has significantly reduced miles travelled, leading to lower fuel costs and a smaller carbon footprint. The impact is not only environmental and financial but also improves customer satisfaction through more reliable and timely deliveries.

However, innovation doesn't always guarantee customer adoption, as evidenced by past examples like the Amazon Dash button (a bizarre product innovation when you think about it now) or the slow uptake of Alexa voice shopping. What feels different now is the accelerating exponential pace of innovation. The ability to experiment and adapt rapidly, or to pivot away from unsuccessful experiments with minimal impact, will be more critical than ever.

As technology evolves, so too will consumer behaviour. Customers will increasingly expect seamless, personalised, and efficient shopping experiences across these new AI-driven platforms. Retailers who fail to keep up with these evolving expectations risk falling behind. Alongside this innovation, customer trust and loyalty will drive a heightened focus on governance and privacy, particularly as AI systems handle sensitive customer data. Ethical AI use and robust data security will grow in importance as key differentiators.

Advice for building in agility to take advantage of this change

This ongoing evolution presents numerous opportunities for forward-thinking retailers. However, to harness these paradigm shifts, businesses need to be able to respond rapidly and optimise their systems for continual change. The core challenge for technology leaders is no longer just about implementing new solutions, but about building platforms and processes that are inherently adaptive, flexible, and resilient.

Here are some practical steps I’d recommend:

1. Continue the drive towards headless architectures 
   This is where the front-end customer experience is decoupled from back-end systems, allowing for greater agility in deploying new channels and integrating with emerging AI platforms. This is not just a technology challenge in selecting the right platforms that meet the business need and building the connectivity between them. It is also an operational challenge in aligning teams into logical domains that enable faster flow of value across the organisation.
2. Invest in robust data strategies 
   There’s never been a greater need for high-quality, easily accessible product information. It’s important to have the platforms that make data readily accessible, at low latency, but also build the culture to drive consistency throughout the organisation (no shadow, stale databases floating around), and make sure this is kept in tandem with the technology strategy. Onboarding of end users, such as commercial teams, and making sure they feel both empowered and accountable for the quality of the metadata about their products is critical.
3. Foster a culture of continuous experimentation and learning 
   This will help technology teams build, learn and respond faster to each innovation opportunity. This requires setting clear strategic goals and empowering teams with enough autonomy, stability and space to be able to experiment with each cycle of change.

The future of retail is dynamic and unpredictable. At Crosstide, we support businesses in building greater agility into their processes and platforms to be better prepared for change. Get in touch if you would like to learn more or follow our author, Paul Stockdale, for more retail insights.