Retailers often expect AI to deliver rapid improvements in customer service, but many find that initiatives lose momentum before delivering value.
With many retail companies wanting to use AI in their CX, they encounter gaps in core foundations rather than bad technology, leading to fragmented pilots, unclear impact, and wasted spend.
Most companies don't know what the right foundation is supposed to be.
As a result, AI in retail CX fails to scale when layered on top of disorganized systems, meaning retailers that skip the readiness work continue to hold back so many implementations.
Hideki Hashimura, CRM/CX Strategist at redk, argues that retailers need to check whether the correct foundations are in place to make a pilot succeed today and scale tomorrow.
"Are we creating the right foundations? That's the question,” he said.
“Are we creating the right foundations to make sure that the technology delivers on its objectives, but also that it will support expansion and over the midterm will deliver transformational impact?"
The Quick Win Trap: Why Retail AI Keeps Stalling
Retail operates under constant pressure, seeing demands spiking overnight, undersupply issues, or social media attention.
On top of this, customer expectations are rising across all channels and time zones, forcing service teams to prioritize speed and fix immediate problems over long-term stability.
A 2024 Gartner report reveals that customer expectations during service interactions continue to rise, resulting in many service leaders deploying quick-fix solutions in customer journey analytics, self-service, and AI-supported operations.
Whilst problems are temporarily resolved, they are often implemented in isolation, creating problems that deliver short-term wins but hinder long-term successes.
“The question we generally hear from brands is: ‘What is the best chatbot out there?’” Hashimura continued.
"That question shows that the approach is not right. The right question orgs should be asking is: ‘Is our foundational framework able to maximise the use of chatbots and LLM technology? ’"
Starting with the chatbot question creates the quick win trap, by falling into a repeatable cycle in which every problem triggers another short-term fix, these eventually stop being solutions and become dependencies.
The Three Foundations Retail Teams Underestimate
Retail AI readiness depends less on the tools used and more on the three core foundations that are often left underdeveloped:
- Knowledge management
- Data integration
- Omnichannel consistency
Knowledge management
This foundation is how an organization captures and maintains what it knows, serving as the information layer that systems rely on to respond accurately.
Being the least visible of the foundations, LLMs depend entirely on structured, accessible information to generate useful responses.
If this knowledge is inconsistent across teams, AI systems will likely produce unreliable answers or fall back to generic responses.
“Knowledge management is becoming a foundational piece to leverage the LLMs,” explained Hashimura.
“An LLM works on natural language and uses natural language processing to deliver its value. If you don't have content, it serves no purpose.”
Strong knowledge management also requires ongoing ownership of a maintained system that reflects how the business operates.
Data integration
This refers to how well customer and operational data are connected across systems, such as purchase behavior, service interactions, and customer feedback.
An incomplete customer view means organizations are working with fragmented signals rather than a full picture.