CX Excellence: Harnessing the Potential of AI in Retail


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With new and rapidly evolving technologies like artificial intelligence (AI) continually transforming the retail industry, many businesses are falling behind their competitors because they’re unclear where to start when it comes to integrating it into their operations. In fact, a 2023 report from Workday found that 76% of business leaders believe their knowledge of AI and machine learning (ML) applications needs improvement, and 72% say their organization lacks the skills to fully and adequately implement AI and ML initiatives. Yet, with an increasing number of consumers wanting and expecting the convenience, speed, efficiency, and personalization that AI tools can deliver, AI integration is no longer a ‘nice to have’, it’s a ‘must have’ to succeed in today’s market.

In Adobe’s 2023 State of Digital Customer Experience report, the consumer experience was shown to have improved over the last three years as businesses have embraced digital transformation including AI and ML. Half of respondents said brands’ platforms and apps have gotten more user-friendly and seamless, 49% noticed more consistency in personalization across in-store and online interactions, and 42% said targeted ads are more relevant to their needs and interests. But to reap the benefits of these efforts on a sustained basis, brands must take a thoughtful and responsible approach to integrating AI and GenAI into their business.

Adopting and Implementing AI for Better CX

How a business delivers its products and services to its customers is just as important, if not more, as the quality of those offerings. In fact, in a survey conducted by my firm, TELUS International, the top reason Americans of all ages would speak poorly about a brand was due to a negative customer experience, not a poor experience with a product.

Consider the benefits of offering customers the ability to interact with your products where, when and how it’s most helpful and convenient for them. For example, an AI-powered virtual fitting room allows customers to visualize how clothing or accessories look on them without physically trying them on, saving the customer time and reducing exchanges and returns. Implementing AI-driven chatbots on your website can offer instant and personalized customer support by answering queries, providing product information and assisting with the purchasing process, thereby improving the overall customer experience.

High quality, trusted and diverse data is key to designing, building and delivering these types of personalized and customized experiences, and to do so effectively, there are some key considerations for companies to keep in mind:

1. Design an AI strategy in line with CX goals.

There is an ever-growing list of use cases for AI in retail, many of which go beyond the responsibilities of one CX leader, like using AI to assemble products, pack boxes in a warehouse, or find items on a shelf. Regarding CX delivery, the strategy must focus on a specific question: “How does AI directly impact the business-consumer relationship?” For example, AI plays a massive role in hyper-personalization that goes beyond using name or location and delves into specific customer preferences and needs. AI algorithms can analyze customer purchase history to provide tailored product recommendations to enhance the shopping experience by presenting items that align with the customer’s individual preferences. However, an effective AI strategy must consider input from all key stakeholders, both inside and outside the business, to understand the particular challenges and opportunities of CX delivery. By understanding what consumers expect from a positive CX, brands can more accurately tailor their strategy and approach while incorporating the right balance of AI-powered services, human agents and automated solutions to meet customer expectations.

2. Ensure you have the right AI expertise.

Brands should adopt a three-pronged approach to building an internal AI team. For starters, employ the right mix of data engineers, data scientists, AI architects, and ML engineers because each role has specialized expertise. Brands should also consider partnering with an experienced third-party vendor that offers the right people, tools and digital and data solutions to provide them with end-to-end support from consultation through to building a platform or providing training datasets to operating a cohesive and differentiated solution that’s designed to delight their end customers. In doing so, brands can ensure they are sourcing enough high quality datasets to properly train their algorithms to advance their AI projects to meet critical CX business needs faster and more strategically. Lastly, because AI technology is constantly evolving, continuously training and upskilling employees is a crucial step to keep up with the rapid pace of technology evolution.

3. Focus on responsible AI and bias mitigation.

For all the great advancements we are able to achieve with the support of AI, there are many concerns surrounding how to develop it and use it responsibly. As GenAI learns from vast datasets, if the training data reflects societal prejudices or stereotypes, the AI model may learn and inadvertently perpetuate and amplify those biases when generating new content. In fact, in a recent survey my firm conducted, 40% of American consumers said they don’t believe companies do enough to protect users from bias and false information, while 77% encouraged algorithm audits to mitigate bias before integration. Meanwhile, KPMG found that 90% of consumers say companies should implement AI ethics policies, but less than half (44%) have embraced them. Most consumers won’t trust organizations that aren’t transparent about data collection and use. Ensuring your organization is transparent in disclosing where AI is being used and how customer data will be used and stored and building robust trust and safety protocols is key to building consumer trust.

4. Scale AI carefully and track ROI.

Implementing AI is a complex undertaking. Rather than immediately integrating it enterprise-wide, CX teams should consider launching a pilot program that allows for testing on a smaller scale — organizations that go full steam ahead risk a very costly failure. Pilots give developers the opportunity to trial the technology and spot errors to course correct and adjust on a smaller scale before a wider roll-out. Brands should also take note of the ROI in areas like time and cost savings, increased revenue, as well as in improved customer experiences and greater business agility. Moreover, it is critical to examine ROI as a whole rather than simply focusing on one point in time or one aspect of the platform. This valuable and crucial data will enable brands to adjust their business operations and strategies in real time to be even more effective and resilient in an ever changing and competitive industry.

Take a Measured and Thoughtful Approach to AI

It’s becoming increasingly critical for retail brands to leverage AI, from transforming customer experiences to driving greater productivity, it should be embedded in the functionality of day-to-day CX operations and workflows. However, the importance of being deliberate in its implementation cannot be overstated — it requires a strategic, iterative approach. Rushing into AI adoption without the fundamentals and proper framework in place will be detrimental to its success. Those that are strategic and deliberate in their adoption strategy will benefit from the transformational power of AI to deliver best-in-class CX.

Michael Ringman
Michael Ringman is the Chief Information Officer at TELUS International and has been with the company since 2012. As CIO, Michael remains focused on driving continuous innovation for both customers and team members, and has built his career on implementing technology services, especially developing public and private cloud solutions for retail, government, technology and finance verticals. Michael holds a Bachelor of Science degree in Aerospace Engineering and a Master of Science in Telecommunications, both from the University of Colorado.


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