How AI Can Help You Create Human-Centric Retail Experiences

Generative AI may be generating buzz, but for tech-savvy retailers, AI is not a new technology. Conversational AI has been foundational to many customer experience technologies we now take for granted. Chatbots, Interactive Voice Response (IVR) and intent analysis can be used to collect information, accelerate responses, automate self-service options and augment an agent’s capabilities, while still other AI models have been in use to recognize trends and patterns in data that would be too massive and complicated for a human to do efficiently.

With the dawn of generative AI, the possibilities are even broader. This type of AI can recognize and classify similarities in a vast pool of data and then generate new text, audio, code, images, simulations and even videos based on those patterns, opening a new realm of possibilities in how customers interact with and experience their favorite brands. And, perhaps ironically, this technology may even help retailers create more human experiences for customers.

Enhancing the Human Touch with AI

The expectation customers have today for the brands they interact with is hyper-responsiveness: being known, valued and helped, in the right way, at the right time. Retail brands that compete on experience must constantly strive for improvement, whether it’s through reiterating processes or retraining employees. As the main point of contact that many customers have with a brand, contact center agents carry a lot of responsibility for maintaining these exceptionally high standards.

Between the current attrition rates at contact centers, long wait times, the ongoing fallout from the Great Resignation and other challenges in the industry, it can be tempting to see generative AI as a one-size-fits-all tool that can quickly and easily improve your customer experience. According to a recent Gartner poll, 38% of respondents stated that customer experience and retention was the primary focus for their generative AI investments, ahead of revenue growth, cost optimization and business continuity.


But if you’re thinking of adding generative AI to your CX toolkit, there are some important considerations to make first.

Human-Centered Design in AI-Driven CX

Google has developed Recommended AI Practices, and the first principle is around using a human-centered design approach: The way actual users experience your system is essential to assessing the true impact of its predictions, recommendations and decisions. How do you ensure that your AI use case keeps humans at the center? Here are some considerations:

Consider your data: You must consider issues like data lineage (i.e., where did this data come from, and was there a bias in how it was created or collected?); input bias (i.e., is your ability to intake information reflecting reality or your expectations of what reality should look like?); prejudice reinforcement (i.e., did you influence the answer or outcome by how you phrased the question?); diversity of perspectives (i.e., who is being represented and who isn’t?); and potentially overlooked angles and consequences (if you can’t be 100% accurate in your results, what is the consequence of a false negative versus a false positive?)

Follow best practices for privacy: There is no single correct model, and balancing privacy against utility will have to be measured in each individual scenario, but there are some best practices. Try to avoid using sensitive data when less sensitive data will suffice. Anonymize and aggregate data where appropriate. I recommend Google’s page on privacy best practices for further reading.

Try and try again: Don’t give up at your first mistake. Identifying an issue in your outcome or process isn’t the end — in fact, it’s an opportunity. If you’re discovering a bias in your field or column, or that you’re producing inadequate outcomes, it’s just that: a bad field, a bad column and an opportunity to clean your data or reassess your process. It does not mean you need to scrap the whole idea. One violation existing in a vacuum doesn’t mean you scrap the entire product, feature or department. (Do make sure it’s in a vacuum, though.)

Keep a human in the loop: So far, AI solutions aren’t adept at ensuring that the data they rely on was collected ethically or provides a nuanced and empathetic response to a customer experiencing a complex problem. This is why AI-powered CX solutions must include a human element. Keeping a human in the loop helps ensure that your AI solution is giving you the outcomes and solutions you want. Human emotions tend to be more complicated than the 90 discrete buckets a programmer can instill, and can provide the human touch that can’t be replicated by a machine.

Incorporating Customer-Worthy AI into Your CX

There is something about AI that feels beyond human emotion, and therefore above human fallibility or illogical reasoning. But AI is only as good as the data it’s fed – and that data is generated by humans.

What this means is that AI is great at augmenting human workers, empowering live agents with the tools and data they need to become more efficient and more effective, helping a human agent anticipate why someone might be calling or providing background gathered from across different channels to help the agent piece together what might be happening. AI is great at repetitive, mundane tasks, or providing relevant information almost instantaneously — but no matter how sophisticated it is, AI still isn’t providing a human touch.

If you’re considering adding AI to your CX toolkit, you first need to answer these two questions:

  • What outcomes are you trying to drive today?
  • What is currently preventing you from accomplishing your CX goals?

While generative AI technology is new and exciting and endlessly promising, it can’t be the magic cure to all your CX problems. It doesn’t matter how sophisticated your technology solutions are if you don’t first understand what your customers want and where your customer experience is falling short. For now, this remains one area where artificial intelligence can’t replace human understanding.

Aaron Schroeder is Director of Analytics and Insights at TTEC Digital, one of the largest global CX technology and services innovators. The company delivers leading CX technology and operational CX orchestration at scale. TTEC Digital’s 60,000 employees operate on six continents and bring technology and humanity together to deliver happy customers and differentiated business results.

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