The retail industry is facing monumental uncertainty. Retailers are faced with the challenge of meeting higher customer demands with smaller teams due to staffing shortages and changes in consumer behavior. Digital transformation driven by the pandemic forced retailers to offer omnichannel sales and support, and now customers expect brands to provide the same level of service across all channels, as these recent statistics demonstrate:
- 87% of retailers globally have already implemented AI into store operations in some form.
- 73% of customers will move to a competitor brand after multiple bad service experiences.
- 67% of customers prefer self-service options like chatbots.
Retailers are turning to AI to meet the elevated level of customer expectations. AI can be implemented across retail outlets, both in-store and across channels, in a wide and varied number of use cases. This post will review common AI implementations in retail and consider future deployment possibilities.
Benefits of AI in retail
There is a lot of fear of AI replacing jobs. However, the reality is that AI simply makes employees more effective and enhances their ability to serve customers. Other benefits include:
- Streamlined marketing efforts across channels.
- Access new layers of analytics and insights in-store by mining data from IoT devices.
- More intelligent chatbots or interactive voice response (IVR) handle low-level requests and free up support agents to handle more complicated questions.
- Streamlined internal operations.
- Faster fulfillment.
AI use cases
In-store
IoT devices are enabling a myriad of possibilities as AI and machine learning, combined with cloud storage, assist retailers in collecting and analyzing new data sets. In location-based marketing, integrated IoT devices and data from customer devices offer advantages such as customer heat maps and personalized promotions based on customer location and buying history.
Security is an omnipresent concern in retail, and IoT cameras—such as those from Cisco Meraki—are used for advanced security monitoring. These devices have multi-functional capabilities to track customer behavior. Because the data is stored in the device or the Cloud, retailers do not have to worry about on-premises servers or data storage.
AI, supplemented with AR technology, is making it possible for customers to try on clothes or other accessories virtually. AI chatbots deployed across retail apps could guide customers to specific products in the store in the near future.
Action steps
- Prioritize IT modernization to support AI implementation in physical store locations.
- Implement integrated IoT devices featuring cameras and sensors for advanced analytics and security.
Customer support
AI is revolutionizing customer support. Intelligent virtual agents (IVA) are next-level chatbots enabled with AI features such as natural language understanding (NLU), allowing chatbots to understand more words and better interpret customer intent. Amazon’s returns chatbot can reportedly recognize up to 100,000 requests. Additionally, AI is better at deciphering a customer’s meaning when grammar errors and typos are present. IVAs can now handle more complex tasks like returns and intelligently elevate calls to human agents.
Customer service agents then receive summaries of the customer interaction so far, so the client will not have to repeat themselves—and the agent gets up to speed faster. AI in the contact center can now auto-record calls and chats, generate summaries and action items from the recording, and automatically send the records to the preferred CRM.
Further, real-time sentiment analysis can help agents “take the temperature” of interactions and adjust tactics to steer the conversation in a more constructive direction. AI can also “listen in” and suggest knowledge database articles that can potentially solve the customer’s issue. AI tools can also automatically schedule follow-up messages and surveys for the customer. New real-time translation tools are only expanding the possibilities of customer service.
Action steps
- Assess what tools your contact center currently uses, and research cloud-native, AI-enabled Contact Center as a Service (CCaaS) platforms such as Five9, Webex Contact Center, or CXsync.
Operations
Mobile devices and specialized retail platforms, empowered with AI are helping managers to stay in touch with employees, efficiently schedule shifts, and communicate quickly with all employees.
The marketing department has also seen a boost from AI. Generative AI allows marketers to generate and re-purpose marketing materials quickly. Analytics mining AIs help marketers create personalized promotions and more accurate customer profiles.
Fulfillment centers also have several use cases, as AI is deployed to speed up shipping processes and automate thank-you notes or invoicing.
Within IT, numerous AI-powered tools support retailers with fast troubleshooting, networking optimization, and advanced security tools that seek out advanced threats.
Learn more: Top retail technology trends of the tech revolution
Action steps
- Identify inventory processes that need improvement within your operations.
- Create guidance and policies around the use of generative AI.
AI best practices
Set realistic expectations of what AI can and cannot accomplish. AI’s capacity and capabilities are rapidly expanding. However, AI is still limited in terms of complexity and creativity. At the end of the day, AI is just a very advanced mimic.
Regarding customer-facing communications, human eyes should always double-check anything created in generative AI, as there are still issues with accuracy and “hallucinations”. For generative AI use cases, retailers need to be careful in terms of security and intellectual property. In a famous case, a phone manufacturer input engineering data into a public LLM. The AI learned sensitive design specifications for unreleased models that it then output to public queries.
To avoid situations like this, retailers should deploy a private LLM and be conscious about what it is being trained with. The golden rule of AI is “garbage in, garbage out.” It is a simple but effective phrase to remind users that AI output will reflect the quality of its input. There are numerous instances of AI taking on bias, so it is important to monitor what goes into an LLM, including chatbots and IVR.
In terms of compliance, retailers need to be aware that rules regarding AI usage are currently emerging and will evolve as AI develops.
Action steps
- Ensure that internal AI LLMs are private and that sensitive data is segmented from networks.
- Seek out the guidance of experienced AI implementation partners.
CBTS is your guide to the AI revolution in retail
Consumer expectations will only grow as AI adoption becomes ubiquitous. Retailers must embrace AI across these various use cases to avoid losing a competitive edge. As an organization, how do you prioritize AI implementation? How can you ensure AI it is safe, secure, and compliant while avoiding inaccuracies and implicit bias?
The answer to these questions is to seek the guidance of an implementation partner like CBTS. CBTS has helped our clients implement AI across the IT spectrum. Our team understands the nuances of the retail sector and formed a retail advisory council to better serve and understand the needs of our retail clients.
Get in touch to learn how to get the most out of AI in retail.
