An article written by Digital Wave Technology’s CEO Lori Schafer. Find the original article published by Retail TouchPoints here.
Co-pilots are essential on a commercial aircraft: they’re there to assist the captain in operating the aircraft, monitor instruments and systems, communicate with air traffic control and generally divide the workload with the pilot. While a co-pilot’s role is one of assistance and support, they are also fully trained to take over operation of the aircraft if needed. As pilots and co-pilots gain experience in working together, it becomes easier over time for the pilot to entrust the co-pilot with more responsibility.
The way companies adopt artificial intelligence mimics this scenario. In the beginning, an AI co-pilot serves as an AI assistant to help humans accomplish routine tasks more efficiently and accurately. Once businesses gain trust in their AI co-pilots, they can increase the difficulty of the tasks assigned to AI and rely on AI as an “autopilot” with final oversight from a human teammate. In other words, the captain can leave the cockpit with full assurance that the right decisions are being made.
How the Co-Pilot Approach Works Today
The co-pilot approach reflects the way that retailers have begun implementing AI over the past few years. In the beginning, the pilot supervises the AI co-pilot, quickly turning over more and more responsibility as AI continues to prove its value, and retailers gain confidence in the results.
During this early stage, retailers apply the power of AI for repetitive but data-intensive tasks, allowing humans to focus on assignments requiring creativity, emotional intelligence and real-world perception. Early use cases for AI in retail include inventory management, dynamic pricing, customer service chatbots, loss prevention and personalized marketing. These tasks benefit from AI’s efficiency in analyzing data and executing decisions autonomously, enhancing efficiency, accuracy and scalability.
Once retail teams gain confidence in AI, they can incorporate the technology into more complex use cases that involve complex decision-making, a nuanced understanding of human behavior and ethical considerations. Examples can include customer service escalations, product recommendations for high-involvement purchases, new product introductions and other more complicated tasks. These tasks require human judgment, empathy and creativity to navigate effectively, ensuring positive customer experiences and upholding ethical standards. Human oversight is essential to mitigate biases, address complex situations and uphold brand reputation.
The co-pilot approach eases the transition between more repetitive, data-driven tasks and complex, revenue-generating projects. This process ensures harmony between AI capabilities and human expertise, laying a strong foundation for sustained AI adoption and operational excellence across key retail areas including content marketing, customer support and fulfillment. Let’s take a look at how this progression works in several areas of the retail organization.
AI in Marketing: Creating Compelling, Engaging Content at Scale
AI has revolutionized content marketing for retailers. Generative AI can automatically write product descriptions and other copy at scale in a company’s brand voice, following the brand/collection style guides, and personalize the content for every channel – website, marketplaces, social selling, etc. Generative AI also improves website searchability and discoverability by enriching attributes and placing products in the right facets or filters within a site’s navigation structure.
That’s just the beginning, though. Once retailers experience these early benefits of AI for marketing, they can build upon that foundation to extend the value of AI into new areas, such as:
Voice search optimization: Improves relevancy of voice search results and caters to voice assistants like Alexa or Siri.
Emotion-based AI for content optimization: Tailors content to resonate emotionally with audiences, deepening engagement and brand connections.
Augmented reality (AR) content experiences: Personalizes content experiences in real time, offering personalized virtual try-on experiences for products.
Content performance prediction and optimization: Anticipates content performance more accurately and optimizes content strategies accordingly.
AI-powered content collaboration and ideation: Generates data-driven ideas and recommendations, improving efficiency and quality.
AI in Customer Support: Improving the Customer Experience
AI also has revolutionized customer support for retailers. By leveraging AI, retailers can provide 24/7 support, personalize interactions and anticipate customer needs proactively without human engagement. The technology also contributes to the improved scalability of individualized customer support processes. In the early stages of the co-pilot approach, retailers can use AI to automate chatbots and ticketing.
As the retailer’s reliance on AI evolves beyond the co-pilot role, leadership can begin to incorporate more complex use cases including:
Advanced sentiment analysis and emotional intelligence: Develop AI to understand nuanced customer emotions for tailored responses and respond empathetically.
Proactive support: Predict and address customer needs preemptively, improving the overall experience.
Visual support: Extend AI capabilities to analyze visual cues for product assistance.
As retailers incorporate more advanced AI-driven techniques, customers will benefit from more personalized assistance, fostering greater satisfaction and loyalty.
AI in Pricing: Automating Markdowns to Increase Sell-Through and Profit
Lifecycle pricing is an intensely data-driven process for retailers, and markdown optimization is a key area where AI can deliver greater value. Retailers can automate markdowns to minimize revenue loss and increase profitability, which can be especially important for seasonal retailers. For example, AI can help retailers ensure that holiday decorations are sold out just before the big event. It’s critical in seasonal apparel, too – think about swimwear, fall and winter accessories and the many other items that have an end to their selling seasons.
AI also can deliver profit-maximizing alerts for strategic decisions that go beyond markdowns, such as buying more product, cancelling orders, diverting inventory or changing business rules.
The Future of AI is Far-Reaching
When it comes to AI adoption, the aircraft has left the runway and is accelerating at exponential speed. As retailers continue to turn over increasingly complex tasks to their AI co-pilots, they can take full advantage of the expanding capabilities of AI, benefiting all stakeholders in the organization, from leadership and shareholders to everyday shoppers. Adopting the co-pilot approach to AI today will ensure the long-term success of AI in any retail business.