AI in Retail? Nope, Start with Automation
Stories of thrilling, new AI use cases in retail have been popping up in our tech news feeds, whether computer vision, facial recognition, or elimination of human workers. But these sensational accounts miss an important piece of the AI learning curve. A complex infrastructure of end-to-end process automation underpins the flashy technology reflected on the front end.
Process gaps shatter customer journeys and lead to a lack of transparency in supply chain and customer interactions, which together form a composite of the AI backbone. If your retail organization suffers from process gaps and manual routing — and 37% of business and technology decision-makers report that they are — you must start with laying the groundwork before leaping to AI bling.
So where do you start? An incremental approach to AI in retail should focus on:
- Automating customer journeys. Most customer journeys are hindered by manual processes and legacy systems, but new tools are arriving to help. Automating customer journeys is a great starting point for retailers. Seamless journeys drive better online shopping experiences with AI-based solutions like enhanced analytics or intelligent recommendation solutions.
- Building software more quickly. Customer journey automation requires more software that many firms don't have the time or resources to build themselves. As retailers identify the gaps and pain points in their shoppers' journeys, new tools like low-code development platforms are stepping up to speed software development. Seamless journeys are the building blocks for predictive AI features.
- Leveraging robotic process automation (RPA). RPA drives client-side automation and integration, which enables businesses to automate manual tasks typically handled by humans. This offers straight-through automation, as well as end-to-end transparency.
- Applying AI to back-end processes. AI has powerful use cases in back-end activities. Retailers should analyze how these can augment current human-centric processes. For example, digital business pros apply natural language processing to written materials to aid decision making and apply analytics to automate frequently asked questions (thereby letting customer service reps focus on more complex service issues).
Focus On Tools That Support End-To-End Automation
Use measured, gradual approaches to enter the world of AI by focusing on back-end processes first. Get executive buy-in on the importance of seamless customer journeys and the importance of supportive technologies like RPA and low-code, which jump-start end-to-end automation. These tools will ultimately pave the way to a foundation that can fully exploit the power of AI and radically transform the customer experience.
The original Forrester blog by Forrester’s Rob Koplowitz, Vice President, Principal Analyst and Sucharita Kodali, Vice President, Principal Analyst is here. The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.
