New Delhi: Successful retail businesses have replicated their profitable models across geographies and have scaled-up fast to grab a sizeable global market share. These mega-players, however, are on the constant lookout for risks posed by disruptions and smaller innovative competitors that can seize a significant portion of their existing customer base.
Irrespective of size, enterprises that leverage automation and artificial intelligence (AI) to simplify decision making are better positioned to achieve profitability through a consistent customer experience, a rationalized and efficient IT application landscape, a strong talent pool and meaningful relationships with their partners.
Business stakeholders must take the lead in AI adoption.
IT teams must be welcomed to give their inputs but the overarching direction of an enterprise-wide retail AI must come from the business leaders. What are the objectives of an AI-led transformation? What’s the overall budget? What is the risk appetite – how much loss are they ready to bear from failed AI initiatives, especially in the early stages? The business teams must also bring together marketing, operations, legal, warehouse and supply chain leaders to decide accountability for the end goals. This sets the tone for clear expectations from AI initiatives. Retailers can yield significant benefits by making AI-led investments in the following areas.
Customer Centricity
Retailers must have a sharp customer-centric view and account for the changing preferences, distractions and competing brands or offers that enter the consideration set of their customers. An AI platform that can derive data-driven insights from user behaviors and choices can be used by retailer players to arrive at optimal product recommendations, loyalty programs, personalized offers and options like the retail wallet/buy-now-pay-later. The database of user queries and their feedback can be fed into an AI system to improve the effectiveness and perception of omnichannel customer service, including chatbots. AI can also help in identifying and winning over customers who prefer a hybrid/phy-gital (physical + digital) experience.
Outage/disaster management
Outages can result in poor customer experience and can significantly impact a company’s ability to operate in a seamless manner. Retailers should invest in automated provisioning, site reliability engineering (SRE), incident management and initiatives to reduce the mean time to repair (MTTR). Investing in resilient/disaster readiness can improve user trust and the overall user experience.
Optimized Operations
Effective AI can improve demand supply predictions, improve the efficiency of warehouse management systems and improve last mile delivery costs through optimized fleet management systems. From a design perspective, retailers must consider making their IT applications more modular and improve application interoperability by employing APIs or a containerized approach for easy movement of data blocks between multiple cloud/on-premise environments. Since AI reduces the human effort of deriving insights from data, retailers can also realize financial benefits of IoT adoption in warehouse operations. Since applications may undergo an upgrade or introduce new features, retail AI must factor in predictive maintenance processes to realize greater savings.
Talent Management
Educating employees about the benefits of AI and getting them to provide regular feedback to improve the machine learning/AI performance is critical in achieving success in these initiatives. Employees must also be prepared to measure the right parameters, keep a track on the ROI and also provide timely feedback on AI initiatives that are failing/need to be shelved. It is important to give employees the guidance that investment in AI is a long term endeavor which may require periodic maintenance and course corrections based on outcomes. Lastly, employees must be asked to track the impact of AI-led decisions, if any, on compliance and regulatory requirements.
Improve efficiencies with partners
API-friendly designs, adoption of microservices/Kubernetes for cloud and DevOps can increase the speed to market and make it easier for retail companies to make decisions based on the nature of API interactions and the data that is exchanged. Retailers will do well to benchmark the performance of their AI solutions against that of their competitors or standards that are set by their partners. Proof of concept (PoCs) are a great way to mitigate risk and drive innovation in the way retailers interact with their partners.
Conclusion
The retail space is in a constant state of flux owing to changes in consumer mindsets, competition and technology adoption. In order to stay relevant, retail players need to invest in AI solutions that will allow business stakeholders to achieve their goals across user experience, retail operations, partner collaboration and performance, application rationalization, reliability and resilience while keeping a close eye on AI investments and ROI to realize profitable growth.
Inputs by: Subbiah Muthiah - SVP - Head of Emerging Technologies, Qualitest.