How to Overcome the AI Hype and Drive Retail Collaboration
Lately, we have all been inundated by artificial intelligence (AI), from the most obvious ChatGPT to content, music, and art generators and even wilder applications. As an industry typically slow to adopt new technology, retailers have a plethora of modern tools available to support and augment their businesses, from merchandise planning and pricing to fulfillment and operations. And many of these tools are increasingly incorporating advanced analytics and elements of artificial intelligence (AI). By most traditional measures, these systems can work well and be powerful assets in driving competitive advantages.
However, there are a few challenges with AI, particularly when the expectation is to drive organizational collaboration and efficiencies. Some of these are technology-related, but others, more importantly, have to do with how some retailers are struggling to effectively incorporate AI into their business strategy and operations.
Let’s unpack these drawbacks.
Challenge 1 | Siloed Views of Demand
Most modern retail systems have built-in analytic tools that provide a myopic interpretation of demand focused on a single functional area. Further, that interpretation is neither consistent across functional areas nor shared with the broader organization. By default, these tools create functional silos and hinder any ability to have a consistent, company-wide understanding of demand needs. These silos impede fluid and agile operational orchestration that can be achieved with AI-driven cross-functional collaboration.
The goal of leveraging AI for retail collaboration is to transform from siloed functions to a connected ecosystem that can tie demand needs with a balanced supply response. Foundational to this connected ecosystem is establishing a shared and consistent understanding of unconstrained demand, organizational risks, and supply chain constraints.
Achieving effective cross-functional retail collaboration requires centralizing and sharing clean and consistent data with AI-powered, accurate predictive insights. Certainly, a modern, AI-driven demand platform with enterprise visibility is the cornerstone of this enablement. But that is only one piece of the solution.
A modern, AI-driven demand platform with enterprise visibility is the cornerstone of this enablement.
But that is only one piece of the solution.
Challenge 2 | Assuming AI is the Silver Bullet
We need to debunk the notion that AI can solve almost everything. While a common forecast removes the barriers associated with multiple versions of the truth, there are still challenges related to the adoption and integration of the forecast signal into business operations.  Moving from functional silos to collaboration requires business process and organizational changes to adapt to new tools and what are often vastly different ways of working together. It is not simply a matter of deploying a new AI-driven system and assuming this type of collaborative nirvana will automatically happen.
Especially with traditional retailers, AI-driven collaborative transformation represents a major mindset shift, since historically, functional areas did not mingle well, if at all.  Organizational structures will likely need to be dissected and rebuilt. Legacy business processes will need to be scrutinized and redesigned to accommodate vastly different tools and meeting cadences. Tough decisions must be made. None of that is easily done. However, to achieve effective collaboration that drives meaningful business results, functional silos must be broken down. Agility and fluidity need to be infused into operational processes to not only combat fierce competition but also get ahead of the continual supply chain disruptions retailers are experiencing.
Challenge 3 | Ignoring Alignment with Strategy and Outcomes
AI is of little value without other key components that seem to be ignored by most AI pundits. That includes vision, strategy, authenticity, connection (with both employees and consumers), accountability, etc., as well as being able to assess AI solution results/recommendations for “reasonableness.” In other words, as with other shiny objects, AI is not perfect, nor does it always truly meet all expectations given that any typical solution’s focus and scope is not all-encompassing.
Understanding and appropriately implementing a given AI solution component within a holistic framework of operating model, organization, and business process is essential.
Implementing an appropriate AI solution component within a holistic framework of operating model, organization, and business process is essential.
Without question, AI is transforming the way organizations work. When AI is implemented correctly, taking into consideration necessary functional and organizational changes, as well as focus on business strategy and outcomes, retailers can be in a much better position to achieve a congruent and optimized supply chain. Certainly, it is a paradigm shift in how organizations think, interact, and orchestrate. However, when done right, the efficiency and productivity gains of AI-infused retail operations can be tremendous.
Contributor
The Parker Avery Group is a leading retail and consumer goods consulting firm that transforms organizations and optimizes operational execution through development of competitive strategies, business process design, deep analytics expertise, change management leadership, and implementation of solutions that enable key capabilities.
