Printable version here

Riding the AI wave: decision-making tools for department stores

The IADS Academy programme, a 29-year-old tailor-made mentoring workshop open only to our members’ high potentials, promotes cooperation and future orientation. Over the years, the IADS Academy has trained 190+ executives from 29 companies in 22 countries, some of whom reached top positions in member and non-member companies (for IADS member companies alone, 4 CEOs).

The 2024 topic was as follows:

AI and department store activities - Given the number of possible AI applications, how can retailers develop a decision-making tool (in terms of investments, teams and time)?

Once in the forecast, how can they decide and prioritise the right areas of application (examples: product development, customer loyalty, in-store operations, productivity-saving operations...)?

The following is an attempt to report all insights the Academy group considered and worked on during the journey to their final presentation shown to the IADS member CEOs.

Introduction: department store's never-ending transformation

The retail industry is undergoing the AI seismic shift and department stores, long-standing symbols of traditional commerce, are in the midst of this transformation. AI is no longer a futuristic concept but a technological breakthrough, as were the printing press, electricity, and the internet, making it a present-day reality and necessity. As for those game-changing innovations, AI is designed to make life easier, especially in an increasingly complex world.

To navigate this complex landscape, department stores need tools that not only assess their readiness for AI but also guide their strategic decisions. Those are the two value propositions introduced by the 2024 IADS Academy cohort. This article explains their findings on creating a decision-making tool for AI initiatives in department stores, exploring the challenges, solutions, and opportunities that lie ahead.

The paradigm shift: from human to “machine customers”

Attracting customers and hopefully catching a share of their wallets is at the core of any retail company. These customers, so far human beings, are difficult to navigate as their emotions drive them. Also, they are impulsive, lazy, highly demanding, and easily distracted, especially with a shrinking attention span. They are often late and lack urgency. To answer this, department stores spent decades and immense resources such as advertising and marketing campaigns, CRM and loyalty programmes, data analytics systems, and sales strategies to target and reach these coveted human customers.

Customers have always evolved over time, but technology drives a new type of evolution. What can be called “bound customers” came to life with the rise of Web 2: these human customers are supported by machines executing requests, such as when they shop online. Department stores had to adapt to answer the needs of this new omnichannel breed. The next type of customer is not totally there yet, but retailers have started dealing with what the Academy called “adaptable customers”: leads are shared with the machine, and the machine executes.

Tomorrow, retailers will serve AI-powered “machine customers”, with the machine leading and executing, a fundamental paradigm shift for retail. While its human counterpart will still exist as a department store customer, the “machine customer” will develop and require new adjustments, yet again. Analytical and logical, highly observant, goal-focused, tireless, and emotionally unaffected, equipped with a strong memory, they will operate autonomously and make purchasing decisions based on extensive research.

This future is already there, as proved by Perplexity AI's latest innovation. In November 2024, they launched an AI-powered shopping assistant in the US that allows users to research and purchase products directly through its platform. This new feature, “Buy With Pro”, streamlines online shopping by enabling one-click checkout for select products, saving users time and enhancing their shopping experience. If “Buy With Pro” is unavailable for a product, users are redirected to the merchant's website to complete their purchase. Additionally, Perplexity AI offers a visual search tool called “Snap to Shop”, which allows users to find products by uploading photos. The assistant integrates with platforms like Shopify to provide unbiased product recommendations tailored to users' searches.

AI disruption is on the way for all economic sectors. In retail, analytical AI already supports better customer segmentation, predictive maintenance and fraud detection, to name a few. For department stores, generative AI has become a reality as it already generates content, code and efficient chatbots. What comes next is multi-modal AI, which can think, feel, process, and create.

This shift underscores the urgency for department stores to adapt to a new reality where human and machine customers coexist. Using the surfing metaphor, retailers should take steps to catch the AI revolution wave, deciding whether to ride it or let it pass and paddling hard to catch it before it crashes over them. The Academy's message was clear: AI is not just a trend but an imminent wave that must be embraced.

Assessing department store AI-readiness: the AIRI framework

To help department stores navigate this wave, the Academy cohort introduced the Artificial Intelligence Readiness Index (AIRI), a comprehensive assessment tool designed by the University of Singapore to evaluate an organisation's readiness for AI adoption. Based on extensive research and testing, AIRI focuses on five critical pillars to evaluate company readiness:

• Business value to understand how AI can generate tangible benefits.
• Organisation to gauge the cultural and structural adaptability of the company.
• Infrastructure to ensure the availability of robust technological foundations.
• Data, as its quality and accessibility, should be assessed.
• Ethical practices to establish governance frameworks to manage risks and ensure compliance.

The assessment tool has four levels:

• Unaware: staff in the organisation perceive AI as a threat to jobs and do not understand its potential.
• Aware: the organisation and its employees trust AI applications.
• AI-ready: the organisation trusts AI applications, has the necessary resources and infrastructure, and is ready to move forward.
• Competent: the organisation is at the forefront of AI and sets the standards for others.

The AIRI framework operates like a game of Jenga: if one pillar is weak or missing, the entire structure risks collapse. By scoring organisations across these four dimensions, AIRI provides actionable insights into where improvements are needed. For instance, some companies might excel in infrastructure but lag in ethical practices or data quality. The tool also offers two approaches to implementation: top-down (organisational level) and bottom-up (functional level). This flexibility allows department stores to tailor their AI strategies based on specific needs and priorities.

The Academy group used the AIRI tool to assess their departments and companies and found that most department stores fall between the Unaware and Aware levels. During the Academy presentation, CEOs were invited to quickly evaluate their AI level (without using the AIRI tool), and most considered their organisations AI-ready. This discrepancy shows how relevant it would be for departments and organisations to use the AIRI tool.

A new team member: Lola, department stores’ AI instructor

The Academy introduced a second value proposition in the form of a department store-only generative AI tool, Lola (a name randomly chosen by the Academy group), a new department store team member. While AIRI provides a roadmap for readiness, Lola, the AI chatbot built by the Academy cohort, acts as a guide for execution. Developed over a few months using data from IADS members and other reliable sources, Lola is specifically tailored for the retail industry. Lola is not just another chatbot but a generative AI tool designed to provide tailored, contextualised insights and actionable recommendations.

Lola’s capabilities were demonstrated live during the Academy presentation to CEOs. Its responses were not only more relevant than any generic gen AI tool, but also enriched with examples from real-world department store information. Unlike generic AI tools like ChatGPT, Lola is fed with precise retail-specific data curated by experts. This makes it uniquely positioned to address challenges faced by department stores, from optimising foot traffic analysis using CCTV footage to improving conversion rates through advanced customer segmentation.

However, Lola is still in its “infancy”, as it is a proof-of-concept that requires continuous learning and refinement. To grow into a robust decision-making assistant, it needs to be nurtured with high-quality data aligned with ethical standards.

Building an experimental culture within organisations and beyond: leveraging the IADS resources

One of the key takeaways from the Academy cohort was the importance of fostering an experimental culture within organisations. AI adoption is not just about technology; it requires a mindset shift at all levels of management. Leaders must champion innovation while ensuring that employees feel empowered rather than threatened by AI. Management support goes beyond CIOs or CTOs, it starts with CEOs and executive sponsors who set the tone for organisational change. They also highlighted the need for upskilling employees to handle new responsibilities brought by AI technologies.

In the future, the Lola experiment should use the endless resources of the IADS. Lola would be fed not only with broad information about AI but also with AI-specific data from IADS members and all the data from the IADS years of research. While it will support IADS members' decision-making about AI, it will become a broader information tool for members to ask about any topic and get an answer instantly. To make Lola a reality, IADS members and the IADS need to establish governance guidelines, guarantee a secure environment and establish the use process.

Conclusion: catching the AI wave together

The journey toward AI adoption in department stores is akin to mastering the art of surfing: it demands observation, vigilance, preparation, and agility. Through their dual value propositions, the AIRI assessment tool and the pioneering Lola generative AI agent, the Academy group has laid a foundation for a structured and practical approach to AI adoption. By identifying readiness gaps and fostering a culture of education and trust, the AIRI tool offers a clear roadmap for organisations navigating the complexities of AI integration. Meanwhile, Lola shows how generative AI can be tailored to the unique needs of the retail sector, providing actionable insights and decision-making support. Lola can certainly become the IADS members’ private agent.

As one presenter aptly noted during the Q&A session, “AI is not just a tool; it’s a way of doing things.” By aligning AI initiatives with business strategies and fostering collaboration across teams, department stores can ride the AI wave with confidence.

Credits: IADS (Christine Montard)