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The rapid evolution of artificial intelligence (AI) technology represents a dual-edged sword for the retail and department store sectors. While AI promises to revolutionise operations, its integration into the corporate fabric demands more than mere technological upgrades—it requires a strategic alignment with each organisation's unique culture, values, and broader objectives. As data becomes the new "gold" and advances in hardware capabilities make previously inconceivable AI applications possible, retailers are presented with tremendous opportunities and significant challenges. This article looks at how department stores can effectively use AI to boost operations and support their mission while considering the financial and risk challenges involved.

Understanding the strategic importance of AI in retail

What’s your problem? 

AI in retail is more than a technology for automating operations—it addresses core business challenges through classification, creation, recommendation and regression systems. However, defining the business problem AI should answer before diving into AI implementation, whether it’s an opportunity to generate growth or reduce costs, is crucial, as a tool is here to solve an issue, not the other way around.

Retailers must consider whether AI is the most suitable solution or whether existing systems or alternatives could address the problem more effectively. AI's success standards should often be measured against current solutions rather than an ideal, flawless system. To help identify these problems, user interviews and market research become essential tools. By speaking directly with users, retailers can gain insight into what issues are most important to address. Market research can reveal existing AI systems, their uses, and how they fit within the organisation’s needs. With this knowledge, department stores can better associate AI initiatives with strategic goals, ensuring AI is both a problem-solving tool and a growth driver. Fundamentally, understanding and addressing business problems starts with recognising that AI systems are data-centric. Even the most sophisticated AI solution can fall short without accurate and sufficient data. Therefore, the planning phase of AI development should involve assessing data availability and quality to ensure the AI system’s performance meets business expectations.

A panorama of how retailers use AI so far

AI is shaking up retail, typically solving four key business problems:

• With data readiness established, the first problem AI often addresses is classification. AI’s power to organise data is a game changer. Take Sephora’s Virtual Artist1: it uses AI chatbots to classify customer queries, delivering fast, efficient responses that streamline operations and elevate service. Department stores are very advanced in using AI for their chatbots as customer queries are all very similar, making them easy to classify.
• Next is content creation, where AI’s potential truly shines. Amazon's AI-powered video ad generator for holiday marketing slashes production costs while generating high-quality, engaging ads from simple text inputs. Similarly, Depop has introduced a generative AI tool that creates product descriptions from photos, making the listing process faster and easier for users. Automating tasks like generating product descriptions, SEO metadata, and imagery for retail platforms can reduce costs by up to 90%. AI helps brands scale operations while keeping content personalised and data-driven, delivering messages that resonate with their target audiences. This approach boosts customer engagement, increases conversion rates, and further reduces production costs.2
• Recommendation systems are another AI powerhouse, as seen in Amazon’s product suggestions. These engines personalise shopping experiences based on customer behaviour, while AI-driven size recommendations reduce returns and boost confidence in online purchases. AI doesn’t just make suggestions; it is transforming the entire shopping journey.
• Finally, AI tackles regression for tasks like sales forecasting. Macy’s leverages AI to predict future trends, though forecasts based on historical data still carry risks due to market volatility.

However, beyond automating tasks, AI supercharges efficiency. Target’s AI streamlines supply chains, cutting costs and improving logistics, while Tapestry uses AI-driven insights to tailor offerings based on customer preferences. H&M and Amazon personalise customer interaction in real-time, while Google’s virtual fitting room reduces returns by showing customers how clothes fit differently on different body types. These innovations drive loyalty and repeat business. AI is not just a tool for operational efficiency; it strategically enhances customer and employee experience, drives innovation, and maintains a competitive edge, transforming organisational performance when integrated with a clear strategic vision.

Shaping AI around people, organisation, culture and purpose

Engaging employees at every level  

CEOs set the tone, but engaging employees at all levels is essential for successfully integrating AI. Education and training help ensure that everyone understands the purpose of AI initiatives and their role in the process. Without this engagement, AI initiatives may face resistance or fail to achieve their full potential. At the CEO level, strategic leadership from the CEO is crucial for successful AI integration, and the CEO must articulate a sharp vision for how AI aligns with the company’s broader goals. This top-down approach ensures that AI becomes a strategic priority, receiving the necessary support and resources to drive success. Walmart’s CEO, Doug McMillon, exemplifies initiative-taking leadership by calibrating AI initiatives with strategic business objectives, setting a solid precedent for top-level advocacy in AI adoption.

At the C-suite and executive level, AI literacy among executives is vital for successful AI integration. Executives must develop a deep understanding of AI’s capabilities and limitations to make informed decisions that coordinate AI initiatives with business goals. Target’s leadership exemplifies this commitment by investing significantly in AI education, ensuring that it supports its omnichannel strategy and enhances the customer experience across all touchpoints.

Middle managers and managers play a compelling role in AI initiatives, bridging higher management and front-line employees. At Lowe’s, AI training programs empower middle managers to critically assess where AI can be most effective and advocate for its adoption within their teams. Managers are key to the day-to-day management and operationalisation of AI initiatives, making it essential for them to be well-prepared to oversee these technologies. Companies like IKEA offer AI training programmes that empower managers to identify opportunities for AI integration within their departments while ensuring that non-AI solutions are considered when appropriate. This balanced approach ensures that AI is implemented effectively without overshadowing other valuable tools.

Regarding employees and associates, it is essential to onboard them, and bottom-up ideas should be encouraged. Front-line employees must be trained to understand how AI can enhance their roles and optimise daily operations. Proper training minimises potential resistance or misunderstandings. Starbucks, for instance, provides comprehensive training for its baristas on using AI-driven tools like the "Deep Brew" system, which helps manage inventory and personalise customer recommendations.

Infrastructure, financial considerations and strategic alignment  

To successfully integrate AI, retailers must carefully assess their infrastructure, financial resources, and long-term business goals. Whether opting for in-house AI development or third-party solutions, considerations like scalability, security, and cost-effectiveness are paramount. In-house systems offer customisation but require significant investments, while third-party solutions offer quicker implementation but need to adapt to existing infrastructure.

Precise planning is essential to avoid wasted resources and misaligned AI initiatives. Retailers must ask critical questions upfront: What outcomes are expected? What improvements are needed? Which resources are available? By tailoring AI solutions to department-specific needs, organisations can secure buy-in, align AI with strategic goals, and ensure initiatives are purposeful. Successful AI deployment requires more than technological alignment; it demands an approach that integrates with the organisation's culture, values, and objectives. Companies like Nordstrom demonstrate this by using AI in inventory management while maintaining human-centric customer service, ensuring that AI enhances, rather than replaces, the customer experience. Beyond that, companies should establish clear guidelines that define how AI is developed, used, and monitored, ensuring alignment with the company's core values. For instance, Patagonia’s integration of AI into its supply chain reflects how aligning AI with organisational values—in their case, environmental sustainability—can enhance brand integrity and customer trust. This approach allows retailers to leverage AI’s potential while fully maintaining company standards, be it consumer rights, sustainability or promoting social responsibility.

Monitoring, coordinating, and purpose-driven AI implementation 

AI implementation requires continuous monitoring and coordination across all departments to ensure that AI tools are leveraged effectively and remain uniform with the company's objectives. AI’s scale, speed, and scope can lead to unforeseen risks if not carefully managed, including reputational, economic, legal, and regulatory risks. Robust oversight ensures that AI initiatives do not become fragmented, leading to inefficiencies and lost opportunities.

Effective risk management is imperative to address these potential pitfalls. With AI’s transformative power, identifying and mitigating risks such as unfair bias, privacy violations, and cybersecurity threats from the outset and throughout the AI lifecycle is fundamental. Failure to do so can have catastrophic impacts, from reputational damage to regulatory fines. As AI technology evolves rapidly, it is necessary to build internal governance structures that actively monitor AI systems and their societal implications.

One example of purpose-driven AI implementation is Walmart’s dynamic pricing strategy, which demonstrates how aligning AI initiatives with specific business objectives can drive success. By adjusting prices in real-time based on demand and competitor pricing, Walmart optimises operational efficiency and customer satisfaction. This strategic use of AI highlights how AI solutions can be tailored to enhance business performance and customer experiences.

However, balancing AI deployment with human expertise is crucial to avoid over-reliance on technology and maintain the human touch. For instance, Best Buy uses AI for inventory management but relies on human expertise for customer interactions. This balance ensures that AI augments, rather than replaces, the human touch in customer service. Similarly, department stores must assess where AI is necessary and where human input adds unique value, thereby optimising the use of both AI and human resources.

To support effective coordination and monitoring, forming cross-departmental committees can help oversee AI deployment and integration. These committees facilitate sharing insights and best practices across departments, ensuring AI initiatives remain consistent with business strategies. As seen at JCPenney, cross-functional coordination is necessary to ensure AI projects remain aligned across departments, preventing siloed efforts and maximising value. Continuous monitoring ensures that AI systems remain transparent, explainable, and aligned with the company’s evolving objectives and societal expectations, fostering a unified approach to AI adoption.

By enhancing monitoring strategies and balancing AI and human interaction, retailers can optimise their AI implementations to meet business objectives better and adapt to evolving market conditions. This approach ensures that AI initiatives are not only practical but also sustainable and in line with the broader mission of the organisation.

AI and ethics

Fostering a responsible AI culture 

As the final critical consideration, fostering a responsible AI culture is essential. Companies should ensure that all AI deployments are conducted within the framework of the established ethical guidelines. This approach mitigates data privacy and bias risks, ensuring that AI integration across the organisation remains responsible, privacy-conscious, and parallel with the company's core values.

This begins with integrating ethical principles into AI training programs, focusing on privacy, fairness, and transparency. By doing so, organisations can ensure their AI initiatives align with legal and ethical standards while minimising risks such as data misuse. Training should teach employees how to identify and mitigate biases in AI systems while exploring AI's broader impact on social responsibility and ethical standards. By combining technical skills with moral considerations, companies ensure that AI tools are used responsibly and align with their values and compliance requirements. Real-world scenarios and case studies further help employees critically assess the ethical dimensions of AI, positioning it as a positive force within the organisation while mitigating risks such as data breaches or biased algorithms. Together, these layers of engagement form a robust foundation for successful AI integration within the organisation. Situating the entire workforce—from the CEO to front-line employees—with the company's AI vision and providing them with the necessary skills and understanding cultivates a cohesive and innovative environment. This comprehensive approach ensures that AI initiatives are implemented effectively while fostering a culture of continuous improvement and adaptability. As AI reshapes the retail landscape, this commitment to holistic employee engagement will be crucial in achieving sustained success and maintaining a competitive advantage.

Lessons from Amazon’s failed AI recruitment tool 

The need for a well-thought-out program is exemplified by Amazon’s experience with its AI recruitment tool, which was scrapped due to its inability to operate without bias. Continuous monitoring, coordination, and a clear understanding of AI’s purpose in each specific application are required to avoid similar pitfalls. Their failed AI recruitment tool is a cautionary tale of AI’s potential pitfalls. The tool developed biases against women due to flawed training data. This incident highlights the need for continuous monitoring, rigorous testing, and readiness to recalibrate or halt AI projects if they deviate from ethical standards. It also underscores the importance of incorporating diverse datasets to mitigate bias and ensure fairness in AI applications.

Incorporating AI into business operations requires strict adherence to ethical guidelines around data privacy, fairness, and transparency. AI systems must respect personal data and comply with global data protection laws, while continuous auditing helps prevent biases and ensures fairness in automated decisions.

Transparency is essential in sensitive areas like recruitment, where biased AI can cause harm, as demonstrated by Amazon’s experience. Clear communication of AI usage and constant monitoring help organisations build trust and ensure that AI solutions fit within both ethical standards and business goals.

Lessons from Patagonia’s AI privacy lawsuit 

While Patagonia has set a positive example of ethical AI integration, it now faces a class action lawsuit for alleged privacy violations involving its customer service operations. The lawsuit claims Patagonia used AI software from Talkdesk to intercept, record, and analyse customer communications without informing customers or obtaining their consent, violating California privacy laws.

This incident highlights the dual-edged nature of AI, as mentioned earlier. While AI can bring about operational efficiencies, improve customer service, etc., its misuse or lack of transparency can lead to severe legal and reputational risks, requiring organisations to manage AI cautiously and embed it within their cultural and ethical frameworks.

The key lessons from the Patagonia lawsuit are:

• Transparency and consent: organisations must disclose when AI is used in customer interactions, ensuring that customers are informed and consent to data monitoring.
• Ethical AI use: even companies with firm ethical commitments, like Patagonia, must ensure their AI tools align with privacy laws and ethical standards.
• Continuous monitoring: regular audits and strong governance are essential to ensure AI systems comply with evolving legal and ethical requirements.

Patagonia’s case serves as a reminder that AI must be implemented responsibly, balancing innovation with privacy and transparency to avoid legal risks and protect customer trust.

AI is poised to revolutionise the retail industry, offering immense potential to transform operations, enhance customer experiences, and drive innovation. However, its true impact comes when it is thoughtfully integrated into a company’s culture and aligned with strategic goals. Rather than adopting technology for its own sake, retailers must ensure that AI addresses specific business challenges, relies on reliable data, and has the support of employees at all levels. Clear leadership, continuous oversight, and adherence to ethical standards are essential for managing risks and ensuring transparency. AI’s real power lies in how carefully it’s integrated into a company’s unique culture and values rather than being hastily adopted to keep up with competitors. The key to success is implementing AI and shaping its use to reflect the organisation’s goals and mission. Before integrating AI, businesses must take a meticulous approach, ensuring it aligns with what truly matters to the organisation. This begins with fostering a culture that understands and embraces AI rather than rushing in recklessly.

AI should be seen as a tool that requires safety precautions—like a seat belt in a car—to ensure it’s used responsibly and sustainably. By taking the time to assess whether AI fits into your long-term vision and building a solid foundation, retailers can harness its transformative potential in a way that leads to lasting success as both the market and the technology evolve. This measured approach helps companies adapt to challenges and handle risks, preventing a “black box” mentality. Ultimately, the heart of the organisation must guide AI’s implementation, creating a foundation for sustainable growth and innovation.

Credits: IADS (Maya Sankoh)