Laying the Foundations for AI Success

Over the last few weeks, we have looked at how GenAI is revolutionising the retail industry. The technology is proving extremely useful for those already using it, bringing new clarity to the complex world of retail decision-making and delivering real results throughout the customer journey.

However, knowing where and how to start is often the biggest challenge. Successful adoption requires the right foundations, strategic focus, and long-term planning.

For this latest article in the series, we explore the key themes that underpin a successful GenAI journey. These principles apply no matter where your organisation is on the data and AI maturity curve.

Build Your Core: Data, Infrastructure, and Collaboration

All data activities thrive on high-quality data, seamless integration, and robust infrastructure. Without these foundational elements, even the most advanced AI models and GenAI solutions will fall short.

• Data Quality and Integration: Unified, clean, and accessible data is critical. Consider where high-quality, relevant data already exists and ensure it can flow freely between online and offline channels to create a 360-degree view of the customer. For example, Walmart’s centralised data platform combines in-store and online data, enabling AI systems to generate actionable insights.
• Scalable Infrastructure: Invest in flexible, modular systems that adapt to future AI advancements. Ikea consistently seeks to customise existing AI models rather than building new ones, reducing costs while delivering tailored solutions.
• Cross-Functional Collaboration: Break down silos and foster teamwork across departments. Collaborative efforts ensure that AI initiatives address real business challenges and deliver results across the organisation. For instance, Lindex achieved measurable results by aligning store operations, IT teams, and data scientists, leading to the successful deployment of AI-powered in-store assistants.

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Identifying Opportunities: Quick Wins and High-Impact Areas

With AI’s vast potential, it’s essential to start small and focus on areas where AI can deliver immediate, tangible value.

• ?Analyse Organisational Needs: Assess pain points and areas of inefficiency and engage your teams in helping you to do so. Look for repetitive tasks, decision-heavy processes, or customer interactions that could benefit from automation, enhanced personalisation, or improved data analysis.
• Prioritise by ROI and Feasibility: Focus on opportunities that offer the greatest return on investment, either financially or operationally but also, those that are most achievable for you in the short term. High-impact areas like demand forecasting, personalised marketing, and customer service automation often deliver quick, measurable results (assuming you have the access to the enablers to deliver them).
• Align with Strategic Goals: Ensure that AI initiatives align with your broader business objectives. For example, if customer experience is a priority, focusing on personalised engagement or smarter product recommendations may be the best starting point.

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Scaling and Sustaining GenAI for Long-Term Success

Once the foundations are in place and early wins have been achieved, the next step is to embed new tools and technologies into your organisation’s core processes. Scaling AI requires both technical readiness and cultural alignment to ensure long-term success.

• ?Agile and Adaptive Systems: As AI evolves, so must the systems that support it. Building flexible infrastructure ensures that solutions remain relevant and capable of addressing new business needs.
• Enterprise-Wide Integration: AI should be embedded across multiple touchpoints, from supply chain optimisation to customer engagement. Put new capabilities into the hands of your best end-users. Use them as advocates for scaling further.
• Upskilling Teams and Aligning Culture: Sustaining GenAI requires investment in people as much as technology. Programmes to upskill teams and foster cross-functional collaboration are essential. Mercado Libre addressed AI talent gaps by introducing AI copilots across software engineering, reducing development time by 60% and accelerating their ability to scale beyond their human constraints.
• Strategic Alignment: Ensure GenAI initiatives are consistently aligned with organisational goals. Cross-functional teams and centralised AI strategies help maintain focus and ensure that every project contributes to long-term value creation.

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Conclusion: A Practical Path to GenAI Adoption

Getting started with GenAI doesn’t have to be daunting. Focus on strong foundations, quick wins, and scalable systems. Make sure you put significant investment into adoption, into managing and embedding the changes that these new tools will bring. The journey to full-scale adoption requires a strategic approach, ensuring that AI becomes an integral part of decision-making, customer engagement, and operations.