10 Tips for Ensuring Visibility and Responsiveness With AI
Using artificial intelligence can strengthen forecasting, streamline operations, and empower teams to make more informed decisions. Here’s how Southern Glazer’s does it.
1. Prioritize business impact over fancy technology. Don’t aim for “more AI,” aim for better results. We began by focusing on high-volume, predictable SKUs (e.g. high-volume vodka) where AI models can quickly prove their value and accuracy. This performance-first approach builds trust and encourages adoption.
2. Create and maintain clean data. AI models are only as good as the data they’re trained on. Invest in strong data platforms and governance practices to ensure your supply chain data is accurate, complete, and usable; this is foundational to visibility. Regular audits and cross-functional ownership of data quality can prevent errors from cascading throughout the supply chain.
3. Integrate AI into existing platforms. Big bang deployment of new AI platforms is tempting for fast transformation but can prove to be costly and sluggish. We leveraged our legacy planning tools and infused AI from Amazon SageMaker into user-familiar workflows. By doing it this way, we minimized disruption and accelerated implementation and adoption.
4. Automate routine processes. AI helps automate repetitive tasks so our planners can spend more time on complex, value-added work, such as managing supply fluctuations in fine wine.
5. Pair AI with human judgement. AI is a powerful decision augmentation tool, not a full replacement for human intelligence and experience. In nuanced categories like new item launches, where demand and availability do not follow predictive patterns, human expertise still drives the final call. The best outcomes come from blending algorithmic insights with the intuition and market knowledge of seasoned teams. By positioning AI as an advisor rather than a decision-maker, distributors gain the benefits of automation while keeping critical judgement in human hands.
6. Collaborate across teams. Success doesn’t come from entirely outsourcing the development of your AI solution. Your supply chain experts should work closely with data scientists to create and fine-tune models and interpret results. Collaboration increases the confidence users have in applying AI outputs to real-world challenges.
7. Invest in user training. Build structured change management programs that include training, clear communication, and hands-on learning opportunities. When users understand the “why” behind AI adoption and see how it simplifies their work, adoption accelerates and resistance decreases.
8. Roll out in phases and share wins. We started small and scaled up as teams saw results. Sharing early successes built momentum and confidence. Celebrate wins and communicate them widely to build trust in the process. This helps create a culture where employees see AI as an enabler of success rather than a disruptive change.
9. Commit to continuous learning. Models improve over time as they’re fed more data, and business conditions evolve constantly. Regularly review AI performance, refresh models, and seek feedback from end users to ensure tools remain aligned with organizational goals.
10. Explore generative AI for real-time insights. We’re currently developing AI agents that can analyze data and emails, flag stockouts, and recommend corrective actions. These agents address particular use cases today as we work to connect them through an orchestration layer, building an end-to-end control tower. Such a platform represents the next frontier in proactive, responsive supply chain management.
SOURCE: Diego Fonseca, Vice President, Supply Chain & Logistics, Southern Glazer’s Wine & Spirits