In my work helping companies across Retail, Consumer Goods, and Automotive, and leading a portfolio of generative AI solutions at Globant, I’ve seen AI adoption accelerate. However, I’ve also seen many of the same mistakes repeated. Too often, organizations jump into AI without a clear strategy, picking the wrong use cases, or expecting the technology to do what it simply can’t.
Here, I explore four key insights to help clients understand how to apply AI smartly and efficiently, and the lessons that can make the difference between a failed pilot and a true transformation.
1. Start with governance, not tools
Many leaders tell employees to “use AI” without providing direction. That only creates confusion and risk.
One crucial step is AI governance: setting clear rules on what the company wants to achieve, which platforms are approved, how compliance, security, and ethics will be handled, and which processes are priorities for transformation. This provides coherence and builds trust across the organization.
With a governance model in place, teams can move with clarity and confidence. Without it, AI adoption risks becoming fragmented and inconsistent.
2. Choose the Right Use Cases
As the leader of the generative AI solutions portfolio, the biggest pitfall I see is companies betting on the wrong use cases and expecting AI to solve problems it isn’t yet ready to tackle. AI is powerful, but it’s not magic. For example, AI is still unreliable for perfect demand forecasting because the models are fundamentally probabilistic.
AI is much more effective today in areas where processes are repetitive, creative, or visual. For example, automating content generation or accelerating design workflows can improve efficiency. The economic impact is direct and tangible when AI augments these kinds of processes.
Implementing AI is ultimately about creating efficiency, which almost always relates to financial impact. Picking the proper use case is where that value begins.
3. Start Small, Fail Fast, and Learn Big
Once the foundation is set, start with simple, fast-to-test use cases, and where you can iterate quickly. Quick wins not only prove value but also help teams learn by doing.
Complexity is a double challenge in AI: not only is the use case itself complicated, but the adoption of the technology also adds another layer of difficulty. Complex AI solutions, especially agentic, multi-agent architectures, can unlock huge potential only once the organization has matured. And don’t fear failure: models evolve with training, and early iterations are rarely perfect.
That’s why early projects should be low-risk but high-learning. Even if the first iterations aren’t perfect, that’s okay, AI systems often need time to train and improve. The key is to treat AI adoption as an evolutionary process. Begin with small wins, gradually expanding as models become stronger and teams build new competencies.
4. Use AI to get closer to the consumer
AI is often applied to supply chain optimization, inventory management, or logistics in industries with razor-thin margins. That’s important, but the most significant breakthroughs I’ve seen come from understanding the consumer more deeply.
Thanks to direct data, retailers already have a head start. However, AI can finally bridge the gap for Retail, CPG, and automotive companies, using synthetic data to model consumer behavior, inform product development, create more innovative marketing strategies, and design smarter marketing campaigns.
Executives’ eyes light up when they see how AI can move beyond efficiency and drive growth, because it is a game-changer. It doesn’t just optimize operations; it fuels innovation and deepens the brand-consumer relationship.
Strategic AI: How Leaders Can Shape the Future of Their Business
AI isn’t about chasing hype or deploying the flashiest models. It’s about choosing wisely, governing responsibly, and moving iteratively toward value. In Retail, CPG, and Automotive, the companies that succeed will be those that don’t just adopt AI, but integrate it strategically into both operations and customer engagement.
AI is a powerful business tool if applied with the proper focus, and it boils down to four essentials:
- Establish governance before scaling.
- Choose use cases strategically.
- Start small, experiment, and evolve.
- Look beyond efficiency to unlock new consumer insights.
For Retail, CPG, and Automotive leaders, these principles can mean the difference between getting stuck in endless pilots and achieving measurable business impact.
Explore how Globant’s Retail, Consumer Goods & Manufacturing, and Automotive Studios can help you lead with AI.
