Retail and consumer packaged goods (CPG) companies are at a pivotal crossroads. The promise of artificial intelligence (AI)—especially generative AI (GenAI)—is reshaping the sector, offering radical new ways to optimize operations, reimagine customer engagement, and unlock new business models. Yet, the path forward is anything but straightforward. Leaders must balance the urgent need to modernize legacy systems and drive operational excellence with the imperative to innovate and experiment, all while navigating sector-specific challenges around data, talent, and return on investment (ROI).
Insights from recent industry roundtables with retail and CPG executives reveal a clear consensus: meaningful progress with AI requires a dual focus. On one hand, organizations must stay laser-focused on core business objectives—streamlining supply chains, improving productivity, and enhancing customer experiences. On the other, they must carve out space for experimentation and future-facing innovation, even when the business case is not yet fully proven.
AI is not just another tool in the digital transformation toolkit. It is a force multiplier that can break through the inertia of legacy systems and outdated processes. For retail and CPG, this means:
Retail and CPG leaders face a unique set of hurdles on the AI journey:
Data quality and strategy are among the top three barriers to GenAI adoption in the sector. Mergers, acquisitions, and sprawling product portfolios create complex, often messy data estates. As one executive put it, “You’re going to get new garbage in all the time; that’s the nature of big business.”
The pursuit of perfect data can become a fool’s errand. Instead, leading organizations focus on making progress with the data they have, using GenAI to optimize and reinvent processes—even when the data is less than ideal. The key is to start small, build trust through proofs of concept, and iterate quickly.
Finding, training, and retaining AI talent is a significant challenge, but the real differentiator is fostering an AI mindset across the organization. Upskilling existing employees, investing in change management, and creating a culture that embraces experimentation are essential. As one transformation lead noted, “Upskill the people, or they will become obsolete. And if upskilling is not available, they will seek employment elsewhere.”
The easiest business case for GenAI is improved productivity—automating repetitive tasks, streamlining workflows, and reducing costs. However, leaders caution against being obsessed with traditional ROI models. GenAI is a new business engine with value that may not be fully captured in initial business cases. Organizations should balance quick wins in productivity with investments in innovation that may yield new revenue streams or competitive advantage over time.
Most RCPG organizations are still in the pilot stage with GenAI, with only a minority having deployed solutions at scale. The advice from industry leaders is clear: start with focused use cases that address real business problems, but don’t get stuck in perpetual experimentation. Build a roadmap that moves from pilots to scaled deployment, and ensure that innovation teams work side-by-side with business and IT.
Ground your GenAI strategy in use cases that align with business strategy and address real pain points—whether it’s supply chain optimization, customer targeting, or product innovation. For example, one retailer used GenAI to analyze real-time in-store and online activity, creating new, more accurate customer personas and enabling more targeted messaging.
Don’t let the quest for perfect data or a flawless business case delay action. GenAI can deliver value even with imperfect inputs. Focus on incremental improvements and build momentum through quick wins.
Balance investments in core operational improvements with space for experimentation. Use R&D budgets to explore new business models and value streams, not just to optimize existing processes. As one executive put it, “Make sure you’re future-proof. AI is a new business engine with no one business case.”
Break down silos between business, IT, and innovation teams. AI-driven modernization is not just a technology initiative—it’s a business transformation that requires joint ownership and governance.
Move beyond pilots by building the foundations for scale: robust data governance, clear talent strategies, and a culture that rewards experimentation and learning. Most RCPG leaders expect to embed GenAI into employee and customer-facing solutions within the next two years—now is the time to lay the groundwork.
AI-driven modernization in retail and CPG is not about waiting for the stars to align. It’s about making tangible improvements to business-as-usual while creating the conditions for breakthrough innovation. Accept that data will never be perfect, ROI will not always be immediate, and the path to scale will be iterative. The winners will be those who improve core operations today while experimenting their way toward the next wave of value.
At Publicis Sapient, we help retail and CPG leaders navigate this balance—combining deep industry expertise, proven AI platforms, and a SPEED (Strategy, Product, Experience, Engineering, Data & AI) approach to deliver both operational excellence and innovation. The future belongs to those who act boldly, experiment wisely, and never lose sight of the core value their business delivers.
Ready to modernize and innovate? Let’s build the future of retail and CPG—together.