In the rapidly evolving CPG landscape, few voices offer as much clarity on the intersection of technology and business strategy as Zainab Hussain. An e-commerce strategist with a deep background in customer engagement and operations, she has a unique vantage point on how companies are navigating the complexities of AI adoption. Today, she’ll break down the divergent paths CPG giants are taking, from aggressive, wide-scale implementation to methodical, foundational rebuilding. We’ll explore the critical importance of fixing internal processes before automation, the power of co-development partnerships, and how a modernized data infrastructure becomes the launchpad for true one-to-one marketing and a hyper-efficient supply chain.
The article highlights Newell’s aggressive “deep end” approach, implementing over 100 AI use cases, versus Clorox’s methodical, four-year foundational strategy. Can you discuss the pros and cons of these different roadmaps and what factors might lead a CPG to choose one path over the other?
It’s fascinating to see these two strategies play out because they represent two fundamentally different corporate philosophies. Newell’s “deep end” approach is incredibly bold and can deliver staggering, immediate results. When you’re in a turnaround situation, you need to show momentum, and achieving a 500% increase in marketing assets with fewer people is a powerful proof point. The risk, of course, is that you might be building on sand. If your underlying processes are flawed, you could be scaling inefficiency. On the other side, you have Clorox, which took the patient, methodical route. Spending four years rebuilding your data infrastructure doesn’t generate flashy headlines, but it creates a rock-solid foundation. The pro is that once that foundation is set, your ability to scale sophisticated applications like hyper-personalization becomes almost limitless and far more stable. The choice really depends on a company’s starting point. If you have a 25-year-old ERP system like Clorox did, you simply have no choice but to rebuild. For a company like Newell, aiming to quickly change its operational DNA, the aggressive, multi-pronged attack makes a lot of sense to force change and discover value rapidly.
Coca-Cola’s CEO warned against automating bad processes, while also noting AI is increasing revenue in sales by suggesting orders. Could you walk us through the steps a company must take to “fix” internal processes before implementing AI and provide an anecdote on how that sales-marketing connection might work?
That warning from Coca-Cola’s CEO is probably the most crucial piece of advice any leader can hear right now. “Fixing” a process isn’t about technology; it’s about people and workflow. The first step is to map the existing process from start to finish. You have to get in the weeds and identify every manual handoff, every data silo, and every bottleneck. You bring in the people who actually do the work—the salespeople, the marketers, the supply chain planners—and ask them what their biggest pain points are. Once you’ve streamlined the human element, you can bring in AI to accelerate the now-efficient process. For that sales-marketing connection, imagine one of Coca-Cola’s salespeople. Previously, they’d show up at a store with a clipboard. Now, an AI-powered app on their tablet not only suggests an order based on that store’s history but also analyzes what the most successful retailers in that zip code are selling. Then it makes the magic connection. The app says, “Our new Christmas ad campaign launches next week. Suggest an endcap display for this product, as it will be heavily featured.” Suddenly, you’ve connected a multi-million-dollar marketing campaign directly to a conversation happening in a mom-and-pop store, creating a direct line between marketing spend and higher revenue.
Newell’s strategy of acting as a beta tester for tech companies yielded a 500% increase in marketing assets. Please elaborate on how a CPG can build these co-development partnerships and describe the practical, day-to-day steps involved in testing and rolling out a new AI tool.
Building those partnerships requires a shift in mindset from being a passive customer to an active co-creator. It starts with being proactive and identifying emerging tech companies that align with your strategic goals. You don’t wait for the perfect, off-the-shelf solution; you offer your company as a real-world laboratory. For the tech startup, access to a CPG’s scale and complexity is invaluable. Day-to-day, this is an intensely collaborative process. A small, dedicated team from the CPG, say from brand management, works directly with the tech company’s engineers. They’d start with a single problem, like asset creation. They would test an early version of the tool, provide instant feedback on whether the generated content meets brand guidelines, and iterate in rapid cycles. This isn’t a long, drawn-out pilot; it’s a series of weekly or even daily sprints. This hands-on approach is how you get a tool that is perfectly tailored to your needs and how Newell was able to drive such a monumental productivity gain, increasing customer service response productivity by 300% to 400% and taking response times from a painful three hours down to just 15 minutes.
Clorox now personalizes 60% of its marketing with a goal of one-to-one engagement, built on a new data infrastructure. What specific types of data are most critical for this hyper-personalization, and how does a modernized ERP system directly enable these advanced marketing and supply chain capabilities?
For that level of hyper-personalization, you need to weave together a rich tapestry of data. It’s not just about purchase history. It’s about combining first-party data, like how a consumer interacts with your website, with real-time, unstructured data from social media and customer feedback channels. The AI’s power is in its ability to synthesize this all, identifying patterns and unmet needs that a human analyst would miss. For example, it can see a cluster of conversations on Twitter about a desire for a certain product feature and flag it as a potential innovation opportunity. The modernized ERP is the central nervous system that makes this all possible. An old, siloed ERP can’t give you a real-time view of your business. The new system allows marketing to see what’s happening in the supply chain at that exact moment. So, when the AI identifies an opportunity to send a personalized offer to 10,000 specific customers, the system can instantly verify that the inventory is available in the right locations to fulfill those potential orders. It breaks down the walls between departments and enables the business to operate as a single, intelligent organism.
What is your forecast for the CPG industry’s AI adoption over the next five years?
Over the next five years, the gap between the AI-enabled CPGs and the laggards will become a chasm. We’ll move beyond isolated use cases and into full-scale, end-to-end process automation. The concept of “one-to-one marketing” that Clorox is chasing will become the industry standard, and consumers will expect it. But the biggest evolution won’t just be in marketing or supply chain; it will be in how CPGs innovate. AI will become the core of the R&D process, analyzing consumer trends and even generating digital prototypes before a single physical product is made. The ultimate winners won’t be the companies with the most algorithms, but those who successfully manage the immense cultural shift required to become a truly data-driven, AI-native organization. It’s as much a human challenge as it is a technological one.
