Why Operating Model Transformation Should be the #1 Priority for CPGs

Modernizing for Agility

Why Operating Model Transformation Should Be the #1 Priority for CPGs

Consumer products (CP) is arguably the industry facing the most disruption and behavioral changes caused by widespread adoption of generative artificial intelligence (AI). From the potential to remove the retailer as the middleman, scale content and marketing personalization and connect with younger customers, CP firms have a lot to gain from innovation.

What will separate the technology leaders from the technology laggards in the consumer products industry? It comes down to whether or not companies are centralizing their digital capabilities and strategies through a digital operating model.

“Without a consolidated digital operating model, employees have to work with fragmented artificial intelligence models across different brands, and will create less intelligent generative AI tools because knowledge and resources aren’t connected. For brands that end up in this scenario, it’s then 3x more expensive to fix things.”

-Daniel Liebermann, Managing Director at Publicis Sapient

For years, CP companies have used a business operating model centralized around their products, siloed by region and even by departments within regions. As technology like generative AI becomes more and more powerful, the importance of operational agility can’t be ignored.

But after a year of economic headwinds, how can CP firms make the transition from a product-based operating model to a digital operating model efficiently and cost-effectively? It comes down to an iterative, step-by-step approach.

Why 2024 is the year to transition to a digital operating model

In 2024, experts predict reducing inflation across regions, passing on potential cost savings across the supply chain to CP firms and, with the advent of AI-based technologies increasing in public usage, consumer firms need to stay ahead of the game.

Reducing costs due to inflation

After a year of sustained inflation, many CP brands are facing smaller-than-expected margins, and they have to make tough decisions on what to invest in and where they can cut costs.

At first glance, the costs of building out a new digital-first operating model (centralizing and consolidating digital capabilities) are daunting for many firms. But in the long term, having a central artificial intelligence and data hub can save CP brands money and time across brands, especially when utilizing first-party (1P) data for new large language models (LLMs) and algorithms.

Digital operating models in practice: centralized consumer insights

A global consumer electronics company wants to create a consumer insights hub, where employees can analyze consumer data to personalize marketing for the Gen Z segment.

Smaller regions don’t have the budget to invest in analytics talent, technology and training resources and because they’re organizationally siloed from larger regions, they can’t scale data insights.

If business units from different brands and regions can all access data from the same centralized structure, smaller regions can still access and utilize centralized consumer insights and apply them to their own decision-making to decrease time-to-market.

Rapidly taking advantage of new technology

Given the complex organizational matrix between regions and brands within global CP firms, the operating model is often the key linchpin to success with new technology.

Digital operating models in practice: generative AI incubator

A global spirits company wants to create a conversational AI-powered chatbot for retailers to answer shipping and delivery questions for several brands in the U.S. market, but some brands have fully siloed sales processes, from manual to self-service as well as in-person.

Some brands don’t have the technical capabilities to pilot a chatbot, and any efficiencies from generative AI couldn’t be scaled or transferred across brands in the future.

If digital capabilities were centralized at a company level, new projects and learnings from generative AI could be efficiently scaled across the company and progressively honed over time.

Three opportunities for conversational AI in the CPG industry

“2024 is the year where being digital-first is fundamental. There’s no choice but to invest in generative AI, which requires a lot of agility, and means there’s also no choice but to transition to a digital operating model.”
Sabrina McPherson , Senior Managing Director at Publicis Sapient

The three stages of AI operating model transformation

Generally, most CP firms start with a decentralized operating model that is not truly “digital-first.” Step by step, firms can begin to consolidate their digital capabilities, like AI, from being decentralized to a digital core.

Here’s how each of these digital operating models works in conjunction with AI capabilities:

1) A decentralized digital operating model

In this model, each business unit manages AI technology, strategy and delivery independently, with very little exchange. This model isn’t scalable, and it often comes with a lot of cross-region duplication. Usually, AI solutions are purpose-built for specific needs or groups, and there’s minimal digital resource allocation.

2) A digital center of excellence (DCoE) operating model

This operating model (the DCoE) creates expertise and consistency around AI across business units and provides a single source of AI metrics and data. However, the AI strategy and execution are still left up to siloed brands and regions—and the AI center’s success still relies on the separate business units’ budgets.

3) A digital core operating model

The digital core operating model gives a centralized hub full control of all AI investments and budgets, as well as digital experience and OKRs.

The digital core operating model gives a centralized hub full control of all AI investments and budgets, as well as digital experience and OKRs.

While regions would still have a limited ability to execute AI strategy, they’d use solutions and resources from a global AI team. When CP companies adopt this top-down mentality to drive acceptance across the organization, it makes it easier for each business unit to adapt to AI changes because they don’t need to reinvent the wheel with new AI projects and can start from a central repository.

How can consumer products firms prepare for an AI-optimized operating model?

While the goal of transitioning to a digital-first operating model is to create more speed and agility to adapt to new technology like AI, successfully transitioning digital capabilities at the center of a company takes longer than expected.

Taking a step-by-step approach

Using a pilot operating model, CP firms can isolate one or two strategic priorities, like generative AI, to test with a new operating model while they still have the rest of the business operating as usual.

This allows the business to work out the kinks and gathers feedback to iterate on and does it in a seamless way that doesn’t fully disrupt the rest of the business.

Focusing on accountability and organizational culture

The other piece to enabling digital operating model success is change management. CP firms can stand up a solid DCoE or hub, but if business units aren’t motivated to utilize that center of excellence through AI projects and AI budgets, they’re not going to be able to advance quickly enough.

Many companies realize only after investing in new technology that it’s more about the people and process components and not about the data, the tool or the luminary behind it.

“It’s been decades since we’ve seen a technology as powerful as generative AI. We’re already seeing that the companies ready to receive this technology, and that are converting it into profits, are the ones with digital-first operating models. If there’s ever been a perfect time to invest in this, it is right now.”
Daniel Liebermann , Managing Director at Publicis Sapient

2024 operating model takeaways, by sector

And with many nuances dependent on the product types CP firms cover, Publics Sapient experts have broken down the key next steps for each of the biggest sectors:

Consumer electronics and white goods industry operating model

B2B customer engagement: As B2B sales go digital, generative AI capabilities can power customer service interactions, from emails to chatbots to product information. A centralized digital operating model ensures that processes and advancements can scale across brands and regions, and avoids duplication of efforts.

Beauty and personal care industry sustainability

Move quickly through test-and-learn: Because beauty and personal care brands have more direct-to-consumer interaction with consumers, generative AI has the opportunity to make more of an impact through personalized marketing messaging faster than other sectors that only engage through retailers. Your digital operating model needs to support centralized AI capabilities that can surface insights and engage with consumers across brands

Tap into consumer insights: Generative AI can quickly surface upcoming trends from consumer channels, like TikTok or YouTube, to inform new products and predict customer needs. A centralized AI-powered insights function can disseminate trends and predictions across brands and regions

Food and beverage industry sustainability

Accurately forecast demand: Generative AI can produce synthetic data to predict demand from retailers and consumers, but this requires centralized analytical and artificial intelligence capabilities