Why Smart CPG Leaders Are Rethinking Their AI Strategy to Maximise Consumer Insight Value

Have you noticed how every technology vendor, consultant, and industry publication is telling you that your AI Strategy is the solution to all your business challenges?


The pressure to invest heavily in AI capabilities has never been greater for CPG executives.

Yet many leadership teams ask a fundamental question: How do we separate genuine opportunities from expensive distractions?

This question becomes particularly challenging when it comes to customer understanding.

While an effective AI strategy promises unprecedented insights into consumer behaviour, the path from investment to measurable business impact remains unclear for many leadership teams.

If you would rather listen than read:

The Reality of AI Strategy Investment in CPG Today

Let’s start with some sobering numbers.
According to McKinsey’s recent analysis, while 70% of CPG companies are implementing some form of AI initiative, only 22% report significant business impact from these investments.
The disconnect between implementation and value creation isn’t surprising when you consider that many companies approach AI as a technology solution rather than a business transformation.

The executives I work with often share a common frustration: they’ve invested millions in AI platforms and data lakes, yet still struggle to generate actionable customer insights that drive revenue growth.
One global beverage company CIO recently confided,

”We have more data than ever, but less clarity on what our customers actually want.”

This disconnect exists because technology alone can’t create customer understanding.
The most sophisticated AI systems are only as valuable as the business questions they’re designed to answer and the actions they enable.

Building a Strategic Framework for AI Investment

When working with CPG leadership teams, I’ve found that successful AI strategies share a common foundation: they start with strategic business objectives rather than technological capabilities.

Rather than asking “How can we implement AI?” successful executives ask:

  1. Which customer-related business challenges, if solved, would create substantial value?
  2. Where are our current gaps in customer understanding creating barriers to growth?
  3. How might AI-generated insights enable better strategic decision-making?

This approach shifts the focus from technological implementation to business transformation. This is a subtle but critical distinction that separates high-ROI investments from expensive experiments.

Consider how Miyoko’s Creamery approached this challenge.
As a pioneer in plant-based dairy alternatives, they faced the dual challenge of understanding both their core vegan consumers and the much larger segment of flexitarians they needed to attract for mainstream growth.
Rather than broadly implementing AI across their organization, they identified specific high-value decision points where enhanced customer understanding would drive measurable business outcomes.

They focused initially on product formulation and messaging optimization, using AI to analyze consumer sentiment around taste, texture, and functionality in both their owned channels and broader food conversations.
Their system identified that “performance” messaging (how the product melts, stretches, etc.) resonated more strongly with flexitarians than sustainability or ethical messaging.

According to Miyoko’s Series C funding announcement, this targeted approach helped them reformulate their flagship products and reframe their marketing to emphasize culinary performance. This delivered a 40% increase in repeat purchase rates among … Click to continue reading

Why Your AI Marketing Strategy Is Failing (And How to Fix It)

The promise of AI in marketing has never been greater!

But if you’re like most CPG CMOs, your AI investment isn’t delivering the promised returns.

IDC’s latest Worldwide Artificial Intelligence Spending Guide shows that the global AI software market is expected to reach $251.4B by 2027, far exceeding earlier projections. However, most organisations are seeing disappointing returns on their AI investments. (Source)

This is confirmed by McKinsey’s 2023 State of AI report, 50% of organizations are using AI in at least one business function, but only 27% report seeing tangible cost savings! (Source

The disconnect? It’s not the technology that’s failing – it’s how we’re using it.

As Laurie Buczek, Group Vice President, Executives Insights and Leadership Services at IDC said when launching their latest FutureScape report:

Marketing’s future is represented by AI-fueled transformation. We only see the tip of the iceberg today, and lying underneath is a world where AI becomes the new operational fabric of marketing and sales, redefining the role of marketing, the people who work within marketing, and the way brands deliver a compelling, engaging customer experience.”

As a CMO, you’ve likely already invested in AI tools. Perhaps you’re using them for content creation, basic analytics, or campaign optimisation.

But here’s the uncomfortable truth: if you’re like 62% of organizations, you’re barely scratching the surface of AI’s potential.

So to help, here are a number of common failures and their solutions. You can thank me later!

If you prefer to listen rather than read:

Failure #1: The Single-Tool Trap
Most CPG brands fall into what McKinsey terms “point-solution paralysis.”

You’ve invested in an AI content tool or basic analytics platform, but it operates in isolation.

According to Adobe’s 2024 Digital Trends Report, this approach captures only 25% of potential ROI. (Source)

The Fix: Consider how P&G transformed their approach. By implementing what their Global CMO calls an “AI ecosystem,” they integrated multiple AI models across their brand portfolio.

The result? A 30% reduction in marketing waste across their brand portfolio. (Source)

Their system connects predictive analytics, consumer sentiment analysis, and dynamic segmentation in real-time.

 

Failure #2: Outdated Mental Availability Measurement
Byron Sharp’s research at the Ehrenberg-Bass Institute shows that 83% of brands either measure mental availability incorrectly or not at all. In today’s rapid-fire digital environment, annual brand tracking studies are like using a sundial to time a sprint.

The Fix: Nike’s revolutionary approach leverages continuous AI monitoring across 50 million weekly customer interactions. Their “Consumer Intelligence Network” automatically tracks and adjusts brand signals, leading to a 40% increase in digital engagement, a 28% increase in consideration rates and identification of $2 billion in new category opportunities. (Source)

 

Failure #3: Static Segmentation Paralysis
In the fast-moving CPG space, traditional annual segmentation is dangerously outdated. McKinsey’s 2024 Consumer Insights report reveals that 71% of brands still rely on annual studies, missing crucial … Click to continue reading

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