The Impact of AI-Enabled Commerce on Consumer Goods Companies and the Foundational Steps Industry Leaders Are Taking Now to Gain Share amid Disruption

This final post in a series of three builds on previous predictions for how consumer shopping behavior is likely to evolve as AI’s role expands across the path to purchase.

One of the most-repeated quotes about consumer goods brands is attributed to Hector Laing, the former Chairman of United Biscuits: “Buildings age and become dilapidated. Machines wear out. People die. But what lives on are the brands.”

For generations of consumer goods leaders, this concept of enduring brand strength has held fast and guided corporate strategy, principles of consumer engagement and countless marketing campaigns. Empirically we know that brand strength is directly correlated with category market share. Put simply, strong brands “own” a disproportionate amount of consumer mindshare, which leads to outsize results at the point of purchase.

But what if that foundation were to crack? What if the disruptive and accelerating role of AI across the path to purchase marks the dawn of a new era where consumer preference is overpowered by dispassionate recommendation algorithms?

This post analyzes these questions while highlighting key implications and roadmap priorities for consumer goods companies looking to capitalize on this generational shift.

How will this impact the CPG business model?

Debate continues to rage around the ultimate shape of AI’s role in commerce. Critically, though, this is not a single monolithic question to answer but rather two interrelated dynamics to explore.

The first relates to the research and discovery phase, where there has already been a pronounced impact. Just this past year, over 40% of consumers used AI tools to discover gifts for holiday shopping. That traction is expected to grow as consumer awareness and understanding of these tools increases.

While the transaction itself remains largely insulated for the moment, accelerating consumer comfort with AI-based shopping tools and rapid ecosystem developments makes the impact at the point of purchase a question of when rather than if. 

In fact, Morgan Stanley estimates that AI-powered shopping assistants could represent 10-20% of the US ecommerce market by 2030.

Beneath the surface of this AI wave, the crosscurrents are more nuanced. Higher-consideration purchases are better suited for AI-driven research, while purchases of more commoditized items will be the first to see an impact in the transaction itself. Importantly, the starting point for shopping missions is shifting from a planned purchase of a trusted brand to a more fluid journey around a “job to be done.” Here consumers input a need in natural language into a chatbot, which then translates that to inferred purchase criteria and outputs a recommended product. 

Quantifiable data on product options is supplanting gut feel and ingrained consumer preference as the driver of choice. In-store merchandising and elegant ecommerce experiences still have a place in the marketer’s arsenal but will move the needle only for specific product categories and a subset of consumer needs. Increasingly, dispassionate algorithms will make decisions for consumers based on machine-readable inputs instead of emotion-based marketing tactics.

Consumer goods leaders see the writing on the wall and are beginning to question their brands’ future in this “new normal.” The difference between thriving and surviving is a combination of strategic foresight and speed. While inaction is not a viable strategy, moving too quickly is a recipe for damaged brands, frayed channel relationships and organizational distraction.

Below is a framework designed to encapsulate the key considerations that should be driving roadmaps for consumer goods companies while striking that elusive balance between urgency and pragmatism.

An actionable roadmap for strategic advantage

To guide trade-offs between warranted pragmatism and opportunistic embrace of “big bets,” we’ve outlined a structured framework for shaping consumer goods roadmaps in the midst of AI disruption:

Eliminate blind spots

The unavoidable reality is that as the consumer path to purchase evolves and activity shifts to new AI-enabled interfaces, brands are losing signal liquidity, as these emerging channels are less open with behavioral data. 

To shed light on these “dark funnel” moments, companies can:

1. Source data — directly from the platforms where it is available and third-party vendors — to shed light on consumer behavior within AI chatbots.

2. Weave that data into the existing customer 360 to reestablish a solid understanding of consumer behavior.

3. Enhance the portfolio of measurement and attribution techniques that play a critical role in optimizing spend allocation and marketing tactics.

4. Generate synthetic data to probabilistically fill in data gaps and deploy a portfolio of advanced modeling techniques, such as collaborative filtering and Bayesian inference modeling, to augment the existing data foundation.

Snowflake Marketplace and Snowflake’s customer 360 and machine learning infrastructure, synthetic data generation capabilities and range of measurement tools are all accelerants for marketers looking to address this challenge.

Modernize strategy and execution

Historically, brands earned outsize levels of mental and physical availability through significant advertising spend and prime placement in retail stores. This combination of brand salience and visibility across sales channels was a reliable algorithm for revenue growth.

Yet as the commerce landscape evolves, that battle-tested playbook is being reshaped. Brands that want to avoid a commoditizing fade into the background will have no option but to rationalize spend, evolve marketing tactics and modernize their prevailing approach to brand building. Critically, not all product categories and price tiers will be equally affected.

In many categories, consumers will still feel a pull toward unique brands that use inspirational creative to stand out from the proliferation of “AI slop” in social feeds and low-quality products across ecommerce platforms. A luxury goods company, for instance, may make a determination that the high-end leather goods from its flagship brand will remain aspirational and avoid commoditization. And while AI will influence aspects of consumer research and discovery for those products, it will be less important relative to other factors such as word of mouth, influencers, trends in popular culture and brand-building advertisements. Much of the marketing approach for a similar brand will remain unchanged over the next 12-18 months.

In others, data-driven AI chatbots are increasingly leveling the playing field and relying on objective data to drive recommendations. For brands in these categories, the magnitude of change required will be much more significant. Brand support will be reimagined with a focus on product metadata and web content that establishes authority and relevance.

One commonality across categories is the difficulty in optimally allocating working spend. A challenge even in the best of times, it becomes infinitely more complex as destabilizing forces such as AI, which reshape the path to purchase and introduce new touchpoints, fracture the measurement foundation driving spend allocation. 

While the principles behind this effort remain consistent — allocate spend across channels and touchpoints in a way that maximizes delivery against priority brand KPIs — the underlying analytical techniques such as MMM, MTA, closed-loop attribution and experimentation require a reboot in the age of AI.

And while the channel-level spend allocation decisions they drive remain critical, marketers also need to evaluate the evolving impact of touchpoints within each of those channels. For example, brands need to not only make a decision around spend allocation for retail media as a channel but also deploy spend to individual retailers and the variety of ad units available in each ad network. A brand may choose to decrease overall RMN spend while increasing funding for a retailer such as Walmart and shifting the focus from sponsored search to ads running within Walmart’s Sparky chatbot.

Cement authority and relevance

Increasingly, brands need to not only win the hearts and minds of consumers (for those purchases where human decisioning still guides the end purchase) but also influence the algorithms responsible for product recommendations in AI chatbots.

This recommendation decision-making optimizes for authority and relevance based on the query itself plus known and inferred context about the user, while factoring in more specific criteria such as price, reviews and ease of use. 

Brands need to take action across four core areas to positively influence how their products are perceived by these algorithms:

• Remedy deficiencies around “table stakes” decision criteria such as pricing. 

• Address perceived gaps in features and functionality surfaced through customer comments on social or negative product reviews (for example, “The screw-top cap is really difficult to remove, especially while driving”). 

• Create centralized data on every product through a “product 360.” This gives algorithms efficient access to the machine-readable data they require.

• Invest in content, both owned and in collaboration with third parties such as influencers and journalists, that builds credibility, authority and relevance.

Snowflake offers a range of scalable solutions — including Snowflake Cortex Search — for consumer goods companies looking to aggregate product-related data into a product 360 and integrate that data into AI commerce initiatives.

Win the ecommerce channel

A disproportionate share of transactions that stem from consumer research within an AI chatbot will flow to ecommerce channels, as this type of research and discovery naturally supports online shopping destinations. The result will be not only an ongoing store-to-online shift in sales but also a reallocation of market share within the ecommerce channel itself. 

For brands, then, the question is not whether to prioritize ecommerce but how.

The quickest path is simply to deepen partnerships with third-party ecommerce platforms. Here, though, it is important to place partnership “bets” on the companies most likely to gain share in the years to come. For more context, see the second blog post in this series.

However, for brands that run their own direct-to-consumer (DTC) ecommerce offering, the calculus becomes more complex. The go-forward role of the DTC channel will boil down to a combination of strategic vision and conviction in the “right to win.”

For years many DTC businesses have scaled through marketing tactics (such as SEO and paid search advertising) that will have an increasingly diluted impact as the centrality of AI accelerates. This fraying of the “DTC playbook” will leave brands with a clear choice: invest and adapt or retreat.

A uniquely compelling DTC offering will have a chance to generate consumer gravity and strong returns, while an undifferentiated one will increasingly lose traction. The former should be a focus for go-forward investment while the latter a candidate for deprioritization or even deprecation.

Invest in the retail ‘winners’

In addition to this prioritization of ecommerce partners, consumer goods companies also need to make a similar decision regarding their overall retail relationships.

AI will aggressively sort retailers into “winners” and “losers,” just as the rise of ecommerce did over two decades ago. Rash actions, like cutting off relationships with the perceived “losers,” is not advisable. But at the same time, treating every retailer the same is also strategically costly.

Brands should be pragmatic and rational but also decisive in identifying the retail partners that are poised to thrive in this new retail landscape. Importantly, though, some of those share gainers may be retailers that today are more niche players. Invest financially (for example, RMN spend), operationally (overallocation of scarce inventory) and strategically (joint development of disruptive commerce offerings) in those relationships to ensure your brands retain pole position as category leaders.

——————————————

For consumer goods companies, the rapid ascendency of AI across the shopping landscape represents an incredible opportunity for growth. While it is difficult to meaningfully move share in stable times in mature markets, everything is up for grabs when truly transformative change arrives. Leaders face a once-in-a-generation opportunity to build durable competitive advantage by rewiring their organizations, reshaping sales channels, rethinking marketing strategies and reengaging consumers in delightful ways.

For more information about how industry leaders are leveraging the Snowflake platform to prepare for the shifts that are reshaping and disrupting consumer goods, watch our on-demand webinar, 2026 Marketing Predictions.