Is AI Commerce Already Here? Lessons From Cahoot’s Ugly Talk Panel

Last week in New York, Cahoot hosted a panel called Ugly Talk: Selling in a World Run by Algorithms. The goal of the discussion was simple: move past the hype around artificial intelligence and have an honest conversation about how algorithms are already shaping ecommerce.

The panel brought together operators and technologists who work directly in the ecommerce ecosystem. The discussion also introduced the concept of an agentic ecosystem—a complex, interconnected system that includes AI platforms, autonomous agents, infrastructure, payment systems, and enablers like traditional e-commerce platforms and fraud prevention tools. Participants included Manish Chowdhary, CEO of Cahoot; Nihar Kulkarni of Roswell NYC; Frank Pacheco, who leads Amazon strategy and execution for Nearly Natural; and YiQi Wu, co-founder of Aimerce. Rather than delivering prepared presentations, the group spent the evening debating how discovery, advertising, and customer data are changing the way products are found and purchased online.

One question kept resurfacing throughout the discussion:

Is AI commerce already here, or are we still early?

The answer, as it turned out, depended on who you asked.

These ideas are part of a broader framework for understanding how AI is reshaping ecommerce. The evolution of ecommerce is being driven not only by AI but also by new technologies that are disrupting traditional commerce and forcing fundamental changes in business models and customer engagement. For a complete breakdown of how discovery systems, product pages, brand authority, behavioral data, and fulfillment infrastructure interact, see The AI Commerce Playbook for Ecommerce Brands.

The Debate Around Agentic Commerce

“For the last twenty years ecommerce has largely been built around interfaces designed for humans — search bars, product grids, ads, landing pages. But something subtle is happening now. The first decision is increasingly being made by machines.” — Manish Chowdhary, Cahoot

Some panelists argued that the shift toward AI-driven discovery is already underway. Consumers are experimenting with conversational search interfaces, recommendation systems are becoming more sophisticated, and AI assistants are beginning to influence how shoppers evaluate products. This represents a significant transformation in the retail and e-commerce landscape fueled by AI advancements.

From this perspective, AI commerce isn’t something that will arrive years from now. It’s already emerging in subtle ways across the ecommerce ecosystem, fundamentally transforming the customer journey at every touchpoint.

Others on the panel took a more cautious view. While AI tools are improving quickly, the amount of ecommerce traffic coming directly from AI discovery interfaces remains small. Most shoppers today still rely on familiar channels: Google searches, marketplace browsing, paid ads, and social media recommendations.

Both perspectives reflect different parts of the same reality. The technology is advancing quickly, but consumer behavior takes longer to shift. This signals the emergence of a new paradigm in commerce driven by AI and automation.

That dynamic is typical whenever a new discovery system begins to emerge.

Ecommerce Has Seen This Pattern Before

For most of the history of online retail, product discovery has been controlled by a small number of dominant platforms. The evolution of e-commerce has seen a transformation from simple online catalogs to intelligent, AI-powered experiences that are reshaping how consumers find and purchase products.

In the early days of ecommerce, Google search became the primary gateway to online shopping. Brands learned to optimize their websites and product pages for search rankings. Entire industries emerged around keyword research, backlinks, and technical SEO, as well as practices to protect product listings from search suppression and other threats. E-commerce platforms were essential components of this ecosystem, enabling transactions and supporting the growth of online retail.

Later, marketplaces like Amazon introduced a different discovery model. Instead of competing for visibility on search engines, sellers competed inside marketplace ranking algorithms. Market and product research for Amazon sellers became critical, and reviews, pricing, fulfillment performance, and sales velocity became key signals influencing which products appeared first.

Social media platforms created yet another layer of algorithmic discovery. Instead of actively searching for products, consumers increasingly encountered them through feeds, influencer content, and targeted advertising, which in turn forced brands to rethink how they built a multichannel fulfillment and sales strategy.

Each shift changed how ecommerce brands competed for visibility. To remain competitive as discovery models evolve, businesses must adapt their existing systems—including legacy e-commerce platforms and fulfillment infrastructures—to support new technologies and consumer behaviors, especially as options like peer-to-peer fulfillment networks and Buy with Prime reshape expectations for fast, low-cost delivery.

The discussion at Ugly Talk suggested that AI-driven discovery may represent the next stage in that evolution.

How AI Changes Product Discovery

“Algorithms are deciding what products get recommended, what ads get shown, and what listings surface. In some cases, they may even decide what products get bought on behalf of the consumer.” — Manish Chowdhary

Traditional ecommerce search relies heavily on keywords. A shopper enters a phrase, and the platform returns a list of products that match those terms.

AI-driven discovery systems operate differently. Because they rely on language models and contextual understanding, they can interpret broader intent rather than just matching keywords. Generative AI leverages natural language processing to understand and process customer queries, enabling more conversational and intuitive interactions.

Instead of typing “carry-on luggage,” a shopper might ask an AI assistant a more natural question:

What’s the best lightweight suitcase for international travel?

Rather than returning a page of links, the AI might generate a synthesized answer that recommends several products, summarizes customer reviews, and explains why certain brands are a good fit.

In that scenario, the customer never performs a traditional search. The AI acts as an intermediary that interprets the question and generates product suggestions, and can even complete transactions or tasks on the user’s behalf, such as tracking price drops or executing purchases automatically.

For ecommerce brands, this creates a new kind of visibility challenge. Products may be surfaced not simply because they contain the right keywords, but because the system interprets them as relevant to the customer’s intent. The integration of AI transforms the entire shopping journey, making it more efficient, personalized, and predictive from product discovery to post-purchase services.

When Optimization Backfires

One moment during the panel highlighted how changes in discovery systems can have unexpected consequences.

Frank Pacheco, who works directly on Amazon strategy and execution for the home decor brand Nearly Natural, described a situation that many ecommerce operators will recognize. Product listings are often optimized aggressively for search algorithms, sometimes by adding keywords that improve ranking but do not accurately reflect the product itself.

In one example discussed during the panel, a product listing was updated to include a feature keyword that appeared highly relevant to search queries. The change improved visibility and conversion rates, at least initially. But over time, customers began purchasing the product with the expectation that it included that specific feature. When they discovered the feature did not exist, return rates increased and customer complaints followed.

The example illustrated an important point raised during the discussion: optimizing for algorithms without aligning with the real product experience can create operational problems later.

As discovery systems become more sophisticated, the signals they interpret may also become more nuanced. Instead of simply matching keywords, AI systems may rely more heavily on product context, reviews, and customer behavior. Additionally, automating tasks such as currency conversions, tax calculations, and compliance processes can streamline business operations and reduce manual effort, further enhancing efficiency across various functions, and educational resources like on-demand ecommerce strategy webinars can help operators keep pace with these changes.

That shift could make traditional keyword-driven optimization strategies less effective over time. As AI-driven systems become more complex, risk management becomes increasingly important to address challenges and vulnerabilities such as systemic failures, accountability issues, and data sovereignty concerns.

Building Consumer Trust in AI Commerce

As AI agents become the primary interface between consumers and online marketplaces, building consumer trust is emerging as a cornerstone for the widespread adoption of AI commerce. In this new era, where AI systems increasingly shape the entire shopping experience, businesses must prioritize transparency, accountability, and security to foster lasting relationships with their customers.

One of the most effective ways to build brand loyalty and customer loyalty is by leveraging AI-powered tools that deliver personalized shopping experiences. Generative AI can analyze customer data to recommend products tailored to individual preferences, while dynamic pricing models ensure that consumers receive fair and competitive offers. These innovations not only meet rising consumer expectations but also help brands stand out in a crowded digital world.

However, personalization alone is not enough. To truly earn consumer trust, businesses must ensure their AI systems are explainable, fair, and unbiased. This means deploying machine learning algorithms that actively detect and mitigate bias, conducting regular audits, and providing clear explanations for how decisions are made. Transparency around the collection and use of customer data is equally critical. By offering tiered access and opt-out options, businesses empower consumers to control their own information, reinforcing a sense of security and respect.

Visibility and credibility also play a vital role in trust-building. By investing in search engine optimization (SEO) and optimizing for search engines, businesses can increase their reach and connect with a broader target audience. A strong presence on online marketplaces, supported by trustworthy product data and transparent business practices, further enhances consumer confidence.

Staying agile is essential in this rapidly evolving landscape. AI-powered analytics platforms, such as those offered by Google Cloud, provide actionable insights into consumer behavior, enabling businesses to adapt quickly to shifting customer needs and preferences. By continuously refining their strategies based on real-time data, brands can future-proof their operations and maintain a competitive edge.

Ultimately, building consumer trust in AI commerce requires a multifaceted approach—one that combines advanced AI-powered tools, a commitment to transparency and fairness, robust SEO strategies, and a willingness to adapt quickly. For senior partners and decision makers at global leaders in commerce, prioritizing consumer trust is not just a best practice—it’s a necessity for thriving in the new era of agentic commerce. By doing so, businesses can ensure they remain relevant, resilient, and ready to meet the demands of tomorrow’s digital consumers.

Early Signals From the Market

Although AI-driven commerce is still developing, several signals suggest that the shift is beginning.

Major technology platforms are investing heavily in conversational shopping tools designed to help consumers compare products and make purchasing decisions. Ecommerce platforms are experimenting with AI-powered assistants that guide shoppers through product categories. Even advertising systems are evolving to incorporate machine learning models that determine which products are shown to which audiences. At the core of these advancements are powerful AI engines, which drive advanced search functionalities, product data enrichment, and supply chain optimization.

Operators on the panel noted that these changes are still subtle. Most ecommerce traffic continues to flow through traditional discovery channels. Google searches, marketplace browsing, and paid advertising remain the dominant sources of product discovery. “Right now the traffic coming from AI agents is still very small — less than half a percent of our sales. But it has already grown from almost nothing to something measurable.” — Frank Pacheco, Nearly Natural

But the emergence of new discovery tools suggests the environment is evolving. Businesses must stay agile to respond to new API strategies and platform interfaces, ensuring they can quickly adapt to technological innovations and maintain seamless agent interactions.

AI-driven tools are also enhancing customer engagement and improving consumer experiences by enabling personalized, dynamic, and tailored interactions that drive loyalty and satisfaction.

In addition, AI is optimizing logistics and fulfillment by improving inventory management, dynamically considering shipping costs, selecting cost effective fulfillment solutions, accommodating delivery preferences, and streamlining the supply chain for greater efficiency and speed—making advanced ecommerce shipping software for warehouse automation a core part of competitive operations.

In the early stages of technological shifts, the numbers rarely look dramatic. What matters is the direction of change.

Why Ecommerce Operators Should Pay Attention

For brands and ecommerce operators, the key takeaway from the panel discussion was not that AI commerce has already transformed online retail.

It hasn’t.

But history suggests that discovery systems tend to reshape the competitive landscape over time. Companies that recognize these shifts early often gain a meaningful advantage by rethinking and expanding their business models to adapt to agentic commerce and AI-driven transformation.

Brands that understood search engine optimization early were able to capture organic traffic before the field became crowded. Sellers who learned how Amazon’s ranking systems worked were able to dominate marketplace categories.

The same pattern could emerge with AI-driven discovery, especially as agent to agent interactions—where AI agents representing buyers and retailers conduct transactions autonomously—become more prevalent and rely on robust order fulfillment integrations with ecommerce partners to execute seamlessly across channels.

Understanding how AI systems interpret product information, brand authority, and customer behavior may eventually become a critical part of ecommerce strategy. Additionally, integrating and evolving payment systems to support AI-driven, autonomous transactions will be essential for staying competitive, just as selecting the right Amazon-focused 3PL shipping partners is critical for meeting service-level expectations in marketplace-driven commerce.

The Shift Is Beginning, But Not Finished

If the Ugly Talk panel made anything clear, it’s that the ecommerce industry is still in the early chapters of the AI commerce story.

The technology is evolving quickly, but the ecosystem has not yet fully adapted. Retail businesses are actively adapting their operations and technology infrastructure to thrive in an AI-native environment, focusing on modernization and strategic innovation. As recent news about ecommerce fulfillment innovations and partnerships shows, consumers are experimenting with new discovery tools, platforms are building new recommendation systems, and ecommerce operators are beginning to observe small changes in how shoppers find products.

For now, traditional discovery channels still dominate.

But the emergence of AI-assisted shopping suggests that the next phase of ecommerce competition may revolve around how algorithms interpret and recommend products, with a strong emphasis on creating seamless experiences for customers.

In other words, the rules of visibility may be changing again, as AI transforms the decision making process for both businesses and consumers.

And as the panel discussion made clear, the brands that begin paying attention now will be better positioned when those changes accelerate, especially if they stay close to the latest ecommerce logistics, fulfillment, and supply chain events shaping the next generation of commerce.

Click to learn how the first layer of modern ecommerce is discovery.

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