Global B2B commerce is increasingly moving away from desktop-only procurement workflows as buyers and suppliers expect sourcing, communication and transaction activities to happen across mobile devices. Ecer.com is positioning its platform around that shift, combining mobile commerce with AI matching, multilingual communication, live video and immersive factory experiences.

The company describes Ecer.com as a mobile-first B2B trade platform designed to connect international buyers with manufacturers while reducing some of the delays traditionally associated with cross-border procurement.

Rather than treating mobile as simply another channel for browsing supplier catalogs, the platform is designed to support multiple stages of the buying process, including inquiries, supplier communication, factory inspections, product evaluation and negotiations.

Market Landscape

Cross-border B2B purchasing remains complicated by fragmented communication, geographic distance, language barriers and the need to establish confidence in suppliers before transactions move forward. Digital commerce platforms are increasingly attempting to compress those steps into connected workflows.

Ecer.com says its platform uses buyer-supplier matching algorithms and multilingual AI customer-response technology to connect purchasing requirements with manufacturing capabilities. Instant messaging and live video are intended to reduce dependence on asynchronous email exchanges, particularly when buyers and suppliers operate across different time zones.

The platform also incorporates VR factory tours and interactive 3D product models. For industrial procurement, these tools can provide additional context before a buyer commits resources to an in-person visit.

The company cites a case involving Sinotruk International and a German buyer as an example. According to Ecer.com, the buyer had originally planned a week-long factory verification process but used a mobile VR tour and live video interaction to complete the assessment remotely. Ecer.com says the decision window fell from seven days to less than 24 hours.

That 85% acceleration figure is a company-reported case-study result, rather than independent evidence that the platform generally reduces procurement cycles by that amount.

The broader opportunity is significant because digital B2B commerce increasingly depends on connecting discovery, communication, product information and transaction processes rather than treating them as separate stages.

Strategic Outlook

Ecer.com's strategy reflects a broader movement toward end-to-end digital commerce infrastructure. Its mobile experience combines AI-assisted customer support, supplier discovery, digital showrooms and direct communication instead of limiting the platform to marketplace listings.

The use of VR and 3D is particularly relevant for categories where physical inspection has historically been an important part of supplier qualification. Factory tours can potentially give international buyers an initial view of production environments without requiring immediate travel.

AI matching introduces another layer by attempting to connect buyer requirements with appropriate suppliers. The effectiveness of that model will ultimately depend on the quality and accuracy of supplier information, product specifications and buyer intent signals.

Ecer.com also supports connections with external communication platforms such as WhatsApp, extending conversations beyond the core marketplace environment.

The more important development, however, is the convergence of these capabilities into a single mobile workflow. If buyers can discover suppliers, communicate requirements, inspect facilities remotely and negotiate without repeatedly switching systems, B2B marketplaces could become more operationally important within international procurement.

For suppliers, the model also changes how manufacturing capabilities are presented. A digital factory tour and interactive product model can become part of the sales process rather than simply supplementary marketing material.

The challenge will be establishing whether digital representations can provide enough confidence for higher-value transactions where physical inspections, certifications and other forms of supplier verification remain important.

Top Insights

• Ecer.com is expanding mobile B2B commerce beyond product discovery by connecting supplier matching, communication, factory inspections and transaction workflows.
• AI-powered multilingual responses and buyer-supplier matching are designed to reduce communication delays and improve connections between international demand and manufacturing capacity.
• VR factory tours and 3D product experiences give overseas buyers digital alternatives to some early-stage physical inspection and evaluation activities.
• The company's Sinotruk International example reports an 85% faster procurement process, although the result is a company-reported case study rather than independent performance data.
• The platform illustrates how mobile commerce, AI and immersive technologies are converging around more connected cross-border procurement workflows.

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Marketing AI is moving beyond content generation and campaign automation toward systems designed to continuously decide which audiences deserve attention and what action should happen next. iCustomer is entering that market with Growth Brain, an always-on audience and decision intelligence platform for marketing, growth and revenue teams.

The system is designed to build a persistent understanding of a company's audiences, connect customer signals across marketing and sales systems, and use campaign outcomes to refine subsequent decisions.

That distinction is important as enterprises accumulate more marketing data and technology without necessarily improving how those systems inform decisions. Gartner's 2025 Marketing Technology Survey found that organizations actively use only 49% of their martech capabilities, while CMOs oversee an average of nine marketing channels. Gartner says high-performing organizations are increasingly focused on modular technology architectures, capability-based measurement and standardized processes that AI agents can operate within.

Growth Brain is positioned as an intelligence layer rather than another campaign execution tool. iCustomer says it continuously learns from customer history, audience behavior, previous marketing decisions and business outcomes, then applies those learnings to goals such as increasing qualified pipeline, improving repeat purchases or reducing customer acquisition costs.

Market Landscape

The shift toward decision intelligence reflects a limitation in much of today's marketing AI: generating more content does not necessarily answer the harder questions of who to target, why behavior changed and which action is likely to produce incremental business value.

Growth Brain attempts to address that problem by creating what iCustomer calls a "living record" of the business. The system combines signals from CRM platforms, products, email, data warehouses, advertising platforms, anonymous website visitors and third-party data.

That identity layer can represent an individual, account, household, cohort or buying group, according to the company. The objective is to create a more complete audience picture before automated decision-making begins.

The platform then applies continuous decision loops. A system observes audience behavior, makes a decision, acts, measures the result and adjusts within rules established by the marketing team.

This model has parallels with the direction of modern personalization technology. McKinsey has argued that effective AI-driven personalization requires integrated customer data, real-time decision engines and models that continuously refine the next-best action.

Growth Brain also includes causal measurement, with experimentation intended to distinguish incremental impact from activity that simply receives attribution. Its Pulse interface is designed to alert teams when audience behavior changes, performance deteriorates or new buying signals appear.

Strategic Outlook

The strategic significance of Growth Brain is its attempt to make audience intelligence a reusable enterprise asset rather than something recreated separately inside individual campaigns.

iCustomer says CRM systems, campaign platforms and sales agents can draw from the same audience intelligence and return outcome data to the system. It also supports connections with platforms including Claude, Salesforce and HubSpot, allowing authorized agents to use the same context.

That architecture aligns with a broader shift toward AI systems that operate continuously rather than waiting for marketers to initiate individual campaigns. McKinsey's recent analysis describes agentic AI as systems capable of planning, deciding and executing marketing tasks with limited human intervention, including continuous optimization of customer journeys.

The challenge will be governance. Continuous decisioning requires accurate identity resolution, reliable outcome data, experimentation and clearly defined boundaries around what automated systems can change.

For marketing leaders, the value proposition therefore extends beyond automation. The more consequential question is whether a company can build a feedback loop in which every audience decision generates better information for the next one.

Growth Brain extends iCustomer's existing Audience Loop product, which runs an always-on decision loop against a single target. The company describes Growth Brain as the broader system connecting those loops across acquisition, conversion, retention and expansion.

If that model gains adoption, marketing technology could increasingly compete on the quality of its learning system rather than simply the number of campaigns it can automate.

Top Insights

• iCustomer's Growth Brain shifts marketing AI toward continuous audience intelligence, using customer signals and campaign outcomes to improve subsequent business decisions.
• The platform connects CRM, product, email, advertising and data-warehouse signals to create a broader view of individuals, accounts, households and buying groups.
• Continuous decision loops observe, act, measure and adjust against goals such as pipeline growth, repeat purchases and customer acquisition costs.
• Causal measurement and experimentation are designed to separate incremental marketing impact from activity that merely receives attribution.
• Connections with Claude, Salesforce and HubSpot extend audience intelligence into existing marketing and AI-agent workflows rather than creating another isolated data environment.

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Marketing agencies are adding AI search visibility to an already crowded collection of SEO, content, analytics and reporting tools. Indexly is targeting that operational problem with an agency-focused marketing technology stack that combines traditional SEO workflows with generative engine optimization (GEO), answer engine optimization (AEO), AI visibility tracking and lead attribution.

The platform is designed for U.S. agencies managing multiple clients across sectors including law, fintech, real estate and direct-to-consumer brands. Indexly says the system can monitor brand visibility across ChatGPT, Claude, Gemini, Perplexity, Grok and Google AI Overviews while bringing content generation and reporting into the same environment.

The launch reflects a broader challenge in marketing technology: adding another channel can create more operational complexity even when the channel itself presents a growth opportunity.

Gartner's 2025 Marketing Technology Survey found that only 49% of marketing technology capabilities are actively utilized, while CMOs oversee an average of nine marketing channels. Gartner says high-performing organizations are increasingly focusing on composable stacks, capability-based measurement and standardized processes that AI agents can operate within.

Indexly is positioning its platform around that consolidation problem. Rather than requiring agencies to assemble separate tools for AI visibility, content production, social-platform monitoring, traffic attribution and client reporting, the company says those functions can be managed from one system.

Market Landscape

AI search is creating a new measurement problem for agencies. Traditional SEO platforms are largely built around rankings, backlinks, keyword positions and organic traffic. AI-generated answers introduce different signals, including whether a brand is mentioned, which sources are cited and how frequently a company appears for relevant conversational queries.

Indexly's platform attempts to bring those signals into an agency workflow. Its AI visibility tracking covers multiple conversational systems, while its content agents are designed around formats intended for AI search environments.

The company also tracks visibility on Reddit and LinkedIn and provides AI traffic analytics intended to connect referrals and leads with the content generating them.

That matters because agencies increasingly need to demonstrate business outcomes rather than simply report rankings. BCG research on commercial technology found that more than half of surveyed organizations had failed to unlock sufficient value from recent commercial-tech investments, with manual processes, poor data quality and unclear ownership among the barriers. BCG also found that organizations that redesign workflows alongside technology deployments can achieve substantially greater productivity gains than those that simply add tools.

For agencies, the equivalent issue is tool proliferation across every client account. A new AI-search service can become expensive to deliver if monitoring and reporting require additional manual work.

Strategic Outlook

Indexly's agency strategy is less about replacing SEO than extending an existing search operation into AI-mediated discovery.

Its AI readiness audits, visibility tracking and content agents are designed to give agencies a repeatable framework for adding GEO and AEO services across multiple accounts. The platform also includes an AI governance and fact-checking layer for clients in areas such as law and fintech, where inaccurate claims can create greater reputational or regulatory risk.

Indexly says Spotlight Media360 has deployed its AI Readiness Audit, AI Visibility Tracking, Content Generation and Custom Reports across four to five client accounts. The agency reports Google organic traffic gains across those accounts and says some clients have begun receiving referral traffic from ChatGPT, Perplexity and Google AI Overviews. Those are customer-reported results rather than independently verified performance data.

The commercial model starts at $200 per site per month, according to Indexly, with custom pricing for agencies managing multiple accounts or requiring industry-specific configurations.

The larger question is whether agencies will view AI visibility as a sufficiently distinct service to justify another platform—or whether consolidation itself becomes the deciding factor.

As AI search becomes part of client reporting, agencies will need systems that connect visibility metrics to content activity, traffic and leads. Indexly is betting that an integrated workflow will be more scalable than managing those functions independently.

Top Insights

• Indexly combines SEO, GEO, AEO, AI visibility tracking and reporting to help agencies add AI search services without assembling separate tools for every client.
• The platform monitors visibility across ChatGPT, Claude, Gemini, Perplexity, Grok and Google AI Overviews, expanding measurement beyond conventional search rankings.
• Content agents and AI traffic analytics connect content production with visibility and lead attribution, giving agencies a broader framework for measuring AI search performance.
• Reddit and LinkedIn monitoring extends the platform beyond search engines, reflecting how AI systems can draw information from wider online sources.
• The agency-focused model addresses a growing martech challenge: adding AI capabilities without increasing tool fragmentation, manual reporting and operational overhead.

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Independent financial advisors are facing a marketing environment in which prospective clients increasingly research firms and professionals online before making contact. XYPN and Wealthtender are responding with a new partnership that combines advisor marketing tools with search, reputation management and AI-focused visibility capabilities.

XYPN, a business support platform for fee-only financial planners, said its members will receive access to Wealthtender's marketing platform through the partnership. The offering includes compliant review and testimonial tools, search engine optimization (SEO), answer engine optimization (AEO), targeted content development and tools intended to improve visibility across traditional search and emerging AI-powered discovery channels.

The partnership reflects a broader shift in professional-services marketing: digital reputation is becoming part of the conversion process rather than simply an awareness channel.

That is particularly relevant in financial services, where credibility can influence whether a prospective client moves from online research to an initial conversation. Research from the CFP Board found that 45% of Americans surveyed seek financial information from finance-focused websites, while 17% reported using generative AI tools such as ChatGPT or Gemini for financial questions.

At the same time, online visibility does not automatically translate into trust. CFP Board's 2025 research found that only 39% of respondents believed online financial information generally serves their best interests, while 57% said they had made a regrettable financial decision based on misleading online information.

That trust gap creates an opportunity for advisors to use verified client experiences, professional credentials and useful educational content as part of their digital presence.

Market Landscape

Wealthtender's platform focuses on turning client feedback into reusable marketing assets. Advisors can collect and display compliant testimonials and reviews and use that material across websites, social media and email campaigns.

The platform also extends beyond conventional SEO. Its AEO capabilities are designed around question-and-answer content intended to help advisors appear when prospective clients research specific financial topics, professional groups, industries or communities.

The shift toward AI-powered discovery adds another layer to this strategy. Consumers can increasingly ask conversational systems for recommendations or explanations instead of relying exclusively on conventional keyword searches.

For financial advisors, however, visibility needs to be accompanied by credible information. CFP Board's research shows that consumers are increasingly exposed to online financial information while remaining skeptical about its reliability. The organization reported that 80% of surveyed Americans question the accuracy of online financial information at least once a month.

That makes reputation management and content quality particularly important in a category where inaccurate information can have direct financial consequences.

Strategic Outlook

The XYPN-Wealthtender partnership points to a broader evolution in professional-services marketing technology. SEO is increasingly being combined with reputation management, content development and optimization for AI-mediated discovery.

For independent advisors, the challenge is also one of scale. Larger financial firms can dedicate teams and budgets to content, search and digital reputation, while smaller practices often have limited marketing resources. A platform that packages these functions could reduce the operational burden of maintaining a consistent digital presence.

The opportunity extends beyond acquiring website traffic. Reviews, educational content and advisor-specific answers can influence how prospects evaluate expertise before initiating a conversation.

AI search makes that process more complicated because traditional ranking positions are no longer the only measure of visibility. Advisors may need to establish a broader body of authoritative, structured and contextually relevant information that can be surfaced across different discovery environments.

The partnership therefore represents more than another marketing benefit for XYPN members. It reflects how professional-services marketing is moving toward an ecosystem where search visibility, social proof and AI discovery increasingly overlap.

Top Insights

• XYPN is giving independent advisors access to Wealthtender's SEO, AEO, review and content capabilities as digital discovery becomes more important to client acquisition.
• Wealthtender combines compliant testimonials with marketing workflows, helping advisors turn client feedback into reusable website, social media and email content.
• AI-powered discovery adds a new visibility layer for advisors, making question-driven content increasingly relevant alongside traditional search optimization.
• CFP Board research highlights both growing reliance on online financial information and widespread skepticism about its accuracy, increasing the value of credible advisor content.
• The partnership illustrates how marketing technology is expanding from traffic acquisition into reputation, trust-building and AI-mediated professional discovery.

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Technical SEO is becoming a larger operational challenge as enterprise websites, ecommerce catalogs and SaaS platforms continue to expand. TechDigital Labs has introduced an enhanced technical SEO service designed to help businesses identify crawling, indexing, architecture and website-performance problems that can limit organic search visibility.

The expanded offering targets large and growing digital properties, where adding new products, categories, locations, content and landing pages can create technical dependencies across thousands of URLs. Its scope includes crawlability, indexation, Core Web Vitals, page speed, mobile performance, structured data, internal linking and site architecture.

The announcement comes as search visibility increasingly depends on whether search engines can efficiently discover, interpret and process a site's content. Google says canonicalization helps it determine the representative URL among duplicate or substantially similar pages, while sitemaps can help search engines discover URLs.

For larger websites, those technical foundations can become difficult to manage manually. A new product category, URL structure or internal-linking change can affect how search engines move through the site and how individual pages relate to one another.

Market Landscape

Technical SEO has evolved from a periodic website audit into an ongoing component of digital infrastructure management.

For enterprise sites and ecommerce businesses, the challenge is not simply finding isolated errors. Crawl inefficiencies, duplicate URLs, broken links, redirect chains, canonical conflicts and poorly structured internal links can interact across large portions of a website.

TechDigital Labs says its expanded service examines these relationships through architecture reviews, crawl-budget analysis, indexation checks, XML sitemap reviews, robots.txt configuration, canonicalization, redirects and internal linking.

The emphasis on indexation is particularly relevant because being published does not automatically mean a page will appear in search. Google recommends checking whether important pages are accessible to crawlers and notes that crawling and re-indexing can take time.

The service also incorporates structured data and performance analysis. Google describes structured data as a standardized way of helping search engines understand page content and recommends validating implementations and using URL Inspection to check how Google sees pages.

That makes technical SEO increasingly connected to development, content operations and marketing teams rather than remaining solely an SEO responsibility.

Strategic Outlook

The larger opportunity for technical SEO providers is shifting from one-time audits toward continuous monitoring and prioritization.

TechDigital Labs says its process categorizes technical issues according to relevance and potential impact, giving technical, content and development teams an actionable sequence rather than an undifferentiated list of errors.

That approach matters for large digital properties because not every technical issue carries the same business consequence. An indexing problem affecting an important commercial category can be more significant than a minor issue on a low-value page.

The company also proposes ongoing monitoring of crawl status, indexation, rankings, organic traffic and website performance as websites evolve.

For ecommerce and SaaS companies, this creates a broader technical SEO operating model in which search visibility becomes part of the website development lifecycle. New URLs, templates, navigation structures and content systems can be evaluated for search implications before problems scale.

The strategic challenge will be connecting technical diagnostics with actual business priorities. Technical SEO tools can identify thousands of issues; the value comes from determining which ones affect discoverability, user experience and commercially important pages.

Top Insights

• TechDigital Labs is expanding technical SEO around enterprise-scale crawling, indexing, architecture and performance issues rather than treating SEO as a one-time audit.
• The service combines crawlability, indexation, canonicalization, internal linking and sitemap analysis to address interconnected technical search problems across large websites.
• Focusing on issue prioritization could help development and marketing teams distinguish high-impact indexing problems from lower-priority technical warnings.
• Structured data and website performance remain part of the broader technical health assessment, connecting SEO requirements with development and user-experience workflows.
• Ongoing monitoring reflects the reality that ecommerce, SaaS and enterprise websites continuously create new URLs and technical dependencies.

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Global Communication Works (GCW) is expanding its proprietary approach to AI-driven brand visibility beyond its own client work, opening SignlLabs™ for licensing to public relations firms, advertising agencies, web development companies, marketing consultancies and in-house communications teams.

The move reflects a broader shift in digital marketing as brands increasingly seek to understand not only whether they appear in AI-generated answers, but whether those systems recommend them when consumers or business buyers ask for products, services or providers.

SignlLabs is designed to measure brand presence and recommendation strength across ChatGPT, Gemini, Claude, Copilot and Perplexity. GCW says the methodology was developed specifically around how AI platforms generate answers rather than adapting conventional search-engine optimization techniques.

At the center of the offering is GCW's Maturity Method™, which generates a Signaling Maturity Score™ from 0 to 100 across six dimensions: Foundation, Endorsement, Alignment, Presence, Warmth and Constancy. The framework then places a brand into a five-stage maturity ladder ranging from Unseen to The Answer.

Unlike an aggregated score, each AI platform is measured separately. That approach is intended to identify differences in how individual answer engines interpret and recommend a brand, rather than allowing stronger performance on one platform to obscure weaker visibility elsewhere.

Market Landscape

The development comes as marketers adapt to an environment where traditional search rankings are no longer the only route through which buyers discover information. AI assistants can synthesize information from multiple sources and produce recommendations without presenting the same results-page structure associated with conventional search.

GCW positions AI Signaling as distinct from both SEO and Answer Engine Optimization. Its methodology focuses specifically on recommendation behavior and the external signals that can influence how AI systems understand a brand.

The company says the practice has been tested through its client campaigns and calibrated over a year across five industries. The licensing model extends that methodology to organizations already responsible for reputation, marketing, communications or digital experiences.

Strategic Outlook

For agencies, a standardized measurement framework could provide a way to turn a relatively difficult-to-measure AI visibility question into a recurring assessment. Licensees receive the scoring methodology, Intent Category Framework™, query protocol, reporting templates and team training.

GCW will also continue updating the methodology as AI platforms and their recommendation behaviors evolve. That maintenance could become important as answer engines change how they retrieve, evaluate and synthesize information.

The larger strategic question is whether AI recommendation becomes a measurable marketing discipline alongside established practices such as SEO, content strategy and reputation management. SignlLabs is betting that it will.

Top Insights

• GCW is licensing SignlLabs to external agencies and in-house teams.
• The framework evaluates five major AI answer engines separately.
• Its scoring model covers six dimensions of AI brand signaling.
• The approach emphasizes recommendation, not simply AI search visibility.
• GCW says the methodology was calibrated across five industries over one year.

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Enterprise email marketing is moving toward a model in which AI agents handle more of campaign production while marketers retain control over strategy, approvals and exceptions. Stensul’s September release expands its AI governance platform with capabilities designed to support that shift across enterprise campaign workflows.

The company is adding broader Model Context Protocol (MCP) capabilities, deeper Figma integration and expanded quality-assurance automation. Together, the features allow AI agents to perform more production tasks while operating within predefined templates, brand rules, permissions and approval processes.

The change reflects a broader evolution in enterprise marketing operations. Instead of treating generative AI as a standalone tool for drafting copy or creating images, marketing teams are increasingly looking at agents as participants in end-to-end workflows.

Stensul's approach is to put governance around those workflows. An agent can begin with a Workfront brief, select an approved template or email, add approved imagery, generate alt text, apply tracking parameters and create content for predefined audience segments. It can then preview the email before handing it to people for review and activation.

That architecture could become increasingly important as campaign volumes and personalization requirements grow. Without centralized controls, adding AI agents to email production can also introduce new risks around brand consistency, tracking, accessibility, audience targeting and approval compliance.

Market Landscape

Enterprise marketing technology has been moving from individual AI assistants toward systems capable of executing multi-step tasks. Email production is particularly suited to this transition because many campaign activities follow repeatable processes governed by templates and organizational rules.

Stensul's expanded MCP capabilities are significant in that context. MCP provides a standardized way for AI systems to interact with external tools and data, potentially allowing agents to perform actions rather than simply generate recommendations.

The company's September release extends that concept to campaign assembly. Agents can work with existing enterprise assets and rules instead of generating campaigns from an unrestricted prompt.

The Figma integration addresses another part of the production bottleneck. Authorized designers can turn completed Figma designs into editable Stensul modules and publish them into the governed asset library. Marketers and AI agents can subsequently modify permitted elements without requiring administrators to manually rebuild every design.

Quality assurance is another important layer. Stensul says administrators can establish requirements that automation checks during creation, including missing subject lines and placeholder text. Catching these errors before approval can reduce the number of campaigns sent back for rebuilding.

The result is less a single AI writing feature than a workflow architecture connecting briefs, design, campaign assembly, QA and approvals.

Strategic Outlook

The strategic question for enterprise marketing teams is not simply how much content AI can generate, but how much production responsibility can be delegated without weakening governance.

Stensul is positioning governance as the mechanism that enables that delegation. Templates, imagery, tracking parameters, audience logic, brand requirements and approval policies become constraints within which both people and AI agents can work.

That could allow marketing operations teams to establish rules once while giving marketers and agents greater freedom to execute campaigns at scale.

For organizations running complex multichannel programs, the model also changes the role of marketing operations. Instead of manually managing every production step, operations teams could increasingly focus on designing the systems, permissions and controls that determine how automated production operates.

The Milwaukee Bucks' early use of the new MCP capabilities illustrates the potential operational benefit. The organization says AI can now handle images, links and copy for pre-templated emails, allowing its team to devote more time to multichannel creative campaigns.

The broader opportunity is higher campaign throughput without requiring production resources to increase at the same rate. The challenge will be maintaining human oversight where judgment, brand risk and unusual campaign requirements still demand it.

Top Insights

• Stensul is expanding AI agents from content assistance into governed email production, covering briefs, templates, imagery, tracking, audience content and previews.
• Expanded MCP capabilities give AI agents greater ability to interact with enterprise marketing workflows while remaining subject to predefined governance and permissions.
• Figma integration turns approved designs into reusable campaign modules, allowing marketers and AI agents to adapt assets without rebuilding them manually.
• Automated QA can identify issues such as missing subject lines and placeholder text earlier, potentially reducing campaign rework and approval delays.
• The release points toward marketing operations becoming a governance function as AI assumes more repetitive campaign production work.

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Artificial intelligence is becoming part of the ecommerce shopping journey without replacing search, according to Similarweb’s State of Ecommerce 2026 report. The research suggests consumers are stacking AI assistants and traditional search rather than choosing one over the other, creating a more fragmented path from product discovery to purchase.

The report, released September 10, finds that direct referrals from dedicated AI platforms increased more than 200% year over year but still generate relatively limited traffic for ecommerce marketplaces and online stores. The more important signal may be what happens before the click: Similarweb says AI recommendations can materially influence purchasing decisions, in some cases giving a recommended brand a 2-to-1 advantage over competitors.

The findings put pressure on ecommerce marketers to rethink what counts as product discovery. A shopper may ask ChatGPT or Google Gemini for recommendations, narrow the options through an AI conversation, then turn to Google Search before making a purchase. According to Similarweb, 89% of consumers who use AI during shopping research also use search.

That makes AI visibility less about replacing search engine optimization and more about becoming visible across multiple discovery environments.

The report, produced by Similarweb in collaboration with Statista, also examines the continuing migration from ecommerce websites toward mobile apps, as well as trends across marketplaces, beauty and cosmetics, consumer electronics, fashion and apparel, and home and garden.

Market Landscape

The ecommerce market is entering a period in which traffic and influence are becoming increasingly disconnected.

Search remains a major source of measurable visits, while AI conversations can influence a shopper without producing an immediate referral. Similarweb's findings therefore point toward an emerging distinction between traffic generation and demand influence.

That distinction is particularly important for brands competing for product recommendations. If an AI assistant helps a consumer narrow five possible products to two, the eventual website visit may not reveal which conversation shaped the decision.

Similarweb has previously reported rapid growth in generative-AI referrals and increasing use of AI for product research, reinforcing the idea that AI is becoming part of the discovery layer of ecommerce rather than simply another traffic channel.

The report's finding that AI users also rely on search complicates the idea that Google, ChatGPT or other platforms will simply replace one another. Instead, shoppers appear to be moving between them based on the stage of the journey.

That creates a more demanding environment for ecommerce teams. Product information must be discoverable, structured and authoritative enough to work across search engines, marketplaces, retailer apps and AI interfaces.

The broader market is also continuing to expand. Statista projects worldwide ecommerce revenue to reach about $4.91 trillion by 2030, compared with approximately $3.86 trillion in 2026.

Strategic Outlook

For retailers and brands, the immediate implication is that traditional acquisition metrics may provide an incomplete picture of how demand is created.

SEO, paid search, retail media and direct traffic remain measurable components of the funnel. AI introduces another layer in which consumers can receive comparisons, product explanations and recommendations before ever visiting a merchant's site.

That could change how ecommerce organizations approach content. Product descriptions, reviews, expert information and other authoritative sources may increasingly serve two audiences: human shoppers and AI systems interpreting product information.

The app-versus-web shift adds another complication. Similarweb says ecommerce app sessions are growing at roughly 1.3 times the rate of web visits, while 86.5% of U.S. consumers surveyed say they primarily shop on a smartphone or tablet. This makes mobile experience, app engagement and AI-assisted discovery interconnected parts of the commerce strategy.

For enterprise marketing teams, the emerging goal is therefore not simply to "optimize for AI." It is to build consistent product visibility across the full discovery ecosystem while measuring where possible how those interactions contribute to consideration and conversion.

Top Insights

• Similarweb finds AI referrals growing rapidly, but search remains central, suggesting ecommerce marketers must optimize for multiple discovery environments rather than replace SEO.
• AI recommendations can disproportionately influence purchasing decisions, making brand visibility inside conversational shopping journeys increasingly important for ecommerce companies.
• The 89% overlap between AI-assisted shopping research and search shows consumers are combining discovery tools rather than abandoning traditional search.
• Growing ecommerce app usage adds another layer to fragmented customer journeys, increasing pressure on brands to coordinate web, app, search and AI experiences.
• Statista's projected $4.91 trillion ecommerce market by 2030 underscores the commercial stakes as AI reshapes how consumers discover and evaluate products.

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