The rise of AI search has created a new vocabulary for digital visibility: AEO, GEO, AI discovery and “organic AI traffic.” But the terminology can obscure an important distinction. Traditional organic search measures clicks generated from unpaid search results, while AI systems can synthesize information from multiple sources and present an answer before a user visits any individual website. As Google expands AI Overviews and AI Mode, the more useful question for marketers may not be whether AI discovery is “organic,” but how a brand becomes eligible to be understood, cited and surfaced by AI systems.

For years, organic discovery had a relatively straightforward definition: a user entered a query into a search engine, found a webpage in the unpaid results and clicked through.

That model created a familiar SEO objective. Improve rankings, increase visibility and earn organic traffic.

AI search complicates that definition.

Google's current AI Search experience is designed to handle longer and more complex questions, while AI Mode can break a query into multiple related searches and synthesize information from different sources. Google reported in May 2026 that the average AI Mode search was three times the length of a traditional Search query in the U.S.

The result is a different form of discoverability. A user may ask an AI system for a comparison, recommendation or explanation without ever seeing a conventional list of ten blue links. A brand can still be represented in that response, but the visibility event is not equivalent to an organic search click.

That is why the phrase “organic AI discovery” can become misleading.

Organic Search and AI Discovery Measure Different Outcomes

Traditional organic search is fundamentally ranking- and click-oriented. A webpage earns visibility in a search results interface, and the user decides whether to visit it.

AI discovery is more about whether a system can identify, understand and use information from a source when constructing an answer.

Google itself describes AI Mode as an experience that combines advanced AI capabilities with web information and provides links for users who want to explore further. Its query-fan-out system can issue multiple related searches across subtopics before assembling a response.

That means a single keyword ranking is not necessarily an adequate measurement of AI visibility.

A company could rank well for a narrow query but fail to appear when a user asks a broader, multi-part question. Conversely, information published across several authoritative pages may give an AI system enough context to understand a company even when no individual page dominates a traditional results page.

AI Discovery Does Not Eliminate SEO

This is where some discussions around AEO and GEO go too far.

Google's May 2026 Search guidance specifically added a guide for optimizing content for generative AI features and said SEO best practices continue to be relevant for success in those experiences. Google also cautioned against misconceptions surrounding AEO and GEO.

The implication is important: AI search is not operating independently of the web infrastructure that supports conventional search.

Technical accessibility, useful content, structured information, clear site architecture and strong source material remain relevant. What changes is how that information can subsequently be processed.

From “Finding” Content to “Understanding” It

Traditional SEO often asks: Can the search engine find and rank this page?

AI visibility adds another question: Can the system understand what this organization, product or source represents well enough to use it in an answer?

That makes context particularly important.

A SaaS company, for example, should not rely exclusively on a product landing page. Its website may need comprehensive information about use cases, integrations, features, implementation, security, pricing, limitations and customer applications.

That does not guarantee an AI citation. It simply gives search and AI systems more complete material from which to understand the entity and its offerings.

E-E-A-T Is Relevant, but Not an AI Ranking Formula

Experience, expertise, authoritativeness and trustworthiness remain useful concepts when evaluating content quality, but marketers should avoid treating E-E-A-T as a formula that automatically produces AI visibility.

AI systems can draw from multiple sources, including websites, news organizations, documentation and other publicly available information. Google also continues to emphasize links to web content within its AI Search experiences.

That makes source consistency increasingly important.

If a company describes its product differently across its website, documentation, third-party profiles and public communications, AI systems may encounter conflicting signals. Clear and current information across those surfaces can reduce that ambiguity.

Press Releases Can Contribute Context, Not Guaranteed AI Visibility

Press releases can play a role in this ecosystem because they often contain new, dated information, executive statements, product details and original company announcements.

But publishing a press release does not automatically make a brand discoverable in AI systems.

Its usefulness depends on the quality and relevance of the information, whether the claims are supported elsewhere, how widely the information is distributed and whether the subject is relevant to the questions users are asking.

The same principle applies to multichannel content. Republishing identical promotional copy everywhere is not the same as establishing authoritative coverage.

The Better Metric Is AI Visibility, Not “Organic AI Traffic”

The biggest measurement problem is that AI discovery can happen without a conventional referral session.

A user might see a brand in an AI answer, remember it, conduct a separate search and eventually visit its website directly. Analytics could attribute that eventual visit to organic search, direct traffic or another channel without revealing the original AI influence.

That makes AI visibility, citation frequency, source inclusion, branded search movement and assisted conversions potentially more informative than trying to force every AI interaction into the traditional organic-traffic category.

Google's own data also shows why the two channels should not simply be treated as opposites. In August 2025, Google said overall organic click volume from Search to websites was relatively stable year over year while reporting that AI features were generating more searches and links for users to explore.

The industry therefore does not need another binary debate over whether SEO or AI discovery matters more.

The more useful framework is that SEO helps make information discoverable, while AI search adds another layer in which that information can be interpreted, synthesized and surfaced in response to increasingly complex questions.

That is not “organic AI discovery” in the traditional SEO sense.

It is a new form of information visibility built on top of the web's existing discovery infrastructure.

Market Landscape

AI search is moving beyond simple question answering. Google says AI Mode queries have continued to grow and that users are increasingly asking longer questions, using follow-ups and exploring complex tasks. In May 2026, Google reported that AI Mode had surpassed one billion monthly active users globally.

At the same time, Google continues to position AI Search as a gateway to the web rather than a replacement for it. AI Overviews and AI Mode include links to supporting content, while Google's Search documentation continues to emphasize conventional SEO fundamentals.

This creates a hybrid discovery environment in which traditional rankings, AI-generated answers, citations, branded searches, referral traffic and direct visits can influence one another.

Strategic Outlook

For SEO and communications teams, the practical shift is from optimizing individual pages exclusively for rankings toward building entity-level information consistency.

That means maintaining accurate product information, publishing genuinely useful expert content, strengthening technical documentation, earning credible third-party coverage and organizing information so both humans and machines can interpret it.

AI visibility should also be measured separately from organic traffic. Teams can monitor how frequently their brand appears in representative AI prompts, which sources are cited, what competitors appear alongside them and whether visibility changes as queries move from informational to commercial intent.

AEO and GEO can therefore complement SEO, but they should not be treated as replacements for it.

The terminology may continue to evolve. The underlying objective is more stable: make authoritative information easy to find, understand, verify and use.

Top Insights

• “Organic AI discovery” blurs two concepts: traditional organic traffic measures unpaid search clicks, while AI visibility can occur without a conventional website visit.
• AI search is becoming more conversational: Google reports that AI Mode users increasingly submit longer and more complex queries than traditional Search users.
• SEO remains relevant: Google explicitly says SEO best practices continue to matter for visibility in generative AI Search features.
• Context matters beyond keywords: AI systems can synthesize information across multiple searches and sources, increasing the importance of comprehensive and consistent information.
• Measurement needs to evolve: AI citations, brand mentions and assisted influence can exist without appearing as a conventional referral source in analytics.

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AI assistants are becoming another discovery layer for B2B software buyers, but visibility remains highly concentrated among a relatively small group of vendors. Zen Media's 2026 B2B SaaS AI visibility benchmark found that the top 100 SaaS companies averaged just 9% visibility across 4,000 AI-generated responses, highlighting a growing gap between conventional search presence and being recommended when buyers ask AI assistants which software they should consider.

For years, SaaS marketers have optimized category pages, comparison content, review profiles and product documentation for search engines. The rise of AI-assisted buying introduces another visibility challenge: whether those same companies are actually named when a prospective buyer asks an AI assistant for a recommendation.

Zen Media's 2026 B2B SaaS AI visibility benchmark provides an early measurement of that shift. The study analyzed 1,000 B2B software buyer prompts across Claude, ChatGPT, Gemini and Grok, producing 4,000 responses. A company was counted as visible when an AI assistant named it in a response. The prompts covered informational, comparison and buying-intent questions.

More than 450 SaaS companies appeared at least once. However, recurring visibility was concentrated among roughly 100 vendors. Across that top 100, average visibility was 9%, according to Zen Media.

The distribution was particularly concentrated at the top. Salesforce recorded 45% visibility, followed by HubSpot at 37% and Tableau at 24%. 6sense reached 16%, while Gainsight registered 13%. These figures represent the percentage of responses in the benchmark that named each company, rather than market share, revenue or product adoption.

The distinction between occasional and recurring visibility is important. A SaaS company can appear in a broad category question without remaining visible when the buyer asks more specific questions about integrations, alternatives, use cases, company size or purchasing requirements.

That makes AI visibility a more dynamic measurement than simply checking whether a brand appears in one generated answer.

The benchmark also suggests that SaaS companies have a significant opportunity earlier in the buying journey. Informational prompts represented 58.6% of the 1,000 questions, compared with 20.8% for comparison questions and 20.6% for buying-intent prompts. In other words, most questions in the benchmark occurred while buyers were still researching categories, problems and possible solutions.

That has direct implications for content strategy. Product pages remain important, but category explanations, use-case content, integration documentation, technical specifications and educational resources can provide the contextual information AI systems need to describe a vendor accurately.

Vendor-controlled documentation was the most frequently referenced source type in the benchmark, appearing in 85% of responses. Zen Media says this included product documentation, integration pages, technical specifications and support content.

This finding adds an important dimension to Answer Engine Optimization (AEO). AEO is not simply about producing content designed to generate an AI citation. It also involves making the underlying information about a company clear, current, accessible and internally consistent.

For SaaS organizations, that puts product marketing, SEO, documentation, PR and communications closer together. Product teams control technical accuracy, marketing teams define positioning, SEO teams structure information for discovery, and external communications can provide third-party evidence that helps establish context around the brand.

The benchmark also illustrates why measuring one AI platform may produce an incomplete picture. Zen Media's research used four assistants, while its own case studies show that visibility can vary substantially between individual engines.

For marketers, the practical takeaway is therefore broader than chasing a single AI ranking. The emerging metric is whether a SaaS brand remains visible as a buyer's questions become more specific.

That shifts AI visibility from an experimental SEO tactic toward a broader B2B demand-generation and product-discovery issue. As AI assistants increasingly compress research into conversational recommendations, companies that are clearly documented and consistently associated with relevant buyer problems may have more opportunities to enter the consideration set before a conventional website visit or demo request occurs.

Market Landscape

The SaaS AI visibility market remains fragmented. Zen Media's benchmark found more than 450 companies across 4,000 responses, while only around 100 achieved recurring visibility across the tested questions.

Other 2026 research also indicates that many B2B organizations are still developing their AI-discovery infrastructure. Mojo Dojo's June 2026 audit of 719 B2B companies across five industries found that only 9% could segment AI-driven traffic, pointing to a measurement gap between AI discovery and conventional analytics.

For SaaS companies, this means AI visibility is developing alongside traditional SEO rather than replacing it. Search rankings, third-party reviews, documentation, earned media and AI-generated recommendations increasingly form overlapping discovery layers.

Strategic Outlook

The next phase of SaaS SEO is likely to involve measuring answer share across buyer questions, rather than relying exclusively on rankings and organic traffic.

Marketing teams can build a representative prompt set covering category discovery, use cases, integrations, competitors, alternatives, pricing, security and purchase requirements. Running those prompts consistently across multiple AI systems can reveal where a brand appears, where competitors dominate and where visibility disappears as intent becomes more specific.

Documentation will also become increasingly important. A product page that says what a platform does is useful; a comprehensive information architecture explaining features, integrations, implementation, security, use cases and limitations gives AI systems substantially more context to work with.

The larger change is organizational. AI visibility cannot sit entirely inside SEO. It touches product marketing, content, technical documentation, PR, digital PR, brand strategy and analytics.

For SaaS companies competing in crowded categories, the question is increasingly not only "Can buyers find us?" but also "Does an AI assistant understand when we belong in the conversation?"

Top Insights

• AI visibility remains concentrated: Zen Media found more than 450 SaaS companies in its benchmark, while recurring visibility was concentrated among roughly 100 vendors.
• The top 100 averaged 9% visibility: Salesforce and HubSpot reached 45% and 37%, respectively, while visibility declined sharply beyond the leading vendors.
• Informational research dominates: 58.6% of benchmark prompts were informational, making early-stage educational content important to AI discovery.
• Documentation is a major information source: Vendor documentation appeared in 85% of responses, according to the benchmark.
• AI visibility requires multi-engine measurement: Results can vary between AI systems, making a single-platform visibility check insufficient for understanding overall exposure.

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Inertia Digital Marketing has acquired SeniorsPlaces.com and plans to relaunch the senior-care directory on a rebuilt platform in the fourth quarter of 2026. The acquisition, completed in May, expands Inertia's digital marketplace capabilities into senior living and home care, with the redesigned platform intended to connect families researching care options directly with communities while giving operators tools for listings, lead management, CRM integration and performance measurement.

The senior-care search market is becoming another area where digital platforms are attempting to simplify a complex offline purchasing decision.

Inertia Digital Marketing's acquisition of SeniorsPlaces.com, completed in May 2026 for undisclosed terms, will result in a rebuilt platform designed around two distinct user groups: families looking for senior living or home-care options and operators seeking qualified inquiries.

SeniorsPlaces.com currently operates as a nationwide directory covering independent living, assisted living, memory care and home care. Inertia says the relaunched platform will retain its free-to-families model and will not charge referral fees.

The planned redesign puts search and comparison at the center of the consumer experience. Families will be able to search by location and care type, filter communities according to monthly budget and compare as many as three communities side by side.

Community profiles are also being structured as a consolidated information layer, bringing together care types, amenities, photographs, video, available pricing and reviews. A care quiz is intended to help users determine which level of care may be appropriate, while a cost guide provides additional context around monthly rates.

The model addresses a significant challenge in senior-care discovery: families often need to evaluate multiple factors simultaneously, including location, care requirements, cost and available services. Bringing those variables into a searchable comparison interface could reduce some of the fragmentation associated with researching communities across individual websites and directories.

Lead routing is another important component of the relaunch. According to Inertia, inquiries submitted through Seniors Places will go directly to the relevant community rather than passing through an intermediary referral process.

For senior-living operators, the rebuilt platform introduces a separate management layer. Communities will be able to control their listings through an operator portal, while incoming inquiries will appear in a lead inbox containing information such as care requirements, timing and budget.

Those leads can also be connected to an operator's existing CRM or calendar, moving the platform beyond directory functionality toward a lead-generation and workflow-management system.

Performance measurement is similarly being built into the operator experience. Monthly reporting will track search impressions, page views, leads, tours and move-ins at both community and brand levels. Operators managing multiple properties will be able to manage listings through a single account and update multiple profiles in bulk.

The platform will also support direct scheduling. Featured communities can add a "Schedule a Tour" function that connects booking directly with the community's calendar.

That functionality reflects a broader movement in digital marketplaces from simply generating traffic toward managing the journey from discovery to conversion. For Inertia, the Seniors Places acquisition provides an opportunity to combine directory search, comparison, lead capture, CRM connectivity and appointment scheduling within one vertical-specific platform.

The challenge will be maintaining accurate and transparent information at scale. Seniors Places says it lists more than 11,000 communities nationwide, making data quality particularly important when consumers are comparing pricing, care types, amenities and availability.

The Q4 relaunch will therefore be more than a visual redesign. It represents a shift toward a two-sided digital marketplace where families receive structured decision-support tools while operators gain greater control over their listings, leads and conversion data.

Market Landscape

Senior-care discovery is particularly suited to digital comparison because families frequently evaluate multiple providers across location, care level, pricing, amenities and availability.

The emerging marketplace model combines several traditionally separate functions: directory search, consumer reviews, lead generation, CRM connectivity, analytics and appointment scheduling.

For operators, this creates a potential alternative to treating directory listings solely as referral sources. First-party lead information and conversion reporting can provide greater visibility into the path from search discovery to inquiry, tour and move-in.

Strategic Outlook

The Seniors Places relaunch places greater emphasis on direct lead relationships between families and senior-care communities.

If implemented as described, the platform's CRM and calendar integrations could make it more useful to operators than a conventional directory, particularly for organizations managing multiple communities.

The longer-term opportunity will depend on data accuracy, consumer trust, search visibility and the quality of leads generated through the platform. Pricing transparency may also become increasingly important as families compare communities across different markets.

Inertia's decision to preserve free consumer access while building paid operator infrastructure follows a familiar two-sided marketplace model: minimize friction for the audience while monetizing the business side through tools and services.

The Q4 relaunch will provide the clearest indication of whether that model can turn Seniors Places from a directory into a more comprehensive digital customer-acquisition platform for senior-care operators.

Top Insights

• Inertia acquired SeniorsPlaces.com in May 2026 and plans to relaunch the platform on a rebuilt technology foundation during Q4.
• Families will receive comparison tools, including location and care-type search, budget filters and side-by-side community comparisons.
• Operators gain lead-management infrastructure, with inquiry details, CRM and calendar connectivity, and reporting across the customer journey.
• Direct scheduling is being integrated, allowing featured communities to connect tour bookings directly with their calendars.
• Scale creates a data challenge, with the platform currently listing more than 11,000 senior-care communities nationwide.

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Lickly is repositioning its marketing technology platform around Decision Intelligence, moving beyond influencer discovery toward a broader system for helping marketers determine what actions to take before campaign investment is committed. The RAD Intel company unveiled the expanded platform and new brand at UNBOUND 2026 in Boston, positioning evidence-backed recommendations, audience intelligence and predictive analysis as an alternative to marketing workflows built primarily around dashboards and generative AI outputs.

Marketing technology has become increasingly effective at collecting data, visualizing performance and generating automated recommendations. The harder problem is translating those capabilities into decisions that marketing leaders can explain and defend.

That gap is the focus of Lickly's expanded positioning. The AI-driven SaaS platform, which originally focused on influencer intelligence, is now describing its technology as a Decision Intelligence platform for marketing. Its stated objective is to help brands and agencies identify audiences, determine which creators and strategies align with those audiences, assess potential risks and forecast campaign outcomes before marketing budgets are committed.

The shift reflects a broader evolution in marketing AI. Rather than positioning AI solely as a content-generation or question-answering layer, vendors are increasingly attempting to use multiple data sources and reasoning systems to support higher-value business decisions.

Lickly's platform begins with the audience rather than the creator. It analyzes behavioral patterns, micro-communities and cultural signals before combining those findings with creator intelligence, competitive information, brand-safety considerations and predictive forecasting.

That approach is significant for influencer marketing because creator selection can otherwise become overly dependent on visible metrics such as follower counts, engagement rates or historical campaign performance. Audience alignment introduces another layer: whether a creator's community and cultural relevance correspond with the consumers a brand actually wants to influence.

Lickly says the same infrastructure can now support decisions across the broader campaign lifecycle, including audience identification, creator selection, campaign strategy, competitive intelligence, brand and legal safety, forecasting and measurement.

The company is also differentiating its AI approach from conventional generative AI. According to Lickly, its proprietary M³VR methodology evaluates information across multiple models, signals and reasoning paths before generating a recommendation. The platform is designed to show marketers both the recommendation and supporting evidence, allowing users to challenge the reasoning rather than treating an AI-generated response as an authoritative conclusion.

That distinction matters as marketing organizations increase their reliance on AI while facing greater pressure to explain how campaign decisions were made. An AI system that produces an answer can accelerate workflow, but a decision-support system needs to provide enough context for a marketer to assess the recommendation against business objectives, risk and available evidence.

Lickly's repositioning therefore places human judgment at the center of its product narrative. The company's argument is not that AI should replace marketing judgment, but that AI can organize audience, cultural, competitive and predictive signals into evidence that makes that judgment more informed.

For agencies, the potential application extends beyond creator discovery. Teams managing multiple clients and audience segments could use a common decision layer to evaluate creator fit, campaign strategy and investment scenarios before execution. That could also make campaign planning more consistent when marketers need to document why particular audiences, creators or channels were selected.

The challenge will be proving that evidence-backed recommendations produce better business outcomes than existing combinations of marketing analytics, audience platforms, creator databases and AI assistants. Decision Intelligence also introduces a governance question: marketers need to understand the quality, provenance and limitations of the signals feeding automated recommendations.

Lickly's new brand consequently represents more than a change in terminology. It reflects an emerging category of marketing technology focused on moving AI from answer generation toward decision support, particularly in areas where marketing investment, audience selection and brand risk are closely connected.

Market Landscape

Generative AI has rapidly entered marketing workflows, from content creation and campaign analysis to customer segmentation and media optimization. The next phase is increasingly centered on how AI participates in decisions rather than simply how much content it can produce.

Lickly's audience-first model also reflects the increasing importance of contextual audience intelligence in creator marketing. Creator selection based solely on surface-level metrics can overlook community characteristics, cultural relevance and brand-safety considerations.

Decision Intelligence platforms are attempting to connect those separate signals into a recommendation layer. For enterprise marketers, the value proposition depends on whether those recommendations are transparent, auditable and sufficiently connected to measurable business outcomes.

Strategic Outlook

The evolution from influencer intelligence to Decision Intelligence could broaden Lickly's addressable market beyond creator discovery. It also places the company in a more competitive category that includes marketing analytics, audience intelligence, predictive analytics and AI decision-support platforms.

The strongest differentiator will likely be evidence quality rather than AI generation itself. As marketers gain access to multiple AI assistants, the ability to explain why a recommendation was generated, which signals informed it and where uncertainty remains becomes increasingly important.

For CMOs and agencies, this points toward a hybrid operating model in which AI handles signal aggregation and scenario analysis while humans retain responsibility for strategic choices, budget allocation and risk decisions.

Top Insights

• Lickly is broadening its platform from influencer intelligence into Decision Intelligence covering audience, creator, strategy, safety and campaign forecasting.
• Audience-first planning remains central, with behavioral, cultural and community signals informing creator and campaign decisions.
• Evidence-backed AI is the differentiator, as Lickly says its methodology evaluates multiple models and signals before producing recommendations.
• Human judgment remains part of the workflow, with marketers able to examine the evidence behind recommendations and make final decisions.
• Marketing AI is moving toward decision support, expanding beyond content generation and automated answers into campaign planning and investment decisions.

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H. Lundbeck A/S has received marketing authorization from Japan's Ministry of Health, Labour and Welfare for Vyepti (eptinezumab), expanding the availability of an intravenous CGRP-targeted preventive therapy for adults with migraine. The approval also marks Lundbeck's first independent product launch in Japan, giving the company a larger direct commercial role in one of Asia's major healthcare markets.

Lundbeck's Japanese approval of Vyepti adds another regulatory milestone to the company's expansion of its migraine portfolio across Asia, while highlighting the growing role of targeted biologic therapies in migraine prevention.

The authorization covers eptinezumab for the prevention of migraine attacks in adults. Eptinezumab is a humanized monoclonal antibody that targets calcitonin gene-related peptide (CGRP), a signaling molecule associated with migraine. Unlike orally administered preventive medicines, Vyepti is delivered intravenously, with clinical studies evaluating dosing at 12-week intervals.

The Japanese decision follows an application accepted by the country's Ministry of Health, Labour and Welfare in November 2025. That filing was supported by data from the Phase III SUNRISE program, which evaluated eptinezumab in a predominantly Asian population with chronic migraine.

SUNRISE enrolled 978 participants, including 621 from Asia. Both the 100 mg and 300 mg eptinezumab groups met the trial's primary and key secondary efficacy endpoints, with greater reductions in monthly migraine days than placebo during the first 12 weeks. The researchers also reported that safety outcomes were similar across treatment groups.

Evidence specifically involving Japanese patients comes from the SUNSET long-term extension study. Among 159 treated Japanese participants, 141 completed the 60-week extension. Researchers reported maintained reductions in monthly migraine days and no new safety signals during the extension period.

The broader clinical program also includes PROMISE-1 and PROMISE-2, which studied eptinezumab in episodic and chronic migraine respectively, and DELIVER, a Phase IIIb study involving adults who had experienced two to four previous preventive treatment failures. In PROMISE-1, eptinezumab 100 mg and 300 mg significantly reduced monthly migraine days compared with placebo.

Japan represents a significant market for migraine prevention. Lundbeck cites estimates that migraine affects approximately 8.4% to 11.0% of the Japanese population and contributes substantially to lost productivity. The company's authorization therefore has implications beyond regulatory access, particularly as healthcare systems consider the economic burden associated with recurrent migraine and inadequate disease control.

The announcement also has a commercial significance for Lundbeck. The company said the approval enables its first independent launch in Japan as the marketing authorization holder, following 25 years of activity in the country. The move follows Vyepti approvals in other Asian markets, including South Korea in May 2026.

For the broader healthcare technology and life-sciences ecosystem, the development illustrates how pharmaceutical companies are increasingly combining targeted biologics, region-specific clinical evidence and direct commercialization strategies when expanding specialty medicines into Asian markets.

Market Landscape

Migraine remains a major neurological health burden globally. The World Health Organization says headache disorders are among the most common neurological disorders and that migraine ranked third in overall neurological disease burden by age-standardized DALYs in 2021.

The competitive landscape for migraine prevention includes several therapeutic approaches, including CGRP-targeted monoclonal antibodies, oral preventive medicines and other established interventions.

Eptinezumab's clinical development is differentiated by its intravenous delivery model and evidence across episodic migraine, chronic migraine and patients with previous preventive treatment failures. Clinical trials have also examined longer-term treatment effects and patient-reported outcomes.

Japan's approval places Vyepti within an expanding Asian migraine-treatment market where regulators and healthcare providers are evaluating evidence generated across both global and regional patient populations.

Strategic Outlook

The Japanese authorization demonstrates the increasing importance of localized clinical evidence when pharmaceutical companies expand specialty therapies internationally.

For Lundbeck, the launch also represents a shift toward greater direct commercial control in Japan. The company's earlier regulatory submissions across Japan, China and South Korea were part of a broader Asian expansion strategy.

The next phase will depend on market access, physician adoption, reimbursement and the ability to integrate preventive migraine treatment into routine neurological care. Clinical evidence alone does not determine patient access; healthcare-system economics and treatment pathways also influence how quickly newly authorized therapies reach eligible patients.

Top Insights

• Japan has authorized Vyepti for adults with migraine, expanding access to Lundbeck's intravenous CGRP-targeted preventive treatment.
• SUNRISE provided Asian clinical evidence, with 621 of 978 trial participants coming from Asia and both studied doses meeting key efficacy endpoints.
• SUNSET added Japanese long-term data, with 159 participants treated and no new safety signals identified over the 60-week extension.
• Lundbeck gains a direct commercial role, describing the Japanese authorization as its first independent product launch in the country.
• CGRP therapies are reshaping migraine prevention, adding targeted biologic approaches alongside established oral and non-drug treatment strategies.

Get in touch with our MarTech Experts.

E-commerce marketing is entering a new phase in which the point of influence may shift from advertisements and search results to AI shopping assistants, deal platforms and savings ecosystems. New research from Northwestern University's Retail Analytics Council and Minty indicates that senior commerce marketers are preparing to redirect a significant share of incremental marketing investment toward AI commerce tools and cashback platforms as shoppers increasingly use technology to compare prices, discover alternatives and evaluate total value.

E-commerce marketers are beginning to treat AI shopping assistants and savings platforms as part of the commerce infrastructure rather than simply another set of promotional channels.

The finding comes from the first wave of the Proactive Commerce Leadership Index, a survey of 150 senior commerce decision-makers conducted by Northwestern University's Retail Analytics Council in August 2026. According to the research, marketers plan to direct 63 cents of every new marketing dollar toward AI commerce tools and cashback and savings applications rather than traditional digital advertising. Ninety-eight percent of respondents represented companies generating more than $100 million in annual revenue.

The shift reflects a change in how consumers discover and evaluate products. Rather than encountering a brand through an advertisement and subsequently beginning their research, shoppers can increasingly start with an AI assistant that compares products, identifies promotions, checks prices and surfaces alternatives.

EMARKETER's September 2026 Future of Digital research similarly describes AI as reshaping the shopping journey, with brands increasingly having to consider how they appear within large language models and how they engage consumers arriving through AI-driven discovery.

The Northwestern-Minty research puts numbers around that transition. Seventy-nine percent of surveyed commerce leaders expect their primary customer to be assisted by AI in 2027. Two-thirds expect AI to become their primary shopping gateway for reaching consumers, compared with 18% who identify traditional search and 13% who identify social as their primary route.

Savings ecosystems are also gaining attention. Eighty-one percent of respondents describe cashback and savings tools as the most helpful form of marketing for shoppers making purchase decisions. Among marketers responding to the shift toward AI and proactive commerce, 61% say they are increasing or reallocating investment toward cashback and savings applications.

That does not necessarily mean conventional advertising disappears. Instead, the role of advertising can change as brands seek to influence consumers earlier in an AI-mediated purchase process.

The study defines proactive commerce as delivering total-value messaging to shoppers and their AI agents before traditional behavioral triggers, such as a search, click or abandoned cart. Thirty-eight percent of respondents say they are already operating proactive commerce programs, while another 36% are moving toward the model. Half say they are preparing to market to AI agents.

Budget allocation provides another indication of the shift. Cashback and savings platforms currently account for 18% of performance-marketing budgets among respondents, with that share expected to reach 30% in 2027. Meanwhile, the proportion of leaders describing these channels as "core or strategic" is expected to rise from 16% today to 41% in 2027.

The development is also reflected in the technology being built around AI shopping. Minty's current platform, for example, provides AI agents with access to merchant offers, coupon codes and cashback information through an MCP interface, illustrating how savings data can become directly accessible inside agentic shopping workflows.

This creates a different optimization problem for e-commerce marketers. Traditional performance marketing has largely focused on impressions, clicks, conversions and retargeting. AI-mediated commerce introduces another layer: whether a brand's product, price, promotion and value proposition can be surfaced and evaluated by an AI agent at the moment a consumer is making a decision.

That could increase the importance of structured product information, real-time offers, pricing data, promotions and machine-readable commerce infrastructure.

At the same time, the survey represents the expectations of senior commerce decision-makers rather than observed consumer behavior. Its 150-person sample is heavily weighted toward large enterprises, so the projected 2027 budget changes should be interpreted as an indicator of enterprise marketing sentiment rather than a forecast for the entire e-commerce market.

For marketers, the central development is therefore not simply a shift from ads to cashback. It is the emergence of an AI-mediated distribution layer where product discovery, comparison, incentives and purchase decisions increasingly happen before a shopper reaches a conventional advertising destination.

Market Landscape

AI is already changing the mechanics of digital commerce. EMARKETER's 2026 research identifies AI search and discovery as a major force affecting digital marketing, while its September Future of Digital analysis describes brands adapting to consumers arriving through AI-driven searches and large language models.

The development of agent-accessible commerce infrastructure adds another dimension. Minty's MCP offering allows AI agents to retrieve live merchant offers and cashback information, demonstrating how promotional data can become part of an AI agent's shopping workflow rather than remaining confined to a website or browser extension.

For retailers and brands, this expands the definition of commerce visibility. Product feeds, pricing, promotions, reviews, availability and value propositions may increasingly need to be accessible to systems that make or influence shopping recommendations.

Strategic Outlook

The emerging model could move performance marketing from behavioral response toward proactive value delivery.

Instead of waiting for consumers to search, visit a product page or abandon a cart, brands can increasingly use AI commerce infrastructure to surface relevant offers during the decision process.

That creates opportunities but also measurement challenges. If an AI assistant introduces a shopper to a product, a cashback platform supplies an incentive and the final transaction occurs on a retailer's site, attribution can become more fragmented.

Marketing organizations will therefore need to evaluate AI commerce channels alongside search, retail media, social, affiliates and traditional advertising rather than treating them as isolated investments.

The 2027 expectations in the Northwestern-Minty research suggest that enterprise marketers are already preparing for that broader distribution environment. Whether those allocations materialize at the projected scale will depend on consumer adoption, incremental economics, measurement standards and how effectively AI agents translate product and offer information into transactions.

Top Insights

• AI commerce is becoming a budget priority, with surveyed marketers planning to allocate 63 cents of each new marketing dollar toward AI and savings ecosystems.
• AI could become a major shopping gateway, as two-thirds of surveyed commerce leaders expect it to become their primary consumer-reach channel in 2027.
• Cashback is moving toward infrastructure, with respondents expecting performance-marketing allocation to rise from 18% currently to 30% in 2027.
• Proactive commerce is emerging, with 38% already running programs and another 36% moving toward delivering value messages before conventional marketing triggers.
• AI agents create a new optimization layer, requiring brands to make product, pricing and promotional information accessible to machine-mediated shopping experiences.

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Customer research is moving closer to the point of interaction as AI enables companies to collect and analyze feedback while customers are actively using digital products. Customer insights platform Marvin has launched Live Intercept, an AI-moderated research capability that conducts voice-to-voice interviews directly inside websites and digital products, connecting real-time customer conversations with broader research and customer data.

Customer research has traditionally been separated from the digital experiences it is intended to improve. A product team may recruit participants, schedule interviews, collect responses and analyze findings weeks after a customer encounters a problem.

Marvin is attempting to shorten that cycle with Live Intercept, a new capability that embeds AI-moderated voice interviews directly into websites and digital products.

The system begins an interview after a customer consents to providing feedback. Marvin's AI Interviewer follows a predefined discussion guide, asks follow-up questions and adapts to responses during the conversation. Participants can speak naturally and share their screens, allowing researchers to capture not only what customers say but also what they are attempting to accomplish.

That distinction matters for product and marketing teams. A conventional survey can identify that a customer encountered friction, but a moderated conversation can potentially reveal what the customer expected, why an interaction was confusing and what they were trying to accomplish at the time.

Marvin says Live Intercept can operate continuously in more than 40 languages, allowing teams to determine where and when interviews appear within a digital journey. Potential applications range from testing landing-page messaging and onboarding flows to understanding pricing questions, checkout abandonment, feature adoption and renewal concerns.

The technology also changes the recruitment model. Instead of recruiting a separate panel and asking participants to return for scheduled research, the intercept can reach customers while they are already interacting with the product.

That approach reflects a wider movement toward real-time Voice of Customer data. Gartner's 2026 research recommends that organizations use direct, indirect and inferred customer feedback to evaluate customer-facing AI agents and monitor whether those experiences are improving customer satisfaction and reducing customer effort.

The growing investment in AI-powered customer interactions makes timely feedback particularly relevant. Gartner reported in August that customer-service leaders had increased AI spending by 38%, while overall service and support budgets grew by only 2%. The research identified GenAI chatbots, voicebots and agentic AI platforms among technologies expected to deliver increasing value.

Marvin's approach is different from using AI simply to automate customer support. The AI is positioned as a research interviewer, collecting qualitative evidence that can subsequently inform product, marketing and customer-experience decisions.

Following an interview, recordings and transcripts flow into Marvin's Knowledge Hub. The company says those conversations can be combined with CRM records, support tickets, existing research, product data and customer calls. Teams can also enable automatic PII redaction across text, audio and video.

This creates a potentially valuable connection between qualitative research and existing customer intelligence. Instead of storing interviews as isolated research artifacts, teams can connect customer statements with other operational data and identify recurring themes.

Marvin also uses AI to surface themes and link findings back to specific moments in the underlying conversations. Notifications can be delivered through Slack or Microsoft Teams when an interview concludes, reducing the time between collecting feedback and distributing it internally.

The larger strategic question is whether customer research becomes part of the product experience itself. As organizations increasingly use AI to accelerate product development, research systems that operate continuously could help teams collect evidence at the same pace.

The model does not eliminate the need for conventional research. Rather, it adds another layer of feedback collection at moments when customer intent and behavior are directly observable.

Market Landscape

AI is changing customer experience from a sequence of predefined interactions toward more dynamic, context-aware journeys. McKinsey's July 2026 research describes companies redesigning CX around AI-enabled, cross-channel orchestration rather than static customer journeys.

At the same time, customers remain selective about how AI is introduced into their interactions. Gartner's 2026 survey of 3,566 B2B and B2C customers found that 87% said companies using GenAI for customer service should provide access to a human agent.

That makes consent, transparency and appropriate handoffs important considerations for AI-powered research embedded in customer-facing environments.

The broader opportunity is the convergence of VoC, customer data, product analytics and AI. Instead of waiting for periodic research studies, organizations can potentially build a more continuous feedback loop between customer behavior, customer statements and product decisions.

Strategic Outlook

Live Intercept represents a shift from customer research as a scheduled activity to research as an embedded digital capability.

For marketing and product organizations, the potential benefit is speed. Feedback collected during acquisition, activation, conversion or retention can be analyzed while the underlying customer journey is still current.

The more significant development may be the integration of qualitative feedback with operational data. CRM records can show what customers did, analytics can show where they stopped and AI-moderated interviews can help explain why.

However, scaling this model will require careful governance. Voice and screen recordings can contain sensitive information, making consent, PII management, data retention and access controls important components of implementation.

Gartner's research similarly emphasizes that AI investments need to align with actual customer behavior rather than simply adding AI to existing processes.

If those requirements are addressed, embedded AI research could become another layer of the modern customer data and experience stack.

Top Insights

• Marvin is embedding AI research into digital journeys, allowing companies to conduct voice interviews while customers are actively using websites and products.
• Live Intercept turns feedback into an interaction, with AI following discussion guides and adapting follow-up questions to individual customer responses.
• Qualitative and operational data can converge, connecting interview transcripts with CRM records, support tickets, product data and existing research.
• Real-time VoC is gaining strategic relevance, as companies increasingly use AI to redesign customer journeys and customer-facing experiences.
• Privacy becomes a core requirement, particularly when AI research involves voice, video, screen sharing and personally identifiable customer information.

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Consumers are increasingly turning to artificial intelligence to discover and evaluate products, but that acceptance does not automatically extend to AI-generated advertising. New research from Net Conversion highlights a growing gap between how consumers use AI as a shopping assistant and how they respond when brands use AI to create advertising, raising new questions for marketers about authenticity, creative strategy and the role of human validation in AI-era customer journeys.

Artificial intelligence is becoming part of the consumer purchase journey, but new research suggests marketers face a credibility challenge when they move the technology from shopping assistance to advertising production.

The third wave of Net Conversion's MORE Intelligence Consumer Pulse found that 54% of consumers say they trust a brand less when its advertising appears to be AI-generated. The effect is particularly notable among younger audiences, with 38% of consumers ages 18 to 29 strongly agreeing with the statement. Meanwhile, 63% of consumers under 40 say AI-generated content has already changed how they interact with brand messaging.

The contrast becomes clearer on the other side of the buying journey. Net Conversion found that 54% of consumers now use AI tools at least sometimes to research purchases, increasing to 60% among consumers under 40. Among AI users, 45% say an AI recommendation increases their trust in a brand.

The findings point to an important distinction for marketing organizations: consumers may value AI when it reduces research effort, while remaining more cautious when they can see the technology being used to automate the brand's own communication.

AI is also expanding the consideration phase rather than simply replacing conventional search. According to Net Conversion, 74% of consumers who use AI for research say its recommendations increase the number of brands they consider.

However, AI recommendations are rarely treated as the final source of truth. Consumers continue moving between AI assistants and traditional digital properties to validate information. Brand websites are used by 59% of AI researchers for cross-checking, followed by search engines at 57%, retailer pages at 55%, YouTube at 53% and Reddit at 53%.

That behavior aligns with other 2026 research showing that AI discovery and conventional web research are increasingly interconnected. The Trade Desk Intelligence and PA Consulting reported in September that 95% of consumers surveyed double-check AI-generated search results, often using the open internet for additional validation.

Reddit's own August 2026 research similarly found that consumers frequently validate AI recommendations with human perspectives before purchasing.

For marketers, this changes what visibility means. A brand may first enter a consumer's consideration set through an AI-generated recommendation, but the subsequent evaluation can happen across search, owned websites, retail marketplaces, video and online communities.

Google's 2026 research describes a similar fragmentation of the customer journey, with AI Search increasingly supporting conversational discovery and product consideration rather than functioning as a separate destination from traditional search.

At the same time, Net Conversion's research indicates that advertising attention itself is becoming harder to secure. Fifty-two percent of consumers say they are tuning out, skipping or blocking digital advertising more than they did a year earlier. Nearly half frequently encounter the same advertisement across multiple platforms.

The findings also highlight the importance of cross-device behavior. Net Conversion reports that 88% of consumers regularly use a second device while watching television, while 25% search for a brand on their phone immediately after seeing an advertisement. Among consumers under 40, that figure rises to 31%.

This creates a more complicated media environment for advertisers. A streaming advertisement may generate a search rather than an immediate conversion, while an AI recommendation may generate a later visit to a brand website that conventional attribution does not clearly connect to the original interaction.

The research therefore points toward an emerging marketing model in which AI discovery, traditional search, community validation, brand content and advertising work as interconnected touchpoints.

For marketing teams, the implication is not simply to increase or decrease AI usage. Instead, brands need to distinguish between using AI to make consumer experiences more useful and using AI to automate creative communication that consumers may perceive as impersonal.

Market Landscape

AI-assisted shopping is moving from experimentation toward a more established component of product discovery.

NIQ and World Data Lab reported in August 2026 that 74% of shoppers globally use AI for product discovery, while their research also examined the interaction between AI, retail media and social commerce.

Other research suggests consumers still place significant value on information beyond algorithmic recommendations. McKinsey's 2026 consumer research found that generative AI responses remain less trusted for product research than several human and traditional information sources, despite growing usage.

That creates a two-stage environment for brands: AI can influence discovery, while external sources can influence confidence.

For marketers, this expands the scope of SEO, AEO and GEO beyond traditional rankings. Brand information needs to be discoverable and understandable across AI assistants, search engines, review environments, video platforms and community discussions.

Strategic Outlook

The emerging challenge is less about whether marketers should use AI and more about where AI adds value without weakening consumer trust.

AI can accelerate content production, audience analysis and media optimization. But the Net Conversion findings suggest that consumers may distinguish between AI helping them and AI being used to communicate with them.

That makes transparency, distinctive creative, useful first-party content and credible third-party validation increasingly important.

Marketing teams also need measurement systems capable of connecting fragmented journeys. An AI recommendation may lead to a branded search, a YouTube review, a retailer visit and eventually a purchase. Last-click reporting can overlook much of that sequence.

The next phase of AI marketing is therefore likely to involve both AI-powered discovery optimization and stronger human-centered brand experiences.

Top Insights

• AI is entering product research, with 54% of consumers surveyed by Net Conversion using AI tools at least sometimes to research purchases.
• AI-generated advertising faces a trust gap, as 54% say they trust brands less when their advertisements appear to be AI-made.
• AI expands consideration, with 74% of AI researchers saying recommendations increase the number of brands they consider.
• Verification remains essential, with consumers checking AI recommendations across brand websites, search engines, retailers, YouTube and Reddit.
• Advertising attention is fragmenting, making cross-device behavior, AI discovery and multi-channel measurement increasingly important to media planning.

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