Loyalty platforms have traditionally separated program strategy from program execution. Marketing and loyalty teams may decide which customers should receive rewards or how tiers should change, but implementing those decisions often requires technical teams, development queues and lengthy approval cycles. Phaedon is attempting to remove that operational bottleneck with an expanded Tally AI Connector that lets loyalty teams build and modify programs through natural-language conversations.

The technology allows teams to work with Tally through AI environments including ChatGPT, Claude and Gemini, as well as Phaedon's own AI platform. Instead of limiting AI to reporting and recommendations, the connector is designed to let users propose changes to loyalty mechanics, including earning rules, tiers, promotions, member segments and rewards.

A human approval step remains part of the workflow. AI can prepare or execute a proposed change only after an authorized person approves it, creating a governance layer around program-level automation.

From Loyalty Analytics to Program Execution

The distinction is important for enterprise loyalty teams. AI is increasingly being used to analyze customer behavior, identify high-value segments and recommend campaigns. Phaedon's approach moves the technology closer to the operational layer where those recommendations become actual program changes.

Through the Tally AI Connector, a loyalty manager could use conversational instructions to identify a segment, develop a win-back campaign and initiate the campaign without switching between multiple operational systems. Customer-care teams can also use the environment to address member issues.

Phaedon says the connector can interact with the underlying mechanics of a loyalty program, including tiers, points, earning rules and member segments. That makes it less of a conventional analytics assistant and more of an AI interface for loyalty operations.

The architecture is built around the Model Context Protocol (MCP), a framework increasingly used to connect AI models with external tools and data. By implementing Tally as an MCP server, Phaedon is positioning the loyalty platform as an operational system that AI assistants can interact with rather than simply a data source they can query.

Human Approval Becomes the Governance Layer

Giving AI access to customer accounts and loyalty mechanics creates a different set of requirements from using AI for content generation or reporting.

A mistaken change to an earning rule could affect thousands or millions of members. An incorrectly configured promotion could create financial exposure, while an inappropriate reward could undermine the economics of a loyalty program.

Phaedon's proposal-and-approval workflow addresses that risk by keeping a person responsible for committing changes. The AI handles the interpretation, preparation and execution workflow, while the human remains accountable for the final decision.

That model reflects a broader direction in enterprise AI: organizations are increasingly exploring agents that can perform actions inside business systems while retaining approval gates for consequential operations.

Loyalty Becomes a Conversational Marketing System

The development also connects loyalty technology with the broader evolution of customer engagement platforms. Instead of treating loyalty as a standalone points engine, conversational AI can potentially connect member intelligence, segmentation, campaign execution and customer service within the same workflow.

For marketers, that could reduce the distance between identifying an opportunity and acting on it. A loyalty team spotting declining activity among a valuable customer segment, for example, could potentially move from analysis to a proposed intervention without waiting for a separate technical implementation cycle.

Phaedon says its platform currently supports more than 550 million loyalty members and 6 billion transactions annually. The company also reports that brands using Tally experience nearly four times average loyalty-membership growth. These are company-reported figures and are not independently verified in the announcement.

The company plans to expand AI integrations and use cases across travel, hospitality and retail through the remainder of the year.

Market Landscape

Customer loyalty platforms are increasingly incorporating AI for personalization, segmentation, predictive analytics and campaign optimization. Major marketing technology ecosystems from companies such as Salesforce and Adobe are also moving toward AI-assisted customer engagement and increasingly autonomous workflows.

Phaedon's approach differs primarily in where it places AI control: inside the operational mechanics of the loyalty program.

That distinction could become more important as MCP and similar interoperability approaches make it easier for enterprise AI assistants to connect with business applications. Instead of asking marketers to learn another AI interface, an MCP-based architecture allows existing AI environments to become conversational entry points into operational systems.

The challenge will be governance. As AI gains permission to change customer-facing systems, enterprises will need granular authorization, audit trails, approval workflows and safeguards against unintended actions.

Strategic Outlook

Phaedon's Tally AI Connector illustrates a shift from AI that explains customer data to AI that participates in customer operations.

For loyalty teams, the potential benefit is shorter execution cycles. Changes that previously required technical intervention could increasingly become conversational workflows, provided organizations establish appropriate controls around who can propose, approve and execute changes.

The human approval requirement is therefore more than a usability feature. It represents an important governance model for agentic marketing technology: AI performs operational work, but people retain authority over consequential customer decisions.

If this model expands across loyalty, CRM and marketing automation platforms, conversational interfaces could become another layer through which marketers manage customer programs. The competitive question will then shift from whether a platform offers AI to how safely and deeply that AI can operate inside the underlying marketing infrastructure.

Top Insights

• Phaedon's Tally AI Connector lets loyalty teams modify program mechanics through conversational AI, extending automation beyond analytics into operational execution.
• The MCP-based architecture connects Tally with ChatGPT, Claude and Gemini, turning existing AI environments into interfaces for loyalty management.
• Human approval remains mandatory before program changes commit, providing governance as AI gains access to customer data, rewards and loyalty mechanics.
• The approach connects loyalty management with agentic marketing, where AI can move from identifying opportunities to preparing and executing customer engagement actions.
• Phaedon reports support for more than 550 million loyalty members and 6 billion annual transactions, although those figures are company-reported.

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Generative AI is making it easier for enterprises to produce content at scale, but it is also widening a longstanding governance problem: much of that content never passes through the systems designed to control it. Aprimo is addressing that gap with Content Portfolio Management (CPM), an AI-powered governance product designed to discover, assess and prioritize content across an enterprise ecosystem rather than only within a digital asset management system.

The announcement expands Aprimo's strategy from managing centralized digital assets to governing content wherever it exists. CPM can discover assets across websites, cloud storage, partner portals, CMS platforms and other external repositories without requiring companies to migrate those assets into the DAM.

That distinction matters as enterprises increasingly distribute content through agencies, dealers, partners, regional teams and multiple digital channels. A DAM can provide a controlled source of approved assets, but it cannot by itself guarantee that outdated, off-brand or non-compliant versions have not already been published elsewhere.

Aprimo Mesh Creates a Visibility Layer Across Distributed Content

CPM's first major component is Aprimo Mesh, which discovers content across an organization's ecosystem and maps each asset to its source. Aprimo says the system fingerprints assets and deduplicates them against known DAM content, creating a unified view without requiring content migration.

That architecture addresses a practical weakness in conventional DAM governance. Enterprises may know which asset is approved inside their repository while having limited visibility into what is actually live across websites, partner environments or external storage.

The second layer comes from Aprimo's Open Agent Framework. Its AI agents can operate beyond the boundaries of the DAM and apply governance policies to distributed content. Librarian Agents enrich discovered content with metadata, Critic Agents identify quality issues and Compliance Agents flag brand or legal violations.

The result is closer to continuous content monitoring than periodic asset auditing.

AI Agents Move Governance From Review to Enforcement

The third component is a Portfolio Risk Dashboard that prioritizes findings according to factors including region, brand, asset type and severity.

This changes how marketing and compliance teams can approach governance. Rather than reviewing thousands of assets in sequence, teams can focus human attention on the issues with the greatest potential business impact.

For organizations managing multiple brands, markets and distribution partners, that prioritization could be particularly useful. A campaign that has expired, a website using an outdated product claim or a partner displaying an unapproved asset can represent different levels of risk, and a centralized risk view can help determine where remediation should begin.

Aprimo says customers are already using CPM to monitor websites, identify compliance problems across partner networks and retire expired campaigns. Helen of Troy, for example, is using the system to identify accessibility and ADA compliance risks across its content ecosystem, according to the company.

Market Landscape

Content governance is becoming more complicated as enterprises combine DAM platforms with CMSs, cloud storage, commerce systems, social channels and generative AI tools.

Aprimo's own cited research says 65% of organizations report growing asset volumes year over year, while only 31% say they can consistently enforce brand guidelines across content. Those figures originate from the research cited by Aprimo and should be treated as vendor-supplied market context rather than an independently established benchmark.

The competitive landscape includes established DAM and content operations platforms from vendors such as Adobe, Bynder and Acquia, alongside governance capabilities increasingly embedded into broader marketing technology stacks.

Aprimo's differentiation is the attempt to govern content in place. Instead of requiring every asset to be consolidated into a single repository, CPM creates visibility across distributed systems and applies AI-driven controls where the content already resides.

That model becomes particularly relevant for enterprises with large partner ecosystems, decentralized marketing organizations or regulated content workflows.

Strategic Outlook

The introduction of CPM points to a broader change in enterprise content operations: governance is moving from repository management toward portfolio-wide control.

As AI increases the volume and speed of content creation, manual review becomes increasingly difficult to scale. The more consequential question is whether AI-generated and human-created content can be governed continuously across the channels where it ultimately appears.

Aprimo is also positioning its broader platform around agentic content operations, with AI agents handling tasks such as enrichment, review, personalization and workflow execution.

For enterprise marketing teams, the value of this approach will depend on more than detection accuracy. They will need reliable policy definitions, auditability, permission controls and clear human escalation paths before autonomous governance can become part of critical brand and compliance processes.

If those controls mature, DAM platforms could evolve from repositories of approved assets into active governance layers spanning the entire enterprise content lifecycle.

Top Insights

• Aprimo CPM extends AI governance beyond the DAM, giving enterprises visibility into content scattered across websites, cloud storage, CMS platforms and partner ecosystems.
• Mesh creates a distributed content map without migration, addressing a governance gap created when approved DAM assets are copied into external environments.
• Librarian, Critic and Compliance Agents automate metadata enrichment, quality checks and policy enforcement, reducing dependence on manual content audits.
• The Portfolio Risk Dashboard prioritizes governance problems by severity and business context, allowing enterprise teams to focus human review on higher-risk content.

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Marketing platforms have spent years accumulating customer signals, but turning those signals into timely decisions remains difficult. Attentive is pushing its AI strategy deeper into that problem with Lifecycle Intelligence, a new set of capabilities within Attentive AI Pro designed to help brands understand customer interests, purchase behavior and long-term value.

The omnichannel marketing platform's latest release adds Product Affinity, Recency, Frequency and Monetary Value (RFM), and Customer Lifetime Value (LTV) capabilities. Attentive is also expanding AI across reporting and external workflows through a new Reporting Agent and Model Context Protocol (MCP) capabilities.

The broader shift is significant for retail and e-commerce marketers. Rather than using AI primarily to generate copy or campaign variations, Attentive is positioning AI as a decision-making and execution layer that can continuously interpret customer signals and update audience targeting.

Lifecycle Intelligence Turns Customer Data Into Dynamic Segments

Product Affinity is generally available to AI Pro customers and allows marketers to create dynamic audiences based on customer interest in particular products and categories.

The RFM capability, currently in beta, adds another layer by examining how recently and frequently customers purchase and how much they spend. Customer LTV, also in beta, is designed to help brands estimate customer value over time and determine where retention resources should be concentrated.

These capabilities address a familiar problem in customer data platforms and marketing automation: marketers may have extensive behavioral data but still rely on manually defined segments that become outdated as customers change.

Attentive's approach is to combine these signals with agentic AI that can act on changing customer behavior. Instead of simply identifying that a customer browsed a product or purchased recently, the platform can use those signals to keep audience segments and campaign delivery more relevant.

That moves AI closer to the operational layer of marketing automation.

For e-commerce teams, the potential application is straightforward. A customer showing increased interest in a particular product category could move into a relevant audience, while high-value customers may receive different retention treatment from infrequent or low-value buyers.

Reporting Agent Moves Analytics Into Conversation

Attentive is also introducing Reporting Agent in beta. The capability allows marketers to ask questions about their Attentive programs conversationally, investigate changes in performance and surface potential next actions.

This is part of a broader movement toward natural-language analytics, where marketing teams can query campaign and customer data without constructing reports manually.

The addition of MCP capabilities expands that concept beyond Attentive itself. The company says marketers can connect external AI tools such as ChatGPT and Claude to Attentive, with more than 70 tools currently available in beta for tasks including campaign creation, audience building and reporting.

That creates a more open architecture than an AI assistant confined to a single marketing application. It also introduces new considerations around permissions, data governance and security when customer data becomes accessible through third-party AI interfaces.

Early Customer Results Point to Revenue Applications

Attentive cites several early customer results for AI Pro. Frye US reported a 13% overall revenue lift after using the broader AI Pro suite, according to the company.

Flag & Anthem used the platform to identify more shoppers and connect additional behavior to customer profiles. Attentive says the brand achieved a 28% increase in people identified for follow-up, a 7% increase in incremental sign-ups and a 20% increase in abandonment-journey revenue.

These figures are vendor-reported customer results rather than independently verified performance benchmarks. They nevertheless illustrate the commercial use case Attentive is pursuing: using AI to improve audience identification and trigger more relevant communications rather than simply accelerating campaign production.

Market Landscape

Attentive competes in a market that includes customer engagement and marketing automation platforms from Salesforce, Adobe and other enterprise vendors, many of which are adding generative and agentic AI capabilities to existing customer-data workflows.

The competitive distinction is increasingly shifting from AI content generation toward what platforms can do with behavioral data. Product affinity, RFM analysis and LTV modeling are established marketing concepts; Attentive's differentiator is attempting to make those signals continuously actionable within an automated engagement platform.

The MCP integration also reflects an emerging enterprise pattern in which AI agents operate across multiple applications. As systems such as ChatGPT and Claude become interfaces for business workflows, marketing platforms need ways to expose governed data and actions without creating disconnected AI silos.

Strategic Outlook

Attentive's release illustrates how agentic AI is changing the role of marketing automation platforms. The next generation is likely to be judged not only by how effectively it creates messages, but by whether it can interpret customer signals, make audience decisions and execute campaigns within defined business rules.

That puts data quality and governance at the center of the AI marketing equation. Poor customer identities, incomplete behavioral signals or inconsistent value calculations can produce automated decisions at scale without necessarily producing better outcomes.

For enterprise marketing teams, the opportunity is a more adaptive lifecycle model in which segmentation, reporting and engagement become continuous processes rather than periodic manual exercises.

The challenge will be proving that these AI-driven decisions generate incremental value while maintaining transparency and control over customer data.

Top Insights

• Attentive Lifecycle Intelligence combines product affinity, RFM and LTV signals with agentic AI to make customer segmentation more adaptive for e-commerce marketers.
• Reporting Agent brings conversational analytics into marketing operations, reducing the need for teams to manually construct reports to investigate campaign performance.
• MCP connectivity allows external AI tools such as ChatGPT and Claude to interact with Attentive capabilities, expanding AI beyond a single marketing platform.
• Attentive's customer results demonstrate potential revenue applications, although the reported performance figures are vendor-provided rather than independent industry benchmarks.

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AI creative platforms are becoming crowded with models, templates and generation tools, but the growing number of options can create another problem for marketers: figuring out which workflow to use in the first place. Vmake Labs is addressing that usability gap with an upgraded AI creative workspace that separates video marketing, video generation and image generation into dedicated creation paths.

The redesigned workspace targets small businesses, e-commerce sellers, creators and marketing teams producing social content at scale. It combines specialized creation workflows with a mobile Agent Room and a library of Viral Creative Templates intended to move users from product assets or ideas to finished campaign content with fewer setup decisions.

The update reflects a broader evolution in generative AI marketing software. Instead of offering a single prompt box, platforms are increasingly packaging AI models into task-specific workflows that guide users toward particular outputs.

Vmake Separates Marketing Workflows by Creative Goal

Vmake's redesigned workspace is organized around three primary paths: Agent, Video Generation and Image Generation.

The Agent experience, also presented as Video Marketing, focuses on finished marketing videos. Users can provide product assets, brand information and campaign preferences, then use guided settings and templates to create social-oriented content, including user-generated-content-style videos.

Video Generation takes a broader production approach. Vmake's AI Video Generator allows users to create videos from text prompts, select models such as Seedance and Kling, choose aspect ratios and access high-resolution output options.

The Image Generation path is designed for standalone visual assets such as social covers, product imagery and campaign graphics. Those images can also become source material for subsequent video production, connecting image ideation with downstream content creation.

For marketing teams, the distinction is less about the underlying AI models and more about reducing workflow ambiguity. A retailer creating a product launch video does not necessarily need the same interface as a creator experimenting with an AI-generated visual concept.

That task-oriented structure is becoming increasingly important as generative AI moves from experimentation into everyday marketing production.

Mobile Agent Room Brings Creative Iteration Beyond the Desktop

Vmake has also extended its Agent Room to mobile devices. Users can continue conversations, modify parameters, review generated assets and refine outputs from a phone.

The mobile workflow is particularly relevant to social media teams working around short publishing cycles. Instead of treating generation as a desktop-only production process, Vmake positions Agent Room as a continuous workspace where users can review variations and make adjustments while away from a computer.

The approach resembles a broader trend across enterprise software, where AI assistants are becoming persistent interfaces rather than standalone generation features.

For marketers, that can make iterative work more practical. Campaign assets often require multiple revisions involving copy, visuals, formats and platform-specific requirements. Keeping those changes within the same conversational environment can reduce the need to move between separate creative applications.

Viral Templates Target the Blank-Prompt Problem

Vmake's Viral Creative Templates address another barrier to generative AI adoption: users may know the type of content they want but not how to construct an effective prompt.

The template library organizes concepts around styles, industries and marketing ideas, including UGC-style videos, AI hook videos, stock avatars, beauty, fashion and sports. Seasonal concepts such as back-to-school, Halloween and Black Friday are also included.

Templates can serve as starting points rather than finished outputs. Users can select a concept, generate an initial asset and then refine it through Agent Room.

That workflow is important for small businesses and e-commerce teams that may not have dedicated creative-production resources. It shifts the role of generative AI from an open-ended prompt interface toward a guided creative system.

Market Landscape

Vmake operates in a market that includes specialized AI video platforms such as Runway and Kling alongside broader creative ecosystems from Adobe and Canva. Other platforms increasingly combine text-to-image, text-to-video, editing and automated content workflows.

The competitive distinction is therefore moving beyond whether a platform can generate an image or video. Model access, output quality, editing controls, templates, brand consistency and workflow integration increasingly determine how useful these tools are for professional marketing teams.

Vmake's strategy emphasizes workflow orchestration: putting multiple generation paths, templates and refinement tools inside one workspace. Its inclusion of tools for video enhancement, translation and asset preparation extends the platform beyond initial generation into post-production.

For enterprise marketing teams, however, larger questions remain around brand governance, asset management, copyright controls and integration with existing MarTech stacks. Those capabilities can become increasingly important as AI-generated creative moves from experimentation into high-volume commercial campaigns.

Strategic Outlook

The next phase of AI creative software is likely to be less about individual generation models and more about how effectively platforms connect ideation, production, refinement and distribution.

Vmake's redesigned workspace reflects that shift. By separating creative tasks while keeping them inside one environment, the company is treating generative AI as a production workflow rather than simply a collection of models.

That could be particularly useful for e-commerce and social marketing teams that need frequent variations of product imagery and short-form video. The larger opportunity is connecting those capabilities with campaign data, brand guidelines and automated publishing systems.

As AI-generated content becomes easier to produce, differentiation may increasingly come from workflow design and operational controls rather than generation alone.

Top Insights

• Vmake's redesigned workspace separates AI video, image and marketing workflows, helping small businesses and e-commerce teams choose a more direct production path.
• Mobile Agent Room extends creative iteration beyond desktops, allowing social teams to review, adjust and refine AI-generated campaign assets from phones.
• Viral Creative Templates address the blank-prompt problem by giving marketers structured starting points for UGC, seasonal and industry-specific social content.
• Vmake's broader production toolkit shows AI creative platforms moving beyond generation toward connected workflows spanning enhancement, translation and campaign asset refinement.

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For pharmaceutical marketers, the search results page is no longer the only place where consumers encounter health information. AI-generated answers from ChatGPT, Gemini, Claude, Perplexity and Google's AI Overviews are becoming another discovery layer, creating a measurement problem for brands that have traditionally tracked rankings, clicks and conversions.

Fullspan Health, the B2B healthcare platform operated by RVO Health, is addressing that gap with AIQ, a measurement and media optimization solution designed to track how health brands appear across AI-powered search experiences.

AIQ is available across campaigns running through Fullspan Health's portfolio, including Healthline, Healthgrades, Medical News Today, Psych Central and Bezzy. The platform tracks brand references, citations and links across major AI search environments and is intended to show marketers how campaign investment affects AI visibility over time.

The development comes as consumers increasingly use AI for health-related research. A March 2026 KFF survey found that 32% of U.S. adults had used AI chatbots for health information or advice during the previous year. A separate Pew Research Center survey conducted in June 2026 found that 34% of U.S. adults had used AI chatbots for at least one of several health-related reasons, including learning about treatments, diagnoses and symptoms.

AIQ Turns AI Search Visibility Into a Marketing Measurement Layer

AIQ is designed to answer a question conventional analytics platforms do not necessarily address: when a consumer asks an AI system about a condition, treatment or healthcare topic, does a particular pharmaceutical brand appear in the resulting answer?

The platform monitors visibility across Google AI Overviews, ChatGPT, Claude, Gemini and Perplexity. Fullspan says marketers can use the data to identify where brands are referenced, cited or linked and then evaluate changes after a campaign launches.

That creates a feedback loop between media investment and AI discovery. Instead of treating AEO as a one-time content optimization exercise, marketers can monitor specific topics and queries, examine changes in visibility and identify areas where additional campaign activity could expand reach.

The approach also extends beyond a brand's own website. Fullspan says AIQ can help marketers understand how its publisher properties contribute to citations and how those properties can be incorporated into campaigns designed around AI discovery.

That distinction is important because AI search visibility depends on more than conventional keyword rankings. AI systems can synthesize information from multiple sources, meaning publisher authority, content quality, entity recognition and citation patterns can all influence how a brand is represented.

Fullspan Reports Strong Citation Performance Across Health Topics

Fullspan's own AIQ analysis provides an early demonstration of the measurement approach. The company says its content appeared in roughly two-thirds of tested ChatGPT, Claude and Gemini answers across a sample of health categories.

Reported citation reach varied substantially by condition, reaching 81% for migraine, 79% for HPV prevention, 73% for hypothyroidism and 71% for type 2 diabetes and obesity.

Fullspan also reports that its content appeared more frequently than competitors in selected categories, including cardiovascular disease, HIV and type 2 diabetes/obesity. For cystic fibrosis, the company says its content appeared in 85.4% of tested prompts.

These figures should be read in context. Fullspan tested an average of 200 unique prompts across selected conditions and therapeutic areas between July and September 2026. Its category-reach methodology counts the percentage of unique questions for which at least one tested AI model cited a Fullspan property. The results therefore describe Fullspan's test environment rather than providing an independently audited ranking of the healthcare publishing market.

Market Landscape

AI search measurement is becoming a new category within digital marketing analytics. Traditional SEO platforms such as Ahrefs focus heavily on rankings, organic traffic, backlinks and search visibility, while emerging AEO tools increasingly track AI citations, mentions and answer-level presence.

Fullspan's strategy differs by combining measurement with its own media inventory and health-content ecosystem. That gives the company a direct connection between AI visibility data and advertising campaigns, rather than positioning AI measurement solely as an independent analytics layer.

The timing reflects a broader change in consumer behavior. Pew found that 22% of U.S. adults get health information from AI chatbots at least sometimes, while younger adults are substantially more likely to do so.

For pharmaceutical marketers, the implications extend beyond traffic. AI-generated health answers can influence how consumers understand conditions and treatment options before they reach a brand site, physician or healthcare provider.

Strategic Outlook

AIQ points toward a broader evolution in marketing measurement: visibility may increasingly need to be evaluated inside the answers produced by AI systems, not only in the links users click afterward.

For pharma companies, that could make AEO part of the media-planning and campaign-measurement process. Marketing teams may eventually need to connect AI citations with audience exposure, brand consideration, website activity and downstream conversion signals.

The more difficult challenge will be standardization. AI platforms can change models, retrieval systems and citation behavior, making cross-platform comparisons difficult. A useful AI-search measurement system therefore needs consistent prompt methodology, transparent definitions and longitudinal tracking.

Fullspan's launch also highlights a competitive question for publishers and ad platforms: whether proprietary content authority can become measurable media value in an AI-mediated discovery environment.

Top Insights

• AIQ extends pharmaceutical marketing analytics into AI answers, giving brands visibility into citations and references across ChatGPT, Gemini, Claude, Perplexity and Google.
• Fullspan combines AI-search measurement with its publisher network, creating a direct connection between AEO performance and healthcare media campaigns.
• Consumer adoption is making AI visibility commercially relevant as millions of Americans increasingly use chatbots to research symptoms, diagnoses and treatment information.
• Fullspan's reported citation results are promising but remain company-generated measurements based on a defined prompt sample, not independent industry benchmarks.

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The shift from traditional search engines to AI-powered discovery is forcing enterprise marketing teams to rethink how brands earn visibility, and Assemble is creating a new industry forum focused on that transition. The company has announced the Digital Marketing & Communications Council (DMCC), bringing together its new AEO Board with its existing SocialMedia.org and SocialMedia.org Health Boards.

The council is designed as a peer network for executives responsible for digital marketing, communications, social media, brand, customer experience and growth as large language models (LLMs) increasingly influence how consumers discover and evaluate companies.

Assemble says the combined community includes 394 member companies and 1,750 individual members, representing more than half of the Fortune 500. The organization plans to use the council to facilitate discussions around answer engine optimization (AEO), AI search visibility, organizational structures, measurement and changes to digital media strategy.

AEO Moves Beyond Traditional SEO

AEO has emerged as a new layer of digital visibility as consumers increasingly use AI interfaces to obtain direct answers rather than navigating a conventional list of search results.

For enterprise marketing teams, that changes the measurement problem. A brand can maintain strong conventional search rankings while receiving little visibility when an AI system generates an answer from multiple sources. Conversely, being mentioned or cited by an AI system does not necessarily translate into website traffic in the same way as a traditional search click.

Assemble says members of its communities are reporting traffic declines of between 40% and 60%, although the figures are based on member-reported experiences rather than an independently audited industry dataset. The company also says nearly two-thirds of members identify operationalizing AEO as a near-term challenge.

That distinction matters. AEO is not simply a replacement for SEO. It introduces questions around brand representation, source authority, structured content, entity recognition and how AI systems interpret information across a company's digital footprint.

The resulting challenge extends into marketing operations. Teams need to determine who owns AI-search visibility, how it should be measured, what content should be optimized and how changes in discovery affect paid media, organic acquisition and conversion.

DMCC Creates a Peer Network for AI-Driven Marketing

The DMCC combines Assemble's AEO Board with its SocialMedia.org and SocialMedia.org Health communities. The broader structure reflects the convergence between previously separate functions as AI changes how organizations manage digital communications.

The council's launch is scheduled for Assemble's North American Marketing Leadership Summit (NAMLS), running September 28–30, 2026. Leaders from digital marketing, AEO, social media, brand, communications, customer experience, growth and digital strategy are expected to participate.

Executives from companies including Amazon, PepsiCo, SAP, Spotify, Unilever, KPMG, AT&T and Bank of America are listed among the 2026 summit speakers.

The involvement of large enterprises is significant because AI-search optimization is becoming an organizational issue rather than a narrowly defined SEO task. Companies operating across multiple brands, markets and content systems face a different problem from smaller businesses experimenting with individual pages: they need repeatable governance and measurement across their entire digital presence.

Market Landscape

Google remains central to conventional search, while Microsoft has integrated AI capabilities into Bing and its broader Copilot ecosystem. Meanwhile, standalone AI platforms such as OpenAI's ChatGPT, Anthropic's Claude and Google's Gemini are changing how users can discover information without starting from a traditional search-results page.

This creates a fragmented visibility environment. Enterprise marketers increasingly have to consider not only rankings and clicks, but also whether their brands are accurately represented in AI-generated answers and which sources influence those responses.

The broader enterprise AI market is also moving toward agentic workflows, automation and AI-assisted decision-making. For marketing organizations, that means AEO is likely to intersect with content management, analytics, brand governance, public relations and customer experience rather than remain isolated within an SEO team.

Strategic Outlook

The launch of DMCC highlights an important development in enterprise marketing: AI search is becoming an organizational and governance challenge as much as an optimization problem.

Traditional SEO provided relatively familiar metrics such as rankings, impressions, clicks and organic traffic. AI-generated discovery introduces less standardized measurements around mentions, citations, recommendations and brand context.

That creates demand for shared methodologies. Marketing leaders will need to determine which AI-search signals matter commercially and how those signals connect to pipeline, customer acquisition and brand outcomes.

For MarTech vendors, the opportunity is equally significant. Platforms that can connect AI visibility data with analytics, content operations, customer data and marketing automation could become increasingly important as enterprises attempt to understand the relationship between AI discovery and revenue.

Top Insights

• Assemble's DMCC brings AEO and social-media leadership together as enterprises confront fragmented AI-search visibility and changing digital discovery behavior.
• Member-reported traffic declines highlight the uncertainty surrounding AI-powered search, although Assemble's figures have not been independently validated as an industry benchmark.
• AEO is expanding beyond SEO into brand governance, content strategy, analytics and communications as LLMs increasingly influence customer discovery and evaluation.
• Enterprise marketing teams will need new measurement frameworks connecting AI mentions and citations with acquisition, conversion, revenue and broader customer outcomes.

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B2B marketing teams are increasingly using large language models to analyze campaigns, customer journeys and performance data, but faster answers can create a new problem: whether those answers can be trusted. Dreamdata is addressing that gap with Dreamdata AI, a set of three products designed to connect LLM-based analysis to governed go-to-market data.

The offering includes the Dreamdata Analytics Agent, Dreamdata MCP Server and Dreamdata Data Warehouse. The products are built around Dreamdata's account-based data model and a governed semantic layer intended to keep definitions and calculations consistent across users and AI tools.

The announcement comes as generative AI becomes more embedded in marketing operations. McKinsey's 2025 research found that 71% of surveyed organizations were regularly using generative AI in at least one business function, with marketing and sales among the most common areas of adoption.

Dreamdata's argument is that enterprise marketers need more than an AI interface that can answer questions in natural language. They also need a way to understand where an answer came from.

Dreamdata AI Puts Data Governance Behind LLM-Based Marketing Analytics

The Dreamdata Analytics Agent allows marketers to query their GTM data using plain English and receive consistent reports across an organization. The company says the agent can also recommend next actions based on the underlying analysis.

The MCP Server takes a different approach. It connects Dreamdata's account-based data context to external LLM environments, allowing teams to use agents they already work with without repeatedly defining their marketing funnel and data model.

The Data Warehouse is aimed at organizations building their own AI workflows. It exports Dreamdata's account-based model as a GTM warehouse with an analytics schema designed for teams developing custom agents and applications.

A key distinction is how the company handles calculations. Dreamdata says its AI products do not independently recalculate the numbers when responding to questions. Instead, they operate against standardized definitions and governed data. The objective is to reduce the possibility that two users ask essentially the same question and receive materially different results.

That issue becomes more significant as B2B journeys grow more complicated. Dreamdata's 2026 LinkedIn Ads B2B Benchmarks Report, cited by the company, describes an average customer journey lasting 272 days, involving 88 touchpoints and 10 stakeholders.

For enterprise marketing teams, this creates a practical challenge. Attribution, pipeline influence and channel performance cannot be reliably interpreted if different AI tools apply different definitions to revenue, opportunities, accounts or campaign influence.

Market Landscape

Dreamdata is entering a market where established marketing platforms already use AI to simplify analytics and decision-making. Salesforce, Adobe and other enterprise MarTech vendors increasingly embed generative AI into CRM, marketing automation, customer data and analytics workflows.

Dreamdata's differentiation is narrower: it is putting the account-based GTM data model and its semantic definitions at the center of AI interaction rather than treating AI primarily as a conversational layer.

That approach reflects a broader shift in enterprise AI. Gartner projects that 90% of marketing analytics workflows will be augmented by generative AI by 2028, while emphasizing measurable ROI, human oversight and data governance.

The implication is that AI-powered marketing analytics may increasingly compete on the quality and governance of the underlying data, not simply on the sophistication of the language model. For marketing leaders, the question is moving from "Can AI answer this?" to "Can the organization verify why AI produced this answer?"

Strategic Outlook

Dreamdata AI illustrates an emerging architecture for enterprise marketing intelligence: governed data underneath, an agent or LLM interface above it, and traceability between the two.

That architecture could become particularly relevant as marketing teams connect AI agents to budget allocation, attribution analysis and campaign optimization. McKinsey estimates that agentic AI could account for more than 60% of the incremental value AI generates from marketing and sales deployments, underscoring the potential importance of agent-driven workflows.

The competitive question will ultimately be whether platforms can preserve data consistency while allowing marketers to work across multiple AI environments. Dreamdata's three-product strategy addresses that problem from the data layer upward rather than relying solely on the LLM itself.

Top Insights

• Dreamdata AI connects LLMs with governed GTM data, giving B2B marketers a way to interrogate complex customer journeys without abandoning standardized definitions.
• The MCP Server extends Dreamdata's account-based context into external AI agents, reducing the need for marketing teams to repeatedly explain funnel structures.
• The Data Warehouse targets enterprises building custom AI agents, positioning governed marketing data as infrastructure for future analytics and automation workflows.
• Dreamdata's approach reflects a wider enterprise AI shift toward traceability, as marketers increasingly need to validate AI-generated analytics before making budget decisions.

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Los Angeles agency True North Social has launched a dedicated Facebook and Instagram advertising practice for brands across Los Angeles County, combining Meta Ads Manager campaign management with in-house photography, video production, creative strategy and conversion measurement. The month-to-month service targets ecommerce, beauty, fashion, real estate, healthcare, legal, hospitality and technology companies.

The launch reflects a broader shift in paid social advertising toward tighter integration between media buying, creative production and measurement. Rather than treating creative development and advertising operations as separate agency functions, True North Social says its new practice keeps media buyers, strategists, copywriters, designers and a production crew within the same operation.

The agency operates from Culver City and says engagements begin with an audit of an existing Meta advertising account, its historical structure and available performance data. Before campaign development, the agency assesses three areas: the client's business objective, available audience data and the budget required to support different stages of the marketing funnel.

Campaigns are structured around awareness, consideration, conversion and remarketing, with separate creative and measurement objectives for each stage. True North Social says audience strategies can combine Custom Audiences, Lookalike Audiences and interest-based targeting, depending on the data available to the advertiser.

The technical infrastructure includes Meta Pixel and Conversions API implementation, with the agency saying it verifies conversion tracking and event deduplication. It also says Meta-reported conversions are compared with backend revenue from platforms such as Shopify, WooCommerce and Klaviyo.

That measurement layer is increasingly important as advertisers attempt to reconcile platform attribution with their own first-party business data. A conversion reported inside an advertising platform does not necessarily represent the same measurement window or methodology used in a company's ecommerce or analytics system.

Creative production is another central component of the offering. True North Social says its internal photography and video team produces static, carousel and vertical video assets, including location-based shoots across Los Angeles. The agency's approach is built around producing multiple creative concepts and testing hooks, formats and messages rather than relying on a single campaign asset.

The emphasis on creative testing comes as advertisers face continued pressure from Meta's advertising auction. Meta reported that average price per ad increased 12% year over year in both the first and second quarters of 2026, while ad impressions increased 19% in the first quarter and 14% in the second, according to the figures cited by True North Social.

Those figures suggest that advertisers are operating in an environment where increased delivery does not necessarily translate into cheaper reach. Rising auction prices can make creative effectiveness, conversion tracking and budget allocation increasingly consequential for performance marketers.

The agency is also positioning local production as a differentiator for Los Angeles advertisers. In-house crews can produce product photography, location footage and short-form video without requiring a separate external production relationship for every creative refresh.

True North Social says it maintains a limited client roster and uses month-to-month agreements after an initial three-month period. The company lists Facebook Agency Partner status and says its client experience spans more than 100 verticals.

Its published client roster includes brands such as RE/MAX, Cutera, Therabody, Bristol Farms and American Needle, while cited case studies include Thrive Societe, The Orlando Hotel and Electrolit.

The new practice ultimately places three components—media buying, creative velocity and measurement quality—at the center of Meta advertising management. For brands facing higher advertising costs, the ability to continuously test creative while maintaining reliable conversion data may become as important as campaign targeting itself.

Market Landscape

Meta remains a major advertising environment for consumer and direct-response brands, but the operating model has become more technically demanding. Privacy changes, signal loss, attribution differences and increasing competition for inventory have pushed advertisers toward first-party data and server-side measurement.

At the same time, short-form video and rapidly changing creative formats have increased the production requirements for paid social teams. Agencies increasingly need capabilities spanning media strategy, creative development, analytics and conversion infrastructure rather than media buying alone.

Strategic Outlook

True North Social's Los Angeles launch illustrates how specialized paid-social agencies are attempting to respond to these pressures through vertically integrated services.

For advertisers, the relevant question is no longer simply whether an agency can purchase Meta inventory. Creative testing capacity, first-party data activation, Conversions API implementation, attribution reconciliation and vertical-specific experience can all affect how effectively a campaign is managed.

As Meta auction costs fluctuate, agencies that can connect those capabilities into a single operating workflow may increasingly position creative iteration and measurement engineering as core parts of paid media management.

Top Insights

• True North Social has launched a dedicated Meta advertising practice combining Facebook and Instagram media buying with in-house creative production.
• The agency says campaigns use Meta Pixel and Conversions API alongside backend revenue reconciliation to improve conversion measurement.
• In-house photography and video production is designed to increase the volume and frequency of creative testing for paid social campaigns.
• Meta's reported 12% year-over-year increase in average ad prices highlights the growing importance of creative and measurement efficiency.
• Month-to-month engagements give advertisers a flexible agency structure while placing continued emphasis on campaign performance and account optimization.

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