Social listening has long promised to turn millions of online conversations into usable consumer intelligence, but the research process can still involve repetitive searches, manual filtering, competitive comparisons and hours spent assembling presentations. YouScan is targeting that workflow with a major upgrade to Insights Copilot, its AI agent for social listening.

The September 2026 release is the fourth major update to Insights Copilot since its 2023 launch. It adds multi-brand comparison, multi-step research, natural-language filter selection and shareable reports, moving the product from conversational analysis toward a more autonomous research workflow.

From Questions to Research Workflows

Insights Copilot is integrated directly into YouScan and lets users query social data using natural language. The system can surface consumer sentiment, complaints, trends and other insights while presenting charts and links to the underlying posts.

That evidence layer is important for enterprise research. Instead of receiving an AI-generated conclusion without context, users can open the social posts associated with an insight and evaluate the evidence themselves. YouScan says the platform can process up to 5,000 mentions per request, with insight counts and a reasoning flow included in results.

The latest release changes how users structure the research itself.

Rather than asking one question about one brand, users can now ask Copilot to analyze several brands or topics simultaneously. A request such as comparing competitors on sentiment, complaints or share of voice can be handled in a single analysis rather than through separate searches that researchers must reconcile manually.

Multi-Step Analysis Adds an Agentic Layer

The more consequential change is the addition of multi-step research.

For complex questions, YouScan says Insights Copilot can determine the steps required to complete the analysis, work through those steps and return a consolidated answer. It can also suggest follow-up questions for deeper investigation.

That puts the product closer to the emerging category of agentic analytics, where AI systems do more than summarize an existing dashboard. The system interprets the research objective, determines how to investigate it and assembles the resulting evidence.

For consumer-insights teams, this can reduce the amount of manual work between defining a research question and producing an initial finding.

Natural-Language Filters Reduce Research Friction

The upgrade also lets users specify research constraints conversationally.

A researcher can request negative TikTok posts from a particular country and time period, for example, and Copilot automatically applies the relevant filters. YouScan's documentation says the system can identify and apply filters based on the wording of a request, while users can also specify parameters such as sentiment, language, source, engagement and dates.

This matters because filtering is often one of the less visible time costs in social intelligence. Researchers may understand exactly what they want to examine but still have to configure multiple controls before they can start interpreting the results.

Natural-language filtering moves that configuration into the research conversation.

Reports Become Part of the AI Workflow

The fourth addition addresses what happens after the analysis.

Insights Copilot can turn a conversation into a structured report containing findings, charts and supporting social posts. Reports can be exported as PDFs or shared through a link, according to YouScan's documentation.

That connects research and stakeholder communication in the same workflow.

For agencies, the use case is particularly direct: an analyst can investigate a client question, refine the findings and generate a shareable report without rebuilding the analysis in presentation software. Brand, marketing and communications teams can similarly package competitive or sentiment research for executives.

Social Data Is Becoming an Intelligence Layer

The timing reflects a broader problem in enterprise marketing: organizations increasingly have access to social data but struggle to convert it into timely decisions.

Sprout Social's 2026 Social Intelligence Report found that 86% of organizations surveyed said they had missed critical business opportunities because consumer insights were delayed, siloed or underused.

That makes speed and accessibility important alongside data coverage.

YouScan is positioning Insights Copilot around that gap. The company says its platform analyzes millions of online conversations, while the Copilot layer allows users to interrogate that data through natural-language questions and receive evidence-backed results.

Market Landscape

AI is changing social listening from a monitoring activity into a more interactive research environment.

Traditional workflows often require analysts to define queries, configure filters, inspect dashboards, compare segments and manually assemble conclusions. AI agents can increasingly perform parts of that process conversationally.

YouScan's latest release combines several of those functions: cross-brand analysis, automated filtering, multi-step reasoning and report generation. The underlying social posts remain accessible, giving researchers a mechanism to validate the conclusions rather than treating the AI response as the final authority.

That combination of automation and evidence is particularly relevant as marketing teams bring generative and agentic AI into research workflows.

Strategic Outlook

The next stage of social listening is likely to be defined less by how quickly platforms can summarize mentions and more by how much of the research workflow they can execute responsibly.

YouScan's update moves Insights Copilot in that direction. A single research question can now span competitors, markets, filters and multiple analytical steps before producing a report that can be shared with stakeholders.

For marketers, researchers and agencies, the potential benefit is not simply fewer dashboard interactions. It is a shorter path from a business question to an evidence-backed view of what consumers are saying, why the conversation is changing and where further investigation may be required.

The remaining challenge for AI-powered research tools is maintaining transparency as autonomy increases. YouScan's continued emphasis on links to underlying social posts provides one mechanism for keeping human researchers involved in validating AI-generated findings.

Top Insights

• YouScan's upgraded Insights Copilot combines competitive comparison, multi-step research, natural-language filtering and report generation in one social-listening workflow.
• The AI agent can analyze multiple brands or topics together, reducing the need for researchers to repeat identical queries and manually reconcile results.
• Users can describe filters conversationally, allowing the system to narrow analysis by source, country, sentiment, language and date.
• Every finding can be traced to underlying social posts, giving researchers a way to inspect the evidence behind AI-generated conclusions.
• The update reflects a wider shift toward AI agents that execute portions of research workflows rather than simply summarize existing dashboards.

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Flytxt is expanding its position in AI-powered telecommunications marketing and sales after being recognized as a Challenger in Gartner's 2026 Magic Quadrant for CSP AI-Enabled Marketing and Sales Solutions.

The recognition, announced September 30, follows Flytxt's 2025 designation as a Niche Player in Gartner research, according to the company. Gartner's current Magic Quadrant evaluates vendors serving communications service providers (CSPs) with AI-enabled capabilities across marketing and sales operations.

The development is notable less for the designation itself than for the technology shift behind the market. CSPs are moving from conventional campaign automation and predictive analytics toward AI systems capable of interpreting customer and market signals, reasoning about possible actions and supporting execution.

From AI Copilots to Agentic Execution

Flytxt is positioning its Niya-X platform around what it calls an "AI Expertforce" rather than a collection of standalone task agents.

The platform is designed around a continuous perceive-reason-act-learn cycle. According to Flytxt, it combines causal reasoning, counterfactual simulation and privacy-preserving federated learning to evaluate factors affecting business outcomes and model alternative scenarios before recommending or taking action.

That approach targets a broader problem facing telecom operators: customer behavior, pricing, product portfolios and competitive conditions can change quickly, while traditional marketing systems often depend on predefined rules and manually managed campaigns.

Gartner's September 2026 research similarly identifies AI agents and agentic automation as important trends in the CSP marketing and sales market, which is evolving from broad business-support-system capabilities toward more specialized AI-driven functions.

What Niya-X Is Designed to Do

Flytxt says Niya-X supports decisions across the CSP commercial lifecycle, including product and proposition design, growth marketing and continuous optimization.

Its current platform combines agentic AI and marketing automation, with capabilities spanning product and pricing, sales and marketing, and customer care and engagement.

Gartner's market definition covers a similarly broad set of functions. Its 2026 research identifies capabilities including predictive growth and retention, personalization, campaign management, customer segmentation, lead management, sales intelligence and marketing performance analysis.

This broadening is important because telecom marketing increasingly depends on connecting data from multiple parts of the business. Customer behavior can influence retention offers, product recommendations and campaign targeting, while sales and market intelligence can feed decisions about acquisition and proposition design.

Privacy and Enterprise AI Architecture

Flytxt's emphasis on federated learning also addresses a central challenge for CSPs: using large amounts of customer data while operating within enterprise privacy and governance constraints.

The company says its AI architecture uses privacy-preserving federated learning alongside causal and counterfactual techniques. Rather than simply predicting what might happen, the system is designed to evaluate potential interventions and their expected effects within a particular CSP's business context.

That distinction matters as agentic AI moves closer to commercial decision-making. The value of an autonomous system depends not only on its ability to generate recommendations, but also on whether enterprises can control how those recommendations translate into actions.

Why CSP Marketing Is Becoming an AI Priority

Telecom operators face high acquisition costs, customer churn and increasingly complex product portfolios. Gartner describes CSP AI-enabled marketing and sales solutions as a way to replace reactive manual processes with AI-driven automation across campaign execution, personalization, cross-sell, upsell and sales management.

The transformation also extends beyond marketing.

Forrester argues that CSPs are increasingly pushing AI across network, engineering, IT, business, service and marketing operations as operators look for new sources of growth beyond traditional connectivity.

This creates an environment in which marketing AI is becoming part of a larger enterprise AI architecture rather than a standalone martech application.

Market Landscape

Gartner's 2026 Magic Quadrant includes 12 vendors in the CSP AI-enabled marketing and sales market, including Amdocs, AsiaInfo, Beyond Now, Cerillion, Comviva, Etiya, Flytxt, Huawei, Oracle, Salesforce, Tecnotree and Whale Cloud.

Gartner's accompanying Critical Capabilities research evaluates the market across use cases such as AI marketing campaigns, agentic multichannel campaigns, agentic microsegmentation, marketing-spend optimization, customer insights, sales-performance intelligence, competitive-intelligence agents and predictive growth and retention.

That breadth shows how quickly CSP AI is moving beyond isolated chatbot or copilot deployments toward systems embedded in commercial decision processes.

Strategic Outlook

Flytxt's latest positioning reflects a wider transition in enterprise AI: the movement from tools that assist employees toward systems designed to coordinate decisions and actions around defined business outcomes.

For CSP marketing organizations, the practical challenge will be balancing autonomy with governance. AI systems that influence pricing, customer retention, targeting or sales execution require access to sensitive data and clear boundaries around what they can change without human intervention.

Niya-X's combination of domain-specific AI, agentic execution and privacy-oriented architecture places Flytxt within that emerging enterprise model. The company's Gartner recognition provides additional visibility, but the more significant development is the market's growing focus on AI systems that can connect customer signals directly to commercial decisions.

Top Insights

• Flytxt's Niya-X combines causal reasoning, counterfactual simulation and agentic execution for CSP marketing, sales and growth decisions.
• Gartner's 2026 research identifies agentic automation, predictive growth and autonomous customer engagement as key developments in CSP AI marketing.
• The platform extends beyond individual AI tasks by connecting customer and market signals with decisions across the commercial lifecycle.
• Privacy-preserving federated learning is positioned as a way to apply enterprise AI while addressing CSP data-governance requirements.
• The CSP market is moving toward AI systems that integrate marketing automation, sales intelligence, personalization and predictive growth rather than isolated AI tools.

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Neato is expanding its e-commerce services around a premise that is becoming increasingly important for consumer brands: producing more content with AI does not necessarily solve the harder problem of turning that content into commerce.

The e-commerce acceleration company has appointed Nicole Lenora as Creative Director and launched Neato Shop Studio, a dedicated content and commerce environment designed to connect social video production with marketplace performance.

The move comes as social platforms increasingly combine entertainment, creator recommendations and shopping. TikTok Shop, for example, allows sellers to connect products with in-feed videos, LIVE shopping and creator affiliate activity, compressing the path between product discovery and purchase.

From Content Production to Commerce Infrastructure

Neato's announcement reflects a broader change in how brands are approaching creative operations.

Generative AI has made it easier to produce variations of advertising assets, product videos and other marketing content. But increasing production volume creates another challenge: deciding which concepts deserve investment, adapting them for different commerce environments and connecting creative performance with actual sales outcomes.

Neato says Shop Studio is intended to address that gap by supporting short-form product storytelling, unboxings, creator-led education, affiliate content and live shopping.

The company is also connecting the studio to its broader e-commerce operation, which includes marketplace management, performance marketing, direct-to-consumer growth, fulfillment and profitability operations.

That integration is significant because creative teams can potentially receive marketplace performance feedback rather than treating content production as a separate workflow.

Nicole Lenora Adds Creative Leadership

The appointment of Nicole Lenora gives the initiative a dedicated creative leadership layer.

Lenora joins Neato as Creative Director with more than two decades of experience across creative direction, entertainment, branded content and commercial production. The company says she has more than 18 years of video-production leadership experience and is a Gold Telly Award-winning creative professional.

Her remit is broader than producing individual assets. Neato says she will help build creative systems, teams and content strategies intended to support higher-volume commerce operations.

That distinction matters as brands increasingly need content that can move between social discovery, creator ecosystems and shoppable experiences.

TikTok Shop Changes the Creative Workflow

TikTok Shop illustrates why commerce platforms are forcing creative and e-commerce operations closer together.

The platform supports product discovery and transactions through videos, LIVE content and creator showcases. Its affiliate model also allows creators to promote products and earn commissions, making creators part of the commercial distribution infrastructure rather than simply an awareness channel.

For brands, this changes the creative brief.

A video needs to capture attention, communicate product value and provide a natural path toward purchase. Content can also be tested and iterated against commercial outcomes.

Recent industry data points toward the same convergence. MADA estimates that U.S. TikTok Shop generated $10.6 billion in sales across 27 categories during January through July 2026, with affiliate creators accounting for an estimated 74% of revenue. The figures are modeled estimates rather than official TikTok reporting, so they should be treated accordingly.

AI Raises the Bar for Creative Strategy

The expansion is happening against a wider AI-driven change in commerce.

Meta has described a shift toward scroll-led shopping in which AI, creators, short-form video and messaging increasingly influence the journey from discovery to transaction. Its 2026 India research also reported that Reels and creator content are becoming significant components of product discovery and purchase decisions.

YouTube is similarly adding AI creator tools and shopping capabilities, illustrating how major platforms are bringing content production and commerce closer together.

The implication for brands is that creative teams increasingly need to understand distribution, commerce mechanics and performance measurement alongside traditional storytelling.

Market Landscape

Social commerce is becoming less about placing an advertisement inside a social feed and more about integrating discovery, content, creators and transactions into one workflow.

DHL eCommerce forecasts social commerce could reach $1 trillion by 2028 and reports that 64% of businesses surveyed plan to increase social selling over the next five years.

That environment creates demand for infrastructure that can connect creative production with marketplace operations.

Neato's Shop Studio is positioned in that intersection. Rather than operating as a standalone production studio, it is being integrated with the company's marketplace and performance capabilities.

Strategic Outlook

The next phase of AI-assisted e-commerce creative is likely to be defined less by how many assets a brand can generate and more by how effectively teams connect creative experimentation with commercial feedback.

For consumer brands, the operational challenge is building a repeatable system in which creators, production teams, marketplace operators and performance marketers can work from the same objectives.

Neato's investment in both creative leadership and Shop Studio reflects that direction. AI can increase production capacity, but human creative judgment, platform-native storytelling and performance data remain important in deciding what content should be produced, where it should appear and how it should contribute to commerce.

Top Insights

• Neato is connecting creative production with marketplace operations, reflecting the growing convergence of content, social commerce and e-commerce performance.
• Shop Studio supports short-form video, unboxings, creator education, affiliate content and live shopping across Neato's broader commerce infrastructure.
• Nicole Lenora's appointment adds dedicated creative leadership as brands face pressure to produce more content without sacrificing storytelling quality.
• TikTok Shop and similar platforms increasingly connect creator content directly with product discovery and transactions, shortening the path from attention to purchase.
• AI increases content-production capacity, but performance data and human creative strategy remain important for deciding which commerce content scales.

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Website privacy and analytics teams often know that a cookie or tag is present without having a reliable record of why it exists, who owns it or whether it was ever formally approved. ObservePoint is targeting that gap with a new Cookie and Tag Database designed to turn website scanning data into a structured governance resource.

The database is built directly into ObservePoint's website-scanning platform and lets teams search for cookies and tags identified during scans. According to the company, each entry includes what the technology does, its vendor, commonly assigned consent category, risk level and supporting references. ObservePoint says the records are reviewed by human experts rather than being generated dynamically by an AI system.

The company announced the product on September 23, positioning it around a recurring problem for enterprise marketing, analytics, privacy and compliance teams: identifying unknown website technologies and determining whether they are still appropriate to run.

From Cookie Discovery to Governance

Finding an unfamiliar cookie is only the first step.

A scan may reveal a cookie or tag whose name provides little indication of its purpose. Teams then have to identify the vendor, determine what information the technology handles, establish the appropriate consent category and decide whether it should remain active.

ObservePoint's database is intended to shorten that investigation by associating each record with a plain-language description, vendor information, consent classification and risk assessment. The company says the underlying dataset draws on more than a decade of scanning experience across real websites rather than being assembled solely from vendor documentation.

That distinction matters because website technology stacks frequently change. Marketing campaigns introduce new tags, analytics platforms are replaced, advertising integrations are added and legacy technologies can remain active long after the original team that implemented them has moved on.

Adding Accountability to Every Cookie and Tag

The more significant enterprise feature may be the governance layer built around the database.

ObservePoint says customers can attach internal information to individual cookies and tags, including a legal approver, technical maintainer, product owner and review date. Custom fields can also be added to reflect an organization's own policies and processes.

That moves cookie management beyond identification.

Instead of maintaining a separate spreadsheet containing ownership and approval information, organizations can associate governance records with the technologies discovered on their websites. For privacy and compliance teams, this can create a more structured record for recurring reviews and audits.

The distinction is particularly relevant as consent frameworks become more detailed. IAB Europe's Transparency & Consent Framework, for example, provides standardized mechanisms for communicating information about vendors, purposes and user consent choices across the digital advertising ecosystem. Its current framework includes a Global Vendor List and technical specifications for consent management.

Why Tag Governance Is Becoming More Complex

Cookie management is no longer limited to checking a handful of first-party cookies.

Modern websites can run analytics, advertising, personalization, conversion measurement, customer-data and other third-party technologies simultaneously. Google Tag Manager, for example, supports consent controls that determine how tags behave according to user choices, including categories such as advertising, analytics, functionality, personalization and security storage.

That makes knowing what is running increasingly important alongside knowing whether a user has consented.

Google's consent-mode documentation similarly emphasizes that websites need to obtain consent, communicate the consent state and ensure tags behave according to that choice.

A database that connects discovery with ownership and review information therefore addresses an operational layer between website scanning and consent management.

Human Curation Versus Automated Classification

ObservePoint is also making human curation part of the product's positioning.

The company says every database definition is reviewed by an expert before being made available to customers. Its CTO Dave Smith argues that years of observing cookies and tags operating on real enterprise websites provide context that cannot necessarily be derived from vendor documentation or an AI-generated classification.

That does not eliminate the need for organizations to make their own legal or compliance determinations. Consent requirements can vary by jurisdiction and implementation, and IAB Europe explicitly notes that its framework does not replace individual participants' legal responsibilities.

Instead, the database provides a documented starting point for teams investigating what their digital properties actually load.

Market Landscape

The launch reflects a broader convergence between digital analytics, privacy operations and marketing technology governance.

Consent-management platforms help organizations capture and communicate user choices, while tag-management systems control when technologies execute. Website scanning provides another layer by identifying what is actually present on a digital property.

ObservePoint is positioning its database between those functions: discovery becomes searchable intelligence, and intelligence can then be connected to organizational accountability.

For enterprise organizations operating large portfolios of websites, that connection could be particularly useful. A cookie inventory becomes more actionable when teams can associate each technology with a vendor, risk classification, owner, approval history and review date.

Strategic Outlook

The practical value of cookie governance increasingly lies in maintaining an accurate record over time.

Websites change continuously, making one-time audits insufficient for organizations with large or frequently updated digital estates. A governance system that combines automated discovery with human-reviewed definitions and internal ownership records could make recurring reviews easier to manage.

The larger trend is toward treating website technology as governed infrastructure. Analytics tags, advertising pixels and cookies are no longer isolated marketing components; they can influence measurement, personalization, advertising and privacy obligations across the customer journey.

ObservePoint's new database is an attempt to make that technology layer more transparent—and more accountable—inside the same environment used to discover it.

Top Insights

• ObservePoint's Cookie and Tag Database connects website scanning with searchable information about cookie purpose, vendor, consent category and risk.
• Human-reviewed definitions are positioned as an alternative to relying exclusively on vendor documentation or real-time AI-generated cookie classification.
• Enterprise teams can associate cookies and tags with legal approvers, technical maintainers, product owners and review dates for governance.
• Consent frameworks and tag-management systems increasingly require organizations to understand not only what technologies run, but how they respond to user choices.
• The product turns cookie discovery into an ongoing governance workflow rather than a one-time technical audit.

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Email deliverability is becoming an increasingly important part of business communication infrastructure as companies rely on website contact forms, transactional notifications and email campaigns to connect with customers.

InboxAlly, an email deliverability platform, is highlighting the risks businesses face when messages generated through website forms fail to reach the intended recipient. While marketing teams commonly monitor campaign inbox placement, operational emails triggered by contact forms can receive less attention despite their direct connection to customer inquiries and potential sales opportunities.

The underlying issue is not simply whether an email was sent. Deliverability determines whether the recipient's mail system accepts the message and where that message ultimately lands, including the primary inbox, spam folder or another filtered location.

Why Contact-Form Emails Can Disappear

Website contact forms often create an immediate expectation: a prospect submits an inquiry and expects a business to respond.

If the notification reaches a spam folder—or fails to be accepted by the recipient's mail infrastructure—the company may never realize that a prospective customer attempted to make contact.

InboxAlly spokesperson Vivian Bastos says businesses need to treat authentication and sender reputation as core elements of reliable communication rather than purely technical configuration tasks.

Several factors can influence the outcome, including domain reputation, sender reputation, authentication configuration and recipient engagement.

For businesses, that means email deliverability extends beyond newsletter campaigns. Contact-form notifications, password resets, customer-service messages and other transactional communications can also depend on the underlying email infrastructure being configured correctly.

SPF, DKIM and DMARC Form the Foundation

Email authentication is one of the most important technical components of deliverability.

SPF (Sender Policy Framework) allows a domain to specify which mail servers are authorized to send messages on its behalf.

DKIM (DomainKeys Identified Mail) adds a cryptographic signature that receiving systems can use to verify that a message was authorized by the sending domain and has not been altered in transit.

DMARC (Domain-based Message Authentication, Reporting and Conformance) builds on SPF and DKIM by allowing domain owners to specify how receiving systems should handle messages that fail authentication checks.

Google and Yahoo have also increased authentication requirements for bulk senders, making proper SPF, DKIM and DMARC configuration increasingly important for organizations sending significant volumes of email. Google's sender guidelines require bulk senders to authenticate email and meet additional requirements around spam rates and unsubscribe mechanisms. (support.google.com)

Yahoo similarly requires bulk senders to implement email authentication and maintain appropriate sending practices. (senders.yahooinc.com)

Deliverability Is Also a Reputation Problem

Authentication alone does not guarantee inbox placement.

Receiving systems also evaluate signals associated with the sender and domain, including engagement and sending behavior. A technically authenticated email can still be filtered if the broader reputation of the sender is poor.

This makes monitoring important for businesses that depend heavily on email-generated leads.

InboxAlly offers tools including automated email warmup, inbox-placement testing, sender-reputation scoring and authentication monitoring. These capabilities are designed to help organizations identify potential deliverability problems before they affect business communication.

The company's emphasis on sender reputation reflects a broader change in email infrastructure: deliverability is increasingly a continuous operational concern rather than a one-time DNS configuration task.

Contact Forms Need Testing, Too

A common weakness in website operations is assuming that a successful form submission means the notification workflow has also succeeded.

A form can technically accept a customer's information while the resulting notification is misconfigured, sent from an unauthenticated domain or filtered by the recipient's mail system.

Businesses can reduce that risk by regularly testing contact forms, checking notification addresses and reviewing email authentication records.

Using a professional domain-based email address rather than a generic consumer mailbox can also improve consistency across business communication and simplify authentication management.

DNS records should likewise be monitored after changes to email providers, website infrastructure or marketing platforms.

Market Landscape

Email has become a more complex infrastructure layer as businesses use multiple platforms to send messages. A website may generate notifications through one provider while marketing automation, CRM, customer support and transactional systems use other sending services.

That fragmentation can make domain authentication and reputation management more difficult.

The shift toward stricter sender requirements from major mailbox providers is also raising the technical baseline for organizations that send email at scale. Google says bulk senders can face enforcement when messages fail authentication requirements or exhibit high spam rates. (support.google.com)

For marketing and IT teams, this makes deliverability a shared responsibility rather than a problem limited to email marketers.

Strategic Outlook

The business impact of email deliverability is ultimately tied to the messages a company cannot afford to miss.

A newsletter landing in spam may reduce campaign engagement. A contact-form notification that never reaches a sales representative can potentially mean a missed customer inquiry.

That makes authentication, reputation monitoring and routine testing important parts of the broader digital customer journey.

As businesses add more automated email workflows, the distinction between marketing infrastructure and core business communication is becoming less clear. Deliverability therefore needs to be considered at the website, CRM, marketing automation and transactional-email levels—not only when a campaign is scheduled.

Top Insights

• Email deliverability affects more than marketing campaigns, with contact-form notifications and transactional messages also dependent on reliable email infrastructure.
• SPF, DKIM and DMARC provide core authentication mechanisms that help receiving systems verify legitimate messages from business domains.
• Sender reputation and recipient engagement can influence inbox placement even when authentication is configured correctly.
• Major mailbox providers have introduced stronger requirements for bulk senders, increasing the importance of authentication and responsible sending practices. (support.google.com)
• Regular contact-form testing can help businesses identify notification failures before they result in missed customer inquiries.

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10Fold has launched MetricsMatter 5.0, an expanded communications intelligence platform that combines earned media, social, digital, competitive, website and AI-search data to give B2B marketing and communications teams a broader view of brand visibility and business performance.

The platform reflects a shift in B2B measurement as marketers increasingly have to track not only traditional search rankings and media coverage, but also whether companies are being surfaced and cited by generative AI systems.

MetricsMatter 5.0 tracks brand and competitor visibility across five AI platforms identified by 10Fold as ChatGPT, Google Gemini, Google AI Mode, Google AI Overviews and Perplexity. It also introduces metrics designed to assess the breadth, consistency and freshness of a company's earned-media presence.

The launch comes shortly after 10Fold published research showing that 58% of surveyed B2B technology marketing and communications leaders include an AI-search or LLM-visibility platform in their integrated reporting. The study, conducted by Sapio Research among 400 B2B technology marketing and communications leaders, also found that only 35% had fully integrated reporting and 38% connected communications and visibility metrics to pipeline or revenue influence.

From Communications Reporting to Intelligence

Traditional PR and communications dashboards tend to measure outputs such as media mentions, potential reach, social engagement and share of voice.

MetricsMatter 5.0 attempts to connect those measurements with competitive context and AI discovery.

The platform provides clients with continuously updated data and allows them to compare performance across competitors and time periods. 10Fold also says customers can connect additional marketing-program data, giving communications teams the ability to examine relationships between visibility and broader business indicators.

That distinction matters as marketing leaders face growing pressure to explain what communications activity contributes to business performance.

10Fold's own research illustrates the measurement gap. While 87% of respondents said their CEO or board generally trusts metrics aligned with business outcomes, only 38% reported connecting communications and visibility metrics to pipeline or revenue influence.

The implication is that the next generation of communications analytics will need to do more than count coverage. It will need to help marketers understand which visibility signals correlate with meaningful business activity.

New Metrics Target AI-Readiness

MetricsMatter 5.0 adds three measurements intended to evaluate the quality of earned visibility rather than simply its volume.

Coverage Diversity Index examines the distribution of coverage across media categories and sources, helping identify whether a brand's authority is concentrated in a narrow group of publications.

Message Inclusion Analysis measures how consistently priority messages appear in earned coverage. That can help communications teams identify which positioning themes are being repeated by external sources and which require additional reinforcement.

Recency Index evaluates the freshness and continuity of relevant coverage, distinguishing sustained visibility from a profile built primarily on older articles.

Together, the metrics move communications measurement closer to an authority model: who is talking about a company, what they are saying, how recently they are saying it and how broadly those signals are distributed.

AI Search Adds a New Visibility Layer

The more significant change is the platform's treatment of AI-generated answers as another discovery environment.

MetricsMatter 5.0 measures overall AI visibility, performance against selected prompts, changes over time, citations and sources influencing AI responses, and AI-driven website referral traffic.

This reflects a broader change in B2B search behavior. Gartner's 2026 research says AI is increasingly influencing B2B buying and recommends that marketers treat AI answer engines as part of a broader search strategy rather than viewing AEO as a replacement for traditional SEO.

Gartner has also published a Market Guide specifically addressing answer-engine visibility tools, noting that brands increasingly need to understand their visibility in LLM-powered search and discovery environments.

For communications teams, that creates a new measurement question: a company can earn substantial media coverage without necessarily knowing whether those sources influence the answers prospective buyers receive from AI systems.

MetricsMatter is designed to connect those two layers.

Competitive Intelligence Moves Into the Same Dashboard

The platform also combines social performance with earned-media, digital and competitive data.

Metrics include follower growth, engagement, click-through rates and performance over time, while 10Fold says customers can examine campaign correlations and compare competitive performance.

This approach addresses a persistent problem in marketing analytics: channel-specific dashboards can show what happened within an individual channel without explaining how different visibility activities interact.

By combining the data, 10Fold is positioning MetricsMatter as a decision-support layer rather than simply a reporting application.

The company's Salesloft customer example reinforces that positioning. Salesloft's Jason Beck said MetricsMatter 5.0 provides visibility into both traditional Share of Voice and AI-oriented Share of Answer. That remains a customer statement rather than independent validation of the platform's measurement methodology.

Market Landscape

The expansion of AI-generated search is creating demand for measurement systems that can capture visibility outside conventional search-result pages.

Gartner says AI answer engines create new risks and opportunities for B2B brands, including the need to monitor how companies are represented in AI-generated answers and address potential hallucination or inaccurate-brand-information risks.

At the same time, 10Fold's research suggests measurement adoption is moving faster than measurement integration. More than half of surveyed leaders said they track AI search visibility or brand citations, while only 35% reported fully integrated reporting across earned, paid, social, content and digital channels.

That gap creates an opening for platforms that combine visibility data across multiple channels and connect it with competitive and business context.

Strategic Outlook

MetricsMatter 5.0 signals a broader evolution in B2B marketing analytics: visibility is becoming multidimensional.

Media coverage, social engagement, website activity, conventional search and AI-generated answers increasingly overlap in the buyer journey. Measuring each channel separately can leave marketers without a clear view of how external authority translates into discoverability and, ultimately, commercial impact.

The challenge for platforms such as MetricsMatter will be proving that these visibility metrics are not merely new dashboard numbers. Their long-term value will depend on data quality, transparent methodologies, meaningful competitive comparisons and credible connections between visibility changes and business outcomes.

For B2B communications leaders, however, the direction is clear: AI visibility is becoming part of the measurement stack alongside traditional media and digital performance.

Top Insights

• MetricsMatter 5.0 combines earned media, social, digital, competitive and AI-search data into a single communications intelligence environment for B2B teams.
• New Coverage Diversity, Message Inclusion and Recency metrics shift measurement beyond media volume toward authority, consistency and freshness.
• The platform tracks brand and competitor visibility across ChatGPT, Gemini, Google AI Mode, AI Overviews and Perplexity.
• 10Fold's research found 58% of surveyed B2B technology marketing leaders already include AI-search or LLM visibility platforms in integrated reporting.
• Only 38% of respondents connected communications and visibility metrics to pipeline or revenue influence, highlighting the measurement challenge the platform is designed to address.

Get in touch with our MarTech Experts.

Braze is expanding its AI portfolio with four capabilities designed to move customer engagement beyond automated content generation and toward continuous decisioning, campaign quality assurance, agentic workflows and conversational experiences.

The customer engagement platform announced BrazeAI Decisioning Studio Go, Agentic Standards, BrazeAI Operator Connect and Conversational Agents at Forge 2026. The products are currently in beta, with Decisioning Studio Go and Agentic Standards expected to become generally available in October 2026.

Together, the releases position Braze as an increasingly agentic layer within the marketing technology stack. The company is giving AI responsibility not only for creating or recommending content, but also for deciding how campaigns should run, checking whether they meet organizational requirements and executing actions from external AI environments.

AI Decisioning Moves Beyond A/B Testing

The most significant expansion is Decisioning Studio Go, a self-service version of Braze's AI decisioning technology.

Traditional marketing experimentation typically evaluates a limited number of predefined combinations. Braze says Decisioning Studio Go can continuously test combinations involving message variants, timing, days of the week and frequency at an individual-customer level.

The company says an agent can evaluate more than one billion combinations simultaneously and make individualized decisions within marketer-defined guardrails.

That changes the role of experimentation. Instead of marketers manually determining which variants should be tested and then applying winning rules to broader audiences, an AI agent can continuously optimize decisions for individual recipients.

Braze says Decisioning Studio Go is designed to be deployed without technical teams or professional services, potentially bringing continuous decisioning to marketing teams that previously could not operationalize sophisticated optimization models.

The product is expected to become generally available on October 14, 2026.

Campaign QA Becomes an Agentic Workflow

Braze is also applying AI to a less visible but operationally important part of marketing: quality assurance.

Agentic Standards reviews campaigns and Canvases for issues involving campaign setup, copy, links, personalization, compliance, naming conventions and brand requirements. Marketers define the rules, after which the system produces pass, warning or fail results.

The more consequential feature is its connection with BrazeAI Operator. When permitted, Operator can help implement corrections, while human approval remains available as a control point.

For enterprise marketing organizations managing multiple markets, brands and regulatory requirements, automated QA could address one of the bottlenecks created by faster campaign production.

Sportsbet, a Braze customer, said the system has already identified issues including missing unsubscribe links and eligibility segments. That is a customer-reported example rather than an independently validated performance measurement.

Braze Becomes Accessible From AI Tools

Operator Connect extends Braze's AI capabilities outside the Braze interface.

The company says marketers and developers can access Braze intelligence and perform actions from AI environments including Claude, ChatGPT, Snowflake Cortex Code and Microsoft Copilot, alongside other MCP-compatible tools.

This is significant because enterprise AI adoption is increasingly moving toward systems that can call external tools rather than simply answer questions.

Under the new model, a marketer could ask an AI tool to analyze recent Braze campaign performance, develop a campaign plan and then move toward execution without repeatedly switching applications.

Braze says actions remain authenticated through the individual user's identity and governed by existing Braze permissions. That permission-aware approach is important as AI agents move from generating recommendations toward taking actions in production systems.

Stitch, a Braze partner, says its early work with Operator Connect has produced speed-to-market improvements of up to 90%. That figure is a partner-reported claim rather than an independent benchmark.

Conversational Agents Add a New Engagement Layer

Braze is also extending AI into direct customer interaction with Conversational Agents.

The beta capability supports WhatsApp, SMS, RCS and web experiences, allowing agents to answer questions, interpret customer intent and recommend products using brand-specific guidance, knowledge sources and customer context.

This moves conversational AI closer to the core customer-engagement platform. Rather than treating chat as a separate support application, Braze is positioning conversations as another stage of the marketing journey—from discovery and consideration through conversion.

The challenge will be balancing responsiveness with accuracy, brand controls and appropriate escalation when an AI agent cannot confidently resolve a request.

Market Landscape

The announcements reflect a broader shift in marketing technology from AI-assisted creation toward AI-assisted execution.

Research firm Gartner has forecast that organizations will increasingly use AI agents across business workflows, while warning that agent adoption alone does not guarantee productivity gains. Data quality, governance, workflow integration and human oversight remain important components of enterprise deployment.

That context matters for customer engagement platforms. Generative AI can produce copy relatively easily; autonomous decisioning and execution require access to reliable customer data, business rules, permissions and measurable objectives.

Braze's architecture attempts to combine those elements inside a single engagement layer while also exposing capabilities through external AI tools.

Strategic Outlook

Braze's latest AI releases point toward a customer engagement model in which AI participates throughout the marketing lifecycle: deciding what to send, checking whether it is safe and compliant, executing the campaign and responding conversationally to customers.

The strategic change is therefore less about any individual AI feature and more about the increasing automation of the operating layer surrounding customer engagement.

For enterprise marketers, the important questions will be whether these agents improve incremental business outcomes, how effectively organizations can govern autonomous actions, and whether AI-driven personalization remains explainable and controllable at scale.

Top Insights

• Braze is extending AI from content assistance into decisioning, campaign QA, workflow execution and customer conversations across its engagement platform.
• Decisioning Studio Go applies continuous AI optimization to message, timing and frequency decisions at individual-customer level rather than relying solely on traditional A/B tests.
• Agentic Standards introduces automated campaign QA covering setup, personalization, compliance and brand requirements before customer communications are delivered.
• Operator Connect brings Braze capabilities into Claude, ChatGPT, Copilot and other AI environments, while retaining user-level permissions for agent actions.
• Conversational Agents extend customer engagement into WhatsApp, SMS, RCS and web, connecting natural-language interactions with customer context and product information.

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Landbase has launched GTM-3 Omni, an AI model designed to automate go-to-market discovery, qualification and outreach, while publishing a benchmark that tests whether agentic prospecting can produce more precise target lists than conventional data and filtering workflows.

The company's AI Lab tested Landbase alongside Clay, Apollo and ZoomInfo using 26 list-building prompts, identical qualification criteria and the same judging process. According to Landbase's published results, GTM-3 Omni achieved 76.7% precision, compared with 47.9% for Clay, 36.0% for Apollo and 23.2% for ZoomInfo. Landbase says its model produced the most precise result on 18 of the 26 prompts.

Those figures are significant, but they should be viewed in context: the benchmark was commissioned and published by Landbase itself. It is evidence of the company's stated performance under its chosen test conditions, rather than an independent industry benchmark.

From Filters to Agentic Qualification

The central product change in GTM-3 Omni is the move from filter-based prospecting toward natural-language reasoning.

Traditional prospecting workflows generally require users to translate an ideal customer profile into fields such as company size, industry, geography, job title and technology usage. Landbase says GTM-3 instead interprets the buyer's criteria, explores potential candidates, evaluates them and removes prospects that do not satisfy the requirements.

That process takes approximately two minutes, according to the company, compared with seconds for conventional list generation.

Landbase argues that the additional processing time is intentional. Its premise is that GTM teams should optimize for the percentage of usable prospects rather than simply the number of records returned.

The company says its benchmark also produced a 76.1% first-page acceptance rate, measuring the proportion of the first 100 results that met the buyer's criteria.

Why List Precision Matters to Agentic GTM

The launch comes as AI moves deeper into sales research and prospecting. Gartner predicts that AI agents will outnumber sellers by 10 to 1 by 2028, while fewer than 40% of sellers are expected to say those agents have improved productivity. Gartner attributes the potential productivity gap partly to fragmented data, workflow integration and seller experience.

That distinction is important for agentic GTM systems. An agent can automate prospect research at scale, but poor underlying data or weak qualification logic can simply increase the volume of incorrect recommendations.

Landbase's approach therefore puts precision at the center of its product positioning.

The company's claim that customers have seen 2x-or-better campaign performance after improving list precision is based on Landbase customer observations, rather than the 26-prompt benchmark itself. That distinction matters when evaluating the commercial impact of the new model.

A Competitive Market Is Also Becoming Agentic

Landbase's benchmark arrives as competitors are also embedding AI into prospecting.

Clay now offers MCP access that allows sellers to use its prospecting, enrichment and message-generation capabilities through AI tools such as Claude and Codex. Its platform combines data from more than 200 providers with AI research and workflow automation.

Apollo has similarly expanded AI research and prospecting capabilities. Its AI tools can identify prospects, qualify leads, summarize account information and generate research using natural-language instructions.

That makes the competition broader than database size. Vendors are increasingly competing over how effectively AI can interpret an ICP, combine multiple data sources, evaluate relevance and move qualified records into downstream sales workflows.

GTM-3 Extends Into AI-Native Workflows

Landbase says GTM-3 Omni is available through its agentic CLI and can run inside Claude Code, Codex and Gemini CLI. The company is also targeting enterprise and private-equity deployments where GTM infrastructure may need to operate across multiple portfolio companies.

That architecture reflects a wider movement toward AI agents becoming interfaces for business software rather than simply features inside individual applications.

Gartner's 2026 research similarly identifies centralized data, workflow integration and seller judgment as important foundations for effective AI agents in sales.

For revenue teams, the practical question is therefore shifting from whether AI can generate prospect lists to whether an agent can reliably understand the company's definition of a qualified opportunity.

Market Landscape

The sales intelligence market is moving from static databases toward AI-assisted research, enrichment, prioritization and workflow execution. Clay supports natural-language prospecting and multi-provider enrichment, while Apollo combines AI research with prospect identification and qualification.

Landbase is positioning GTM-3 around another dimension: reasoning over the qualification criteria before returning the list.

That distinction could become increasingly important as GTM teams evaluate AI systems based on the quality of actions they enable rather than the size of their databases.

Strategic Outlook

GTM-3 Omni highlights a fundamental challenge for agentic sales technology: automation does not eliminate the need for accurate qualification; it makes qualification logic more consequential.

If an agent can reliably translate complex ICP requirements into account-level decisions, it can reduce the manual research burden on SDRs, marketers and RevOps teams. But the quality of those decisions still depends on data freshness, validation, governance and clearly defined business criteria.

Landbase's benchmark provides one data point in that transition. The broader market will ultimately need comparable, independently validated testing across more use cases, datasets and buying environments to determine how consistently agentic prospecting performs against established GTM platforms.

Top Insights

• Landbase's GTM-3 Omni uses agentic reasoning to evaluate prospects against natural-language qualification criteria instead of relying primarily on predefined filters.
• The company's benchmark reported 76.7% precision across 26 prompts, but the results remain vendor-published rather than independently validated.
• Clay and Apollo are also embedding AI research and natural-language prospecting into their GTM platforms, intensifying competition around agentic sales workflows.
• Gartner warns that agent proliferation alone will not guarantee productivity gains without connected data, workflow integration and effective seller experiences.
• The emerging competitive battleground is shifting from database volume toward qualification quality, contextual reasoning and the ability to turn prospect intelligence into GTM actions.

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