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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

Braze has announced the winners of its 2026 Torchie Awards, highlighting customer engagement programs that combine AI decisioning, personalization, creative execution and modern data infrastructure with measurable business outcomes.

The winners were announced at Forge 2026 in Las Vegas, where Braze is positioning AI-powered customer engagement as a major focus for marketers. The company's official Forge agenda includes sessions on AI decisioning, agentic marketing workflows, personalization and data-driven growth.

Rather than focusing solely on campaign creativity, this year's awards provide examples of how marketing organizations are applying technology to operational problems, from lifecycle optimization and loyalty growth to emergency communications and fragmented martech stacks.

AI Decisioning Moves From Experimentation to Execution

Compare the Market received the AI in Action Award after deploying BrazeAI Decisioning Studio for car insurance renewals in 57 days.

According to Braze, the program coordinated six customer-level decisions across more than 195 content modules, generating more than 1 million personalized experiences for a 34-million-person customer base. The company reported a 9.07% conversion increase against business as usual, later reaching 17%, while CRM's revenue contribution increased 55% over nine months despite 25% fewer sends.

The example illustrates a broader change in lifecycle marketing: AI is increasingly being applied to determine which message, offer or experience a customer should receive rather than simply generating marketing content.

Braze's Forge program separately features Compare the Market's migration from legacy CRM toward AI-driven engagement, including changes to its data foundation, lifecycle journeys and operating model.

Creativity Becomes a Data and Engagement Strategy

AB InBev's SAB/Brahma campaign with Zé Delivery won Campaign of the Year after connecting purchases with Brazilian football fandom. The campaign generated more than R21 million in investment for customers' selected football clubs and more than R30 million in incremental revenue, according to Braze.

Wendy's received Creative Excellence recognition for its Meal of Misfortune activation tied to Netflix's Wednesday. The experience extended into customer engagement channels and an interactive in-app tarot experience, producing 1.1 million new loyalty accounts, seven million tarot interactions and 311,000 incremental loyalty purchasers.

These examples point toward a more integrated definition of campaign performance, where creative concepts are connected directly to identity, loyalty, personalization and measurable customer behavior.

Customer Engagement Expands Beyond Marketing

Wealthsimple was named Collaborative Team of the Year for its Monthly Millionaire experience, which brought marketing and engineering teams together and attracted more than one million participants during its first month.

The Braze for Impact Award went to Ridery for an earthquake alert system developed in Venezuela. Connected to USGS seismic data, the system was built in less than 24 hours and designed to send location-specific alerts to customers in affected cities. Braze says it has since become permanent infrastructure for future earthquakes of magnitude 5.0 or greater.

The latter example is particularly significant for enterprise customer-engagement architecture because it demonstrates that messaging infrastructure can support operational and public-safety use cases alongside conventional marketing.

Data Infrastructure Becomes Part of the Engagement Stack

The partner awards also highlight the infrastructure underneath modern engagement programs.

Databricks was named Technology Partner of the Year following a zero-copy data-sharing integration developed with Braze. The companies reported more than 185 shared customers.

CACI and Legal & General won Transformation Partner of the Year for a customer engagement engine combining Snowflake, Tealium, Optimizely CMS and Braze. Braze reported increases in registrations and logins, meaningful customer actions and transfer completion rates following the transformation.

The 2026 Torchies therefore reflect an increasingly connected MarTech architecture: customer data, decisioning, content, orchestration and analytics are converging rather than operating as isolated systems.

Braze says the 2026 program received more than 230 submissions across 12 categories. The award program itself includes eight customer categories and four partner categories.

Market Landscape

Customer engagement platforms are increasingly competing on their ability to combine real-time customer data, AI decisioning, orchestration and experimentation. Braze's Forge agenda reflects that shift, with dedicated programming around agentic marketing workflows and AI-powered engagement.

For enterprise marketing teams, the practical implication is that AI adoption is becoming less about isolated content-generation tools and more about embedding decisioning and automation into existing customer journeys.

Strategic Outlook

The 2026 Torchie winners illustrate several directions shaping enterprise customer engagement: AI-assisted decisioning, personalized lifecycle orchestration, creative experiences tied to loyalty data, cross-functional marketing and engineering teams, and shared data infrastructure.

The common architectural thread is integration. As customer engagement platforms increasingly connect data, AI and execution, the competitive question is shifting from whether brands use AI to how effectively they can connect AI-driven decisions with customer context, experimentation and measurable outcomes.

Top Insights

• Braze's 2026 winners demonstrate how AI decisioning is moving beyond experimentation into personalized lifecycle marketing and measurable conversion programs.
• Compare the Market's implementation shows how AI can coordinate large numbers of customer-level decisions across content, journeys and audiences.
• Wendy's and AB InBev demonstrate how creative customer experiences increasingly depend on loyalty, behavioral data and engagement infrastructure.
• Ridery's earthquake-alert system shows how real-time engagement platforms can support operational communications beyond conventional marketing campaigns.
• Databricks and CACI highlight the growing importance of data infrastructure and technology integration behind modern customer engagement.

Get in touch with our MarTech Experts.

Informa TechTarget is opening its buyer-intelligence data to AI applications through a new Model Context Protocol (MCP) server, allowing B2B marketing and sales teams to bring person-level intent signals directly into tools such as Claude, ChatGPT, Microsoft Copilot, Gemini and custom AI systems.

The move changes where buyer intent data can be used. Rather than requiring GTM teams to work inside a dedicated intent-data interface, Informa TechTarget's Buyer Intelligence can now provide context inside AI-assisted workflows for tasks such as account analysis, sales battlecards and personalized outreach.

The company says its Buyer Intelligence is based on observed research behavior from business and technology decision-makers across its media and webinar properties. Those signals are intended to show what potential buyers are researching and which subjects may indicate emerging commercial interest.

From Intent Platform to AI Context Layer

The significance of the MCP integration is less about another interface and more about making proprietary buyer signals available to AI systems at the point where work is being performed.

MCP is an open protocol designed to connect AI applications with external data sources and tools. That architecture is becoming increasingly relevant as enterprises move AI beyond standalone chat experiences and into business workflows. TechTarget itself recently reported on Alteryx using an MCP server to connect enterprise data with AI applications and agents.

For Informa TechTarget customers, the practical application could be an AI-generated account strategy that incorporates current research behavior rather than relying only on CRM records or static firmographic information.

The company says users can create account battlecards, analyze markets and accounts, and develop personalized buyer messaging using Buyer Intelligence within their preferred AI environment.

That makes the data more actionable, but it also changes the requirements for intent platforms. AI-generated recommendations are only as useful as the signals supplied to them.

Buyer Intent Meets Agentic GTM

The launch arrives as B2B buying behavior is becoming more difficult for traditional marketing analytics to observe. Forrester's 2026 Buyer Insights research, based on more than 17,500 global buyers, says AI is reshaping how buyers research, evaluate and make purchasing decisions.

Forrester also reports that 94% of business buyers use AI during their buying process, while buying decisions increasingly involve large networks of internal and external stakeholders.

That creates pressure on GTM teams to understand buyer behavior before a prospect explicitly enters a conventional sales funnel.

Intent data can help fill part of that visibility gap, but Forrester cautions that organizations need to evaluate intent sources for signal accuracy, relevance, uniqueness and business impact rather than simply maximizing signal volume.

Informa TechTarget's MCP strategy therefore puts the emphasis on accessibility. Its Buyer Intelligence can be brought into CRM, marketing automation, ABM and modern data-stack environments, while the new MCP connection extends that accessibility into AI workflows.

Competing in the AI-Native GTM Stack

The competitive landscape is moving toward a more connected GTM architecture. Salesforce, Microsoft, HubSpot and other major platforms are embedding AI into sales and marketing workflows, while specialist intent-data providers compete on the quality and breadth of signals feeding those systems.

The differentiator is increasingly what happens after a signal is detected.

An intent platform that identifies an interested account can inform prioritization. An AI workflow with access to that signal can potentially turn it into an account summary, messaging recommendation, research brief or sales action without requiring a marketer to manually move information between systems.

Informa TechTarget is extending that model with its recently introduced Buyer Intelligence AI capabilities, including Pain Points, Pitch Assist and Persona and ICP Intelligence.

For enterprise marketing teams, the resulting architecture could mean treating intent data less as a reporting destination and more as an intelligence layer that supplies context to AI-powered GTM operations.

Market Landscape

B2B data platforms are increasingly becoming part of the infrastructure supporting AI-assisted revenue operations. Forrester's 2026 B2B CDP landscape identifies AI-augmented decisioning and coordinated revenue engagement as important applications for customer data platforms.

At the same time, Forrester says many organizations still struggle to extract broader value from intent data, particularly beyond identifying accounts that appear to be actively researching a purchase.

MCP adds another potential layer to this evolution by allowing external intelligence sources to become available within AI applications. The resulting competition is likely to focus not only on proprietary data but also on how easily that data can be incorporated into the workflows where marketers and sellers already operate.

Strategic Outlook

The Informa TechTarget launch reflects a broader change in B2B marketing technology: proprietary buyer data is increasingly being treated as context for AI rather than information that users must retrieve manually.

For GTM teams, the value will depend on signal quality, freshness, identity resolution, governance and how effectively AI applications translate those signals into useful actions.

If that connection works reliably, MCP could make buyer intelligence a more persistent part of AI-assisted account planning, personalization and sales execution rather than another destination marketers have to monitor.

Top Insights

• Informa TechTarget's MCP server brings person-level buyer intent into AI workflows, allowing GTM teams to use proprietary signals inside existing AI applications.
• MCP shifts intent data from a standalone destination toward contextual infrastructure that can inform account analysis, messaging and sales workflows.
• Forrester's 2026 research shows AI is increasingly embedded in B2B buying, increasing the importance of understanding buyer behavior beyond conventional website analytics.
• Intent-data providers face greater pressure to demonstrate signal accuracy and incremental value as AI systems consume multiple external data sources.
• AI-ready buyer intelligence could become an important component of agentic GTM stacks, connecting proprietary behavioral signals with automated research and engagement workflows.

Get in touch with our MarTech Experts.

Banks and credit unions are increasingly using generative AI to produce marketing content, but a new Cornerstone Advisors study suggests the industry is still at an early stage when it comes to AI systems that can actually execute marketing actions.

The report, “Speed Is a Revenue Strategy: How AI Agents Accelerate Bank Marketing,” commissioned by Persado, examines how financial institutions are approaching AI agents and identifies speed to market as an underdeveloped part of marketing performance. Cornerstone says the research focuses on bank and credit union marketing leaders and examines applications including compliance, customer-journey orchestration, content optimization and real-time segmentation.

The distinction between generative AI and agentic AI is central to the findings. Generative AI can help marketers create copy, ideas and other content. AI agents are designed to perform defined tasks or actions within specified controls, potentially shortening the distance between identifying a customer opportunity and responding to it.

From Content Generation to Marketing Execution

According to the research, 66% of bank and credit union marketing executives currently use generative AI for content creation, while only about one in 10 institutions have adopted agentic AI. Just 5% of respondents describe AI agents as a top strategic priority, while 47% classify agentic AI as an experimental or emerging investment area.

The gap highlights a broader issue with enterprise AI adoption. Producing a campaign asset is only one stage of financial-services marketing. Before a message reaches a customer, institutions may need audience validation, compliance review, approval, channel selection and journey coordination.

That process can be particularly consequential in banking, where marketing activity operates alongside regulatory requirements and strict controls around customer communications.

Cornerstone's research identifies campaign and journey orchestration as the AI-agent application with the highest perceived business impact at 27%, followed by offer and product recommendations at 25%.

Those use cases move AI beyond content production toward decision support and workflow execution.

Compliance Becomes Part of the AI Workflow

The report identifies compliance review as a significant obstacle to faster marketing execution, with some institutions taking four to six weeks to complete review cycles.

AI agents could potentially shorten parts of that process by checking content against predefined requirements before human approval. That does not eliminate regulatory responsibility; instead, it changes where automated systems can assist within an existing governance framework.

The distinction is important for financial institutions. Federal Reserve officials have emphasized that banks adopting AI should use appropriate risk-management frameworks and controls tailored to individual applications. In July, Federal Reserve Vice Chair for Supervision Michelle Bowman said financial institutions should add appropriate controls based on the risks associated with particular AI applications.

The regulatory environment is also evolving around technology providers. In September, U.S. banking regulators proposed updated third-party risk-management guidance as financial institutions manage increasingly complex relationships with technology vendors.

For marketing organizations, this means agentic AI cannot be evaluated solely as a productivity tool. Governance, auditability, permissions and accountability become part of the technology decision.

The Shift Toward Governed Autonomy

Despite limited current adoption, the research indicates that expectations are changing. Sixty-three percent of respondents said they expect their organizations to be somewhat or very comfortable allowing AI agents to execute marketing actions autonomously within approved boundaries within two years.

That creates a significant transition point for financial-services marketing teams.

The emerging model is not necessarily fully autonomous marketing. Instead, institutions can define what an agent is permitted to do, establish approval thresholds and use AI to handle repetitive steps while marketers and compliance teams retain control over higher-risk decisions.

For example, an agent could identify a customer segment, recommend an offer, check a campaign against predefined requirements and prepare a journey for approval. A human could then authorize the final activation.

Market Landscape

Financial institutions are moving toward AI adoption while regulators continue to emphasize responsible implementation. The Federal Reserve's 2026 guidance on model risk management narrowed the scope of its traditional model-risk framework and explicitly addressed the changing treatment of generative and agentic AI, reflecting the difficulty of applying older governance approaches to rapidly evolving technologies.

That environment creates a market opportunity for marketing platforms that combine AI capabilities with governance rather than treating compliance as a separate downstream process.

Persado's position in this market is centered on AI-generated and optimized customer communications, while broader enterprise platforms from companies such as Salesforce and Adobe are also developing AI capabilities across customer data, marketing automation and journey orchestration.

The competitive landscape is therefore shifting from AI that creates marketing assets toward AI that can operate within the marketing workflow.

Strategic Outlook

The Cornerstone research points to a measurable change in how financial-services organizations may evaluate AI marketing technology. Speed is becoming connected to the ability to identify a signal, determine an appropriate response and move through required controls without unnecessarily restarting the process at every stage.

For banks and credit unions, the next challenge is building the infrastructure and governance required to make that possible. Agentic AI may reduce execution time, but its usefulness will depend on clean customer data, clear permissions, reliable compliance controls and measurable attribution.

The result could be a marketing stack in which AI agents handle increasingly structured tasks while human teams retain responsibility for strategy, oversight and final decisions.

Top Insights

• Generative AI adoption in banking marketing is substantially ahead of agentic AI, revealing a gap between content production and automated marketing execution.
• Cornerstone respondents identify journey orchestration and product recommendations as leading agentic AI use cases, shifting attention toward customer-level actions.
• Compliance remains a major execution constraint, making governed AI review an important requirement for financial-services marketing automation.
• Most respondents expect greater comfort with bounded AI autonomy within two years, indicating a potential transition from experimentation toward controlled deployment.
• Financial institutions will need data, governance and attribution infrastructure alongside AI agents to translate faster marketing execution into measurable outcomes.

Get in touch with our MarTech Experts.