At NAB Show 2026, TwelveLabs introduced a new generation of video intelligence tools, signaling its transition from a model provider to a full-stack AI platform capable of transforming how enterprises and creators analyze and operationalize video data.

Video has long been one of the most valuable—and least accessible—forms of enterprise data. Despite representing the majority of digital content globally, extracting meaningful insights from video has traditionally required manual tagging, time-intensive review processes, and fragmented workflows. TwelveLabs is aiming to change that equation.

At NAB Show 2026, the company unveiled a series of product and ecosystem updates centered on a single idea: making video data as searchable, structured, and actionable as text. The announcement marks a strategic shift toward becoming a full-stack video intelligence platform, combining foundation models, applications, and ecosystem integrations.

At the core of this evolution is Pegasus 1.5, TwelveLabs’ latest video foundation model. The model introduces what the company describes as time-based metadata extraction—a capability that allows users to define specific criteria and automatically identify relevant segments within video content, complete with timestamps and structured outputs.

Unlike traditional video analysis tools, which rely heavily on pre-defined tags or manual annotation, Pegasus 1.5 dynamically interprets context. It can identify transitions, key events, and objects within a video in a way that mirrors how human editors review footage. This enables organizations to move from raw video to structured data with minimal intervention.

The implications are significant for industries that rely heavily on video. Media companies can transform archival footage into searchable assets, sports broadcasters can automatically index plays and highlights, and enterprises can eliminate manual tagging workflows that often consume thousands of hours annually. TwelveLabs claims that early benchmarks show Pegasus 1.5 outperforming competing models, including Google Gemini 2.5 Pro, in segmentation quality.

While the model itself represents a technical leap, TwelveLabs is also focusing on usability. The introduction of Rodeo, its first application-layer product, brings AI agents directly into the creative workflow. Designed as a co-pilot for video production, Rodeo allows users to search, edit, and assemble footage using natural language commands.

This approach reflects a broader trend in AI: moving from tools that assist with tasks to systems that actively participate in workflows. With Rodeo, AI agents can surface relevant clips, suggest edits, and help assemble sequences, reducing the time required to produce content from hours or days to minutes.

The company’s ecosystem strategy further extends its reach. Through a partnership with Autodesk, TwelveLabs’ video intelligence capabilities are now embedded into Autodesk Flow Capture, a platform used in film and television production. This integration introduces features such as Smart Search and Smart Actions, enabling production teams to locate specific moments within footage and automate media organization.

For creative industries, this integration addresses a longstanding challenge: the fragmentation of production and post-production workflows. By embedding AI directly into tools already used by professionals, TwelveLabs reduces the need for additional systems and simplifies adoption.

The broader significance of these announcements lies in how they reposition video within enterprise data strategies. Historically, video has been treated as unstructured data—valuable but difficult to analyze at scale. By enabling structured extraction and real-time interaction, platforms like TwelveLabs are effectively turning video into a first-class data source.

This shift aligns with wider trends across the AI and cloud ecosystem. Companies such as Microsoft, Google, and Amazon are investing heavily in multimodal AI, where systems can process text, images, and video simultaneously. Video intelligence is emerging as a key component of this evolution.

From a market perspective, the demand for video analytics is growing rapidly. According to Gartner, multimodal AI is expected to become a core capability for enterprise platforms, while IDC highlights the increasing importance of unstructured data in digital transformation initiatives.

TwelveLabs’ strategy reflects these dynamics. By combining advanced models with application-layer tools and ecosystem integrations, the company is positioning itself as a comprehensive solution for video intelligence. This approach contrasts with competitors that focus primarily on either infrastructure or end-user applications.

For enterprises, the value proposition is straightforward. Faster access to video insights can improve decision-making, reduce operational costs, and unlock new use cases—from content monetization to compliance monitoring. For creators, the ability to interact with video through natural language could fundamentally change how content is produced and edited.

However, challenges remain. Scaling video intelligence requires significant computational resources, and ensuring accuracy across diverse content types is complex. There are also questions around data privacy and governance, particularly when dealing with sensitive or proprietary footage.

Even so, the direction is clear. As video continues to dominate digital content, the ability to analyze and act on it efficiently will become a competitive differentiator. TwelveLabs’ latest announcements suggest that the industry is moving closer to that reality.

Market Landscape

The video intelligence market is evolving alongside advances in multimodal AI. Gartner identifies multimodal systems as a key trend shaping enterprise AI adoption, while IDC emphasizes the growing role of unstructured data, including video, in analytics and automation.

Major technology providers such as Google, Microsoft, and Amazon are expanding capabilities in video and AI, increasing competition in this space. TwelveLabs’ full-stack approach positions it within a rapidly emerging category focused on operationalizing video data at scale.

Top Insights

• TwelveLabs launches Pegasus 1.5, introducing time-based metadata extraction that enables structured, searchable video data without manual tagging or re-indexing workflows.
• Rodeo brings AI agents into video production, allowing creators to search, edit, and assemble footage using natural language, significantly reducing production time.
• Integration with Autodesk Flow Capture embeds video intelligence into professional workflows, improving collaboration and efficiency in media production environments.
• The shift toward full-stack video intelligence platforms reflects growing demand for multimodal AI solutions capable of transforming unstructured video into actionable insights.

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BearingPoint has introduced GenAIQ, an agentic AI platform designed to help enterprises move beyond isolated generative AI pilots and deploy automation at scale across knowledge-intensive workflows.

As generative AI adoption accelerates, a familiar pattern is emerging across enterprises: widespread experimentation, but limited operational impact. BearingPoint is targeting that gap with the launch of GenAIQ, a platform aimed at turning AI experimentation into scalable, production-grade automation.

GenAIQ is built around the concept of agentic AI—systems that not only generate outputs but also execute multi-step tasks across business processes. Unlike traditional AI tools that operate in silos, agentic platforms orchestrate workflows, interact with enterprise systems, and deliver outcomes with minimal human intervention.

The challenge GenAIQ addresses is structural. Many organizations have deployed generative AI in narrow use cases—content generation, coding assistance, or customer service automation—but struggle to extend those capabilities across departments. Fragmented data, legacy IT systems, and regulatory requirements often slow adoption.

BearingPoint’s approach combines modular architecture with deep enterprise integration. GenAIQ connects to existing IT landscapes, enabling organizations to automate document-heavy workflows and knowledge-intensive processes without overhauling core systems. This integration layer is critical, as enterprise adoption depends not only on AI capabilities but also on compatibility with existing infrastructure.

At its core, GenAIQ offers a library of more than 60 industry-specific AI agents, each designed to handle distinct business tasks. These agents are accessible through an “agent store,” allowing organizations to deploy pre-configured workflows or customize them for specific use cases. This model reflects a broader shift in enterprise AI—from building models from scratch to assembling modular components that can be quickly deployed.

The platform supports a progression from task-level assistance to end-to-end automation. For example, an organization might begin by using AI to summarize documents or generate reports, then expand into fully automated workflows that handle data extraction, decision-making, and execution across systems.

This staged approach aligns with how enterprises typically adopt new technologies. According to Gartner, organizations that successfully scale AI tend to focus on incremental deployment, governance, and integration rather than isolated pilots. Meanwhile, IDC highlights that automation of knowledge work is a key driver of productivity gains in the next wave of digital transformation.

A notable aspect of GenAIQ is its emphasis on governance and transparency. As AI systems take on more responsibility in business processes, organizations face increasing pressure to ensure compliance, explainability, and control. GenAIQ incorporates mechanisms for monitoring agent behavior, tracking decisions, and maintaining auditability—features that are becoming essential for enterprise adoption.

This focus reflects broader concerns around AI deployment in regulated industries. Financial services, healthcare, and public sector organizations, in particular, require systems that can demonstrate accountability and align with compliance frameworks.

From a competitive perspective, GenAIQ enters a rapidly evolving market. Major technology providers such as Microsoft and Google are embedding generative AI into enterprise platforms, while software vendors like Salesforce are introducing AI-driven automation within their ecosystems. BearingPoint’s differentiation lies in its consulting-led approach, combining technology with implementation services and domain expertise.

This combination could be particularly relevant for organizations that lack in-house AI capabilities. By offering end-to-end support—from identifying use cases to deployment and scaling—BearingPoint positions GenAIQ as both a platform and a transformation framework.

The concept of an “agent store” also signals a shift toward ecosystem-driven AI adoption. Instead of relying on a single model or vendor, enterprises can select and deploy specialized agents tailored to their needs. This modularity not only accelerates implementation but also allows organizations to adapt as requirements evolve.

For enterprise marketing teams and operational leaders, the implications are significant. AI is moving beyond isolated productivity gains to become an operational backbone, capable of managing workflows across departments. Platforms like GenAIQ enable this transition by providing the infrastructure needed to coordinate multiple agents and processes.

However, scaling AI remains a complex undertaking. Data quality, change management, and integration challenges continue to pose barriers. Success will depend on how effectively organizations align technology with business objectives and governance frameworks.

GenAIQ’s launch underscores a broader industry transition—from experimentation to execution. As enterprises look to extract tangible value from generative AI investments, platforms that combine automation, integration, and control are likely to play a central role.

Market Landscape

The enterprise AI market is shifting toward agentic and workflow-driven systems. Gartner identifies autonomous and agent-based AI as a key trend, while IDC projects strong growth in AI-driven automation across knowledge-intensive industries.

Technology leaders such as Microsoft, Google, and Salesforce are embedding generative AI into enterprise platforms, increasing competition. BearingPoint’s GenAIQ enters this landscape as a consulting-led, modular solution focused on scaling AI adoption across complex environments.

Top Insights

• BearingPoint launches GenAIQ, an agentic AI platform designed to scale generative AI from isolated pilots into enterprise-wide automation across knowledge-intensive workflows.
• The platform’s 60+ industry-specific agents and modular architecture enable organizations to deploy AI incrementally, moving from task-level assistance to full process automation.
• Strong emphasis on governance, transparency, and integration addresses key barriers to enterprise AI adoption, particularly in regulated industries.
• GenAIQ reflects a broader shift toward agent-based AI systems that orchestrate workflows and deliver measurable operational impact across business functions.

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At Adobe Summit, Semrush unveiled a new Brand Visibility operating model designed to help enterprises navigate a rapidly shifting discovery landscape shaped by AI search, autonomous agents, and fragmented digital touchpoints.

The way brands are discovered online is undergoing a structural transformation. Traditional keyword-based search—long the foundation of digital marketing—is giving way to AI-driven discovery systems that surface answers, not links. In response, Semrush has introduced a new framework aimed at redefining how organizations approach visibility in this environment.

The company’s Brand Visibility framework positions discoverability as a measurable, orchestrated outcome rather than a byproduct of channel execution. It defines brand visibility as the extent to which a company is discoverable, accurately represented, and commercially actionable across both human-driven and machine-mediated environments.

At the center of this model is a new concept: Agentic Search Optimization (ASO). Unlike traditional SEO, which focuses on ranking web pages, ASO is designed to ensure that brands are recognized, interpreted, and selected by AI systems—including chatbots and autonomous agents—as they evaluate information and generate responses.

This shift reflects a broader change in user behavior. According to Gartner, traditional search volume is expected to decline by 25% by 2026, as users increasingly rely on AI-generated answers from platforms like ChatGPT and Google Gemini. In this context, visibility is no longer about appearing on a results page—it is about being embedded within the answer itself.

Semrush’s research highlights a critical challenge for enterprises: the “alignment gap.” While many organizations have invested heavily in digital marketing, few have adapted their operating models to account for AI-driven discovery. The result is fragmented execution across SEO, content, and AI initiatives.

The data underscores the issue. Only 22.6% of organizations have a unified process for managing content across traditional search and AI environments. Meanwhile, more than half of enterprise teams report being only partially aligned—or entirely siloed—when it comes to brand visibility strategy.

This lack of alignment has measurable consequences. Fully aligned teams are significantly more likely to report that their visibility efforts are actionable and measurable, while disconnected teams struggle to quantify performance in AI-driven channels.

To address this, Semrush is proposing a structured operating model built around orchestration rather than execution. The framework introduces a four-stage lifecycle: foundation, content, distribution, and feedback. Together, these stages create a continuous loop in which brand narratives are defined, deployed across channels, and refined based on performance signals.

A key element of this approach is the concept of a unified content supply chain. Instead of creating separate strategies for SEO, social media, and AI platforms, organizations define topics and messaging once and distribute them across all discovery surfaces. This consistency is critical for building authority in AI systems, which rely on patterns and signals across multiple sources to determine relevance.

The framework also introduces a new organizational role: the Brand Visibility Orchestrator. This role is designed to bridge the gap between strategy and execution, ensuring that brand narratives remain consistent across channels and that performance data is fed back into decision-making processes.

This reflects a broader trend in enterprise marketing. As the number of channels and platforms increases, coordination becomes more complex. Companies such as Adobe and Salesforce have already begun integrating AI-driven insights into their marketing clouds, emphasizing the need for unified data and workflows.

Semrush’s approach extends this idea into the realm of discovery itself. By treating visibility as a system-level outcome, the company is encouraging organizations to rethink how they measure success. Metrics such as share of voice, AI citations, and sentiment within generated responses are becoming as important as traditional rankings and traffic.

Early results suggest the potential impact of this shift. Semrush reports that it was able to nearly triple its own AI share of voice—from 13% to 32%—within a month by applying the principles outlined in its framework. While internal benchmarks should be interpreted cautiously, they highlight the potential gains from coordinated execution.

From a market perspective, the introduction of a formal operating model signals a maturation of AI-driven marketing strategies. According to IDC, organizations that successfully integrate AI into their marketing operations are more likely to achieve measurable improvements in efficiency and customer engagement.

However, implementing such a model is not without challenges. It requires changes in organizational structure, investment in new tools, and a shift in mindset from channel-specific optimization to system-wide orchestration. For many enterprises, this represents a significant transformation.

Still, the direction is clear. As AI systems become the primary interface for information discovery, brands must adapt to a world where visibility is determined not just by algorithms, but by how effectively they communicate across interconnected platforms.

Semrush’s Brand Visibility framework is an attempt to provide a roadmap for that transition—one that aligns strategy, technology, and execution in an increasingly complex digital ecosystem.

Market Landscape

The shift toward AI-driven discovery is reshaping the marketing technology landscape. Gartner forecasts a decline in traditional search, while IDC emphasizes the growing importance of AI in customer engagement and marketing operations.

Technology leaders such as Google, Microsoft, and Amazon are investing heavily in AI-driven discovery systems. Meanwhile, platforms like Adobe and Salesforce are embedding AI into marketing workflows, increasing the need for unified visibility strategies.

Top Insights

• Semrush introduces a Brand Visibility framework that shifts marketing from channel-based execution to orchestrated discovery across AI and traditional search environments.
• Agentic Search Optimization (ASO) emerges as a new discipline focused on ensuring brands are selected and represented within AI-generated answers and autonomous systems.
• Research reveals significant alignment gaps in enterprise marketing teams, with most organizations lacking unified processes for managing visibility across search and AI channels.
• The framework’s lifecycle and new “Brand Visibility Orchestrator” role reflect a broader move toward integrated, system-level marketing operations in the AI era.

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At Adobe Summit, Siteimprove unveiled a significant update to its AI search capabilities, introducing advanced Answer Engine Optimization (AEO) insights designed to help enterprises measure and improve visibility across generative search and answer engines. The move signals a broader shift in how brands compete for attention as AI-powered interfaces redefine digital discovery.

The rise of generative AI is rapidly changing how users find and consume information online. Instead of scrolling through search engine results pages, buyers increasingly rely on AI-generated summaries, conversational assistants, and answer engines to make decisions. Siteimprove’s latest update to its Siteimprove.ai Search solution is built around that reality.

The new AEO insights capability is designed to help enterprise marketing teams understand how their content surfaces inside AI-generated responses—what is often referred to as “AI visibility.” In practical terms, this means tracking whether a brand is cited, how frequently it appears in answers, and how it is positioned against competitors within AI-driven outputs.

Answer Engine Optimization is emerging as a critical extension of traditional SEO. While SEO focuses on ranking web pages, AEO focuses on being included in AI-generated answers. This distinction matters because large language models and AI search interfaces—such as those from Google and Microsoft—increasingly act as intermediaries between brands and users. If a company’s content is not structured or trusted enough to be referenced, it risks disappearing from the decision-making journey altogether.

Siteimprove’s platform attempts to close that gap. The updated AI Visibility dashboard aggregates performance metrics across both traditional search and AI-driven environments. These include AI citation frequency, share of voice within generated answers, sentiment analysis of brand mentions, and even revenue attribution linked to AI visibility.

That last metric—tying AI visibility to revenue—is particularly notable. Enterprise marketing teams have historically struggled to connect SEO performance with direct business outcomes. By extending attribution models into AI-generated experiences, Siteimprove is aligning AEO with measurable ROI, a requirement for large organizations managing complex digital ecosystems.

CEO Nayaki Nayyar framed the launch as a response to changing buyer behavior. According to IDC, 79% of B2B buyers are expected to rely on AI tools for decision-making by 2028. That statistic underscores a fundamental shift: discovery is no longer confined to search engines but distributed across AI interfaces.

The competitive implications are significant. Platforms like Adobe, Salesforce, and Amazon are all investing heavily in AI-driven marketing and customer experience tools. Siteimprove’s approach differentiates itself by focusing specifically on content intelligence and governance—ensuring that enterprise content is accurate, structured, and accessible enough to be surfaced by AI systems.

This is where AEO becomes less about keywords and more about content quality and context. AI systems prioritize signals such as authority, clarity, and trustworthiness. Siteimprove’s platform addresses these factors by integrating accessibility, SEO, and content analytics into a unified workflow. The result is a system that not only optimizes for search engines but also for AI interpretation.

Another notable aspect of the launch is its alignment with emerging industry frameworks. Gartner recently identified AEO as a growing category, emphasizing the need for organizations to measure presence in AI-generated answers and conversational interfaces. Siteimprove’s inclusion in that market guide suggests that AEO is transitioning from an experimental concept to an operational priority.

For enterprise marketing teams, the implications are immediate. Content strategies must now account for how information is parsed and synthesized by AI models. This includes structuring content for machine readability, maintaining factual accuracy, and ensuring consistent brand messaging across digital channels.

The integration of AEO insights into a broader “agentic content intelligence” platform also reflects a wider trend toward automation. Siteimprove.ai already includes conversational analytics and keyword intelligence agents, and the addition of AEO capabilities suggests a future where AI systems continuously monitor and optimize content performance without manual intervention.

In that sense, Siteimprove is positioning itself not just as an analytics provider, but as a control layer for enterprise content in AI-driven environments. As generative search becomes more dominant, that control could prove essential for brands trying to maintain visibility and influence in increasingly opaque discovery systems.

Market Landscape

The AEO category is still in its early stages but is gaining traction as generative AI adoption accelerates. According to IDC, nearly four out of five B2B buyers will depend on AI-assisted decision-making by 2028, fundamentally reshaping digital engagement models. Meanwhile, Gartner highlights that traditional SEO metrics alone are no longer sufficient, pushing enterprises toward hybrid strategies that combine SEO, AEO, and content intelligence.

Vendors across the martech ecosystem are responding. Adobe and Salesforce are embedding AI into customer experience platforms, while Microsoft and Google continue to evolve AI-native search interfaces. Siteimprove’s focus on visibility analytics positions it within a niche but increasingly strategic layer of this ecosystem.

Top Insights

• Siteimprove introduces advanced AEO insights, enabling enterprises to measure AI visibility across generative search platforms, including citation frequency, share of voice, and brand sentiment in AI-generated responses.
• The platform connects AI visibility metrics to revenue attribution, helping marketing teams quantify the business impact of appearing in answer engines and conversational AI interfaces.
• With IDC predicting 79% of buyers will rely on AI for decisions by 2028, AEO is becoming a core enterprise marketing strategy beyond traditional SEO rankings.
• Integration into Siteimprove.ai reflects a shift toward agentic content intelligence, where AI systems continuously optimize content for both search engines and generative AI environments.

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European martech group Candid has appointed Andrew Shaw as Chief Product & Technology Officer (CPTO), signaling a renewed push to scale its AI-powered Live Marketing platform amid rising enterprise demand for integrated marketing infrastructure.

Candid’s decision to bring Andrew Shaw into a group-level CPTO role reflects a broader shift underway across the marketing technology landscape: the convergence of product, data, and AI into unified platforms designed for enterprise-scale execution.

Shaw steps into the role with immediate responsibility for product strategy, technology infrastructure, and platform scalability across Candid’s portfolio of agencies operating in the Netherlands and the United Kingdom. His appointment comes as the company sees growing demand for its proprietary Live Marketing platform—an integrated system that connects strategy, media, creative, and campaign execution within a single AI-enabled environment.

At its core, Live Marketing is designed to unify fragmented marketing workflows. Instead of relying on disconnected tools for analytics, campaign management, and creative production, platforms like Candid’s aim to consolidate these capabilities into a continuous, data-driven feedback loop. This approach mirrors a broader industry movement toward “full-stack martech,” where execution and intelligence are tightly coupled.

Shaw’s background suggests a focus on product-led scaling. He previously served as Director of Product at OLX in Amsterdam, where he worked on large-scale digital platforms, and held a senior product role at adidas in Germany. His experience in managing complex, international product ecosystems is likely to shape how Candid evolves its platform architecture.

The timing of the appointment is notable. Enterprise marketing teams are under increasing pressure to deliver measurable outcomes while navigating an expanding set of channels, data sources, and AI tools. According to Gartner, by 2026, organizations that fail to integrate AI into their marketing operations risk falling behind competitors in both efficiency and customer engagement. Meanwhile, McKinsey estimates that AI-driven marketing and sales use cases can unlock up to 10–20% revenue uplift in certain sectors.

Against this backdrop, Candid’s strategy appears to center on differentiation through integration. While major platforms from Salesforce and Adobe dominate enterprise martech stacks, smaller players are carving out space by offering more flexible, modular, or specialized solutions. Candid’s Live Marketing platform positions itself as a hybrid—combining agency services with proprietary technology.

This hybrid model is gaining traction. Enterprises increasingly expect agencies not just to execute campaigns, but to provide technology-enabled insights and scalable infrastructure. In this context, the role of a CPTO becomes central—not only overseeing engineering, but aligning product development with client outcomes.

Shaw’s mandate to bring the platform to “enterprise scale” suggests a focus on reliability, interoperability, and performance—areas where many emerging martech platforms struggle. Scaling an AI-driven system across multiple markets and clients requires robust data pipelines, standardized architectures, and strong governance frameworks.

It also requires a clear approach to AI integration. Platforms like those from Google and Microsoft are rapidly embedding generative AI into marketing workflows, from content creation to campaign optimization. For Candid, maintaining a competitive edge will depend on how effectively it can incorporate similar capabilities while preserving differentiation.

Another challenge lies in unifying agency operations under a single platform. Candid operates a group of agencies, each with its own processes and client relationships. Shaw’s role will involve standardizing these operations without sacrificing the flexibility that clients expect from agency partnerships.

From an enterprise perspective, the appeal of a platform like Live Marketing is straightforward: fewer silos, faster execution, and better visibility into performance. By integrating strategy, creative, and media within a single system, organizations can reduce friction and improve decision-making speed.

Yet adoption will depend on trust. Enterprises need assurance that such platforms can handle large-scale data, comply with regulatory requirements, and integrate with existing systems. This is where Shaw’s experience in global product environments could prove critical.

Candid’s move also highlights a broader trend toward “platformization” in marketing. As the industry shifts away from point solutions, companies are increasingly investing in platforms that can orchestrate the entire customer journey. This trend is reshaping not only technology stacks but also organizational structures, with roles like CPTO becoming more prominent.

In that sense, Shaw’s appointment is less about a single executive hire and more about positioning. It signals Candid’s intent to compete not just as an agency group, but as a technology-driven platform provider in an increasingly AI-centric market.

Market Landscape

The global martech market continues to expand as enterprises invest in AI-driven platforms to manage complex customer journeys. Gartner estimates that marketing technology now accounts for a significant share of marketing budgets, while McKinsey highlights that AI adoption in marketing is accelerating across industries.

Large ecosystems led by Salesforce, Adobe, and Microsoft dominate enterprise deployments. However, emerging players like Candid are focusing on integrated, AI-powered platforms that combine services and technology—offering an alternative to traditional martech stacks.

Top Insights

• Candid appoints Andrew Shaw as CPTO to scale its AI-powered Live Marketing platform, reflecting a broader shift toward integrated, platform-based martech strategies for enterprise clients.
• Shaw’s experience at OLX and adidas positions him to lead product innovation and infrastructure scaling across Candid’s multi-agency ecosystem and international markets.
• The Live Marketing platform aims to unify strategy, media, and creative workflows, addressing enterprise demand for streamlined, data-driven marketing execution.
• Competition with major platforms like Salesforce and Adobe highlights the growing importance of AI integration and platform scalability in the evolving martech landscape.

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5WPR has released a new research report, The SaaS Content Paradox 2026, highlighting a growing disconnect in B2B marketing: SaaS companies are spending up to $1.09 million annually on content marketing, yet fewer than one-third report meaningful results.

The economics of content marketing in SaaS are becoming increasingly difficult to ignore. Despite years of investment and a well-established belief in content as a growth engine, new research from 5WPR suggests that most B2B software companies are failing to translate spend into measurable business impact.

The report, based on aggregated data from organizations including Content Marketing Institute, HubSpot, and SaaS Capital, paints a picture of a channel caught between potential and underperformance. While content marketing can deliver up to 702% ROI from SEO over three years and contributes to nearly 44.6% of B2B SaaS revenue via organic channels, only 29% of companies consider their strategies effective. Nearly half do not measure ROI at all.

This gap—between what content marketing can achieve and what it actually delivers—defines what 5WPR calls the “SaaS content paradox.”

At the center of the issue is a shift in how buyers discover and evaluate software. Traditional SEO strategies, built around capturing informational search traffic, are losing effectiveness as AI-powered search interfaces reshape user behavior. Platforms from Google and Microsoft increasingly provide direct answers within search results, reducing the need for users to click through to websites. In March 2025, only 40.3% of U.S. Google searches resulted in a click—an inflection point for content-driven acquisition models.

For SaaS marketers, this shift exposes a structural flaw: much of their content is optimized for algorithms rather than buyers. Informational blog posts, long a staple of SEO strategies, are precisely the type of content most vulnerable to being absorbed into AI-generated answers.

The report identifies five systemic failures driving this inefficiency. One of the most critical is the reliance on surface-level metrics such as traffic and lead volume. Without clear attribution models, marketing teams often optimize for visibility rather than revenue. This is particularly problematic given that SEO-generated leads convert at significantly higher rates—51% from MQL to SQL compared to 13% overall—yet many organizations lack the infrastructure to track this difference.

Another overlooked dimension is expansion revenue. According to the report, as much as half of new annual recurring revenue (ARR) in high-performing SaaS companies comes from existing customers. Yet content strategies remain overwhelmingly focused on acquisition. Customer education, onboarding, and retention content—arguably more valuable in the long term—receive comparatively little investment.

Distribution is another weak point. While most SaaS companies concentrate on owned channels such as blogs and email, buyer research increasingly happens elsewhere. Peer communities, private Slack groups, forums like Reddit, and AI-generated recommendations are shaping initial vendor shortlists. In fact, the report notes that 90% of B2B SaaS deals go to vendors already on a buyer’s shortlist—often formed before direct engagement with brand-owned content.

The role of AI in content marketing further complicates the picture. While 87% of marketers now use AI tools for content creation, only a small fraction apply AI to strategic functions such as audience analysis, distribution optimization, or content planning. This imbalance risks flooding the market with undifferentiated content at a time when differentiation is becoming the primary driver of performance.

Case studies included in the report illustrate both failure and success scenarios. HubSpot is cited as a cautionary example, with a reported sharp decline in organic traffic in late 2024 highlighting the vulnerability of keyword-driven content models in an AI-first search environment. In contrast, Zapier is presented as a model for content-led growth, having built a scalable acquisition engine through programmatic SEO aligned with user intent. Meanwhile, Ahrefs demonstrates the value of product-led content, with its YouTube strategy directly showcasing product capabilities while generating substantial organic reach.

For enterprise marketing teams, the implications are clear. Content marketing is not losing relevance, but its execution model must evolve. Strategies need to shift from volume-driven production to intent-driven content, from channel ownership to ecosystem distribution, and from isolated metrics to full-funnel attribution.

This transition aligns with broader trends across the martech landscape. Platforms from Salesforce and Adobe are increasingly integrating AI-driven analytics and customer data capabilities, enabling more precise measurement of content impact. At the same time, emerging tools are focusing on Answer Engine Optimization (AEO), helping brands maintain visibility within AI-generated responses.

The 5WPR report ultimately reframes content marketing not as a failing channel, but as a misaligned one. When executed with a clear understanding of buyer behavior, distribution channels, and measurement frameworks, it remains one of the most powerful drivers of SaaS growth. The challenge lies in bridging the gap between investment and impact—before inefficiencies become systemic.

Market Landscape

The findings come at a time when the global SaaS market is expanding rapidly, with increasing competition driving higher marketing spend. According to Gartner, marketing budgets are shifting toward digital channels, while McKinsey & Company notes that companies leveraging advanced analytics and AI in marketing can achieve significantly higher ROI.

At the same time, AI-driven search from Google and Microsoft is redefining discovery, forcing SaaS companies to rethink SEO, content strategy, and distribution. The rise of community-led research and alternative discovery channels is further fragmenting the buyer journey.

Top Insights

• SaaS companies spend up to $1.09 million annually on content marketing, yet only 29% report strong performance, highlighting a growing efficiency gap in B2B content strategies.
• AI-driven search is reducing click-through rates, making traditional SEO-focused content less effective and increasing the importance of Answer Engine Optimization and brand visibility in AI-generated responses.
• Nearly half of SaaS marketers do not measure content ROI, limiting their ability to connect content efforts with pipeline impact and revenue generation.
• High-performing companies like Zapier and Ahrefs demonstrate that intent-driven, product-led content strategies outperform volume-based approaches focused on keyword traffic.
• Distribution channels are shifting toward communities and AI platforms, requiring SaaS brands to rethink how and where they engage buyers during the research phase.

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As artificial intelligence reshapes how audiences discover brands, Brandpoint is positioning itself at the center of a new category: AI visibility measurement. The company’s latest launch, Brandpoint Optimize, aims to give PR and MarCom teams a way to track how their content performs not just in search rankings, but within AI-generated answers that increasingly define digital discovery.

The shift toward AI-driven search is no longer theoretical. With platforms like Google rolling out AI Overviews and conversational search experiences, and competitors such as Microsoft embedding generative AI into Bing and enterprise tools, the mechanics of brand discovery are undergoing a structural change. Traditional metrics—clicks, impressions, and even rankings—are becoming incomplete indicators of performance.

Brandpoint’s new platform is designed to address that gap. In simple terms, Brandpoint Optimize measures whether a brand’s content is being surfaced inside AI-generated responses, not just whether it ranks on a results page. That distinction is becoming critical as more users receive answers directly from AI systems without clicking through to websites.

According to the company, the platform connects content distribution, earned media coverage, and performance analytics into a single workflow. PR teams can publish content at scale, track pickup across media networks, and evaluate how that presence translates into AI visibility—an emerging metric that reflects whether a brand is referenced or cited by AI systems.

The timing is notable. Industry estimates suggest that nearly 60% of searches now result in zero clicks, as users increasingly rely on summarized answers. Data from Gartner indicates that by 2026, traditional search traffic could decline by as much as 25% due to the rise of AI assistants and generative interfaces. That shift puts pressure on marketing and communications teams to rethink how visibility is defined—and measured.

Brandpoint is effectively arguing that the new battleground is not search ranking, but AI inclusion.

“AI visibility” in this context refers to how often and how prominently a brand appears in AI-generated summaries, recommendations, and conversational outputs. It’s a metric that blends elements of SEO, digital PR, and content authority—yet until now has lacked standardized tools for measurement.

The company claims its advantage lies in its distribution network and historical data. With decades of experience in content syndication and a network of high-authority media placements, Brandpoint can map how content flows from distribution to editorial pickup—and ultimately into AI systems that rely on authoritative sources.

That closed-loop approach is significant. Competing platforms in the martech stack—such as analytics tools from Adobe or CRM-driven insights from Salesforce—typically focus on owned and paid media performance. They offer limited visibility into how earned media influences AI-generated outcomes.

Brandpoint’s model attempts to bridge that gap by tying earned media directly to measurable AI impact. For enterprise teams managing complex, multi-channel campaigns, this could provide a missing layer of intelligence: understanding not just where content is published, but how it shapes AI narratives about a brand.

The platform also introduces competitive benchmarking. Users can analyze how their AI visibility compares with competitors, offering insights into content gaps and positioning opportunities. This aligns with a broader shift toward predictive marketing analytics, where teams use data not only to evaluate past performance but to guide future strategy.

From an operational standpoint, the tool aims to simplify campaign planning. Instead of treating PR distribution, SEO, and analytics as separate functions, Brandpoint integrates them into a unified system. The result is a more continuous feedback loop—publish, measure, optimize—adapted to the dynamics of AI-driven discovery.

Still, the category itself is nascent. While Brandpoint positions itself as a first mover, the concept of AI visibility is likely to attract competition. Large martech vendors and search platforms are already investing heavily in AI analytics, and it remains to be seen how quickly standardized metrics will emerge.

What is clear is that the definition of “being found” is changing. In an environment where AI systems act as intermediaries between brands and audiences, visibility is no longer just about ranking—it’s about representation.

Brandpoint’s roadmap reflects that shift. The company plans to expand the platform with predictive insights, campaign simulation tools, and consumer intent data. These capabilities would move the product beyond measurement into decision-making—helping teams design campaigns optimized for AI discovery from the outset.

For PR and MarCom leaders, the implication is direct: success will increasingly depend on whether AI systems recognize and surface their brand as a credible source. Tools that quantify and influence that outcome may soon become as essential as traditional SEO platforms.

Market Landscape

The launch of AI visibility platforms signals a broader evolution in the martech ecosystem. As generative AI reshapes search and content consumption, vendors are racing to redefine analytics around AI-driven engagement rather than page-level interactions.

Research from Forrester highlights that enterprises are prioritizing AI-powered marketing intelligence to better understand customer intent across fragmented digital touchpoints. Meanwhile, platforms across the ecosystems of Google, Microsoft, Adobe, and Salesforce are converging toward unified data environments that combine content, analytics, and automation.

Brandpoint’s approach sits at the intersection of PR distribution and AI analytics—two areas that have historically operated independently. If the model gains traction, it could push the industry toward new standards for measuring brand authority in AI-generated environments.

Top Insights

• Brandpoint introduced an AI visibility platform that measures how brand content appears in AI-generated search responses, addressing a growing gap in traditional SEO and PR analytics frameworks.
• The launch reflects a major shift as zero-click searches dominate, forcing marketing teams to optimize for AI inclusion rather than just rankings and website traffic.
• Enterprise PR teams gain unified workflows combining content distribution, earned media tracking, and AI performance measurement within a single platform environment.
• Competitive benchmarking and predictive insights position the platform as a strategic tool for planning campaigns in AI-driven discovery ecosystems.
• The move signals the emergence of a new martech category focused on AI visibility, likely to attract competition from major platforms and analytics vendors.

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The structure of enterprise marketing teams is undergoing a quiet but profound transformation. At the upcoming Singapore B2B Marketing Summit, SailPoint and The Ortus Club are set to examine how artificial intelligence is redefining not just workflows, but the very composition of marketing organizations.

Artificial intelligence is no longer a tool layered onto marketing operations—it is becoming embedded within them. From generative content systems to automated campaign orchestration, AI is reshaping how marketing teams function, collaborate, and make decisions.

That shift is at the center of a keynote session titled “The AI Imperative: AI in B2B Marketing, Automation, and the AI Realism.” The discussion will focus on how enterprises are rethinking team structures as AI transitions from experimental deployments to operational infrastructure.

At its core, the question is straightforward: what does a marketing team look like when machines participate in execution?

The answer is less clear. While adoption is accelerating, organizational clarity is lagging. Many enterprises are still defining the boundaries between human-led strategy and machine-led execution. Tasks once handled by specialists—content creation, campaign optimization, data analysis—are increasingly shared with or delegated to AI systems.

This creates a hybrid operating model. In practice, marketing teams are evolving into environments where human expertise and AI-driven automation coexist. The shift mirrors broader changes across enterprise software ecosystems, particularly within platforms from Salesforce, Adobe, and Microsoft, all of which are embedding generative AI into marketing, analytics, and customer engagement tools.

But efficiency gains are only part of the story. The deeper challenge lies in governance.

As AI becomes integrated into everyday workflows, it introduces new layers of complexity around ownership, accountability, and control. Who is responsible for decisions made by AI systems? How should organizations audit automated outputs? And where should human oversight remain non-negotiable?

These questions are becoming increasingly urgent as AI systems take on more autonomous roles within marketing stacks.

SailPoint’s perspective highlights a less visible but critical dimension of this transformation: identity. As enterprises deploy more AI-driven tools, the number of “digital identities” within their environments expands. These identities are no longer limited to employees. They now include applications, automated workflows, and AI agents operating across systems.

Each of these entities requires access—sometimes to sensitive data, customer insights, or campaign infrastructure. Managing those permissions is emerging as a key leadership concern.

In simple terms, the more AI a marketing organization adopts, the more complex its identity ecosystem becomes.

This has direct implications for security, compliance, and operational integrity. Marketing teams, traditionally focused on engagement and growth, are now intersecting with identity governance and IT security in new ways. The boundary between marketing technology and enterprise infrastructure is blurring.

According to IDC, global spending on AI-enabled enterprise applications is expected to grow at double-digit rates through the decade, driven by automation and data-driven decision-making. Meanwhile, McKinsey & Company estimates that generative AI could automate up to 30% of work activities across industries, including marketing functions.

Those projections underscore the scale of the transition underway.

For marketing leaders, the challenge is not simply adopting AI, but deciding how it should be integrated into team structures. Some tasks are clear candidates for automation—data processing, reporting, and repetitive campaign execution. Others, such as brand strategy, creative direction, and ethical decision-making, remain firmly human-led.

Between those extremes lies a growing category of augmented work, where AI supports but does not replace human input.

This spectrum—automation, augmentation, and human control—is becoming a framework for redesigning marketing organizations. It requires new roles, new skill sets, and new management approaches. Data literacy, AI oversight, and cross-functional collaboration are quickly becoming core competencies.

The Singapore summit session aims to move beyond theory and examine how enterprises are navigating these decisions in practice. Leaders are expected to share how they are restructuring teams, redefining roles, and building governance models that can scale alongside AI adoption.

What emerges is a picture of marketing teams in transition. The traditional model—structured around channels, campaigns, and functional silos—is giving way to more fluid, technology-driven environments.

In this new model, AI is not just a tool. It is a participant.

And that changes everything—from how work is assigned to how success is measured.

Market Landscape

The evolution of AI-driven marketing teams reflects a broader shift across the martech ecosystem. Enterprise platforms are increasingly converging around automation, data integration, and AI-powered decisioning.

Vendors such as Salesforce, Adobe, and Microsoft are embedding AI capabilities directly into customer data platforms, marketing automation tools, and analytics suites. This integration is accelerating the move toward unified marketing infrastructures where workflows are orchestrated across systems rather than managed in isolation.

At the same time, identity and access management—an area traditionally led by IT—are becoming critical to marketing operations as AI agents and automated systems proliferate. Companies like SailPoint are positioning themselves at this intersection, where security, governance, and marketing technology converge.

The result is a redefinition of enterprise marketing: less about execution alone, and more about managing complex ecosystems of humans and intelligent systems.

Top Insights

• SailPoint and The Ortus Club highlight how AI is transforming marketing teams into hybrid environments where human expertise and machine-driven execution operate together across workflows and decision-making processes.
• The rise of AI introduces governance challenges around ownership, accountability, and control, forcing enterprises to rethink how decisions are made and monitored within automated marketing systems.
• Digital identities are expanding beyond employees to include AI agents and workflows, making identity management a critical component of modern marketing infrastructure and security strategy.
• Enterprises are adopting a three-tier model—automation, augmentation, and human control—to determine how AI should be integrated into marketing roles and responsibilities.
• The shift signals a long-term restructuring of marketing organizations, with new skills, roles, and cross-functional collaboration required to manage AI-driven operations effectively.

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