U.S. communications firm 5W Public Relations has been named a finalist in the 2026 North America edition of the SABRE Awards for its B2B marketing campaign with enterprise AI platform provider AISquared. The nomination highlights a communications strategy that helped elevate the AI infrastructure startup’s profile within the rapidly expanding enterprise artificial intelligence ecosystem.

Recognition from the North American SABRE Awards places the campaign among the public relations industry’s most visible examples of strategic B2B communications. Organized by PRovoke Media, the awards program honors campaigns that demonstrate measurable impact in branding, reputation management, and stakeholder engagement.

The finalist nomination recognizes a campaign titled “Putting AI Squared On The Map,” which focused on establishing AISquared as a visible voice in enterprise artificial intelligence discussions. The company develops software designed to operationalize AI systems within enterprise environments—particularly where machine learning models must integrate with complex data pipelines, compliance frameworks, and business workflows.

The campaign was led by 5W Public Relations, one of the largest independently owned public relations firms in the United States. According to the agency, the strategy emphasized executive thought leadership, targeted media outreach, and rapid-response commentary tied to evolving discussions around AI adoption in enterprise settings.

In practical terms, that meant positioning AISquared executives in conversations around workforce transformation, data infrastructure modernization, and the broader shift toward production-scale AI deployment.

Elevating Visibility in the Enterprise AI Market

Enterprise adoption of artificial intelligence has accelerated significantly over the past two years. Organizations across sectors are attempting to move beyond experimental machine learning projects toward operational AI systems that directly support decision-making, automation, and customer engagement.

This transition—from pilot projects to production AI—has created demand for infrastructure platforms that connect models with enterprise data environments. Companies such as Microsoft, Google, and Amazon have expanded their enterprise AI ecosystems, while startups and specialist vendors are focusing on orchestration, governance, and data integration layers.

AISquared positions its platform within this infrastructure category. The company’s software is designed to embed machine learning insights directly into operational workflows—such as CRM platforms, data dashboards, or internal applications—without requiring users to interact directly with model outputs.

For enterprise marketing and analytics teams, this type of integration can allow predictive models or AI-driven recommendations to appear directly inside tools already used by analysts and decision-makers.

Industry research suggests demand for this type of operational AI capability is rising rapidly. According to Gartner, more than 55% of enterprises are expected to move AI models into production environments by 2026, compared with fewer than 30% just a few years earlier.

Media Strategy and Campaign Execution

Within that evolving landscape, the communications campaign led by 5W focused on positioning AISquared executives as subject-matter experts in AI adoption challenges.

The program combined thought leadership placements, proactive media relations, and rapid commentary tied to breaking AI news cycles. The agency reported securing 165 media placements during 2025, with coverage appearing in outlets such as:

• The Wall Street Journal
• Fast Company
• Barron's
• Fortune

The approach relied heavily on “newsjacking,” a communications tactic in which companies respond quickly to emerging industry news with expert commentary.

In AI-related sectors—where regulatory debates, product launches, and ethical discussions frequently dominate headlines—timely commentary can significantly increase visibility for emerging technology vendors.

Business Impact and Growth Metrics

The visibility generated by the campaign coincided with a year of rapid expansion for AISquared.

According to the company, annual recurring revenue (ARR) grew by 1,100% in 2025, while net revenue retention exceeded 115%. The firm also reported expanding its customer base by four times across commercial, regulated, and federal sectors.

While growth metrics in emerging AI companies often reflect broader market demand, increased media exposure can play a meaningful role in enterprise sales cycles—particularly in categories where buyers rely heavily on credibility signals.

Enterprise technology procurement decisions frequently involve months of vendor evaluation, proof-of-concept testing, and executive approval. Media visibility and thought leadership can therefore function as an early trust-building mechanism.

The Role of PR in the AI Economy

The nomination also reflects a broader shift in how AI companies approach communications.

In earlier technology cycles, product announcements often drove media attention. In the current AI market, however, the conversation increasingly revolves around strategy, governance, and real-world impact.

As a result, companies that can articulate how AI fits into enterprise workflows—rather than simply highlighting algorithmic capabilities—tend to attract stronger engagement from business audiences.

That dynamic is particularly relevant in sectors intersecting with marketing technology and customer data infrastructure. Platforms used by marketing teams increasingly incorporate machine learning features ranging from predictive segmentation to automated content generation.

Large enterprise platforms such as Salesforce and Adobe have embedded AI capabilities across marketing clouds, analytics suites, and customer data platforms. As those ecosystems expand, specialized AI infrastructure vendors are emerging to bridge gaps between models, data, and operational tools.

Industry Recognition and Upcoming Awards

The winners of the 2026 North America SABRE Awards will be announced on May 5 during a ceremony at Cipriani 42nd Street in New York.

The awards are widely considered among the public relations industry's most prestigious recognitions, evaluating campaigns across criteria including creativity, execution quality, and measurable business outcomes.

For agencies working in technology communications, finalist recognition often reflects the increasing importance of strategic storytelling in shaping how emerging technologies—particularly artificial intelligence—are understood by enterprise audiences.

Market Landscape

The enterprise AI market is rapidly evolving as organizations seek ways to integrate machine learning models into real-world operations rather than isolated data science projects.

Research from IDC estimates that global spending on AI-centric systems could exceed $300 billion by 2026, driven largely by enterprise automation, predictive analytics, and AI-powered decision tools.

In marketing technology specifically, AI is becoming a core component of modern digital infrastructure—from predictive audience segmentation to automated campaign optimization. As a result, vendors that can bridge AI models with operational data systems are increasingly positioned as strategic infrastructure providers.

For communications agencies working with AI startups, this environment creates opportunities to frame companies not simply as software vendors but as contributors to broader discussions about workforce transformation, governance, and enterprise data strategy.

Top Insights

• 5W PR’s campaign for AISquared earned finalist recognition at the 2026 SABRE Awards, highlighting how strategic communications can elevate emerging enterprise AI platforms within competitive technology markets.

• The campaign centered on executive thought leadership and rapid-response media engagement, positioning AISquared within broader discussions around AI adoption, enterprise data infrastructure, and workforce transformation.

• AISquared reported significant business growth during the campaign period, including 1,100% ARR growth and a fourfold increase in customers across commercial and regulated markets.

• Enterprise demand for operational AI infrastructure is accelerating, as companies move from experimental machine learning models toward fully integrated AI-powered workflows.

• The nomination underscores the growing role of strategic PR in the AI economy, where visibility, credibility, and narrative positioning increasingly influence enterprise adoption decisions.

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Creative AI startup RYA has introduced RYA 2.0, a new version of its audience intelligence platform designed to help marketers predict the potential impact of advertising campaigns before they reach the market. The platform combines proprietary audience data, AI-assisted creative generation, and a new evaluation model called RYA Score to help brands assess whether a marketing concept is likely to resonate with audiences before investing in production or media spend.

As generative AI tools reshape the marketing industry, a growing challenge has emerged: the risk that AI-generated content begins to look increasingly similar across brands. With many marketing teams relying on the same underlying models powering platforms like ChatGPT and other generative tools, differentiation has become harder to achieve.

RYA 2.0 attempts to address that challenge by focusing not just on content generation but on predictive audience intelligence—an approach that aims to evaluate the cultural and emotional resonance of marketing ideas before campaigns are launched.

Developed by RYA, the platform is positioned as a creative AI partner designed specifically for marketing teams. Unlike general-purpose generative AI systems, the platform combines AI models with proprietary audience datasets built over nearly a decade.

From Creative Agency to AI Platform

RYA originated within a creative agency environment, where marketers faced a recurring challenge: translating audience insights into effective creative campaigns quickly.

Traditional campaign development cycles often require weeks of research, strategic planning, and creative exploration. According to the company, early versions of the RYA platform already reduced this timeline from six to eight weeks of strategy work to roughly a day.

With the release of RYA 2.0, the company is expanding beyond insight generation into predictive campaign evaluation.

The central feature of the new platform is the RYA Score, a proprietary framework designed to measure the likely cultural impact of a marketing concept. The system evaluates creative ideas across two key dimensions:

• Radical (R-Score) — measuring how bold or attention-grabbing an idea is
• Acceptable (A-Score) — measuring how likely the idea is to remain aligned with audience expectations and brand safety

The combination produces an overall RYA Score, which attempts to forecast how audiences will react to a campaign before it is launched.

For marketers, the concept addresses a long-standing tension in advertising strategy: campaigns must push boundaries enough to attract attention, yet remain relatable enough to avoid alienating audiences.

The Data Behind the Platform

One of the platform’s key differentiators is its underlying data model.

While many generative AI tools rely heavily on large internet datasets scraped from publicly available sources, RYA says its platform is trained on proprietary audience passion data collected directly from real participants.

The dataset is built from weekly surveys of roughly 1,000 individuals, conducted by PhD researchers and designed to track evolving cultural interests, emotional triggers, and emerging trends across audience segments.

This approach allows the platform to map creative ideas against behavioral signals rather than simply generating content from statistical patterns in web data.

The system also incorporates insights from creative professionals across multiple industries, including leaders from agencies such as:

• BBDO
• Ogilvy
• Wieden+Kennedy

By combining expert interviews with audience behavior data, the platform attempts to bridge the gap between creative intuition and predictive analytics.

Introducing RYA Chat

Another major component of the new platform is RYA Chat, a conversational interface designed to guide marketers through campaign development.

The tool functions as a context-aware AI workflow where users can explore audience trends, test creative concepts, and refine campaign messaging in real time.

Through the interface, marketing teams can:

• identify emerging cultural trends tied to passion-based audience segments
• develop campaign positioning frameworks
• generate creative ideas across multiple channels
• test and refine messaging based on predictive audience feedback

The result is a continuous dialogue between marketers and the AI system, designed to simulate a collaborative creative process rather than a one-time content generation task.

Why Predictive Creativity Matters

The release of RYA 2.0 comes at a moment when AI adoption across marketing departments is accelerating rapidly.

Research from Gartner indicates that more than 70% of marketing organizations are experimenting with generative AI for content creation and campaign development.

Yet the widespread adoption of similar AI tools has also introduced new strategic risks.

When marketing teams rely on the same AI models trained on the same datasets, creative outputs can converge—leading to campaigns that feel interchangeable across brands.

This phenomenon has become particularly visible in digital advertising and social media campaigns, where AI-generated visuals, copy, and storytelling patterns increasingly resemble each other.

RYA’s approach reflects a broader shift in the AI landscape: competitive advantage is moving from access to algorithms toward access to proprietary data.

A Changing Role for Agencies and Marketing Platforms

The introduction of platforms like RYA 2.0 also highlights an evolving relationship between marketing agencies and technology platforms.

Traditionally, agencies built value through creative strategy and campaign execution. Increasingly, however, agencies are developing technology-driven platforms that package their expertise into scalable tools.

Large marketing ecosystems—including platforms from Adobe and Salesforce—have already embedded AI across marketing automation, customer data platforms, and analytics tools.

But those systems tend to focus on optimization and personalization, rather than evaluating the core creative concept behind a campaign.

Platforms like RYA are attempting to move AI further upstream in the marketing process—into the earliest stages of idea generation and creative strategy.

The Future of Audience Intelligence

If predictive creative tools prove reliable, they could fundamentally reshape how marketing campaigns are developed.

Instead of relying primarily on instinct, focus groups, or post-launch analytics, brands could evaluate multiple campaign concepts before committing to production budgets.

This approach aligns with broader industry trends around data-driven marketing strategy.

According to research from Statista, global spending on AI in marketing is expected to grow significantly through the end of the decade as companies invest in predictive analytics, automation, and AI-assisted creativity.

For marketers, the long-term goal is clear: reduce uncertainty while preserving creative originality.

RYA’s leadership believes the platform’s evolution reflects a shift in how AI should support creative work.

As CEO and co-founder Mark Himmelsbach explained, the company’s journey from agency to platform revealed an unexpected insight: the most valuable asset was not the creative tools themselves but the data infrastructure built around audience behavior and cultural trends.

In a marketing environment where generative AI tools are rapidly commoditizing, platforms grounded in proprietary intelligence may become the next competitive frontier.

Market Landscape

The marketing technology sector is undergoing a rapid transformation as artificial intelligence becomes embedded across campaign strategy, audience analytics, and content production.

Research from IDC estimates that global spending on AI technologies could surpass $300 billion by 2026, with marketing and customer experience platforms among the fastest-growing segments.

Within this environment, audience intelligence platforms are evolving beyond traditional segmentation tools. Modern systems increasingly combine behavioral data, predictive analytics, and generative AI to guide both strategic planning and creative execution.

The emergence of platforms like RYA 2.0 signals a new category within martech: predictive creative intelligence, where AI helps evaluate not just audience targeting but the cultural impact of campaign ideas themselves.

Top Insights

• RYA 2.0 introduces a predictive audience intelligence platform, enabling marketers to evaluate the likely cultural impact of creative campaigns before investing in production or media spending.

• The platform’s proprietary RYA Score evaluates campaign ideas across radical and acceptable dimensions, helping marketing teams balance bold creativity with audience resonance.

• RYA combines generative AI with proprietary audience passion data collected weekly from surveyed participants, creating a dataset designed to capture real-world behavioral signals.

• RYA Chat provides a conversational AI interface for campaign development, allowing marketers to explore audience insights, refine creative strategies, and generate multi-channel campaigns in real time.

• The launch reflects a broader shift in marketing AI toward proprietary data advantage, as generative models become increasingly commoditized across the industry.

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Communications firm Zilker Media has introduced a new AI Discoverability Ecosystem, a communications framework designed to help companies improve their visibility and credibility within AI-powered search environments. The initiative reflects a growing shift in digital marketing strategy as generative AI systems increasingly influence how users discover brands, information, and expert voices online.

The launch of the AI Discoverability Ecosystem by Zilker Media signals a strategic shift in how public relations and brand marketing are adapting to an AI-driven information landscape.

As generative AI platforms reshape the discovery process for business information, companies are increasingly realizing that traditional SEO alone may not be enough to maintain visibility. Instead, credibility signals across media coverage, content ecosystems, and digital platforms are becoming critical for influencing how AI systems evaluate authority.

The new framework was announced as the agency marks its ninth year in operation. According to the firm, the offering formalizes strategies it has already been applying for clients as generative AI platforms such as ChatGPT, Claude, and Google AI Overviews increasingly shape how information is surfaced online.

The Rise of AI-Driven Brand Discovery

Search behavior is evolving rapidly as AI assistants and generative search features begin synthesizing information rather than simply listing links.

Instead of navigating traditional search results pages, users now frequently receive summarized answers that draw from multiple sources across the web. In this model, visibility is determined not only by search rankings but by the authority signals that AI systems identify when generating responses.

For brands, that shift introduces a new strategic challenge: ensuring that their expertise, leadership, and credibility are visible not just to search engines but to large language models and AI knowledge systems.

According to research from Gartner, traditional search engine volume could decline significantly over the next several years as conversational AI interfaces increasingly become the entry point for information discovery.

Within that environment, communications strategies must extend beyond conventional SEO tactics to include generative engine optimization (GEO)—a discipline focused on ensuring that brands are recognized as authoritative entities within AI-generated responses.

Inside the AI Discoverability Ecosystem

Zilker Media’s framework combines several components designed to strengthen the signals that AI systems use to evaluate brand credibility.

The approach integrates three primary media layers—earned, owned, and rented—within a unified strategy aimed at increasing authority across the digital ecosystem.

AI Discoverability Audits serve as the starting point. These assessments analyze how brands currently appear across search engines, social media platforms, and AI systems. The goal is to identify visibility gaps and opportunities to strengthen authority signals.

The next layer focuses on earned media and public relations, including placements in national and trade publications, podcast appearances, industry awards, and executive thought leadership initiatives. These third-party validations often serve as critical training signals for AI models evaluating the credibility of entities and organizations.

The ecosystem also emphasizes owned media optimization, which includes restructuring website content, executive thought leadership articles, and blog content so that expertise is clearly expressed and easily understood by both search engines and AI systems.

Finally, rented channels—such as social media platforms, partnership networks, and external publishing platforms—are used to amplify authority signals and extend visibility across digital ecosystems.

Why Authority Signals Matter for AI

The concept of AI discoverability is closely tied to how large language models process information.

Modern AI systems are trained on massive datasets containing news coverage, blogs, academic papers, and structured knowledge sources. When these systems generate answers, they rely heavily on patterns of credibility and consistency across multiple sources.

For example, if a company consistently appears in high-authority media outlets, maintains expert-driven website content, and has recognized leadership voices in its industry, AI systems are more likely to reference or recommend that company in generated responses.

Conversely, brands with fragmented digital footprints or limited authoritative coverage may struggle to appear in AI-generated answers—even if they rank well in traditional search.

This dynamic is creating a new intersection between public relations, content marketing, and search strategy.

The Expanding Role of PR in the AI Era

The rise of AI-powered discovery tools is prompting many communications firms to rethink their approach to digital visibility.

Historically, PR focused primarily on reputation management and media relationships. In an AI-driven landscape, those functions are expanding to include data visibility, knowledge graph presence, and entity recognition across digital platforms.

Major marketing ecosystems are already integrating AI into discovery and analytics tools. Platforms from companies such as Google and Microsoft increasingly rely on AI-driven search experiences that summarize information directly for users.

At the same time, marketing platforms from vendors like Adobe and Salesforce are embedding generative AI capabilities within content creation, campaign automation, and analytics systems.

Within this ecosystem, the role of communications strategies is expanding from storytelling and reputation building to influencing how brands appear within AI-generated knowledge environments.

Building Consistent Credibility Signals

Zilker Media’s approach reflects a broader marketing principle that has become increasingly relevant in the AI era: credibility signals must be consistent across every digital touchpoint.

If a company’s messaging, leadership voice, and industry expertise appear fragmented across different platforms, AI systems may struggle to identify clear authority signals.

Conversely, when a brand demonstrates consistent expertise across press coverage, executive content, social channels, and owned media, those signals reinforce each other—strengthening the likelihood that AI systems recognize the brand as a credible source.

This concept aligns with broader trends in entity-based SEO and generative engine optimization, where digital authority is increasingly determined by the interconnected presence of entities across trusted platforms.

A New Visibility Playbook

For marketing leaders, the implications of AI discoverability are significant.

Traditional SEO strategies focused on keyword optimization and link-building are evolving into more comprehensive authority-building initiatives that incorporate PR, thought leadership, and digital content strategy.

Research from Statista suggests that AI adoption across marketing departments continues to accelerate, with companies investing heavily in tools that help automate analytics, personalization, and content generation.

As AI systems become central to information discovery, the brands most likely to succeed may be those that build holistic authority ecosystems rather than relying on isolated marketing tactics.

Zilker Media’s new offering reflects that shift, positioning AI discoverability not as a standalone tactic but as a coordinated strategy spanning media relations, content infrastructure, and digital presence.

Market Landscape

The convergence of AI search, generative content platforms, and conversational assistants is reshaping how businesses approach digital visibility.

According to research from IDC, global spending on artificial intelligence technologies could surpass $300 billion by 2026, with marketing, customer experience, and data analytics platforms among the fastest-growing categories.

At the same time, generative search experiences are changing how users access information online. Rather than navigating multiple websites, users increasingly rely on AI-generated summaries that synthesize information from trusted sources.

This shift is giving rise to new marketing disciplines—including generative engine optimization (GEO) and AI discoverability strategy—designed to ensure brands remain visible within AI-driven knowledge environments.

Top Insights

• Zilker Media has launched an AI Discoverability Ecosystem, a framework designed to help brands improve visibility and authority within AI-powered search platforms and generative information systems.

• The framework integrates earned, owned, and rented media channels, combining PR placements, content strategy, and digital amplification to strengthen authority signals recognized by AI systems.

• AI discoverability strategies focus on ensuring brands appear credible across search engines and AI assistants, including platforms like ChatGPT, Claude, and Google’s AI-driven search features.

• The initiative reflects broader shifts in marketing toward generative engine optimization, where brand authority and media credibility influence how AI systems generate answers and recommendations.

• As AI increasingly mediates digital discovery, integrated PR and content strategies are emerging as key tools for ensuring brands remain visible and trusted in AI-generated results.

Get in touch with our MarTech Experts.

Creative content marketplace Shutterstock has launched a dedicated app inside ChatGPT, enabling users to discover licensable images, videos, music, and sound effects directly within conversational AI workflows. The integration reflects a broader shift toward AI-native creative production, where ideation, asset discovery, and content creation increasingly happen inside AI assistants rather than traditional search platforms.

The introduction of a Shutterstock app within ChatGPT signals a deeper convergence between generative AI platforms and the digital creative economy.

As conversational AI tools become central to how creators brainstorm, research, and develop projects, the need for commercial-ready content embedded directly within those workflows is growing. Shutterstock’s new integration addresses that demand by enabling ChatGPT users to search, preview, and access licensable media assets without leaving the conversation interface.

For marketers, designers, and content teams, the integration means the transition from idea generation to production-ready creative assets can happen within a single environment.

Embedding Licensed Media into AI Workflows

The new app allows users to explore content from Shutterstock’s extensive catalog—one of the largest collections of commercial imagery and multimedia assets globally—while interacting with ChatGPT.

Rather than searching through external stock media websites, users can discover relevant assets during a conversation, preview potential images or media elements, and move toward licensing them directly through the Shutterstock platform.

The company says the integration is designed to support AI-native creative workflows, a model where generative AI platforms serve as the primary interface for ideation and project development.

For example, a marketing team drafting a campaign concept inside ChatGPT could simultaneously search for hero imagery, background music, or video clips aligned with the campaign theme without switching applications.

Paul Teall, Vice President of Marketplace Strategy at Shutterstock, described the integration as an effort to bring commercial confidence into conversational AI environments, allowing users to move seamlessly from concept development to licensed production assets.

The Rise of AI-Native Discovery

The timing of the launch reflects a broader transformation in how people discover digital resources.

According to internal metrics shared by OpenAI, ChatGPT users generate more than one billion queries per day, illustrating the scale at which conversational AI platforms are becoming gateways for information discovery and creative ideation.

In traditional creative workflows, asset discovery often involved searching specialized marketplaces, browsing catalog libraries, and manually evaluating licensing terms.

By embedding access to licensable content directly inside AI assistants, companies like Shutterstock are attempting to reduce friction between creative ideation and asset acquisition.

The shift also reflects a growing trend in software design: rather than building standalone creative tools, companies are integrating capabilities directly into AI ecosystems where users already spend time.

Addressing Licensing and Copyright Concerns

The move also touches on a critical issue in generative AI: content rights and licensing compliance.

Many AI-generated images and media outputs have raised questions about copyright ownership, dataset sourcing, and the legal status of generated assets.

Shutterstock has attempted to differentiate itself by emphasizing rights-cleared content and transparent data provenance. The company’s platform includes licensed media created by professional contributors, along with structured metadata that defines usage rights.

Embedding this content directly into ChatGPT provides users with access to commercially safe assets, rather than relying solely on generative outputs whose licensing terms may be unclear.

For enterprise marketing teams and large creative organizations, that distinction can be particularly important when developing advertising campaigns or branded content.

Shutterstock’s Strategy in the AI Economy

The ChatGPT integration is part of a broader strategy by Shutterstock to position itself as a creative infrastructure provider for AI-driven production.

In recent years, the company has expanded beyond its traditional stock media marketplace to include:

• generative AI image tools
• AI-assisted editing features
• model training data services
• curated datasets for AI developers

Shutterstock also provides data licensing services designed to support organizations training generative AI models.

Through these offerings, the company supplies large-scale multimodal datasets containing images, video, and audio assets with clear licensing structures, which can be used to train and fine-tune machine learning models.

Data Licensing and AI Model Training

The company has increasingly positioned itself as a partner for organizations building AI models.

Its AI services include curated datasets, model evaluation tools, and human-in-the-loop feedback workflows designed to improve model performance. These systems help AI developers fine-tune models using structured preference data and expert creative input.

The datasets themselves are drawn from Shutterstock’s global content library, which contains millions of licensed assets spanning photography, illustration, video, and audio.

In addition to supplying training data, the company also offers tools for model alignment, benchmarking, and continuous evaluation, helping organizations refine generative models over time.

Competing in the AI Creative Stack

Shutterstock’s move reflects a broader competitive race across the creative technology industry.

Major technology companies including Adobe and Microsoft have embedded generative AI features into creative software platforms, enabling users to generate images, edit visuals, and automate design workflows.

At the same time, conversational AI systems like ChatGPT are increasingly functioning as creative hubs, where users develop ideas, generate drafts, and coordinate project workflows.

By integrating directly into ChatGPT, Shutterstock is positioning its licensed media catalog as a foundational layer within these AI-driven environments.

Rather than competing solely as a content marketplace, the company is attempting to become a licensed content infrastructure provider within the AI ecosystem.

The Future of AI-Driven Creativity

The integration highlights a broader transformation underway across the creative industries.

Historically, digital content creation involved a sequence of separate tools—research platforms, creative software, media libraries, and publishing systems.

AI platforms are beginning to unify these stages into a single workflow.

As conversational interfaces increasingly guide project development, companies that can integrate content discovery, creation tools, and licensing frameworks directly into AI environments may gain a strategic advantage.

For Shutterstock, embedding its content catalog inside ChatGPT represents a step toward that vision: a future where licensed creative assets are accessible at the exact moment inspiration occurs.

Market Landscape

The creative technology sector is rapidly evolving as generative AI becomes embedded across design, marketing, and content production workflows.

Research from IDC estimates that global spending on artificial intelligence technologies could exceed $300 billion by 2026, with creative automation and AI-driven media production among the fastest-growing categories.

At the same time, conversational AI tools are becoming primary gateways for information discovery and creative brainstorming. As these platforms grow, integrations that embed professional content libraries directly into AI workflows may become a critical part of the AI-powered creative infrastructure stack.

Top Insights

• Shutterstock has launched an app within ChatGPT, allowing users to discover and preview licensable images, videos, music, and sound effects directly within AI-powered conversations.

• The integration embeds licensed media assets into AI-native creative workflows, enabling marketers, designers, and creators to move from idea generation to production-ready content within a single interface.

• The launch reflects rising demand for commercially safe AI content, as organizations seek rights-cleared assets that avoid the copyright uncertainties associated with generative media outputs.

• Shutterstock is expanding its role as a creative infrastructure provider, offering data licensing, model training datasets, and AI-assisted creative tools to support generative AI development.

• AI assistants are becoming central creative hubs, prompting content marketplaces to integrate directly into conversational platforms where ideation and project planning increasingly begin.

Get in touch with our MarTech Experts.

Consent management provider CookieYes has launched a Cookie Policy Generator, a tool designed to help businesses automatically generate and maintain accurate cookie disclosure policies as global privacy regulations intensify. The platform scans websites for active tracking technologies and produces policies tailored to a site's real-time configuration, aiming to simplify compliance for startups, small businesses, and enterprise marketing teams.

As digital privacy enforcement expands worldwide, businesses face increasing pressure to clearly communicate how user data is collected and used. To address that challenge, CookieYes has introduced Cookie Policy Generator, an automated tool designed to create and maintain up-to-date cookie disclosure policies based on a website’s actual tracking technologies.

The launch reflects growing demand for compliance tools that help organizations navigate an increasingly complex privacy landscape. Modern websites often deploy dozens of cookies and tracking technologies tied to analytics, advertising, and personalization systems. Documenting these technologies manually can be time-consuming and error-prone, particularly for small and mid-sized organizations with limited legal resources.

CookieYes says its new tool is designed to automate that process by scanning websites, identifying active cookies, categorizing them by purpose, and generating policies that update automatically when site configurations change.

Rising Global Pressure for Privacy Transparency

The introduction of Cookie Policy Generator comes amid a wave of global privacy regulations reshaping how companies manage digital data.

Laws such as the General Data Protection Regulation and the California Consumer Privacy Act have established strict requirements for data transparency, consent management, and user rights.

These frameworks require organizations to disclose what types of data they collect, how that data is used, and which third parties may receive it.

According to research from Statista, privacy legislation now affects roughly 80% of the global population, making compliance a central concern for companies operating across digital markets.

At the same time, enforcement activity by regulators is increasing, pushing organizations to ensure that their privacy documentation accurately reflects real-world data practices.

Automating Cookie Policy Creation

Traditional cookie policy management often relies on static templates or manual legal documentation. However, these methods can quickly become outdated as websites add new analytics tools, advertising platforms, or marketing integrations.

CookieYes’ new generator attempts to solve this problem through automated scanning.

The system analyzes a website to detect active cookies and then classifies them based on their purpose—such as analytics, marketing, functional operations, or security. The resulting policy document reflects the actual technologies running on the site rather than a generic template.

The platform also updates policies automatically when new cookies appear or existing ones change, reducing the risk that businesses unknowingly publish inaccurate privacy disclosures.

For organizations operating across multiple markets, the tool also supports multi-language policies and compliance frameworks linked to major privacy regulations.

Built on Consent Management Expertise

While cookie policy generators exist across the privacy technology ecosystem, CookieYes positions its offering as distinct from bundled solutions included within larger consent management platforms.

Unlike add-on tools that simply generate static policy text, the company says its system is built on the data infrastructure used by its broader Consent Management Platform (CMP).

This integration allows the generator to use detailed cookie classification data already gathered through consent management processes.

The tool also supports frameworks such as Google Consent Mode v2, which enables websites to adjust tracking behavior based on user consent preferences.

By linking policy generation directly with consent management infrastructure, the company aims to create a unified system where privacy disclosures, consent records, and tracking technologies remain synchronized.

The Expanding Privacy Technology Market

The launch highlights the growing importance of privacy technology within digital marketing and data infrastructure.

As organizations rely increasingly on analytics tools, advertising platforms, and personalization technologies, maintaining transparent data practices has become both a regulatory requirement and a brand trust issue.

Marketing platforms from companies such as Google and Adobe continue to evolve to accommodate stricter consent requirements, particularly in regions governed by GDPR and similar frameworks.

Meanwhile, privacy-focused browser policies and regulatory enforcement are reshaping how advertisers track and measure user behavior.

In this environment, automated compliance tools are emerging as a critical layer within the broader marketing technology stack.

Privacy as a Trust Signal

Beyond regulatory compliance, privacy transparency is increasingly viewed as a competitive differentiator.

Consumers are becoming more aware of how their personal data is used online, prompting companies to emphasize transparency in their privacy communications.

Anvar T., founder and CEO of CookieYes, described the new tool as part of a broader effort to make privacy communication accessible to organizations without specialized legal expertise.

The platform’s goal, he said, is to ensure that businesses—from startups to enterprise teams—can clearly explain how data is collected and used without relying on complex legal documentation processes.

Compliance Challenges for Growing Businesses

For startups and small businesses, privacy compliance often presents a particular challenge.

Large enterprises typically maintain dedicated legal and compliance teams responsible for reviewing privacy documentation and tracking regulatory changes. Smaller companies, however, may lack those resources.

Tools like Cookie Policy Generator attempt to bridge that gap by automating compliance workflows that would otherwise require specialized legal review.

The platform’s free tier supports scanning for websites containing up to 100 pages, while paid plans expand scanning capabilities to sites with thousands of pages and offer additional features such as scheduled scans and multi-user access.

For agencies managing multiple client websites, automation also reduces the operational complexity of maintaining privacy documentation across numerous digital properties.

Market Landscape

The privacy technology market is expanding rapidly as governments introduce new data protection regulations and consumers demand greater transparency.

Research from IDC suggests that global spending on privacy and data protection technologies is expected to grow steadily through the decade as organizations invest in tools for consent management, data governance, and compliance automation.

At the same time, marketing technology platforms increasingly incorporate privacy controls directly into analytics, advertising, and personalization tools.

Within this evolving landscape, automated compliance solutions—such as cookie scanning and policy generation platforms—are becoming a core component of digital infrastructure for organizations operating in regulated markets.

Top Insights

• CookieYes has launched Cookie Policy Generator, an automated tool that scans websites and creates privacy-compliant cookie policies tailored to real-time tracking technologies.

• The platform helps businesses keep privacy disclosures accurate, automatically updating policies as new cookies or tracking tools are introduced on a website.

• Global privacy regulations such as GDPR and CCPA are driving demand for compliance tools, with privacy laws now covering roughly 80% of the world’s population.

• The tool integrates with CookieYes’ consent management platform, creating a unified system that connects consent tracking, policy generation, and cookie classification.

• Automated privacy tools are becoming essential infrastructure for digital businesses, particularly as marketing technologies increasingly rely on user data and tracking systems.

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Customer-led marketing platform Optimove has introduced a suite of AI-powered agents and capabilities designed to improve and accelerate the marketing content lifecycle. The new tools help marketers create, validate, optimize, and deploy content faster while ensuring brand compliance, quality assurance, and data-driven decisioning across campaigns.

Marketing technology provider Optimove has unveiled a new set of AI agents and AI-powered capabilities aimed at streamlining the entire marketing content lifecycle—from creation to optimization and performance insights.

The announcement expands Optimove’s Positionless Marketing framework, which aims to empower marketers with the ability to independently manage data, creative production, and campaign optimization without relying heavily on specialized teams.

By embedding AI agents directly into marketing workflows, the company says marketers can move faster from concept to execution while maintaining brand integrity, compliance standards, and personalization accuracy.

Addressing the Content Operations Challenge

Generative AI tools have significantly accelerated the ability to produce marketing content, but many organizations still struggle with operational challenges after content creation.

Marketing teams frequently spend significant time validating AI-generated content, ensuring it meets brand guidelines, passing compliance checks, and testing variations to determine what resonates with audiences.

According to Optimove, these operational bottlenecks slow down campaign execution and limit the ability of brands to deliver timely, relevant messages at scale.

Shai Frank, SVP of Product and GM of the Americas at Optimove, said the company’s new AI agents focus on solving that gap.

While generative AI can quickly produce marketing copy, the new capabilities aim to ensure that content remains brand-aligned, compliant, and continuously optimized based on performance data.

AI Agents Across the Content Lifecycle

The newly announced capabilities are structured around three major phases of the marketing content lifecycle: creation, assurance, and decisioning.

AI for Content Creation

To accelerate content production, Optimove introduced several tools designed to help marketers develop campaigns more efficiently.

The Optimove AI Assistant functions as a collaborative AI agent that guides marketers through content generation and optimization using structured prompts.

Another tool, the Template Creation Agent, allows marketers to generate new emails from natural language prompts while referencing existing approved templates to ensure messaging remains aligned with brand voice and style.

These tools operate within Content Studio, a centralized workspace where marketers can create, edit, and manage campaign content across channels from a single interface.

Ensuring Quality and Compliance

While speed is critical, marketing teams also face pressure to ensure AI-generated content remains compliant with brand standards and regulatory requirements.

To address this, Optimove introduced a set of AI agents focused on content validation.

Global Brand Guidelines enable organizations to define tone of voice, brand values, localization requirements, and compliance policies that AI systems must follow when generating marketing content.

The Content Advisor Agent evaluates generated content against these guidelines and scores it based on quality and potential compliance risks.

Another capability, the Content QA Agent, automatically scans campaigns before they are launched, identifying issues such as broken links, missing personalization fields, and other potential errors.

This automated review process helps reduce the need for manual approvals and quality checks that traditionally slow marketing workflows.

Data-Driven Content Decisioning

Beyond creation and quality assurance, Optimove also introduced AI agents designed to optimize campaign performance.

The Content Decisioning Agent generates multiple content variations and performs A/B/n testing to identify which messages perform best across audience segments.

The system dynamically delivers the highest-performing variant to each customer based on real engagement data.

Meanwhile, the Content Intelligence Agent analyzes campaign results and extracts insights from messaging performance.

By automatically identifying factors such as tone of voice, promotion type, and product category, the agent helps marketers understand which messaging approaches resonate most strongly with different audiences.

Supporting the Positionless Marketing Vision

The new capabilities represent another step in Optimove’s broader Positionless Marketing strategy.

This approach aims to eliminate traditional role-based constraints within marketing organizations by giving individual marketers access to tools that previously required specialized teams in analytics, creative production, and optimization.

According to the company, Positionless Marketing gives marketers three key capabilities:

• Data Power – access to customer data and insights
• Creative Power – the ability to generate and deploy marketing content
• Optimization Power – the ability to test, refine, and improve campaigns

The newly introduced AI agents specifically enhance the Creative Power component by enabling marketers to produce and refine content without waiting for cross-functional approvals or manual reviews.

Delivering Personalized Experiences at Speed

The ultimate goal of these capabilities is to help brands deliver personalized messages at the speed of customer interactions.

Modern consumers expect relevant communications across email, mobile, and digital channels, often in real time.

When content production and optimization lag behind customer engagement signals, marketing teams struggle to deliver the right message at the right moment.

Optimove believes AI-driven content workflows can help close that gap.

By automating creation, compliance checks, and performance testing, marketers can focus more on strategy and less on operational tasks.

Expanding the AI Marketing Platform

The announcement follows the company’s recent launch of AI Decisioning Studio, a centralized environment where marketers can monitor and collaborate with AI-powered marketing agents.

Together, these capabilities reflect the broader shift toward agentic marketing platforms, where AI systems operate as autonomous assistants that support decision-making, automation, and optimization across marketing operations.

As AI continues to reshape marketing technology, platforms that integrate content generation with performance analytics and automation are becoming increasingly central to the modern martech stack.

Key Insights

• Optimove launched new AI-powered agents designed to accelerate the marketing content lifecycle.

• The tools help marketers create, validate, and optimize campaign content while maintaining brand compliance and quality standards.

• AI agents support three stages of the content lifecycle: creation, assurance, and decisioning.

• New capabilities include AI assistants, automated QA tools, and content performance intelligence agents.

• The launch expands Optimove’s Positionless Marketing strategy, enabling marketers to manage campaigns independently without relying on specialized teams.

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Digital marketing agency SEOtive has launched a new suite of AI-powered SEO services designed to help businesses improve visibility across AI-driven search platforms and traditional search engines. The offering combines advanced data analysis, intelligent automation, and human expertise to help brands adapt to evolving search algorithms and generate sustainable organic traffic.

Search optimization is entering a new phase as search engines increasingly integrate artificial intelligence and generative experiences into their ranking systems. In response to these shifts, digital marketing provider SEOtive has introduced AI-powered SEO services designed to help companies maintain visibility in AI-driven search environments.

The company says the new services focus on improving discoverability across both traditional search results and emerging AI-generated search experiences.

As search engines integrate conversational AI, semantic search, and automated ranking signals, traditional SEO tactics alone may no longer be sufficient for sustained performance.

Adapting SEO for the AI Search Era

Major search platforms—including those developed by Google and Microsoft—are increasingly incorporating artificial intelligence into their search interfaces.

These changes are reshaping how users discover information online. Instead of browsing multiple web pages, users can receive summarized answers generated by AI systems.

This shift toward AI-assisted search and generative search results is forcing businesses to rethink how their content is structured, optimized, and distributed across digital channels.

SEOtive’s new AI-powered services aim to help organizations adapt to these changes by using machine learning models and automation to identify ranking opportunities within large datasets of search signals and user behavior patterns.

Core Features of SEOtive’s AI SEO Services

According to the company, the platform combines several core capabilities designed to improve organic search performance.

These include:

• Advanced keyword research powered by AI-driven data analysis
• Technical SEO optimization to improve site performance and crawlability
• AI-assisted editorial strategies to create relevant and discoverable content
• Competitor analysis to uncover ranking opportunities
• Search intent optimization to align content with evolving user queries

Together, these features aim to help organizations develop content strategies that better match how modern search engines interpret user intent.

Supporting Emerging Search Experiences

In addition to traditional search rankings, SEOtive’s platform focuses on optimizing content for emerging AI search experiences such as voice queries, conversational search interactions, and semantic search algorithms.

Voice search, for example, has grown rapidly with the expansion of digital assistants like Google Assistant and Amazon Alexa.

These systems often rely on natural language processing and contextual search understanding rather than keyword matching alone.

As a result, content optimized for semantic meaning and conversational queries may perform better across both voice search and AI-generated search responses.

SEOtive says its AI-driven analysis helps identify opportunities within large datasets of search behavior that traditional SEO tools may overlook.

Combining AI and Human Expertise

While automation and machine learning play a central role in the new service offering, the company emphasizes that human expertise remains a key component of effective SEO strategy.

According to SEOtive, the platform combines AI-powered analysis with human-led optimization strategies to ensure content remains relevant, accurate, and aligned with brand messaging.

This hybrid approach is designed to help companies adapt quickly to search engine algorithm changes while maintaining high-quality content standards.

Growing Demand for AI-Driven SEO

The launch reflects broader changes within the digital marketing industry as organizations invest more heavily in AI-powered marketing technologies.

Research from Gartner and Forrester indicates that businesses are increasingly adopting automation and AI tools to manage complex marketing operations and improve digital performance.

At the same time, competition for organic visibility continues to intensify across industries.

Startups, e-commerce brands, and global enterprises alike are seeking new strategies to maintain strong search rankings while adapting to algorithm updates and new search interfaces.

Helping Businesses Navigate Algorithm Shifts

SEOtive says its new AI-powered SEO services are designed to help businesses remain competitive as search technologies evolve.

By analyzing large volumes of search data and identifying patterns in user behavior, the platform aims to uncover opportunities that may not be visible through traditional optimization methods.

The company expects the new services to support organizations ranging from startups and local businesses to large enterprises looking to expand their digital reach in highly competitive markets.

As search engines continue integrating artificial intelligence into their ranking systems and user interfaces, tools that combine AI insights with strategic optimization may become essential for maintaining strong online visibility.

Key Insights

• SEOtive has introduced AI-powered SEO services designed for the evolving AI search landscape.

• The platform uses advanced data analysis, automation, and machine learning to identify search ranking opportunities.

• Features include keyword research, technical SEO, competitor analysis, and AI-assisted content strategy.

• The services focus on optimizing content for AI-generated search results, voice search, and semantic search experiences.

• The approach combines AI technology with human expertise to help businesses maintain long-term organic growth.

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Enterprise procurement platform Zip has appointed Michael Denari as General Manager of AI, bringing in a seasoned technology executive who previously led global IT and enterprise AI strategy at Canva. In his new role, Denari will oversee Zip’s AI business, including go-to-market strategy, revenue growth, product development collaboration, and internal AI transformation initiatives.

AI-powered procurement platform Zip has announced the appointment of Michael Denari as General Manager of AI, strengthening the company’s leadership team as enterprises accelerate investments in artificial intelligence.

Denari joins the company from Canva, where he served as Global Head of IT and played a central role in building and scaling the company’s enterprise AI initiatives across a global workforce of more than 5,000 employees.

At Zip, Denari will lead the company’s AI business strategy end-to-end, including go-to-market execution, customer success, revenue growth, and internal AI adoption across departments. He will also collaborate closely with Zip’s product and engineering teams to shape the development of AI-powered procurement solutions.

Strengthening AI Leadership at Zip

The appointment comes as enterprises face increasing pressure to demonstrate measurable returns on artificial intelligence investments.

According to Rujul Zaparde, organizations are moving beyond AI experimentation and now expect tangible operational impact.

Zaparde noted that Denari brings unique experience from building AI systems within large organizations—an expertise that aligns with Zip’s goal of delivering enterprise-grade AI solutions that improve how businesses operate.

Enterprise AI Experience at Canva

During his tenure at Canva, Denari led the company’s global IT organization, overseeing a team responsible for enterprise technology infrastructure, governance, and AI-driven transformation.

Over the past several years, he implemented multiple AI initiatives that restructured internal business operations, including:

• Automated internal support systems
• AI-powered sales enablement tools
• AI-assisted employee performance reviews
• Procurement compliance automation agents

These initiatives helped integrate AI into core business workflows across the organization.

Denari’s experience spans both procurement leadership and enterprise IT strategy—an uncommon combination that aligns closely with Zip’s platform focus on procurement orchestration and AI-powered enterprise operations.

From Customer to Leadership Role

Denari also played an early role in adopting Zip’s technology during his time at Canva.

When Zip launched its procurement orchestration platform in 2021, Canva became one of the company’s earliest enterprise customers under Denari’s leadership.

This firsthand experience implementing the platform at scale gave him direct insight into how procurement technologies can transform financial operations and internal workflows.

Driving AI in Procurement

Procurement is increasingly viewed as a high-impact area for enterprise AI adoption.

Large organizations often manage complex supplier networks, approval processes, and compliance requirements, creating opportunities for automation and intelligent decision-making.

Zip’s platform aims to address these challenges by integrating AI into procurement workflows, helping companies manage purchasing, vendor management, and financial controls more efficiently.

Denari believes procurement represents one of the most underutilized opportunities for AI-driven business value.

He noted that organizations often underestimate the operational and financial impact that AI-powered procurement systems can deliver.

Leading Zip’s AI Strategy

In his new role, Denari will oversee several critical areas of Zip’s AI operations, including:

• Developing the company’s AI go-to-market strategy
• Expanding enterprise adoption of Zip’s AI capabilities
• Driving customer success and measurable ROI from AI deployments
• Leading internal AI transformation initiatives
• Collaborating with product and engineering teams on AI innovation

The role also includes scaling the use of AI agents across business functions, reflecting a broader shift toward agentic systems that automate enterprise workflows.

A Career at the Intersection of Procurement and Technology

Before joining Canva, Denari built and led the procurement function at Procore Technologies, where he helped scale operations prior to the company’s public listing.

His experience across procurement leadership, enterprise IT management, and AI strategy positions him to guide Zip’s expansion as companies look to integrate artificial intelligence into core financial and operational processes.

Growing Demand for AI in Enterprise Procurement

The appointment reflects growing interest in AI-powered procurement tools as enterprises seek ways to improve operational efficiency and reduce costs.

Research from Gartner suggests that procurement automation and intelligent sourcing technologies are becoming key priorities for CFOs and operations leaders looking to optimize enterprise spending.

As AI adoption expands across enterprise systems, procurement platforms that integrate automation, analytics, and workflow orchestration are gaining traction within the broader enterprise technology ecosystem.

Zip’s leadership move signals its intention to position AI at the center of procurement transformation.

Key Insights

• Zip has appointed Michael Denari as General Manager of AI.

• Denari previously served as Global Head of IT at Canva, where he built enterprise AI programs across the organization.

• In his new role, he will lead Zip’s AI strategy, go-to-market operations, and enterprise adoption initiatives.

• Denari previously helped build procurement operations at Procore Technologies prior to its IPO.

• The appointment highlights growing enterprise demand for AI-powered procurement platforms and operational automation.

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