Bitly is expanding its push into AI-driven marketing intelligence with the launch of Bitly Assist and Weekly Insights, two new features designed to help marketing teams quickly analyze link and QR code performance. The updates aim to reduce the manual effort required to interpret campaign data and allow marketers to move faster from analytics to action.

The new capabilities arrive as enterprise marketing teams face an expanding volume of performance data across channels, from social media campaigns to email marketing and digital advertising. Bitly’s latest AI integrations attempt to simplify that process by embedding conversational analytics and automated reporting directly inside its link management platform.

For years, marketers have relied on shortened links and QR codes not only for distribution but also as measurement tools for campaign engagement. Platforms like Bitly provide granular performance data — including clicks, geographic engagement, and traffic sources — but extracting meaningful insights often requires time spent navigating dashboards and exporting reports.

Bitly’s newest AI features are designed to address that bottleneck.

Bitly Assist, an AI-powered conversational interface integrated directly into the Bitly platform, allows users to ask natural-language questions about link and QR code performance. Instead of manually searching analytics dashboards, marketing teams can ask questions such as which campaign links generated the most engagement during a specific period or which traffic sources are driving conversions.

The assistant then surfaces the relevant analytics in seconds.

Beyond answering questions, the tool also supports conversational creation of links and QR codes, reducing the number of steps required to launch new marketing assets. According to Bitly, the goal is to streamline the entire workflow — from campaign setup to performance analysis — within a single AI-driven interface.

“Customers don’t have time to dig through dashboards for answers,” said Kelsey Stevenson, Chief Product Officer at Bitly. The company built Bitly Assist and Weekly Insights to remove friction between accessing analytics data and acting on it.

The second feature, Weekly Insights, focuses on automated analytics interpretation. Integrated within Bitly Analytics, the system identifies notable changes in link performance across dimensions such as geographic regions, referral sources, and device types.

Rather than requiring marketers to manually run reports, Weekly Insights highlights patterns and anomalies automatically. For example, the system might surface spikes in engagement from a specific region or identify a campaign link that is outperforming others across multiple channels.

The feature effectively acts as a weekly intelligence report for marketing teams managing multiple campaigns simultaneously.

Early users say the combination of conversational analytics and automated insights can significantly reduce the time required for performance analysis. According to Ania Cotton, SEO and Data Analytics Manager at Americas’ SAP Users’ Group, tasks that once required navigating dashboards for several minutes can now be completed almost instantly using the assistant.

The launch reflects a broader shift toward AI-assisted marketing analytics, where platforms increasingly interpret data rather than simply displaying it.

Major technology vendors — including Salesforce, Adobe, Google, and Microsoft — have all introduced AI copilots or analytics assistants designed to automate data interpretation for marketing teams. These tools attempt to solve a growing problem in enterprise marketing operations: the gap between data collection and actionable insight.

Bitly’s approach focuses specifically on link-based engagement data, an often-overlooked layer of marketing analytics that spans multiple channels.

Links and QR codes serve as connective infrastructure across marketing ecosystems, bridging platforms such as social networks, email campaigns, mobile apps, and websites. As a result, they can provide a unified signal for cross-channel engagement.

By embedding AI interpretation into this layer, Bitly is attempting to turn link analytics into a more strategic marketing intelligence tool.

The company has also been expanding integrations with generative AI ecosystems. Recent updates include integrations with large language models such as ChatGPT, Claude, Perplexity AI, and Microsoft Copilot.

Through its Model Context Protocol (MCP) server, Bitly allows its link management capabilities to operate directly inside external AI tools and enterprise workflows.

The integration strategy reflects a broader trend in SaaS platforms embedding functionality into AI assistants rather than forcing users to work inside standalone dashboards.

From an industry perspective, the timing aligns with growing demand for AI-driven marketing intelligence platforms.

According to Gartner, marketing organizations are expected to increasingly rely on AI-enabled analytics tools to interpret complex datasets and automate campaign optimization. Meanwhile, research from IDC indicates that global spending on AI-powered enterprise software is expected to surpass $300 billion by the end of the decade.

For enterprise marketing teams, tools that reduce analytical friction could have significant operational value. Marketing departments often manage campaigns across dozens of platforms — social media, search advertising, influencer marketing, and CRM systems — each generating its own stream of performance metrics.

Consolidating insights from those systems typically requires multiple analytics tools and manual data interpretation.

Bitly’s AI-driven approach attempts to reduce that complexity by turning link engagement data into a central layer of campaign intelligence.

The company’s scale gives it a large dataset to train and refine such insights. Bitly reports more than 5.7 million monthly active users, more than 600,000 paying customers, and usage across 190 countries.

If the company’s AI features gain adoption, link management platforms could evolve from simple utilities into broader marketing analytics infrastructure.

Market Landscape

AI-powered marketing analytics is rapidly becoming a core capability across enterprise marketing platforms. Analysts at Forrester report that marketing teams increasingly expect software to interpret data, generate insights, and recommend actions automatically, rather than simply visualizing performance metrics.

Platforms such as Salesforce Marketing Cloud, Adobe Experience Platform, and Google Analytics are embedding AI copilots to assist with analytics interpretation.

Bitly’s new features position the company within this emerging category of AI-assisted marketing intelligence tools, with a specific focus on cross-channel engagement data generated through links and QR codes.

Top Insights

• Bitly launched Bitly Assist and Weekly Insights, two AI-driven features that automate link analytics and surface actionable marketing insights directly inside the platform.
• The conversational assistant allows marketers to query campaign performance and create links using natural language, significantly reducing manual analysis and operational friction.
• Weekly Insights automatically identifies trends across channels, geographies, and referral sources, helping marketing teams quickly understand what is driving engagement.
• The launch reflects a broader shift toward AI-powered marketing analytics platforms competing with tools from Salesforce, Adobe, and Google.
• With over 5.7 million monthly users and 600,000 paying customers, Bitly is expanding beyond link management toward AI-driven marketing intelligence infrastructure.

Global video and television revenues are projected to exceed $1 trillion by 2030, according to new research from Omdia. The forecast highlights a major transformation in the media and entertainment industry, with social video advertising emerging as the primary growth driver, accelerating the shift from traditional TV to digital video platforms.

New insights from Omdia reveal that global revenues from traditional television and online video services are expected to grow from $775 billion in 2025 to approximately $1.03 trillion by 2030, signaling a significant structural shift in how content is produced, distributed, and monetized.

The projections were presented by Maria Rua Aguete during the FED Show in Madrid, where she outlined how digital platforms—particularly those driven by social video—are rapidly reshaping the economics of the global media landscape.

Social Video Advertising Driving Industry Growth

According to the report, online video advertising will become the primary engine of industry expansion over the next five years.

Advertising revenues in this segment are expected to increase from $309 billion in 2025 to $540 billion by 2030, boosting its share of total video industry revenue from 40% to 53%.

Social video platforms will play a central role in this growth. Major platforms such as Meta, TikTok, and YouTube are projected to generate around $400 billion in streaming advertising revenues by 2030.

The shift reflects broader changes in audience behavior, including increased consumption of mobile-first, short-form video content powered by sophisticated discovery algorithms and creator-driven ecosystems.

These platforms are enabling advertisers to reach highly targeted audiences while delivering scalable monetization opportunities through algorithmic content distribution.

Subscription Video Growth Slows as Market Matures

While subscription-based video services will continue expanding, the pace of growth is expected to slow compared with advertising-led models.

Online video subscription and transaction revenues are forecast to rise from $174 billion in 2025 to $216 billion by 2030.

This growth signals continued demand for premium streaming services, but analysts note the segment is entering a more mature phase, where competition among streaming platforms and rising subscription costs are influencing consumer spending patterns.

Traditional TV Segments Continue to Decline

In contrast, traditional broadcast and cable television models are projected to lose market share over the next decade.

Linear TV advertising revenues are expected to decline from $123 billion in 2025 to $113 billion by 2030, reducing its share of total video industry revenues from 16% to 11%.

Similarly, pay-TV revenues, including subscriptions and transactional services, are forecast to decrease from $169 billion to $159 billion over the same period.

These declines are largely attributed to the continued trend of cord-cutting, as audiences migrate toward digital streaming services and social video platforms.

A Structural Shift in Media Monetization

According to Maria Rua Aguete, the evolving media landscape reflects a deeper transformation in how video content is monetized.

Social video advertising is increasingly becoming the dominant force in the industry, enabling platforms to combine creator-driven content with highly targeted advertising models.

This approach contrasts with traditional television’s reliance on fixed programming schedules and broad audience targeting.

Digital platforms, by comparison, leverage algorithmic content discovery, user-generated content ecosystems, and advanced advertising technology to drive engagement and revenue at scale.

The Road to a $1 Trillion Video Economy

As the global media industry approaches the $1 trillion revenue milestone, analysts believe the balance of power will increasingly favor digital platforms.

Advertising—particularly social video advertising—is expected to remain the central driver of growth, while traditional TV business models continue to shrink in relevance.

The transformation reflects broader shifts in consumer behavior, technology innovation, and advertiser priorities as the industry moves deeper into the AI-driven, creator-led digital media era.

Key Insights

• Omdia forecasts global video and TV revenues will reach $1.03 trillion by 2030.

• Online video advertising is projected to grow from $309B in 2025 to $540B by 2030.

• Platforms including Meta, TikTok, and YouTube are expected to generate around $400B in streaming ad revenue.

• Subscription-based video revenues will grow moderately to $216B by 2030.

• Traditional linear TV advertising and pay-TV revenues are projected to decline due to cord-cutting and digital migration.

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Global consumer intelligence company NielsenIQ has introduced Ask Arthur Chat, an AI-powered conversational interface designed to simplify how businesses access retail and consumer insights. The new tool allows users to ask natural-language questions about product performance, market trends, and category dynamics using data from NIQ’s extensive datasets.

As organizations across the retail and consumer goods industries seek faster access to actionable insights, NielsenIQ (NIQ) has launched Ask Arthur Chat, an AI-powered conversational interface that enables clients to retrieve market intelligence through natural-language queries.

The new tool expands the capabilities of NIQ’s analytics ecosystem by enabling businesses to interact with consumer data through a conversational AI experience rather than traditional dashboards or complex analytics platforms.

Ask Arthur Chat reflects NIQ’s broader strategy to embed artificial intelligence into its data services, making insights more accessible to a wider range of users, including small and medium-sized businesses.

Simplifying Access to Consumer Intelligence

Retailers and consumer goods companies increasingly rely on large datasets to understand product performance, market dynamics, and shopper behavior. However, accessing and interpreting these insights often requires advanced analytics tools and specialized expertise.

Ask Arthur Chat aims to reduce those barriers.

The AI interface allows users to ask questions in natural language—for example, about category growth trends, product performance, or regional market changes—and receive immediate answers grounded in NIQ’s verified datasets.

By eliminating the need to navigate complex analytics systems, the platform allows business users to quickly access insights that support faster decision-making.

AI Built on Verified Retail Data

Unlike general-purpose AI systems that rely on publicly available data sources, Ask Arthur Chat draws directly from NIQ’s proprietary consumer and retail datasets.

This approach ensures that responses are based on trusted, validated market intelligence rather than unverified information from the open web.

According to Troy Treangen, the goal is to combine AI’s conversational capabilities with the reliability of NIQ’s long-established consumer data platform.

By doing so, the company aims to make sophisticated analytics insights accessible to a broader audience without compromising data accuracy.

Supporting SMBs and Enterprise Clients

Ask Arthur Chat is designed to support a wide range of users across NIQ’s client base.

For large enterprises, the tool provides a faster way to access insights without navigating multiple analytics dashboards.

For small and medium-sized businesses (SMBs), it introduces a lower-friction entry point into NIQ’s data ecosystem.

SMBs often lack dedicated analytics teams, making it more difficult to extract value from complex data platforms. By enabling conversational access to insights, NIQ aims to democratize access to market intelligence.

Strengthening Client Engagement

The launch of Ask Arthur Chat also reflects a broader trend across the data analytics industry toward AI-driven insight delivery.

Organizations increasingly expect analytics platforms to provide answers rather than just dashboards.

Through conversational interfaces powered by AI, companies can ask questions and receive actionable insights in real time, improving both accessibility and engagement.

NIQ expects Ask Arthur Chat to support several key objectives:

• Expanding access to insights among diversified clients
• Increasing engagement across its analytics platforms
• Accelerating adoption of AI-powered analytics tools

Expanding the Ask Arthur Ecosystem

Ask Arthur Chat will initially serve as a conversational gateway into NIQ’s data platform, but the company plans to expand its capabilities further.

Future updates are expected to integrate the feature more deeply into NIQ’s Ask Arthur and Discover platforms, enabling additional workflows and use cases.

Planned enhancements include:

• Integration with existing client analytics workflows
• Expanded data insights and business intelligence capabilities
• Availability across additional global markets
• Multilingual support for international clients

These improvements aim to extend the value of the conversational interface while making NIQ’s insights accessible across a wider geographic footprint.

The Rise of AI-Powered Analytics Platforms

The launch highlights a broader shift within the analytics industry as companies integrate artificial intelligence into data discovery and decision-making processes.

Research firms such as Gartner and IDC have noted growing demand for augmented analytics platforms that combine machine learning, natural language processing, and automated insight generation.

These platforms help organizations move from traditional analytics dashboards toward systems that proactively deliver insights through conversational interfaces.

For companies operating in fast-moving sectors such as retail and consumer packaged goods, the ability to access insights quickly can provide a significant competitive advantage.

Positioning for the Future of Data Access

By introducing Ask Arthur Chat, NIQ is positioning itself to meet the evolving expectations of modern data users.

As organizations increasingly rely on AI-powered tools to navigate large datasets, platforms that combine trusted data with intuitive interfaces may become essential components of the analytics ecosystem.

Through its continued investment in AI-driven innovation, NIQ aims to strengthen its role as a leading provider of consumer intelligence in a rapidly changing data landscape.

Key Insights

• NielsenIQ launched Ask Arthur Chat, an AI-powered conversational interface for accessing consumer insights.

• The tool allows users to ask natural-language questions about product performance, market trends, and category dynamics.

• Ask Arthur Chat draws on NIQ’s verified consumer and retail datasets, ensuring reliable analytics insights.

• The platform aims to expand access to market intelligence for SMBs and enterprise clients.

• NIQ plans to integrate the tool across its Ask Arthur and Discover analytics platforms and expand it globally.

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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.

Get in touch with our MarTech Experts.

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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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.

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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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.

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