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.

Get in touch with our MarTech Experts.

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.

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.

Get in touch with our MarTech Experts.

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.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

Oracle NetSuite is expanding its artificial intelligence strategy with new capabilities designed to help businesses integrate the AI models and assistants of their choice directly into enterprise workflows. The company announced several enhancements to its NetSuite AI Connector Service, introducing tools that allow organizations to securely connect external AI platforms to NetSuite data while maintaining governance over how those models access finance, operations, and analytics information.

The update includes the launch of NetSuite AI Connector Service Companion, support for Model Context Protocol (MCP) Apps, and deeper integration with NetSuite Analytics Warehouse. Together, these additions aim to help enterprise teams apply AI across finance, reporting, and operational analysis without requiring complex integration work or advanced prompt engineering expertise.

Enterprise software providers are racing to embed generative AI capabilities into business applications. But many organizations are already experimenting with multiple AI assistants—from enterprise copilots to custom models—creating a new integration challenge: how to connect these tools to operational data safely.

With its latest announcement, Oracle is positioning NetSuite as a flexible foundation for that emerging AI ecosystem.

Rather than forcing customers to rely on a single proprietary AI model, NetSuite’s strategy focuses on letting companies connect their preferred AI systems to ERP data, while controlling permissions and governance through the ERP platform.

“Many customers are already working with AI assistants,” said Evan Goldberg, founder and executive vice president at Oracle NetSuite. “These extensions make it easier to securely connect their own AI to their data and workflows.”

The approach reflects a broader shift across enterprise software markets, where vendors increasingly support open AI architectures instead of tightly locked ecosystems.

Bringing External AI Into ERP Workflows

At the center of the announcement is NetSuite AI Connector Service, a standards-based integration framework designed to link AI models with ERP data.

The service supports the Model Context Protocol (MCP), an emerging framework that enables AI systems to interact with enterprise software while respecting application permissions and workflows.

In practical terms, this means companies can connect AI assistants—whether developed internally or through third-party platforms—to NetSuite while controlling:

• what data the AI can access
• which workflows it can trigger
• how outputs are governed and audited

This capability is becoming increasingly important as generative AI expands into finance operations, marketing analytics, and forecasting workflows.

Research from Gartner estimates that over 80% of enterprises will use generative AI APIs or models in production applications by 2026, up from less than 5% in 2023. That surge is pushing ERP vendors to rethink how AI integrates with core business systems.

AI Connector Service Companion: A Finance-Focused AI Layer

One of the most significant additions is the NetSuite AI Connector Service Companion, which aims to make AI assistants more reliable when interacting with financial systems.

Finance workflows require strict accuracy, permissions, and auditability—areas where general-purpose AI models often struggle.

The Companion tool addresses this challenge by providing a structured layer of prompts, context, and governance aligned with NetSuite data models.

Among its key features:

A finance-specific prompt library

The system includes more than 100 curated prompt templates designed for finance and operational use cases. These templates reflect NetSuite’s internal data structures, terminology, and permissions.

Users can modify the prompts or create their own variations to match internal workflows.

Reusable AI “skills”

The platform introduces reusable instruction sets that guide AI models when interacting with NetSuite data. These skills help convert generic AI assistants into NetSuite-aware agents capable of performing specialized finance tasks.

Role-based governance

Preconfigured role templates align AI access with specific enterprise roles such as:

• CFO
• Controller
• Accounts Receivable Analyst
• Accounts Payable Analyst
• Treasury Analyst

This structure ensures AI interactions remain consistent with enterprise security policies.

MCP Apps Bring NetSuite Interfaces Into AI Assistants

Another major component of the update is NetSuite MCP Apps, which introduces structured interfaces inside AI assistants.

Instead of relying solely on free-form text prompts, MCP Apps allow users to interact with NetSuite through visual components embedded within AI tools.

Examples include:

• Prompt Library access
• Report selection tools
• Record pickers for NetSuite data

These structured interfaces reduce the trial-and-error often associated with generative AI prompts.

For enterprise teams, the benefit is efficiency: users can navigate financial reports, select records, and configure queries through familiar NetSuite-style menus.

This approach mirrors broader trends in enterprise AI design. Many platforms are moving toward guided AI interactions, combining conversational interfaces with structured UI elements.

Companies like Microsoft, Salesforce, and Adobe are also building similar hybrid interfaces that blend generative AI with traditional application workflows.

Expanding AI Analytics Through NetSuite Analytics Warehouse

The final component of the announcement focuses on analytics.

The NetSuite AI Connector Service for NetSuite Analytics Warehouse extends AI access beyond transactional ERP data.

With this capability, AI systems can analyze:

• historical operational data
• aggregated analytics datasets
• third-party business data integrated into the warehouse

The result is a broader analytical view that enables AI-driven forecasting and cross-system insights.

According to research from IDC, global spending on AI-enabled analytics platforms is expected to surpass $300 billion by 2027, as enterprises adopt AI-driven decision support systems.

For NetSuite customers, extending AI into the analytics warehouse opens the door to use cases such as:

• financial forecasting
• supply chain trend analysis
• marketing performance modeling
• cross-system operational insights

Why the Announcement Matters for Enterprise Marketing and Finance Teams

ERP platforms sit at the center of enterprise data infrastructure.

That makes them an increasingly important integration point for AI systems used by marketing, finance, and operations teams.

For marketing leaders in particular, access to ERP-level data can unlock deeper insights into revenue performance and customer lifetime value.

Platforms like NetSuite often connect with broader marketing ecosystems that include tools from Google, CRM platforms from Salesforce, and marketing experience systems from Adobe.

By enabling external AI assistants to interact with ERP data, NetSuite effectively turns the ERP platform into an AI data backbone for enterprise operations.

The flexibility to integrate multiple AI systems may also appeal to organizations experimenting with different generative AI tools across departments.

A Competitive Shift Toward Open AI Ecosystems

NetSuite’s strategy highlights a growing competitive dynamic in enterprise software: AI openness versus AI lock-in.

Many vendors are building tightly integrated AI copilots tied to their own platforms. Others are embracing more open architectures that allow enterprises to plug in external AI models.

NetSuite’s AI Connector Service leans toward the latter approach.

Instead of replacing existing AI assistants, the platform acts as a secure gateway between ERP data and AI tools.

That flexibility could become increasingly valuable as organizations deploy AI systems across marketing automation, finance analytics, and operational planning.

Market Landscape

The ERP market is undergoing a significant transformation as AI capabilities become embedded into enterprise systems.

Research from Statista suggests the global ERP software market could exceed $110 billion by 2030, driven by cloud adoption and AI-powered automation.

At the same time, enterprise leaders are demanding platforms that integrate with broader AI ecosystems rather than forcing them into a single vendor’s AI stack.

NetSuite’s AI Connector Service enhancements reflect that demand.

By allowing companies to bring their own AI models while maintaining governance through ERP permissions and workflows, the platform positions itself as a central AI integration layer for enterprise operations.

Top Insights

• Oracle NetSuite introduced new capabilities in its AI Connector Service that allow enterprises to integrate external AI assistants with ERP workflows while maintaining strict data governance and role-based access controls.
• The NetSuite AI Connector Service Companion introduces finance-specific prompts and reusable AI skills, helping general-purpose AI models interact reliably with ERP financial data and operational workflows.
• New MCP Apps embed NetSuite interfaces directly inside AI assistants, allowing users to navigate reports, records, and data structures through structured menus instead of complex prompt engineering.
• Expanded integration with NetSuite Analytics Warehouse enables AI systems to analyze historical, transactional, and third-party datasets, opening new use cases for forecasting and cross-system analytics.
• The announcement reflects a broader enterprise trend toward open AI ecosystems where companies connect multiple AI tools to core business platforms rather than relying on single-vendor AI solutions.

Get in touch with our MarTech Experts.

CEI, a global technology services firm specializing in enterprise AI systems integration, is accelerating its strategic shift toward AI-driven enterprise transformation. The company announced the launch of a new digital platform, cei.ai, alongside the appointment of two senior executives—Prathap Rao as Chief Sales Officer and Ken Kundis as Chief Marketing Officer.

The domain transition from ceiamerica.com to cei.ai signals a broader repositioning of the company from a traditional IT services provider to an AI-focused enterprise transformation partner, reflecting growing demand from organizations seeking to operationalize artificial intelligence at scale.

Enterprise adoption of artificial intelligence is entering a new phase. After years of experimentation with machine learning models and pilot projects, companies are increasingly focused on production-grade AI systems that deliver measurable business outcomes.

Against this backdrop, CEI’s latest announcements—spanning leadership changes and a brand transformation—reflect how technology services providers are repositioning themselves to support enterprise AI adoption.

The company’s new domain, cei.ai, serves as a strategic signal of that shift. Rather than emphasizing legacy IT consulting services, the brand now highlights CEI’s focus on designing, building, and scaling enterprise AI systems.

According to D. Raja, CEO and co-founder of CEI, the transition represents more than a branding change.

“These announcements are signals of where CEI is going,” Raja said. “CEI.ai represents our commitment to AI-first thinking.”

A Leadership Shift to Support AI-Driven Growth

Alongside the domain launch, CEI announced two executive appointments aimed at strengthening its go-to-market capabilities for enterprise AI.

Prathap Rao Named Chief Sales Officer

Prathap Rao joins CEI as Chief Sales Officer after previously holding senior sales leadership roles at Fractal Analytics, a global AI and analytics services provider.

During his tenure at Fractal, Rao led enterprise relationships with global companies and helped expand adoption of generative AI and agent-based AI systems within large organizations.

His experience spans engagements with multinational corporations including Unilever and Philips, where AI initiatives often require coordination across global data infrastructure, governance frameworks, and operational workflows.

At CEI, Rao will oversee enterprise sales strategy and portfolio alignment. His mandate includes helping organizations move from early-stage AI experimentation toward scaled AI deployments that integrate with core business operations.

“Enterprises are past the point of asking whether AI matters,” Rao said. “They’re asking how to operationalize it responsibly and at scale.”

Ken Kundis Appointed Chief Marketing Officer

CEI also appointed Ken Kundis as Chief Marketing Officer to lead the company’s brand repositioning and market narrative.

Kundis brings decades of marketing leadership experience across major global technology services organizations including Tata Consultancy Services, Capgemini, and Infosys.

Across these roles, Kundis has worked on global platform launches, enterprise brand repositioning initiatives, and thought leadership strategies aimed at aligning marketing with enterprise technology buying cycles.

At CEI, he will lead marketing strategy, brand transformation, and go-to-market messaging designed to position the company as an AI systems integration partner for enterprise clients.

“AI has moved from promise to outcomes,” Kundis said. “Enterprises are now focused on solutions that are engineered, governed, and measured.”

The Rise of AI Systems Integrators

CEI’s repositioning reflects a broader shift within the technology services market.

As generative AI technologies mature, enterprises are increasingly looking for partners that can integrate AI into operational environments rather than simply provide advisory services.

Traditional IT consulting models focused heavily on infrastructure modernization and application development. AI integration introduces additional layers of complexity, including:

• data engineering and pipeline architecture
• model deployment and lifecycle management
• governance and regulatory compliance
• organizational change management

These capabilities are becoming central to the emerging role of AI systems integrators.

According to research from Gartner, more than 70% of enterprises will shift from AI experimentation to operational AI deployments by 2027, creating new demand for partners capable of managing AI infrastructure and lifecycle governance.

From AI Experiments to Enterprise Deployment

One of the key challenges enterprises face today is transitioning from AI prototypes to enterprise-scale deployments.

Many organizations have launched pilots involving generative AI or predictive analytics. However, turning those prototypes into production systems requires integrating AI into enterprise platforms, data ecosystems, and operational workflows.

CEI’s strategy focuses on bridging that gap.

The company’s growing portfolio of AI-enabled services includes capabilities spanning:

• AI strategy and advisory
• data engineering and analytics pipelines
• applied AI model development
• enterprise AI deployment and monitoring
• organizational change enablement

By combining these services, CEI aims to help enterprises move beyond isolated AI experiments toward enterprise-wide AI adoption.

Why Domain Strategy Matters in the AI Economy

The move to the .ai domain also reflects a broader trend across technology companies repositioning themselves in the AI economy.

The .ai domain extension, originally associated with the Caribbean nation of Anguilla, has become synonymous with artificial intelligence companies.

Major startups and technology platforms increasingly adopt the domain as a signal of AI specialization.

For CEI, the transition from ceiamerica.com to cei.ai serves both branding and strategic positioning purposes.

It signals to enterprise clients, partners, and investors that the company is prioritizing AI-driven transformation services.

Enterprise Technology Giants Are Betting on AI Integration

The transformation underway at CEI parallels broader shifts across the enterprise technology ecosystem.

Major technology platforms—including Microsoft, Google, Amazon, and Salesforce—are embedding generative AI capabilities across cloud infrastructure, analytics tools, and business applications.

However, deploying these capabilities within enterprise environments often requires integration across multiple platforms.

That integration challenge is creating opportunities for systems integrators and technology services providers that can connect AI tools to operational data infrastructure.

According to IDC, global spending on AI-centric systems could exceed $500 billion by 2027, driven largely by enterprise investment in AI-enabled automation and analytics platforms.

Companies like CEI are positioning themselves to capture a portion of that rapidly expanding market.

Market Landscape

Enterprise AI adoption is accelerating as organizations seek to automate decision-making, optimize operations, and extract value from large data ecosystems.

Research from McKinsey & Company suggests that generative AI could contribute up to $4.4 trillion annually to the global economy, particularly across knowledge-intensive industries.

Yet many organizations struggle to scale AI initiatives due to fragmented data environments and lack of deployment expertise.

This gap between AI experimentation and operational deployment is creating demand for specialized service providers capable of bridging strategy and execution.

By repositioning itself as an AI systems integrator, CEI is aligning its services with this emerging enterprise need.

Top Insights

• CEI launched cei.ai, signaling a strategic shift from traditional IT consulting toward enterprise AI systems integration focused on production-grade AI deployments and measurable business outcomes.
• The company appointed Prathap Rao as Chief Sales Officer and Ken Kundis as Chief Marketing Officer to strengthen enterprise sales strategy and brand positioning in the rapidly expanding AI services market.
• CEI’s transformation reflects a broader industry trend where technology services firms evolve into AI systems integrators, helping organizations operationalize generative AI and advanced analytics.
• Enterprises are increasingly moving beyond AI pilots toward scalable deployments, creating demand for partners that can integrate AI models with enterprise data infrastructure and operational workflows.
• With the launch of CEI.ai, the company is positioning itself as an AI-first enterprise partner, aligning strategy, data engineering, and AI deployment services for measurable ROI.

Get in touch with our MarTech Experts.