Storyteq has been named a Leader in the 2026 Gartner Magic Quadrant for Content Marketing Platforms, marking the fourth time the company has received the designation. The recognition highlights growing demand for platforms that combine AI-driven content operations, marketing workflows, and digital asset infrastructure into a unified enterprise content supply chain.

Enterprise marketing teams are producing more content than ever before, yet managing that content across campaigns, channels, and regions remains a persistent challenge. As artificial intelligence accelerates content creation and personalization, many organizations are discovering that fragmented content infrastructure limits their ability to scale those capabilities.

The latest Gartner Magic Quadrant for Content Marketing Platforms reflects this shift. Storyteq, the content marketing platform developed by Inspired Thinking Group (ITG), has been named a Leader in the 2026 edition of the report.

The ranking places Storyteq among the most prominent vendors in the evolving content marketing platform category, which is increasingly focused on integrating AI, digital asset management, and workflow orchestration into unified marketing infrastructure.

Content Platforms Move Toward AI-Native Architecture

Content marketing platforms have historically focused on planning and publishing workflows. However, the rise of generative AI and automated content production is pushing these platforms toward a more comprehensive operational role.

Organizations now require systems capable of coordinating content creation, managing digital assets, and ensuring consistent brand governance across global campaigns.

According to Storyteq CEO Andrew Swinand, the company’s platform was designed to function as the structural backbone of enterprise content operations.

He describes the platform as a system that integrates AI-driven planning, automated production, and ongoing optimization throughout the content lifecycle.

In practical terms, that means enabling collaboration between creative teams, marketing strategists, and technology systems within a shared environment.

Building an Intelligent Content Supply Chain

A key theme in Storyteq’s positioning is the concept of the content supply chain—a framework that treats marketing content as a managed production pipeline rather than a series of isolated projects.

Within that model, platforms must connect multiple components of the marketing technology stack, including digital asset management systems, creative automation tools, analytics platforms, and workflow orchestration engines.

Major enterprise ecosystems from companies such as Adobe, Salesforce, Microsoft, and Google are also expanding their marketing platforms to integrate AI-driven content generation and personalization features.

Storyteq’s approach focuses on embedding AI deeply within the platform rather than layering it on top of existing systems.

The company says this integration enables marketers to predict content performance, coordinate production workflows, and optimize campaigns using machine learning insights.

AI Transparency and Governance

One of the challenges facing enterprise marketing teams adopting AI tools is maintaining transparency and governance across automated workflows.

To address this concern, Storyteq has introduced AI trust features designed to provide visibility into how its systems make decisions. These features allow organizations to review the data sources, context, and reasoning used by AI models when generating recommendations or automating content production.

Such transparency is becoming increasingly important as marketing organizations incorporate AI into regulated environments and global brand operations.

Storyteq’s proprietary Halo Intelligence layer also plays a role in this system. The technology analyzes customer data, brand intent, and campaign performance history to recommend content strategies before assets are produced.

AI Agents Enter Content Operations

Another component of the platform is Agent Console, which functions as a centralized interface for managing AI agents used across marketing workflows.

These agents can assist with tasks such as content creation coordination, campaign optimization, and workflow automation.

John Kirk said the goal is to transform the content marketing platform from a planning tool into an operational system for managing AI-driven marketing processes.

He noted that many organizations are experimenting with AI tools in disconnected environments, which can lead to duplication and inefficiencies rather than productivity gains.

A unified infrastructure, he argues, allows companies to coordinate data, automation, and execution within a single operational layer.

The Expanding CMP Market

The recognition from Gartner reflects broader momentum in the content marketing platform market.

As digital channels multiply and marketing teams adopt AI tools for content production, organizations are searching for platforms capable of managing increasingly complex content ecosystems.

Research from Forrester indicates that enterprise brands are investing heavily in content supply chain technologies to reduce production costs while increasing campaign velocity.

Meanwhile, McKinsey & Company estimates that companies implementing advanced content operations and automation can improve marketing productivity by more than 20 percent.

The challenge for vendors in the category is balancing automation with governance, especially as generative AI becomes deeply embedded in marketing workflows.

Storyteq’s continued recognition in the Magic Quadrant suggests that platforms capable of combining AI orchestration, content lifecycle management, and marketing collaboration may play a central role in the next generation of enterprise marketing technology.

For organizations navigating the complexities of AI-driven marketing, the future of content operations may depend less on producing more assets and more on building systems capable of managing them intelligently.

Market Landscape

The global content marketing platform (CMP) market is evolving rapidly as organizations seek to manage large-scale content production across digital channels.

Enterprise marketing stacks increasingly combine CMP platforms with digital asset management systems, marketing automation tools, and AI analytics engines.

According to research from IDC, enterprises are investing in content supply chain infrastructure to improve collaboration between creative and marketing teams while supporting AI-driven content generation.

As AI adoption grows, platforms that integrate content creation, workflow automation, and analytics into a unified environment are emerging as key components of modern martech stacks.

Top Insights

• Storyteq has been named a Leader in the 2026 Gartner Magic Quadrant for Content Marketing Platforms, marking its fourth recognition in the report and highlighting its growing presence in enterprise content operations.

• The platform integrates AI-driven content planning, production workflows, and digital asset management to help marketing teams manage complex content supply chains.

• Storyteq’s Halo Intelligence layer analyzes brand data and campaign history to predict content performance before assets are created.

• The platform also introduces Agent Console, allowing enterprises to deploy and manage AI agents that automate content workflows and campaign optimization.

• As AI adoption accelerates, content marketing platforms are evolving into operational infrastructure that connects data, creative production, and marketing execution.

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Vonage, a subsidiary of Ericsson, has been recognized as a Leader in the latest Frost & Sullivan CPaaS Radar while also receiving the APAC CPaaS Company of the Year award. The recognition highlights the company’s continued investment in AI-powered communications infrastructure and programmable network APIs that enable enterprises to build secure, scalable digital communication services.

Enterprise communications platforms are evolving rapidly as businesses seek to integrate voice, messaging, video, and authentication capabilities directly into digital applications. The latest industry recognition for Vonage reflects how communications platform as a service (CPaaS) providers are becoming central to modern enterprise software ecosystems.

Vonage has been named a Leader in the Frost & Sullivan CPaaS Radar for the fifth time, underscoring the company’s position within a highly competitive market for programmable communications services. In addition, the firm received the APAC CPaaS Company of the Year recognition, marking its sixth consecutive year earning that distinction.

The dual recognition from Frost & Sullivan highlights Vonage’s continued focus on developer-centric tools, network-level APIs, and artificial intelligence capabilities that support digital transformation initiatives across industries.

CPaaS Expands Beyond Messaging and Voice

Communications platform as a service initially gained traction by allowing developers to integrate voice calls, SMS messaging, and video communication into applications through APIs. Over time, the category has evolved to include a broader range of digital interaction capabilities, including authentication, fraud prevention, and customer engagement tools.

Vonage’s platform strategy reflects that shift. The company has introduced a portfolio of network-powered APIs designed to expose telecom network intelligence directly to developers and enterprise applications.

Among the capabilities highlighted in the Frost & Sullivan evaluation are APIs for location verification, silent authentication, SIM swap detection, and quality-on-demand connectivity. These services allow companies to incorporate security and identity verification directly into digital services without building complex telecom integrations from scratch.

Such features are increasingly important in sectors such as fintech, healthcare, and e-commerce, where secure customer verification and fraud prevention are essential.

AI-Enhanced Communications Infrastructure

Artificial intelligence is also becoming a major component of CPaaS platforms.

Vonage has incorporated AI-powered tools across its communication services, including capabilities within its video APIs that support real-time transcription, live captions, translation, and content moderation.

These features are designed to support enterprise use cases such as telehealth consultations, online education platforms, and global customer engagement systems.

According to Krishna Baidya, Vonage’s recognition reflects its ability to combine global network reach with programmable API infrastructure.

He noted that the company’s integration with Ericsson provides a strategic advantage, enabling deeper access to telecom network capabilities that can be exposed to developers through APIs.

Developer Ecosystems Drive Adoption

Another factor driving CPaaS adoption is the growing importance of developer ecosystems in enterprise technology.

Companies are increasingly seeking flexible platforms that allow internal development teams to build communication features directly into business applications rather than relying on standalone communication systems.

To support this approach, Vonage offers tools designed to simplify developer onboarding and accelerate application development. These include sandbox testing environments and Bring Your Own AI (BYOAI) connectors that allow developers to integrate their preferred AI models into communication workflows.

Such capabilities enable enterprises to experiment with new communication features, automate workflows, and deploy customer engagement solutions more quickly.

Network APIs as a Strategic Layer

The concept of network APIs—which expose telecom network intelligence through programmable interfaces—is emerging as a key battleground in the communications platform market.

Major technology ecosystems from companies such as Microsoft, Amazon, and Google are also expanding their cloud communications capabilities as enterprises seek deeper integration between cloud infrastructure and telecom networks.

Vonage’s integration with Ericsson positions the company to leverage global carrier infrastructure while delivering programmable services to developers.

Christophe Van de Weyer said the company’s strategy focuses on enabling developers and enterprises to access advanced network intelligence directly through APIs.

Those capabilities, he noted, allow organizations to automate processes, strengthen security systems, and improve customer engagement across digital channels.

The Growing CPaaS Market

The CPaaS market itself is experiencing rapid growth as enterprises move toward API-first communication architectures.

According to research from Gartner, organizations are increasingly embedding communication capabilities directly into customer applications, internal collaboration platforms, and digital services.

Meanwhile, IDC estimates that global spending on programmable communication platforms will continue to expand as businesses modernize customer engagement infrastructure.

In that environment, vendors that combine AI capabilities, telecom network intelligence, and developer-friendly APIs are likely to play a central role in the future communications technology landscape.

Vonage’s continued recognition in industry reports suggests the company remains one of the prominent players shaping that evolution.

Market Landscape

The Communications Platform as a Service (CPaaS) market is expanding as enterprises increasingly embed messaging, voice, and video capabilities into digital applications.

Rather than relying on standalone communication systems, companies are adopting API-driven communications infrastructure that integrates directly with customer engagement platforms, mobile apps, and enterprise software systems.

Analysts at Statista report that demand for cloud communications services continues to rise globally as organizations prioritize digital customer experiences and automated communication workflows.

At the same time, the integration of AI and telecom network APIs is transforming CPaaS platforms into strategic infrastructure for modern digital services.

Top Insights

• Vonage has been named a Leader in the Frost & Sullivan CPaaS Radar for the fifth time and awarded APAC CPaaS Company of the Year for the sixth consecutive year.

• The platform integrates AI-driven communication tools with programmable network APIs that enable enterprises to build secure messaging, voice, and video capabilities into digital applications.

• Network APIs such as silent authentication, location verification, and SIM swap detection help businesses strengthen fraud prevention and identity verification systems.

• AI-powered video capabilities including transcription, translation, and content moderation support enterprise use cases in telehealth, education, and retail.

• As the CPaaS market grows, vendors combining AI intelligence, telecom infrastructure, and developer ecosystems are emerging as key players in digital communications platforms.

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Exiger has achieved “Awardable” status in the Platform One (P1) Solutions Marketplace, enabling U.S. government agencies to evaluate and potentially procure its Exiger Cyber platform. The designation allows organizations within the U.S. Department of War and other federal agencies to review Exiger’s cybersecurity and supply-chain risk management capabilities through the government’s digital solutions marketplace.

As governments accelerate the adoption of AI and DevSecOps practices, securing software supply chains has become a growing priority for defense and national security organizations. The latest recognition for Exiger reflects increasing demand for automated risk analysis tools capable of managing complex software ecosystems.

Exiger announced that its Exiger Cyber platform has been designated “Awardable” within the Platform One Solutions Marketplace, a procurement environment designed to help government agencies quickly identify vetted technology solutions.

The P1 Solutions Marketplace serves as a centralized digital repository where government decision-makers can access post-competition solutions, including pitch videos and technical capabilities for software, hardware, and service platforms.

Strengthening Software Supply Chain Security

Modern defense systems rely heavily on software and open-source components, creating potential vulnerabilities if those elements are not continuously monitored.

Exiger Cyber addresses this challenge by providing automated software supply chain security capabilities designed for DevSecOps environments. The platform uses graph analytics, machine learning, and rule-based risk assessments to analyze relationships between software components, suppliers, vulnerabilities, and geopolitical risks.

By connecting these variables into a unified risk framework, the system provides real-time insights through dashboards and APIs that integrate directly into DevSecOps pipelines.

According to Brandon Daniels, the rapid adoption of AI in software development has created new challenges for organizations attempting to manage software risks.

Daniels noted that while AI can accelerate code generation and software delivery, it can also increase the likelihood of introducing insecure or poorly understood software components into mission-critical systems.

Supporting DevSecOps Initiatives

Exiger’s participation in the Platform One marketplace aligns with broader efforts by the U.S. government to modernize software development practices.

Platform One is a DevSecOps program designed to standardize and accelerate secure software development across government agencies.

The initiative promotes tools and platforms that enable organizations to integrate security checks directly into development pipelines rather than applying them later in the process.

Exiger Cyber supports several key Platform One focus areas, including:

• DevSecOps engineering
• Cybersecurity automation
• Pipeline platform integration
• Identity management
• Supply chain risk management

Through these capabilities, the platform aims to help agencies monitor software risks continuously rather than relying on periodic compliance checks.

Accelerating Security Authorization

One of the major operational benefits highlighted by Exiger is the ability to accelerate Authority to Operate (ATO) and re-authorization cycles for government systems.

Traditional security assessments can take weeks or months to complete due to manual reviews and fragmented risk data. Exiger Cyber uses automated analysis to reduce that process to minutes, allowing security teams to identify vulnerabilities and make faster decisions.

The platform also identifies leading risk indicators and provides intelligence that helps government agencies prioritize remediation efforts.

Expanding Government Adoption

Exiger’s AI-powered risk intelligence technology is already deployed across several U.S. government agencies.

Recent contracts include engagements with the United States Army and U.S. Customs and Border Protection, where the company’s solutions are used to support trade enforcement initiatives and defense readiness programs.

These deployments demonstrate how AI-driven risk analysis platforms are becoming increasingly important for managing national security supply chains.

A Growing Focus on Software Transparency

The recognition from Platform One highlights the broader shift toward software transparency and supply chain visibility in government technology programs.

As organizations rely on larger networks of third-party software providers and open-source components, maintaining visibility into those dependencies has become a key security requirement.

Platforms like Exiger Cyber aim to address that challenge by combining AI analysis, data intelligence, and automated risk monitoring to create a comprehensive operational view of software ecosystems.

For defense organizations operating in complex and high-risk environments, such capabilities are becoming essential for maintaining secure digital infrastructure.

Market Landscape

Software supply chain security has emerged as a major focus area across both government and enterprise sectors.

Research from Gartner highlights growing concerns around software supply chain vulnerabilities as organizations increase their reliance on open-source software and distributed development models.

At the same time, intelligence platforms that combine AI analytics, cybersecurity automation, and DevSecOps integration are becoming critical tools for identifying risks earlier in the software lifecycle.

According to industry analysts at IDC, spending on cybersecurity and supply chain risk management technologies is expected to continue rising as governments strengthen digital infrastructure protections.

Top Insights

• Exiger achieved “Awardable” status in the Platform One Solutions Marketplace, enabling U.S. government agencies to evaluate its cybersecurity solution.

• The Exiger Cyber platform provides automated software supply chain security using graph analytics, machine learning, and risk intelligence.

• The technology integrates with DevSecOps pipelines to provide continuous monitoring of software components, suppliers, and vulnerabilities.

• Government agencies can use the platform to accelerate ATO authorization cycles and security risk assessments.

• Exiger’s AI-driven supply chain intelligence is already deployed across organizations including the U.S. Army and U.S. Customs and Border Protection.

Get in touch with our MarTech Experts.

BMC Software has announced new AI-powered innovations for its mainframe platform, embedding purpose-built artificial intelligence directly into the tools used by developers and operations teams. The new capabilities aim to address the growing mainframe skills gap, enabling organizations to capture institutional knowledge, improve application insights, and accelerate modernization across mission-critical systems.

As enterprises continue to rely on mainframes for high-volume transaction processing and critical workloads, maintaining and modernizing these systems has become increasingly challenging. A large portion of experienced mainframe professionals are nearing retirement, leaving organizations with limited access to the expertise needed to manage complex legacy environments.

To address this challenge, BMC Software has introduced new artificial intelligence capabilities designed specifically for mainframe workflows. These enhancements integrate AI directly into the operational tools developers and administrators use daily, providing contextual insights and automation across development, security, and operational processes.

AI for Mainframe Application Intelligence

One of the major additions expands the capabilities of the BMC AMI zAdviser Enterprise platform, which delivers AI-driven insights into mainframe development productivity.

The new zAdviser Enterprise Application Analysis feature combines multiple data sources—including source code analysis, telemetry from the BMC AMI DevX platform, and development performance metrics—to generate an AI-powered narrative intelligence report.

By consolidating information from previously disconnected systems, the platform enables IT leaders to identify application complexity, analyze stability patterns, and determine modernization priorities. The system also helps organizations evaluate application risk levels and pinpoint areas where institutional knowledge is heavily concentrated.

This capability is particularly valuable as organizations attempt to modernize legacy applications without losing operational continuity.

Addressing the Mainframe Skills Gap

According to BMC’s 2025 State of the Mainframe report, approximately 66% of the current workforce consists of Gen Z or millennial professionals, many of whom lack direct experience managing legacy mainframe systems.

To help bridge this gap, BMC has expanded the capabilities of BMC AMI Assistant, an AI-powered knowledge tool embedded across the BMC AMI platform.

The assistant integrates with organizational knowledge sources such as runbooks, service tickets, log files, and historical incident resolutions. By surfacing relevant insights directly within operational workflows, the system enables newer developers and operators to diagnose issues and resolve incidents faster.

John McKenny said the company’s approach focuses on embedding AI directly into the environments where mainframe teams work.

Rather than requiring engineers to search through legacy documentation or manually correlate system data, the AI tools provide contextual guidance and operational intelligence in real time.

Automated Security and Certificate Management

BMC also introduced a new security solution designed to address upcoming changes in digital certificate management requirements.

The BMC AMI Digital Certificate Management platform automates certificate lifecycle management for mainframe environments. The solution is designed to help enterprises prepare for upcoming industry changes that will shorten SSL/TLS certificate lifespans from 398 days to just 47 days by 2029.

By automating certificate provisioning, monitoring, and renewal processes, the platform reduces the risk of outages and compliance issues caused by manual certificate management.

The system integrates with enterprise certificate platforms from vendors including Venafi and Keyfactor, allowing organizations to automate certificate lifecycle management without modifying existing infrastructure.

Expanding AI-Driven Workflow Orchestration

Beyond mainframe operations, BMC also announced new enhancements to its Control-M workload automation platform.

The company introduced Control-M Archive Service, a new cloud-native capability that automatically archives job logs and execution data. The archive system provides a long-term repository designed for auditing, compliance reporting, and post-execution analysis.

Control-M plays a central role in orchestrating workflows across hybrid IT environments, connecting mainframe workloads with cloud infrastructure and distributed applications.

These capabilities are part of BMC’s broader strategy to support agentic AI orchestration, where autonomous AI agents coordinate business processes across multiple systems and technologies.

Analyst Perspective on AI-Driven Mainframe Transformation

Industry analysts note that AI is becoming an essential component of modern mainframe operations.

Steve Dickens stated that BMC’s latest innovations demonstrate how AI can capture decades of institutional knowledge and apply it through context-aware automation.

By embedding AI directly into operational workflows, organizations can maintain the reliability of mainframe systems while enabling new generations of engineers to manage increasingly complex IT environments.

The Future of Mainframes in the AI Era

Despite predictions that cloud computing would replace legacy systems, mainframes remain a cornerstone of enterprise infrastructure for industries such as banking, government, healthcare, and telecommunications.

Analysts at IDC report that enterprises continue to modernize mainframe environments rather than replace them entirely, integrating them with cloud platforms and AI-driven automation tools.

Solutions that combine AI intelligence, automation, and institutional knowledge capture are becoming essential for organizations seeking to maintain operational resilience while preparing for the next generation of IT professionals.

With these new AI-powered capabilities, BMC aims to ensure that mainframes remain a vital component of enterprise computing in the era of intelligent automation.

Market Landscape

Mainframes remain critical infrastructure for industries that require high reliability, security, and transaction processing scale.

Research from Gartner indicates that many organizations are investing in mainframe modernization strategies rather than replacing these systems, integrating them with cloud platforms, DevOps pipelines, and AI-driven automation tools.

At the same time, organizations are addressing the mainframe skills shortage by adopting AI-powered tools that help new developers understand legacy systems and maintain operational continuity.

Top Insights

• BMC introduced AI-powered enhancements for its mainframe tools to help organizations modernize and manage critical applications.

• The new zAdviser Enterprise Application Analysis provides AI-generated insights into application complexity, stability, and modernization priorities.

• BMC AMI Assistant surfaces institutional knowledge from runbooks, tickets, and logs to help developers resolve issues faster.

• The BMC AMI Digital Certificate Management solution automates SSL/TLS certificate lifecycle management for mainframe environments.

• Enhancements to the Control-M platform introduce automated archiving capabilities to support auditing and compliance in hybrid environments.

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quantilope has introduced Ad Optimizer, a new AI-powered research solution designed to help brands evaluate and refine advertising campaigns before launch. The platform analyzes creative assets using advanced consumer insight methodologies, enabling marketers to align advertising strategies with key purchasing triggers and maximize return on marketing investment.

As advertising channels become increasingly fragmented and competition for consumer attention intensifies, marketers are under pressure to ensure that every campaign delivers measurable impact. In response, companies are turning to AI-powered insights platforms that combine marketing science with automation to optimize creative performance.

quantilope’s newly launched Ad Optimizer aims to address this challenge by providing brands with a data-driven framework for evaluating advertising concepts before they reach the market.

The tool combines artificial intelligence with established consumer research methodologies, allowing marketing and insights teams to test creative concepts and identify how effectively their ads trigger consumer purchase motivations.

AI-Powered Creative Analysis

The Ad Optimizer platform enables users to upload advertising assets—including video, audio, and visual content—for automated evaluation.

Using AI-driven analysis, the system performs a frame-by-frame assessment of visual elements and accompanying audio to determine whether the ad effectively communicates the intended brand message.

A central feature of the tool is its analysis of Category Entry Points (CEPs)—the specific situations, needs, or consumer motivations that trigger people to think about a product category or brand.

By mapping advertising content against defined CEPs, marketers can understand whether their creative assets reinforce the right consumer cues.

According to Jannik Meyners, the approach helps brands determine whether their advertising builds the mental availability required to influence purchasing decisions.

Rather than relying solely on intuition or traditional research cycles, marketers can identify gaps in messaging early in the creative process.

Bridging Creative Strategy and Data

One of the major challenges in advertising is balancing creative instinct with data-driven decision-making.

While creative teams often rely on storytelling and brand experience to shape campaigns, marketing leaders increasingly require measurable evidence that advertising will perform effectively.

The Ad Optimizer platform seeks to bridge that gap by combining creative evaluation with automated consumer insights.

By delivering recommendations on how messaging can better align with consumer motivations, the tool enables brands to refine campaigns before committing media budgets.

Faster Insights for Campaign Optimization

Traditional advertising research can take weeks to produce results, limiting the ability of marketers to iterate on creative ideas quickly.

quantilope’s Ad Optimizer is designed to accelerate that process by delivering insights significantly faster than conventional research methods.

The platform provides interactive dashboards and visual analytics, allowing marketing teams and stakeholders to quickly identify areas where creative assets can be improved.

This rapid feedback cycle enables brands to test multiple creative concepts, refine messaging, and optimize campaign performance before launching into large-scale media distribution.

Expanding quantilope’s Consumer Intelligence Platform

The introduction of Ad Optimizer also represents a strategic expansion of quantilope’s broader Consumer Intelligence Platform, which integrates automated research solutions across brand strategy, product development, and marketing insights.

Peter Aschmoneit said the new solution bridges the gap between creative intuition and data-driven precision.

By embedding research methodologies directly into the platform, brands can move quickly while still ensuring their decisions are supported by reliable consumer insights.

The Ad Optimizer solution works alongside quantilope’s existing research tools to help organizations understand consumer behavior and refine marketing strategies based on actionable data.

AI and the Future of Advertising Research

Artificial intelligence is rapidly transforming how brands approach advertising effectiveness.

Rather than relying solely on post-campaign analysis, AI tools are enabling marketers to predict and optimize creative performance before campaigns go live.

Industry analysts at Gartner have noted that AI-powered marketing analytics platforms are becoming increasingly important as organizations seek to improve ROI from advertising investments.

Meanwhile, research from Statista shows that global digital advertising spending continues to grow, intensifying the need for tools that help brands maximize campaign effectiveness.

By combining AI-driven analysis with established marketing science frameworks such as Category Entry Points, platforms like Ad Optimizer are helping brands navigate this increasingly complex advertising landscape.

Market Landscape

The advertising technology landscape is rapidly evolving as brands adopt AI-powered creative analytics and consumer insights platforms.

Companies are increasingly integrating artificial intelligence into marketing workflows to analyze consumer behavior, predict campaign performance, and optimize messaging strategies.

According to research from IDC, investments in AI-driven marketing analytics tools are expected to grow significantly as organizations seek to improve marketing efficiency and maximize campaign ROI.

Tools that combine automation, consumer research, and predictive analytics are becoming critical components of modern marketing technology stacks.

Top Insights

• quantilope introduced Ad Optimizer, an AI-powered advertising research tool designed to evaluate creative concepts before campaigns launch.

• The platform analyzes visual and audio components of ads using frame-by-frame AI analysis.

• Ad performance is measured against Category Entry Points (CEPs) to determine whether campaigns trigger key consumer motivations.

• The system delivers fast insights and visual dashboards to help marketing teams refine creative messaging before investing in media budgets.

• Ad Optimizer expands quantilope’s Consumer Intelligence Platform, integrating advertising analysis with broader consumer research capabilities.

Get in touch with our MarTech Experts.

mktg.ai has announced the launch of version 2.1 of its platform, introducing what it calls the Creative Intelligence System, a new category designed to serve as a unified system of record for marketing operations. The platform connects creative assets, campaign performance, and marketing spend into a single intelligence layer, enabling marketing teams to make faster decisions and better understand what drives growth across channels.

Marketing organizations have historically relied on a patchwork of dashboards, analytics tools, and reporting systems to track campaign performance. While these tools provide valuable data, they often operate in silos, making it difficult for teams to see the full picture of how creative assets and campaigns perform across platforms.

mktg.ai aims to address this long-standing challenge with the launch of its Creative Intelligence System, a platform designed to unify marketing data and enable real-time decision-making.

The company describes the platform as a system of record for marketing, similar to how enterprise finance and sales departments rely on systems such as ERP and CRM to manage operations.

From Dashboards to Decision Intelligence

Traditional marketing dashboards typically report historical performance data, leaving marketers to manually interpret results and determine the next steps.

The mktg.ai platform replaces static dashboards with a real-time intelligence system that connects creative assets, campaign performance metrics, and advertising spend across channels.

Through this unified framework, marketing teams can:

• View all creative assets and campaigns across multiple channels
• Identify which creative elements and messages drive results
• Detect wasted media spend or sudden performance changes
• Generate automated reports for internal teams and executives
• Ask questions about campaign performance through conversational AI

The platform’s Ask mktg.ai feature enables users to query marketing data in natural language and receive instant insights, reflecting a broader shift toward conversational analytics.

Introducing Creative Intelligence

A core concept behind the platform is Creative Intelligence, a discipline focused on analyzing marketing performance at the level consumers actually experience it—creative content.

Many analytics and attribution tools focus primarily on channel performance, such as social media platforms or search advertising networks. However, mktg.ai connects campaign results directly to the creative assets driving engagement.

By linking creative performance with media spend and campaign outcomes, marketers gain a deeper understanding of which messages, formats, and visual elements generate the strongest results.

Kevin Wassong explained that the platform was designed to simplify the complexity of modern marketing environments.

He noted that marketing teams have spent years relying on fragmented dashboards and delayed reporting cycles, limiting their ability to act quickly on performance insights.

A Unified View of Marketing Performance

The Creative Intelligence System introduces several core features designed to provide a comprehensive view of marketing performance.

The Campaign View delivers a cross-channel perspective on campaign performance, highlighting the drivers behind growth and engagement.

Meanwhile, the Asset View creates a complete inventory of creative assets, linking each asset with its performance metrics and media spend.

The platform also includes Daily Alerts AI, which automatically identifies performance changes or anomalies that require attention. This helps marketing teams respond quickly to emerging opportunities or issues without waiting for weekly or monthly reporting cycles.

In addition, the system automatically generates marketing reports tailored for both operational teams and executive leadership.

Connecting Marketing Insights to the C-Suite

One of the persistent challenges for marketing leaders is translating campaign performance into clear business outcomes for executive stakeholders.

The mktg.ai platform addresses this challenge by linking campaign activity to business impact metrics that resonate with leadership teams.

Christina Lowris Panos noted that the platform helps provide a unified view of marketing activities across the organization.

By offering visibility into what campaigns are running and how they contribute to brand performance, the system enables more transparent decision-making across departments.

AI and the Future of Marketing Infrastructure

Industry experts believe that AI-driven marketing platforms will play an increasingly important role in shaping the next generation of marketing technology infrastructure.

Shiv Singh emphasized that artificial intelligence can only deliver meaningful insights when it operates on structured and integrated data.

By organizing creative assets, performance data, and marketing spend into a unified system, platforms like mktg.ai enable AI to produce actionable insights rather than simply automating reporting tasks.

Early Industry Partnerships

To support the development of the Creative Intelligence System, mktg.ai has collaborated with several early partners helping define the future of marketing operations.

Organizations including The Corcoran Group, Newman's Own, ICP, and Sandy Hook Promise are working with the company to shape how modern marketing teams adopt unified intelligence systems.

These partnerships highlight growing demand for platforms capable of connecting marketing strategy, creative development, and performance analytics into a single workflow.

A New Model for Marketing Operations

The launch of mktg.ai version 2.1 reflects a broader transformation in how marketing teams manage data and decision-making.

Rather than relying on disconnected dashboards and delayed reporting cycles, organizations are moving toward integrated intelligence platforms that combine data visibility, AI insights, and operational workflows.

For marketers navigating increasingly complex digital ecosystems, tools that provide real-time clarity on creative performance and campaign effectiveness may become essential infrastructure.

Market Landscape

Marketing technology is evolving rapidly as organizations adopt AI-powered intelligence platforms that unify campaign analytics, creative performance data, and media spending insights.

Research from Gartner indicates that enterprises are increasingly investing in marketing data integration platforms to overcome fragmented reporting and gain clearer visibility into marketing ROI.

Meanwhile, market data from Statista shows continued growth in global digital advertising spending, increasing demand for tools that help organizations optimize campaign performance and allocate budgets more effectively.

Top Insights

• mktg.ai launched version 2.1 of its platform, introducing the Creative Intelligence System as a unified system of record for marketing operations.

• The platform connects creative assets, campaign performance, and marketing spend into a single real-time intelligence layer.

• Features include Campaign View, Asset View, Daily Alerts AI, automated marketing reports, and conversational insights through Ask mktg.ai.

• The platform focuses on Creative Intelligence, analyzing marketing effectiveness at the level of creative assets rather than just channels.

• Early partners such as The Corcoran Group and Newman’s Own are helping shape the future of unified marketing intelligence platforms.

Get in touch with our MarTech Experts.

Digital experience platform provider Optimizely has once again secured a leadership position in the 2026 Magic Quadrant for Content Marketing Platforms, published by Gartner. The recognition marks the ninth consecutive year the company has been placed in the Leaders quadrant, highlighting its continued influence in the evolving enterprise content marketing technology landscape.

Enterprise marketing teams are under mounting pressure to produce more content across more channels while maintaining consistency, governance, and measurable impact. In this environment, technology platforms that unify planning, production, and distribution have become foundational to modern marketing operations.

Against this backdrop, Optimizely announced it has been named a Leader in the 2026 Magic Quadrant for Content Marketing Platforms by Gartner—a milestone that extends the company’s leadership streak in the category to nine consecutive years.

The Magic Quadrant is one of the technology industry's most closely watched evaluations, assessing vendors on their ability to execute and completeness of vision. Sustained recognition in the Leaders quadrant signals strong product capabilities, consistent innovation, and broad enterprise adoption.

For Optimizely, the recognition reflects a broader shift occurring across marketing technology stacks: the move toward AI-driven content operations platforms that automate large portions of the marketing workflow.

AI Agents Move Into Marketing Workflows

At the center of Optimizely’s strategy is Optimizely Opal, the company’s AI orchestration platform designed to operate directly inside content marketing workflows.

Unlike earlier generations of AI tools that acted primarily as assistants or standalone generators, Opal embeds AI agents directly into the Content Marketing Platform (CMP) environment. These agents can draft content, localize assets for global markets, chain together multi-step marketing workflows, and enforce governance rules for brand consistency.

The approach reflects a growing industry trend toward autonomous marketing operations, where AI systems manage operational tasks while marketers focus on strategy and creativity.

“AI is shifting from something marketers consult to something that performs work inside the system,” said Rupali Jain, Chief Product Officer at Optimizely, describing how the platform integrates automation with enterprise governance.

The company's vision for “Autonomous Ops” aims to remove friction across the entire content lifecycle—from campaign planning and editorial collaboration to production and distribution.

Why Content Marketing Platforms Are Becoming Strategic Infrastructure

Content marketing platforms have evolved far beyond editorial planning tools. Today they function as central orchestration systems for enterprise marketing operations, integrating with analytics platforms, CRM systems, and digital experience stacks.

Major technology ecosystems—including Salesforce, Adobe, Microsoft, and Google—have increasingly embedded AI capabilities into their marketing clouds. As a result, CMP vendors are racing to differentiate through automation, workflow intelligence, and deeper data integration.

Optimizely’s platform is designed for large global enterprises across industries such as banking, healthcare, and technology. Its system consolidates content planning, collaboration, creation, and publishing into a unified workflow layer.

That integration is becoming essential as marketing teams manage an expanding number of digital touchpoints—from websites and mobile apps to social media, email campaigns, and paid advertising channels.

A Competitive and Expanding Market

The content marketing platform market is growing quickly as enterprises invest in scalable marketing infrastructure.

According to research from Gartner, marketing leaders are increasing spending on content supply chain technologies to manage complex content ecosystems. Meanwhile, Statista estimates global spending on marketing automation platforms could exceed $25 billion by 2030, fueled by AI-driven campaign management and personalization.

Within this competitive landscape, Optimizely competes with vendors offering specialized content orchestration, marketing automation, and digital experience solutions.

The company strengthened its CMP capabilities after acquiring Welcome in 2021, a platform that had already appeared in previous Magic Quadrant reports. That technology was later rebranded as Optimizely CMP, forming the backbone of the company’s content operations strategy.

The latest Gartner recognition also builds on a series of analyst acknowledgments for the company. In recent months, Optimizely was also named a Leader in the 2026 Magic Quadrant for Personalization Engines and recognized in The Forrester Wave: Digital Experience Platforms, Q4 2025 by Forrester.

What This Means for Enterprise Marketing Teams

For enterprise marketing organizations, the shift toward AI-driven content operations represents a structural change in how campaigns are executed.

Traditional marketing teams often rely on disconnected tools for planning, asset management, collaboration, and distribution. That fragmentation can slow campaign launches and introduce governance risks, especially in regulated industries.

Platforms like Optimizely’s CMP aim to consolidate these processes into a single environment where AI assists with operational execution.

In practice, that means marketers can plan campaigns, generate content drafts, coordinate global localization, and manage approvals within one platform while automated workflows handle routine tasks.

The broader implication is that content supply chains are becoming automated digital infrastructure, similar to how DevOps transformed software development pipelines.

For organizations managing high volumes of digital content across markets and channels, the ability to orchestrate these operations through AI-enabled platforms could become a competitive advantage.

As marketing technology stacks continue to consolidate, vendors capable of combining AI orchestration, governance, and enterprise workflow automation are likely to shape the next phase of the MarTech ecosystem.

Market Landscape

The content marketing platform sector sits at the intersection of marketing automation, digital experience platforms (DXPs), and AI-driven content operations. Vendors such as Adobe and Salesforce integrate content workflows into broader marketing clouds, while specialized CMP vendors focus on workflow orchestration and content supply chain management.

Industry analysts increasingly view AI-powered marketing operations as the next evolution of enterprise MarTech stacks. Research from IDC suggests that by 2027, more than 60% of enterprise marketing workflows will incorporate AI-assisted automation, accelerating campaign execution and improving personalization capabilities.

Platforms capable of combining content lifecycle management, AI orchestration, and enterprise governance are expected to become core infrastructure for global marketing organizations.

Top Insights

• Optimizely secured a Leader position in the 2026 Gartner Magic Quadrant for Content Marketing Platforms, extending a nine-year leadership streak and reinforcing its role in enterprise marketing infrastructure.

• The company’s Optimizely Opal platform introduces AI agents embedded directly into marketing workflows, enabling automated content drafting, localization, governance, and campaign orchestration.

• Content marketing platforms are evolving into enterprise content supply chain systems that unify planning, collaboration, and distribution across digital channels and global markets.

• Analysts say AI-driven marketing operations are becoming central to modern MarTech stacks as enterprises scale content production and campaign management across complex customer journeys.

• Enterprise marketing teams increasingly rely on integrated platforms that combine automation, governance, and AI orchestration to manage growing content demands across digital channels.

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Enterprise data infrastructure company Nasuni has introduced a broader platform strategy aimed at helping organizations unlock the value of unstructured file data for artificial intelligence and distributed collaboration. The announcement includes new platform capabilities—Active Everywhere and AI Activate—designed to allow enterprise teams and AI systems to access governed data directly from a unified cloud-based file infrastructure.

Enterprise organizations are rapidly adopting artificial intelligence across operations, but many still struggle with a fundamental challenge: most enterprise data remains locked in unstructured files scattered across global systems.

To address this gap, Nasuni unveiled an expanded product and brand strategy focused on what it calls file data activation—the ability to turn large volumes of enterprise file data into a usable foundation for both human collaboration and AI-driven workflows.

The move signals a shift in positioning for Nasuni, which historically focused on cloud-based file storage. The company now describes its platform as a broader unstructured data infrastructure for enterprise teams and AI systems, reflecting the growing importance of operational file data in modern digital transformation initiatives.

Why File Data Is Becoming Critical for AI

While enterprises increasingly deploy generative AI and automation platforms, the underlying data needed to power these systems often remains fragmented across legacy file systems.

Operational assets such as engineering designs, financial documents, project files, media content, and research data typically live in distributed file environments. These repositories represent some of the most valuable corporate information but are often difficult for AI systems to access securely.

According to research from Gartner, more than 80% of enterprise data is unstructured, stored in files, documents, and media assets rather than structured databases. As AI adoption accelerates, unlocking this data layer has become a top priority for CIOs and data leaders.

Nasuni’s platform attempts to solve this challenge by creating a global file data layer that centralizes governance, access controls, and versioning while enabling distributed teams to work with data stored in the cloud.

Active Everywhere: Edge Access Without Hardware

One of the company’s key product announcements is Resilio Active Everywhere v6, a technology that enables distributed teams to access file data at local network speeds while maintaining centralized governance.

The feature builds on Nasuni’s acquisition of data synchronization provider Resilio and integrates it more deeply into the Nasuni platform.

Active Everywhere allows edge offices and remote teams to access shared file data directly without relying on traditional WAN optimization appliances or proprietary caching hardware. Instead, the solution uses software-based synchronization built into the platform’s global namespace.

This approach addresses a growing enterprise challenge: the cost and complexity of maintaining physical infrastructure across geographically distributed operations.

Companies operating in industries such as manufacturing, architecture, engineering, construction (AEC), energy, and life sciences often rely on large file assets that must be accessed across multiple locations. As file sizes increase and collaboration expands globally, infrastructure bottlenecks can slow workflows.

Nasuni’s strategy is to replace these hardware-heavy architectures with a software-defined file infrastructure model built on cloud storage.

AI Activate: Giving AI Direct Access to File Data

The second major announcement is AI Activate, a new capability that enables AI agents and large language models to interact directly with enterprise file data stored within the Nasuni platform.

Through integration with Model Context Protocol (MCP), AI Activate allows authorized AI tools to discover, read, and act on file data while respecting existing permissions and governance controls.

This design addresses a common challenge in enterprise AI deployments: the need to create separate data pipelines or duplicate datasets before AI models can use them.

By enabling AI to operate directly on file data stored in its platform, Nasuni aims to reduce the need for additional infrastructure while maintaining enterprise security controls.

The approach aligns with broader industry trends in AI-ready data infrastructure, where platforms are evolving to support AI-native workflows.

Technology ecosystems including Microsoft, Amazon, and Google are increasingly embedding AI capabilities into their cloud platforms, prompting infrastructure vendors to ensure enterprise data can be accessed safely by these systems.

A Growing Market for Unstructured Data Platforms

The rise of generative AI is reshaping enterprise data strategies, particularly around unstructured content.

According to IDC, the global datasphere will reach 175 zettabytes by 2025, with the majority of that growth coming from unstructured data sources such as documents, images, videos, and design files.

Organizations that can operationalize this data—by making it searchable, governed, and AI-accessible—are expected to gain competitive advantages in automation, analytics, and innovation.

Nasuni already serves more than 1,300 enterprise customers across industries including manufacturing, media, life sciences, and energy. These sectors often generate large volumes of file-based operational data that must be shared across global teams.

The company has also expanded its cloud ecosystem in recent years, supporting multi-cloud deployments across platforms such as Microsoft Azure and Amazon Web Services.

What It Means for Enterprise IT and Data Leaders

For enterprise CIOs and infrastructure leaders, Nasuni’s expanded strategy highlights a broader industry shift: file infrastructure is becoming part of the AI data pipeline.

Traditional file storage solutions were designed primarily for archiving and collaboration. In the AI era, however, file systems must support real-time access, governance, and integration with intelligent systems.

Platforms capable of unifying file storage, collaboration, governance, and AI access may play an increasingly central role in enterprise technology stacks.

As organizations invest heavily in generative AI and automation, the ability to activate previously untapped file data could determine how effectively those AI systems deliver business value.

Market Landscape

The enterprise file data platform market is evolving as organizations move away from hardware-heavy storage architectures toward cloud-native, software-defined data infrastructure.

Major cloud providers such as Microsoft, Amazon, and Google continue expanding storage and AI services, while specialized vendors like Nasuni focus on operational file systems that integrate governance, collaboration, and AI access.

Analysts at McKinsey & Company estimate that generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy. Unlocking enterprise data—particularly unstructured content—will be critical to capturing that value.

As a result, platforms capable of activating file-based data for AI workflows are emerging as a new category within enterprise data infrastructure.

Top Insights

• Nasuni expanded its enterprise platform strategy to focus on file data activation, helping organizations unlock unstructured data for AI systems and distributed teams.

• The new Active Everywhere v6 capability enables edge teams to access governed file data at LAN speeds without relying on WAN optimization hardware or proprietary caching infrastructure.

• AI Activate introduces AI-ready access to enterprise file data, allowing large language models and AI agents to work directly on governed datasets using Model Context Protocol.

• Enterprise organizations are increasingly prioritizing unstructured data platforms as AI adoption accelerates across global operations and collaborative workflows.

• Analysts say activating file-based operational data could become critical for enterprises seeking to maximize ROI from generative AI investments.

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