Enterprise CX platform provider Sprinklr has secured a Leader position in the 2026 Voice of the Customer (VoC) Platforms Magic Quadrant, according to research firm Gartner. The recognition underscores the growing importance of AI-powered tools that help companies collect, interpret, and act on customer feedback across an expanding universe of digital touchpoints.

For marketing, customer experience, and service teams navigating a fragmented digital landscape, Voice of the Customer technology has evolved from a niche analytics tool into a core enterprise capability. Sprinklr’s placement signals its continued push to unify customer signals—from surveys and support conversations to social media posts—into a single, AI-driven platform.

Why Voice of the Customer Platforms Matter Now

Customer feedback used to come primarily through structured channels like surveys and support tickets. Today, it’s scattered across app reviews, social media posts, messaging platforms, community forums, and product usage data.

That sprawl creates both an opportunity and a problem. Brands have more customer insight available than ever before—but extracting meaning from it is increasingly difficult.

Voice of the Customer platforms aim to solve that challenge by aggregating structured and unstructured feedback, analyzing sentiment and intent, and connecting insights to operational workflows. In other words, they don’t just listen to customers; they help companies act on what they hear.

Sprinklr has been betting heavily on that model through its Unified Customer Experience Management (Unified-CXM) platform, which integrates marketing, customer service, social media management, and research capabilities into a single system.

Sprinklr’s AI-Native Approach

According to the company, its VoC solution is designed to unify both solicited feedback (such as surveys) and unsolicited feedback (like social posts or reviews) into a single view of customer sentiment.

“Customers share what matters in countless ways—not just in surveys, but across everyday conversations, reviews, and interactions on a variety of channels,” said Karthik Suri, Chief Product Officer at Sprinklr. “Too often, that feedback becomes fragmented, and the real, human intent gets lost.”

The company’s AI-native architecture is intended to address that fragmentation by automatically ingesting signals from dozens of sources and transforming them into actionable insights.

What’s New in Sprinklr’s 2026 VoC Capabilities

Sprinklr is expanding its Voice of the Customer offering this year with several AI-driven enhancements aimed at improving insight discovery and operational activation.

Unified feedback intelligence.
The platform aggregates structured and unstructured signals into a single analytics environment, enabling cross-functional teams—including marketing, customer support, and research—to access the same data foundation.

Broad digital channel coverage.
Sprinklr integrates with more than 30 social and digital channels, giving enterprises a wider lens on how customers interact with brands across public and private spaces online.

AI-driven root cause analysis.
Machine learning models analyze patterns across large volumes of feedback to identify drivers behind customer satisfaction—or dissatisfaction—and prioritize the issues most likely to impact loyalty or revenue.

Conversational, adaptive feedback experiences.
Instead of static surveys, Sprinklr’s AI-powered feedback tools dynamically adjust questions in real time based on user responses, potentially increasing response quality and engagement.

Taken together, these capabilities reflect a broader industry trend: turning passive feedback collection into an active operational intelligence layer.

From Insight to Action

One persistent challenge with VoC platforms has been turning insights into measurable business outcomes. Collecting feedback is easy; translating it into action across teams is harder.

Sprinklr’s pitch centers on closing that loop.

By embedding VoC insights directly into marketing campaigns, customer service workflows, and product research initiatives, the platform aims to help organizations move faster when addressing customer pain points.

For example:

• A spike in negative sentiment about a product feature could trigger alerts for product and support teams.

• Social media complaints could automatically route to customer care agents.

• Marketing teams could adjust messaging based on emerging customer sentiment trends.

Enterprises using the platform report improvements in operational efficiency and customer understanding, according to peer reviews and analyst evaluations across multiple Sprinklr product suites.

The Competitive Landscape

The VoC market has become increasingly competitive as customer experience moves to the top of the enterprise technology agenda.

Platforms in the space now compete on several fronts:

• AI and automation capabilities

• Data ingestion across channels

• Integration with CX and marketing stacks

• Actionability of insights

While legacy survey platforms once dominated the category, the market is shifting toward unified CX systems that combine listening, analytics, and activation in one place.

This trend aligns with the broader rise of experience management platforms, which treat customer feedback as an operational data source rather than just research input.

The Bigger CX Trend: AI Everywhere

Sprinklr’s positioning also reflects a wider transformation across the customer experience ecosystem: the shift toward AI-native platforms.

Enterprises increasingly expect CX technology to do more than gather feedback. They want systems that can:

• Automatically detect emerging customer issues

• Predict sentiment trends

• Recommend operational changes

• Trigger workflows across departments

In that environment, VoC tools are evolving into what analysts often call experience intelligence platforms—systems that convert customer conversations into business decisions.

Why Gartner Recognition Still Matters

Despite growing skepticism around analyst rankings in some corners of the tech industry, the Magic Quadrant remains one of the most closely watched benchmarks in enterprise software.

A Leader placement can significantly influence buying decisions for large organizations evaluating technology vendors, particularly in complex categories like customer experience management.

For Sprinklr, the recognition reinforces its strategy of positioning itself as a comprehensive CX platform rather than a collection of point solutions.

What It Means for Enterprise CX Teams

For marketing and customer experience leaders, the key takeaway is less about the ranking itself and more about the direction of the market.

Customer feedback is no longer confined to survey dashboards or quarterly research reports. It’s becoming a real-time operational input that shapes product development, marketing messaging, and support strategy.

Platforms capable of unifying those signals—and translating them into automated actions—are likely to become central components of modern CX stacks.

Sprinklr’s continued presence in the Leader quadrant suggests the company is positioning itself to play a major role in that shift.

Whether enterprises adopt unified platforms like Sprinklr or continue stitching together specialized tools will depend on their existing technology stacks and data strategies. But the trajectory is clear: customer voice is moving from passive listening to active decision intelligence.

And in the experience economy, the brands that act fastest on what customers are saying—everywhere they’re saying it—may have the advantage.

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Customer communications technology provider DataOceans has once again secured a Leader position on the Aspire Leaderboard, the interactive market evaluation from Aspire Customer Communications Services that tracks vendors shaping the customer communications and experience landscape.

The company earned top placement across two key segments of the 2026 Aspire Leaderboard: CCM-CXM Communications Outsourcing and Communications Experience Platforms (CXP)—a sign that enterprises are increasingly looking for integrated solutions that blend operational communications with digital customer experience.

The recognition highlights a growing shift in how organizations approach customer communications: less as a back-office function and more as a strategic experience channel.

Communications Are Becoming an Experience Layer

For decades, enterprise customer communications—statements, bills, policy documents, compliance notices, and other regulated messages—were largely treated as operational necessities. They had to be accurate, compliant, and delivered on time.

Today, that mindset is rapidly evolving.

Organizations are rethinking these interactions as critical moments in the customer journey, where clarity, accessibility, and digital engagement can influence satisfaction, loyalty, and brand perception.

According to DataOceans President Lee Nagel, that transformation is driving demand for platforms that combine communications delivery with modern experience capabilities.

“Organizations are rethinking communications as a strategic experience layer—not just an operational necessity,” Nagel said. “This recognition reflects our role in helping regulated enterprises turn critical communications into governed, consistent, and modern experiences.”

That shift is particularly visible in industries like financial services, insurance, healthcare, and utilities, where high-volume communications must meet strict regulatory requirements while also supporting modern digital engagement.

Why the Aspire Leaderboard Matters

The Aspire Leaderboard—developed by analyst firm Aspire Customer Communications Services—is widely used in the customer communications management (CCM) and customer experience management (CXM) markets to assess vendor positioning.

Unlike static analyst reports, the leaderboard is an interactive market model that evaluates providers based on:

• Product capabilities

• Innovation and strategy

• Market presence

• Customer engagement outcomes

Companies included in the Leader quadrant typically demonstrate both strong technology platforms and a forward-looking product roadmap.

DataOceans’ dual-segment leadership suggests the company is successfully bridging two traditionally separate areas: communications outsourcing services and cloud-based customer experience platforms.

Two Segments, One Integrated Strategy

The Aspire evaluation places DataOceans in leadership positions across two distinct—but increasingly connected—categories.

CCM-CXM Communications Outsourcing

This segment focuses on providers that design, manage, produce, and deliver communications on behalf of enterprise clients.

These services often include:

• High-volume print and digital communications

• Compliance-sensitive messaging

• Document generation and distribution

• Customer correspondence management

Outsourcing these functions allows organizations to reduce operational complexity while maintaining regulatory compliance and consistent brand messaging.

For industries with strict compliance frameworks, such as banking and healthcare, managed communications services can also help mitigate regulatory risk.

Communications Experience Platforms (CXP)

The second segment—Communications Experience Platforms—represents a newer and rapidly expanding category.

CXP platforms combine traditional CCM tools with experience-driven capabilities, including:

• Customer portals and self-service interfaces

• Digital document access and archiving

• Interactive messaging and engagement tools

• Multichannel communication orchestration

In short, CXP platforms aim to transform transactional communications into interactive digital experiences.

For example, a traditional billing statement may evolve into a digital dashboard where customers can view documents, ask questions, and complete transactions—all within the same environment.

This shift reflects a broader enterprise trend toward self-service engagement, where customers increasingly prefer digital access over phone-based support.

A Strong Focus on Regulated Industries

One of DataOceans’ differentiators, according to Aspire, is its focus on highly regulated sectors.

Kaspar Roos, Founder and CEO of Aspire Customer Communications Services, pointed to the company’s ability to combine cloud technology with regulatory expertise.

“DataOceans combines innovative cloud-based capabilities with a strong focus on regulated industry communications and digital experience needs,” Roos said.

That positioning is particularly relevant as industries like banking and insurance modernize legacy communications infrastructure.

Many of these organizations still rely on decades-old document management systems, which struggle to support modern digital channels or self-service experiences.

Replacing those systems requires not just new technology, but also expertise in compliance-driven communications workflows—something specialized vendors like DataOceans aim to provide.

The Rise of Cloud-Based Communications Platforms

Another key factor driving growth in the CCM and CXP markets is the migration toward cloud-hosted communications platforms.

Historically, many enterprises ran communications infrastructure on-premises due to security and compliance concerns.

But the rise of secure cloud architectures—and the need for faster digital transformation—has accelerated the shift toward managed platforms.

Cloud-based communications systems offer several advantages:

• Faster deployment and updates

• Integrated digital engagement channels

• Scalable document generation and distribution

• Improved analytics and reporting capabilities

For organizations dealing with millions of communications each month, cloud platforms can significantly improve operational agility.

Self-Service Is Becoming the Default

Another trend highlighted by the Aspire Leaderboard is the growing importance of customer self-service.

Modern consumers increasingly expect to:

• Access documents online

• Manage preferences through digital portals

• Resolve issues without contacting support

Communications platforms are evolving to meet those expectations by embedding self-service capabilities directly into communication workflows.

Instead of simply sending static documents, organizations can now provide interactive portals where customers can explore information, complete transactions, and engage with support tools.

This transformation effectively turns communications systems into digital engagement platforms.

The Bigger Picture: Convergence of CCM and CXM

DataOceans’ dual recognition also reflects a larger industry convergence.

Traditionally, Customer Communications Management (CCM) focused on document creation and delivery, while Customer Experience Management (CXM) focused on engagement and personalization.

Today, those categories are increasingly overlapping.

Customer communications—whether a billing statement, insurance policy update, or healthcare notification—are often among the most frequent interactions customers have with organizations.

Improving those touchpoints can therefore have an outsized impact on customer experience.

Vendors that combine communications infrastructure with digital engagement capabilities are well positioned to benefit from that convergence.

What It Means for Enterprise Buyers

For enterprises evaluating communications technology, the Aspire Leaderboard serves as a strategic reference point.

Recognition in both the communications outsourcing and experience platform segments suggests that DataOceans is pursuing a hybrid strategy: combining managed services with cloud software.

That approach may appeal to organizations seeking to modernize communications infrastructure without taking on the full operational burden internally.

As digital engagement expectations continue to rise—and regulatory requirements remain complex—vendors capable of balancing compliance, scale, and experience innovation will likely gain ground in the CCM and CXM markets.

DataOceans’ continued presence among Aspire’s leaders suggests it intends to remain part of that evolving landscape.

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Artificial intelligence in marketing may be approaching its next major evolution. A new whitepaper from Appier argues that the industry is moving beyond AI-assisted workflows toward agentic systems capable of autonomous marketing execution.

Titled “The Future of Autonomous Marketing with Agentic AI,” the report explores how agentic AI could become a new operational layer for modern marketing organizations—automating not just individual tasks but entire decision loops.

The idea is simple but ambitious: instead of AI tools that merely recommend actions, future systems could plan, execute, and optimize campaigns on their own, dramatically accelerating how marketing teams respond to customer signals.

According to Appier, that transition could fundamentally reshape the MarTech stack—and the role marketers themselves play inside it.

Marketing’s “Autonomy Gap”

For years, marketing teams have invested heavily in automation tools, analytics platforms, and AI-powered recommendations. Yet many workflows remain stubbornly manual.

Campaigns still require:

• Audience segmentation

• Testing frameworks

• Cross-channel orchestration

• Continuous optimization

These processes often involve multiple platforms and hours of human oversight.

Appier describes the result as an “Autonomy Gap”—the widening mismatch between the speed of digital data signals and the slower pace of human-driven workflows.

Customer journeys today span dozens of touchpoints, from paid media and social platforms to messaging apps and e-commerce environments. Every interaction generates signals that could influence targeting, messaging, and timing.

But translating those signals into action still requires layers of manual decision-making.

Agentic AI, the company argues, is designed to close that gap.

From Automation to Autonomous Execution

Traditional marketing automation operates on rules-based logic. A typical system might follow simple triggers such as:

• If a user abandons a cart, send a reminder email.

• If a campaign hits a budget threshold, pause the ad.

Agentic AI takes a different approach.

Instead of executing predefined rules, agentic systems continuously analyze incoming data, generate hypotheses, test strategies, and adjust execution—often without requiring direct human input.

The whitepaper describes this as a closed-loop decision cycle, where systems repeatedly:

1. Observe data signals

2. Plan strategic actions

3. Execute campaigns

4. Learn from outcomes

5. Refine the next round of decisions

In practice, this could mean marketing platforms that automatically discover audiences, launch experiments, optimize targeting, and reallocate budgets across channels in near real time.

One deployment scenario cited in the report reduced activation timelines from three days to under one hour, representing a 24× increase in operational velocity.

While such results will vary across organizations, the example illustrates the potential scale of automation agentic AI could introduce.

Why Large Language Models Aren’t Enough

The rapid rise of generative AI has already reshaped marketing workflows. Large language models (LLMs) can generate ad copy, summarize campaign insights, and even draft marketing strategies.

But according to Appier, LLMs alone don’t deliver autonomy.

The company compares the relationship between LLMs and agentic systems to a car engine and its driver.

• LLMs provide the reasoning and content generation—the engine.

• Agentic systems provide direction and coordination—the pilot.

Without that orchestration layer, LLMs remain reactive tools rather than independent operators.

Agentic AI architectures bridge that gap by combining several capabilities:

• Reasoning and planning

• Workflow orchestration

• Continuous learning from outcomes

• Autonomous execution across connected systems

The result is a platform capable of self-directing marketing workflows rather than merely supporting them.

Building a Closed-Loop Marketing Engine

A key theme in Appier’s report is the idea of a connected agent ecosystem.

Rather than relying on a single AI model, agentic platforms typically use multiple specialized agents working together. Each agent focuses on a specific function within the marketing lifecycle.

Examples might include:

• Data intelligence agents that analyze behavioral signals and audience trends

• Activation agents that execute campaigns across ad platforms and owned channels

• Commerce or conversational agents that interact directly with customers

When connected, these agents form what Appier describes as a closed-loop growth engine—a system capable of translating insights directly into coordinated actions across marketing touchpoints.

In this model, signals from one channel—say, a surge in product searches—could automatically trigger adjustments across advertising, messaging, and on-site experiences.

Instead of waiting for weekly campaign reviews, optimization happens continuously.

The Changing Role of the Marketing Team

Perhaps the most intriguing implication of agentic AI is its potential impact on how marketing teams work.

As AI systems take on operational tasks—audience discovery, campaign testing, performance optimization—marketers may shift toward higher-level responsibilities.

The whitepaper suggests a future where marketing professionals focus more on:

• Strategic planning

• Creative storytelling

• Brand governance

• Cross-functional collaboration

In other words, the technology could reduce the manual orchestration that dominates many marketing roles today.

Rather than managing dozens of campaign parameters across multiple tools, marketers would oversee AI systems that handle execution at scale.

This transition, the report argues, restores what it calls the “dignity of strategy” to marketing work.

The Rise of an “Agentic Workforce”

Appier frames agentic AI not as a single product innovation but as a new operating model for marketing organizations.

In this model, companies build an “agentic workforce”—a network of AI agents responsible for continuous growth optimization.

These agents would operate alongside human teams, handling high-volume operational tasks while humans guide strategic direction.

The idea echoes a broader shift occurring across enterprise software: AI moving from isolated features toward autonomous digital collaborators.

If the model succeeds, marketing organizations could shift from reactive campaign management to self-driving growth engines.

Implications for the MarTech Stack

The emergence of agentic AI could also reshape the broader MarTech ecosystem.

Today’s marketing stacks often contain dozens of specialized tools for analytics, campaign management, personalization, and experimentation.

Agentic systems promise to connect these components into coordinated decision frameworks, potentially reducing fragmentation.

Instead of juggling multiple dashboards and manual integrations, marketers could rely on AI-driven orchestration layers that manage workflows across the entire stack.

This concept aligns with a growing industry trend toward AI-native marketing platforms, where automation, analytics, and execution converge.

The Strategic Challenge Ahead

Despite the promise, agentic AI introduces new challenges.

Organizations adopting autonomous marketing systems must consider:

• Governance and oversight mechanisms

• Transparency in AI decision-making

• Data quality and integration

• Ethical and compliance implications

Autonomous execution may accelerate growth strategies, but it also requires strong guardrails.

According to Appier CEO and co-founder Chih-Han Yu, the central issue facing modern marketing teams isn’t simply collecting data—it’s acting on it effectively.

“The core challenge today is not simply access to data, but the ability to translate insight into coordinated action,” Yu said. “As marketing environments grow more complex, embedding autonomy into decision loops enables organizations to respond with greater agility while maintaining strategic oversight.”

Marketing’s Next Operating System?

Whether agentic AI becomes the dominant paradigm in marketing remains to be seen. But the concept reflects a broader shift already underway in enterprise AI.

Tools are evolving from assistive technologies to autonomous systems capable of executing complex workflows.

For marketing teams struggling to keep pace with fragmented channels and real-time customer behavior, that shift could prove transformative.

If Appier’s vision plays out, the next generation of marketing technology won’t simply analyze campaigns or suggest optimizations.

It will run them.

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As artificial intelligence continues to reshape marketing, one agency founder is raising a question many companies haven’t fully confronted: What happens if businesses replace the very humans they depend on as customers?

That’s the premise behind a new campaign from Jessica Alex Marketing, a Toronto-based agency founded by Jessica Alex. The firm has launched a message aimed at businesses rapidly adopting AI tools across creative, marketing, and service workflows: “If your business sells to humans, we should totally work together.”

The slogan, Alex says, is less about rejecting artificial intelligence outright and more about pushing the industry toward a deeper conversation about its economic and ethical consequences.

“Humans are being replaced by AI, and not enough people seem to be talking about it,” Alex said. “AI can be a beneficial tool if used responsibly and ethically, but companies are increasingly using it to replace jobs—from modelling and photography to service-based roles—and that’s concerning.”

AI’s Expanding Role in Creative and Marketing Work

Artificial intelligence has moved quickly from experimental technology to everyday marketing infrastructure.

AI tools can now generate:

• Marketing copy

• Product images and ad creatives

• Social media campaigns

• Video and voice content

• Data analysis and targeting strategies

For businesses under pressure to move faster and cut costs, these capabilities are appealing.

Platforms built around generative AI promise faster production cycles and lower operating expenses. In many cases, they allow companies to produce campaigns without hiring traditional creative teams.

But that shift also raises difficult questions about the future of human roles in marketing and creative industries.

Photographers, models, designers, writers, and other professionals are increasingly competing with AI-generated alternatives that can deliver content in seconds.

Alex says the trend became clear through conversations with clients and peers.

A Conversation That Sparked the Campaign

The idea behind the agency’s campaign didn’t emerge from a formal research report or strategic planning session. Instead, it came from a casual conversation with a potential client.

During that discussion, the client explained they were using AI tools for nearly every aspect of their marketing operations.

That moment triggered what Alex describes as a “light-bulb realization.”

“In one-on-one conversations with friends and colleagues, I’ve often asked: if businesses keep replacing people with AI, how will those same people be able to afford the products and services those businesses sell?” she said.

It’s a simple but provocative question that touches on a broader economic debate surrounding AI adoption.

If automation eliminates large numbers of jobs—or compresses wages across creative and service industries—consumer spending power could decline. That, in turn, could affect the businesses adopting the technology in the first place.

Alex’s campaign attempts to distill that concern into a straightforward message: companies that sell to humans should value human involvement in the economy.

The Message: Humans on Both Sides of the Business

The slogan “If your business sells to humans, we should totally work together” acts as both a marketing pitch and a philosophical statement.

It positions the agency as a partner for companies that still value human creativity, collaboration, and strategic thinking alongside technological tools.

But Alex emphasizes that the campaign is not anti-AI.

Instead, she advocates for a balanced approach where artificial intelligence augments human capabilities rather than replacing them entirely.

“I do believe AI can be a vital, complementary piece to one’s work processes,” Alex said. “The key word is complementary.”

That perspective echoes a broader conversation happening across the marketing industry as organizations explore how AI should fit into their workflows.

The Industry’s Automation Debate

The marketing sector has historically embraced new technologies faster than many other industries. From marketing automation platforms to predictive analytics and programmatic advertising, innovation has long been central to the field.

Generative AI, however, represents a different kind of shift.

Unlike previous technologies that automated repetitive tasks, generative systems can now perform creative and strategic functions once considered uniquely human.

That capability is fueling both enthusiasm and anxiety across the industry.

On one hand, AI tools promise unprecedented productivity. Teams can generate content, test campaigns, and analyze performance faster than ever before.

On the other hand, critics worry about:

• Job displacement in creative industries

• Declining demand for human freelancers

• Ethical concerns around synthetic content

• Economic impacts on labor markets

Alex’s campaign taps directly into that tension.

Marketing Agencies at a Crossroads

Agencies, in particular, are navigating a delicate balance.

Clients increasingly expect agencies to integrate AI into their services to deliver faster insights and lower costs. But those same agencies rely heavily on human talent—strategists, designers, writers, analysts—to produce high-quality work.

The result is a complex transformation of the agency model.

Some firms are aggressively adopting AI to reduce operational overhead, while others are emphasizing human creativity as a premium differentiator.

Jessica Alex Marketing appears to be leaning toward the latter approach, framing human collaboration as a strategic advantage rather than an inefficiency.

AI as a Tool, Not a Replacement

The campaign ultimately argues for what many industry leaders call human-AI collaboration.

In this model:

• AI handles repetitive or data-heavy tasks

• Humans focus on creative direction, strategy, and storytelling

The combination can potentially produce better results than either approach alone.

For example, AI might generate dozens of ad variations or analyze large datasets, while human marketers interpret insights and craft brand narratives that resonate emotionally with audiences.

Alex believes this collaborative approach preserves both efficiency and authenticity.

A Conversation the Industry May Need

While the campaign originates from a small agency, the broader issues it raises are increasingly relevant across the global marketing ecosystem.

As AI technologies continue advancing, organizations will need to decide:

• Which tasks should be automated

• Which roles require human expertise

• How to balance cost efficiency with creative authenticity

Those decisions will shape not only marketing strategies but also the future structure of the workforce.

For Alex, the goal of the campaign isn’t necessarily to change every company’s technology roadmap.

It’s to spark a conversation that she believes the industry has been avoiding.

“My hope is that this campaign at least gets people talking more about the ethics and ramifications of replacing human capital with artificial ones,” she said.

Whether companies ultimately agree with that perspective or not, the debate over AI’s role in marketing—and its impact on human work—is likely just beginning.

Get in touch with our MarTech Experts.

The fragmented world of media analytics may soon get a much-needed upgrade. Outset Media Index (OMI) has entered a soft launch phase with a platform designed to bring standardized, data-driven benchmarking to media analysis—an area that has long relied on scattered metrics, opaque rankings, and incomplete traffic signals.

Developed by communications firm Outset PR, the index currently tracks more than 340 publications covering the cryptocurrency sector, including dedicated crypto outlets alongside finance, technology, and mainstream news platforms with specialized crypto coverage.

The broader goal: create a transparent measurement framework that helps marketers, advertisers, PR teams, and publishers better understand which media outlets actually deliver meaningful reach and engagement.

In a digital ecosystem where traditional traffic metrics are increasingly unreliable, that kind of clarity is becoming essential.

Why Media Measurement Is Getting Harder

For years, marketers and communications teams have relied heavily on indicators like traffic estimates, domain authority, and SEO rankings to evaluate media outlets.

But those signals are becoming less reliable as the structure of online news distribution changes.

According to data cited by the Reuters Institute for the Study of Journalism, referencing analytics from Chartbeat, global Google organic search traffic to news websites declined by roughly 33% between November 2024 and November 2025.

The outlook is even more dramatic. Many publishers expect referral traffic to drop another 43% over the next three years as AI-generated summaries and conversational search tools increasingly answer queries without sending users directly to news sites.

In this environment, short-term traffic spikes or high search rankings no longer necessarily indicate a publication’s true influence or audience stability.

A site might briefly trend due to viral coverage or algorithm changes, while another outlet with a smaller but loyal readership could generate more meaningful engagement.

OMI is designed to capture those deeper dynamics.

A Framework Built Around 37 Performance Metrics

The index analyzes media outlets using 37 metrics spanning several key performance dimensions:

• Audience reach

• Reader engagement

• Distribution patterns

• Operational collaboration factors

To build this dataset, OMI combines third-party analytics from providers like Similarweb and Moz with proprietary indicators developed through internal research.

The goal is to go beyond surface-level traffic metrics by contextualizing them with behavioral and operational insights.

All inputs are reviewed and normalized within the platform’s scoring model to prevent inflated results or inconsistent methodology across outlets.

That emphasis on methodological transparency is central to OMI’s positioning.

Unlike many existing “top media lists” that offer little visibility into how rankings are calculated, the platform aims to provide a consistent and objective infrastructure for media evaluation.

Beyond Traffic: Measuring Real Media Impact

One of OMI’s defining features is its focus on how audiences behave once they encounter content, rather than simply measuring how many visitors arrive.

The index introduces several proprietary indicators designed to capture these dynamics.

Unique Score

This metric tracks consistent unique readership across multiple months, helping identify outlets with stable, loyal audiences rather than those driven primarily by short-term traffic bursts.

For media strategists, this distinction is critical.

A publication with reliable readership may deliver more predictable exposure for campaigns than one dependent on viral spikes.

Reading Behavior

Reading Behavior combines metrics such as:

• Time spent on page

• Pages viewed per visit

• Bounce rate

Together, these indicators reveal how deeply readers engage with content after clicking through.

For advertisers and PR teams, that information helps determine whether audiences are actually consuming stories—or simply skimming headlines before leaving.

Reprints and Syndication

Another metric, called Reprints, measures how frequently an article is republished by aggregators or secondary outlets.

This signal highlights platforms where original coverage often triggers broader distribution across the media ecosystem.

In PR terms, it helps identify outlets capable of amplifying coverage beyond their own readership.

Two Core Ratings: Performance and Collaboration

To simplify analysis, OMI aggregates its indicators into two primary rating frameworks.

General Rating

This score reflects overall media performance, combining reach, engagement, and distribution signals into a single benchmark.

For marketers and media buyers, it provides a quick reference for comparing outlets across the ecosystem.

Convenience Rating

The second score evaluates operational collaboration factors that influence day-to-day media relations.

These include:

• Editorial responsiveness

• Turnaround speed

• Flexibility in collaboration

• Price-to-reach alignment

While these elements rarely appear in traditional media rankings, they can significantly affect campaign planning and execution.

By incorporating them into its index, OMI attempts to bridge the gap between media analytics and practical PR workflows.

Side-by-Side Media Intelligence

Within the platform, users can explore outlets through detailed profiles that include historical performance data and contextual insights.

Publications can also be compared directly using filters tied to business impact, allowing teams to evaluate outlets based on the factors most relevant to their goals.

For example, a marketing team might prioritize engagement metrics, while a PR agency might focus on syndication potential or editorial collaboration factors.

This level of customization aims to make OMI useful across multiple functions, including:

• Media planning

• Advertising strategy

• PR outreach

• Market research

• Publisher benchmarking

A Controlled Soft Launch

The current release represents a soft launch phase, meaning access to the platform is being rolled out gradually.

During this stage, the development team plans to work closely with early users to test real-world workflows and refine the index based on feedback.

Participants who contribute insights during the soft launch will be recognized and rewarded for helping shape the platform ahead of broader availability.

This collaborative approach reflects the experimental nature of media analytics today, where methodologies continue evolving alongside changes in search algorithms, distribution channels, and reader behavior.

Part of a Larger Media Intelligence Ecosystem

OMI is not a standalone project.

It sits within a broader analytics ecosystem developed by Outset PR.

Within that framework, the index works alongside Outset Data Pulse (ODP)—a research and interpretation platform currently undergoing a rebrand.

According to Sofia Belotskaia, product lead at OMI, the two systems serve complementary roles.

“Data on its own rarely helps unless it is comparable,” Belotskaia said. “While OMI shows how media performance and distribution patterns evolve across outlets, ODP focuses on explaining why those changes happen and what they mean for teams working across the media market.”

Supporting these tools are additional infrastructure components developed by the agency.

These include:

• A syndication map that tracks how articles spread across aggregator networks

• An internal media parser that automates republication tracking

Together, these technologies enable large-scale analysis of how content moves through the digital media ecosystem.

Rethinking Media Intelligence

For founder Mike Ermolaev, the ultimate goal of OMI is to preserve the human side of media relations while providing better analytical tools.

He describes media work as “a human craft first,” supported by systems that bring transparency to how visibility is created.

Reliable analytics, he argues, can help communications professionals understand that media exposure isn’t simply the result of chance or relationships—it’s something that can be systematically analyzed and improved.

What Comes Next

Looking ahead to 2026, Outset PR plans to integrate its various analytical layers more closely.

The objective is to make media intelligence easier to use within everyday workflows, replacing the patchwork of spreadsheets, dashboards, and isolated analytics tools many teams rely on today.

If successful, the Outset Media Index could provide a clearer framework for navigating an increasingly complex media landscape—where search traffic is shrinking, distribution channels are shifting, and understanding true audience engagement matters more than ever.

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Website experience platform Webflow is expanding its AI ambitions with the acquisition of Vidoso.ai, a startup focused on generating brand-aligned creative assets using multi-modal artificial intelligence.

The move signals Webflow’s latest step toward transforming its platform from a visual web development tool into what it calls an “agentic web marketing platform”—an environment where AI agents operate inside structured workflows to help teams design, generate, and manage digital experiences at scale.

For marketing teams grappling with the rapid adoption of generative AI tools, the acquisition addresses a growing challenge: how to maintain brand consistency and governance while scaling AI-generated content.

The Problem With Today’s AI Content Tools

Over the past two years, generative AI has flooded marketing teams with new capabilities. From copywriting assistants to image generators and video tools, AI can now produce large volumes of content in seconds.

But that speed has come with a downside.

Many of these tools operate outside of the systems of record companies use to manage websites, brand assets, and campaign workflows. Content may be generated quickly, but it often lacks guardrails that enforce brand guidelines, approval processes, and design standards.

According to Webflow CEO Linda Tong, that fragmentation is becoming a major operational challenge.

“Right now, marketing teams are experimenting with AI in disconnected tools,” Tong said. “Content gets generated, but no one owns the guardrails.”

Without centralized governance, organizations risk producing inconsistent messaging, off-brand visuals, or assets that bypass compliance and approval workflows—particularly problematic for larger enterprises.

What Vidoso Brings to the Table

Founded in the San Francisco Bay Area, Vidoso.ai focuses on multi-modal AI generation, meaning its systems can create multiple types of assets—such as images and videos—while adhering to predefined brand frameworks.

Unlike generic AI generation tools trained on broad internet datasets, Vidoso’s models are designed to operate within structured brand environments.

That includes respecting:

• Brand guidelines

• Visual identity rules

• Template systems

• Campaign frameworks

• Approval workflows

The goal is to allow marketing teams to scale content production without sacrificing consistency.

“Frontier models are trained on the average of the internet, not on the specifics of your brand,” said Sharad Verma, CEO and co-founder of Vidoso.

“The first wave of AI gave marketing teams powerful but ungoverned tools capable of generating generic content, but blind to brand systems, templates, and approval workflows.”

Vidoso’s technology was built to bridge that gap by embedding AI generation into structured marketing environments.

Building an “Agentic” Marketing Platform

The acquisition aligns closely with Webflow’s broader vision of an agentic web marketing platform.

Agentic systems refer to AI architectures where autonomous agents can plan, execute, and optimize tasks within defined workflows.

In Webflow’s case, those workflows revolve around building and managing websites and digital experiences.

Over time, the company plans to integrate Vidoso’s underlying AI agents and generation technology into its platform. This would enable marketers to create brand-aligned visual and video assets directly within Webflow while maintaining governance and workflow control.

In practice, that could mean:

• Automatically generating campaign visuals that match brand templates

• Producing localized creative variations for different markets

• Creating video and visual content optimized for multiple channels

• Ensuring all generated assets pass through defined approval systems

Instead of relying on separate creative AI tools, marketing teams could operate inside a single governed environment.

From Web Builder to Marketing Infrastructure

For much of its history, Webflow has been known primarily as a visual website development platform—a tool that allows designers and marketers to build websites without writing extensive code.

But the company has steadily expanded its ambitions.

The platform now aims to support the entire lifecycle of web-based marketing, including:

• Planning digital experiences

• Designing and building websites

• Managing content and updates

• Optimizing performance and engagement

The addition of AI-driven creative generation pushes Webflow further into full-stack marketing infrastructure territory.

As AI becomes embedded across marketing workflows—from analytics to campaign creation—platform vendors are racing to provide integrated environments that unify these capabilities.

AI Governance Is Becoming a Key Differentiator

The acquisition also highlights an emerging theme in enterprise AI adoption: governance.

Early generative AI tools emphasized speed and experimentation. But as companies begin using AI for production workflows, they need systems that enforce brand, compliance, and operational standards.

This shift is particularly relevant for large organizations where marketing output must align with strict guidelines.

Governed AI systems can ensure that:

• Brand voice remains consistent

• Design elements follow approved templates

• Content passes required review processes

• Permissions and workflows remain intact

By embedding AI directly within Webflow’s platform, the company hopes to provide those guardrails without slowing down content production.

What Happens to Vidoso’s Existing Customers

According to Webflow, customers currently using Vidoso’s products will continue to have access to them following the acquisition.

Over time, the company plans to integrate the startup’s technology into the broader Webflow ecosystem, though detailed timelines for those integrations have not yet been announced.

For Webflow, the deal brings both technology and expertise in AI agent development for structured creative workflows.

The Bigger MarTech Shift

Webflow’s acquisition reflects a larger transformation happening across the marketing technology landscape.

As generative AI becomes mainstream, vendors are moving beyond standalone AI features toward AI-native platforms—systems where automation, analytics, and content generation operate together inside structured workflows.

In this model, AI doesn’t just assist marketers; it becomes part of the operational infrastructure that powers digital experiences.

The challenge for vendors is balancing automation with governance—giving teams the speed of AI while maintaining control over brand and business rules.

Webflow’s bet is that agentic AI systems, embedded directly within marketing platforms, offer the best path forward.

A Glimpse of AI-Powered Web Operations

If Webflow’s vision materializes, the process of building and managing websites could look very different in the near future.

Instead of manually designing each asset or page, marketers might work alongside AI agents capable of generating visuals, adapting layouts, and optimizing content based on real-time data—all within the platform itself.

That would transform websites from static digital properties into dynamic marketing environments continuously shaped by AI-driven workflows.

With the addition of Vidoso’s brand-aware generation technology, Webflow appears to be positioning itself at the center of that shift.

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Digital marketing platform Semrush is redefining its identity as the search landscape undergoes one of its biggest transformations in decades. The company announced a sweeping brand refresh that positions it not simply as an SEO toolkit, but as a brand visibility platform built for the age of AI-driven discovery.

The rebrand reflects a broader strategic shift: helping marketers track and influence how brands are discovered across traditional search engines, generative AI answers, social platforms, and emerging AI agent ecosystems.

For a company long synonymous with search engine optimization, the change signals a recognition that the concept of “search” itself is evolving.

From SEO Toolkit to Visibility Intelligence Platform

Founded 17 years ago, Semrush built its reputation by helping marketers analyze keywords, track rankings, and optimize websites for search engines.

But today, brand discovery rarely happens in just one place.

Consumers now encounter brands through:

• AI-generated answers

• Social media feeds

• Online communities and forums

• Video platforms

• Voice assistants

• Conversational AI interfaces

According to Semrush, AI-driven search activity has surged 527% year-over-year, accelerating the fragmentation of discovery channels.

That shift has prompted the company to reposition its platform around a broader mission: helping organizations understand where discovery happens and how to win visibility across it.

“Today, discovery is happening everywhere—AI answers, social platforms, community forums, and more,” said Andrew Warden, Chief Marketing Officer at Semrush.

“Many brands are struggling to navigate it and getting left behind. We’ve reimagined Semrush to solve that.”

Enter “Agentic Search Optimization”

At the center of Semrush’s strategy is a concept it calls Agentic Search Optimization, a framework designed to help marketers understand how brands appear in AI-generated answers and automated discovery environments.

Traditional SEO focused primarily on ranking web pages in search engine results.

But generative AI systems—like chatbots and AI assistants—often summarize content rather than linking directly to websites. That means visibility now depends on whether a brand is mentioned, cited, or referenced in AI responses.

Agentic Search Optimization aims to help marketers track and influence that process.

According to Warden, AI agents are beginning to fundamentally reshape how people research products and services online.

“AI agents are changing how people search, compare, and buy,” he said. “The brands that combine SEO with agentic discovery strategies will define the next era of visibility.”

The Data Powering the Platform

Semrush’s repositioning is backed by one of the largest proprietary marketing datasets in the industry.

The platform currently aggregates:

• 27 billion keywords

• 43 trillion backlinks

• 213 million+ large language model prompts

This data feeds into the company’s analytics tools, which help marketers track competitors, analyze audience behavior, and identify opportunities for content and search visibility.

Over the years, Semrush has expanded its capabilities beyond SEO into areas such as content marketing, competitive intelligence, advertising analytics, and social media management.

The new brand positioning attempts to unify those capabilities into a single intelligence engine for digital visibility.

The Four-Pillar Visibility Model

As part of the transformation, Semrush is framing its platform around four strategic pillars designed to connect marketing intelligence with measurable business impact.

Intelligence

The foundation is Semrush’s proprietary data and algorithms, which collect and process massive volumes of search, content, and competitive intelligence signals.

Insights

The platform’s tools translate that data into actionable insights, helping marketers identify content gaps, keyword opportunities, and competitive weaknesses.

Action

Workflow and campaign management features allow teams to execute optimization strategies directly within the platform.

Impact

Finally, Semrush tracks performance outcomes—helping marketers measure how visibility improvements translate into traffic, engagement, and revenue.

The model reflects a broader industry shift toward end-to-end marketing intelligence platforms that combine analytics, execution tools, and performance measurement.

Why Visibility Is the New SEO

The term “SEO company” has followed Semrush for much of its history.

But the company believes that label no longer captures the complexity of digital discovery.

Search engines themselves have evolved dramatically in recent years. Results pages now include:

• AI-generated summaries

• Video content

• Shopping integrations

• Local listings

• Community discussions

At the same time, many users begin product research on platforms like social media or forums rather than traditional search engines.

The rise of generative AI adds yet another layer of complexity.

Instead of scanning multiple links, users may simply ask an AI assistant for recommendations.

In that scenario, the brands that appear in the AI-generated answer gain immediate visibility—while others effectively disappear from the conversation.

“The reality is simple,” Warden said. “You’re either the answer AI provides, or you’re invisible.”

A New Look for a New Era

Alongside its strategic shift, Semrush is rolling out a refreshed visual identity and updated user interface.

The redesign aims to reflect the platform’s broader scope and make it easier for teams of different sizes—from small businesses to large enterprises—to navigate its growing feature set.

The updated branding will appear across Semrush’s digital properties and customer touchpoints starting in March 2026.

While visual changes often accompany corporate rebrands, the deeper message here is about how the company views the future of marketing technology.

The Bigger MarTech Trend

Semrush’s repositioning reflects a broader transformation underway across the marketing technology industry.

For decades, SEO was primarily about optimizing websites for search engine algorithms.

Today, the challenge is far more complex.

Marketers must now optimize for multiple discovery systems simultaneously, including:

• Traditional search engines

• Generative AI models

• Social media algorithms

• Online communities

• Voice and conversational assistants

This convergence is pushing marketing platforms to evolve from single-channel optimization tools into cross-channel intelligence engines.

Semrush’s new positioning as a brand visibility platform is a direct response to that shift.

The Next Phase of Digital Discovery

Looking ahead, the company believes the next wave of change will come from AI agents that actively assist users in researching, comparing, and purchasing products.

These agents could automate tasks like:

• Evaluating product options

• Comparing pricing and features

• Summarizing reviews

• Making recommendations

In that environment, brands will need strategies not just for human search behavior—but for how AI systems gather and interpret information about them.

By merging traditional SEO insights with AI discovery analytics, Semrush hopes to give marketers the tools to navigate that emerging landscape.

Whether the industry ultimately adopts terms like “Agentic Search Optimization” or not, the underlying trend is clear: the battle for brand visibility is expanding far beyond the search results page.

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Artificial intelligence is rapidly reshaping how people search for information online—and marketers are beginning to rethink the strategies that once defined digital visibility.

For decades, search engine optimization revolved around ranking webpages in traditional search results. But the rise of AI-powered search experiences and generative answer systems is introducing a new layer to the discovery process.

Instead of scanning a list of links, users increasingly receive AI-generated summaries that compile information from multiple sources across the web. That shift is fueling industry conversations around an emerging concept known as Generative Engine Optimization (GEO)—a framework designed to help digital content remain visible in AI-driven search environments.

While the discipline is still evolving, GEO represents one of the latest attempts by the marketing industry to adapt to a search landscape increasingly influenced by generative AI.

The Changing Nature of Search

Traditional search engines have long relied on ranking algorithms that evaluate web pages based on signals such as relevance, authority, and link structure.

Users would enter a query, receive a list of results, and choose which pages to visit.

Generative AI is beginning to change that experience.

Modern search interfaces increasingly include AI-generated responses that summarize information directly on the results page. These responses often combine insights from multiple sources, allowing users to get answers without navigating to several different websites.

The result is a subtle but meaningful shift: search engines are evolving from directories of links into information synthesis platforms.

For marketers and publishers, this raises new questions about how their content is discovered—and whether it is included in those synthesized answers.

What Generative Engine Optimization Means

Generative Engine Optimization focuses on ensuring that digital content remains understandable and usable for AI systems that generate answers for users.

Unlike traditional SEO, which emphasizes keyword rankings and link authority, GEO considers how artificial intelligence models interpret, summarize, and reference content.

That means marketers must think beyond search algorithms alone.

AI systems analyze content using broader contextual signals, including:

• Topic relationships

• Entity recognition

• Source credibility

• Structural clarity

Content that clearly communicates its subject matter and context may therefore have a greater chance of appearing in AI-generated responses.

According to digital marketing agency Arwenus SEO, organizations are increasingly aware of this shift.

“AI-driven search experiences are introducing new dynamics in how information is discovered,” a spokesperson from the agency said. “Organizations are starting to recognize that content needs to be understandable not only for traditional search algorithms, but also for AI systems that interpret and summarize information for users.”

Why Content Structure Matters More Than Ever

One of the recurring themes in GEO discussions is the importance of structured, clearly organized information.

Generative AI models rely heavily on context when analyzing web content. Well-structured articles—those with logical headings, concise explanations, and clear topic boundaries—are often easier for AI systems to interpret.

This structure can influence how effectively content is summarized or referenced.

For example, an article that clearly defines key concepts and explains relationships between topics may provide stronger signals for AI systems compared with content that is loosely organized or ambiguous.

While structured writing has always been good practice in SEO, AI-driven discovery environments make it even more valuable.

The Rise of Entity-Based Optimization

Another concept gaining attention in GEO discussions is entity-based optimization.

Search engines and AI models increasingly rely on entities—distinct concepts such as people, brands, organizations, or locations—to understand information.

By identifying and connecting these entities, AI systems can build a clearer understanding of how topics relate to one another.

For marketers, this means ensuring that content clearly associates brands and products with relevant topics.

For example, articles that consistently connect a brand with specific expertise areas may help reinforce its presence within AI-generated knowledge graphs and summaries.

Topical Authority Is Becoming a Strategic Priority

In the early days of SEO, many content strategies focused heavily on individual keywords.

Companies would publish isolated articles designed to capture search traffic for specific phrases.

The shift toward AI-assisted search is encouraging a different approach.

Experts increasingly recommend building comprehensive topic coverage, where organizations develop clusters of content around key subject areas.

This approach helps demonstrate topical authority—a signal that indicates deep expertise in a particular field.

When AI systems generate answers, they often favor sources that consistently provide reliable and detailed information about the subject being discussed.

As a result, organizations are investing more heavily in long-term content strategies rather than quick keyword-driven tactics.

Credibility and Reliable Sources Matter

Generative AI systems frequently rely on signals of trustworthiness when selecting and summarizing information.

Content that references credible sources, includes clear evidence, and demonstrates subject expertise may be more likely to appear in AI-generated outputs.

This trend aligns closely with broader search industry principles emphasizing expertise, authority, and trust.

For marketers, it reinforces the importance of producing content that prioritizes accuracy, clarity, and reliability rather than purely promotional messaging.

SEO and GEO Will Likely Coexist

Despite the growing attention around generative AI, analysts widely expect traditional search engines and AI-generated discovery systems to coexist for the foreseeable future.

Many users will continue to rely on conventional search results when conducting deeper research or exploring multiple sources.

At the same time, AI-generated summaries will likely become a common entry point for quick answers and high-level information.

This dual environment means businesses may need strategies that support visibility in both contexts.

Traditional SEO techniques—such as technical optimization, backlink development, and keyword targeting—will remain relevant, while GEO principles help ensure content is interpretable by AI systems.

A Broader Shift in Digital Marketing

The conversation around Generative Engine Optimization reflects a wider transformation across the digital marketing landscape.

Artificial intelligence is not just changing search results—it is altering how information is organized, summarized, and delivered to users.

For organizations, adapting to this shift may require rethinking how content is structured and how knowledge about their brand is distributed across the web.

As AI technologies continue evolving, the ability to create clear, authoritative, and well-structured information could become a defining factor in digital visibility.

What Comes Next

Generative AI remains a rapidly developing field, and the exact mechanics of AI-driven discovery are still evolving.

Search platforms continue experimenting with different approaches to integrating AI-generated answers, while marketers explore strategies to ensure their content remains visible.

As these systems mature, the relationship between AI and search optimization will likely become more sophisticated.

For businesses navigating this transition, understanding how AI technologies interpret and present information may become an increasingly important component of long-term digital strategy.

Generative Engine Optimization may still be an emerging concept—but the broader trend it reflects is clear: the future of search is no longer just about ranking pages, but about being part of the answers themselves.

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