Cloudinary is doubling down on video automation with a fresh set of AI-powered upgrades to MediaFlows, its no-code workflow engine. The new capabilities aim to tackle one of the most stubborn bottlenecks in digital marketing: the slow, manual grind of video post-production.

With features like automated subtitles, metadata generation, and chapter creation, the company is positioning MediaFlows as more than a workflow tool—it’s becoming an end-to-end video optimization engine built for scale.

Automating the Parts No One Wants to Do

Video may dominate engagement metrics, but producing it efficiently—especially at scale—remains a challenge.

According to Wyzowl, 63% of consumers prefer learning about products through video. That demand is pushing brands to produce more content, faster, and for more channels. But behind every polished video is a stack of manual tasks: captioning, tagging, localization, and formatting for discoverability.

Cloudinary’s latest update targets exactly those pain points.

MediaFlows now uses AI to automate key post-production workflows that traditionally require specialist tools and teams, including:

• Generating and translating subtitles
• Creating titles, descriptions, and metadata tags
• Adding chapter markers for easier navigation

The goal is straightforward: reduce time-to-publish while improving accessibility and search performance.

Built for a Multi-Platform, Multi-Language World

One of the standout features is automated subtitle generation and translation.

As brands expand into global markets, localization has become essential—not optional. Yet translating video content has historically been resource-intensive and slow. By automating subtitle extraction and translation, Cloudinary is making it easier to adapt content for different regions without duplicating effort.

This also ties directly into accessibility requirements, which increasingly mandate subtitled or captioned content across industries.

In parallel, AI-generated metadata helps videos perform better not just in traditional search engines, but also in emerging discovery environments—particularly LLM-powered or “agentic” search systems.

That’s a subtle but important shift. Video SEO is no longer just about keywords; it’s about structured, machine-readable context that AI systems can interpret.

Smarter Navigation for Long-Form Content

Another addition—automated chapter generation—addresses a growing trend in long-form video.

From product demos to explainer content, longer videos are becoming more common in B2B and e-commerce. But without proper structure, they can be difficult to navigate.

By automatically inserting chapter markers, MediaFlows improves the viewing experience while also making content more usable across platforms that support segmented playback.

It’s a small feature with outsized impact, particularly for brands investing in educational or high-intent video content.

No-Code Meets Natural Language

Cloudinary is also leaning into usability.

Teams can build custom workflows using MediaFlows’ no-code interface or simply describe what they want using a natural language workflow agent. That lowers the barrier for marketing and content teams who may not have technical expertise but still need to manage complex video pipelines.

This aligns with a broader industry push toward democratizing AI—moving advanced capabilities out of engineering teams and into the hands of everyday users.

Why This Matters for Marketers

The bigger story here isn’t just automation—it’s operationalization.

Most brands already know video drives engagement, conversions, and trust. The challenge has been scaling production without ballooning costs or timelines.

Cloudinary’s approach reframes video not as a creative bottleneck, but as a process that can be optimized and automated like any other part of the marketing stack.

For industries like e-commerce, media, and enterprise tech—where speed, localization, and compliance all matter—this could be a meaningful shift.

Same-day publishing across regions, for example, becomes far more achievable when subtitles, metadata, and structure are handled automatically.

Competitive Context

Cloudinary isn’t alone in bringing AI into video workflows. A range of platforms—from video editing tools to marketing suites—are adding automation features.

What differentiates MediaFlows is its focus on orchestration rather than creation. Instead of generating video content itself, it optimizes and prepares that content for distribution, discovery, and compliance.

That makes it a natural fit for teams already producing video but struggling to scale post-production efficiently.

The Bottom Line

With these MediaFlows updates, Cloudinary is targeting a critical gap in the video pipeline: everything that happens after the edit.

By automating subtitles, metadata, and navigation, it’s helping brands move faster while improving accessibility and search performance across both traditional and AI-driven channels.

As video consumption continues to rise—and as discovery shifts toward AI-powered systems—the ability to produce optimized content at scale could become a defining competitive advantage.

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Ndovesha AI is betting big on a future where marketing teams don’t just use AI—they delegate to it.

The company has announced an expanded version of its all-in-one AI agent platform, designed to help businesses generate marketing assets, automate content production, and accelerate digital growth using specialized, task-focused AI agents. The pitch is simple: replace fragmented tools and manual workflows with a unified system that actually produces finished marketing outputs.

It’s a crowded category, but Ndovesha AI is leaning into a clear differentiator—execution over assistance.

From Prompts to Production

Most generative AI tools still operate as assistants. They generate text, suggest ideas, or create drafts, leaving users to stitch together the final output across multiple platforms.

Ndovesha AI is aiming to collapse that process.

Its platform bundles a wide range of AI-powered agents into a single workspace capable of producing:

• Social media visuals, carousels, and video content
• Ad creatives and marketing captions
• Flyers, logos, and branded assets
• Landing pages and full websites
• Presentations and blog articles

The idea isn’t just speed—it’s completeness. Instead of moving from prompt to draft to design tool to publishing platform, users can generate ready-to-use assets in one environment.

That shift reflects a broader industry move toward agentic AI—systems that don’t just assist with tasks but execute them end to end.

The Rise of Specialized AI Agents

At the core of Ndovesha AI’s platform is a growing library of purpose-built agents, each focused on a specific marketing function.

These include tools like a prompt generator, AI ad creator, carousel generator, video content agent, and website builder, among others. While individually these capabilities aren’t new, packaging them into a coordinated system is where the company sees its edge.

This modular approach mirrors a wider trend in AI development: breaking down workflows into discrete, automatable units that can be orchestrated together. For marketing teams, that could mean faster campaign execution with fewer handoffs between tools and teams.

Targeting Speed—and Cost Pressure

The timing of this expansion is no coincidence.

Businesses across sectors are under pressure to produce more content across more channels, often with limited budgets and leaner teams. At the same time, expectations for quality and personalization continue to rise.

AI-powered content generation has emerged as a solution, but many tools still require significant human oversight and integration effort.

Ndovesha AI is positioning itself as a more practical alternative—one that reduces both production time and creative costs by automating the full lifecycle of asset creation.

That value proposition is particularly relevant for startups and SMEs, though the platform is also targeting agencies and enterprise teams.

Competing in a Crowded AI Stack

Ndovesha AI enters a competitive landscape filled with generative AI platforms, design automation tools, and marketing suites—all vying to become the central hub for content creation.

What sets this approach apart is its emphasis on consolidation. Instead of excelling at one function, the platform aims to cover many—design, copy, web development, and campaign assets—under a single interface.

That breadth can be a double-edged sword. While it simplifies workflows, it also raises expectations around quality and depth in each category, where specialized tools often still have an advantage.

Still, as AI models improve and workflows become more standardized, all-in-one platforms are gaining traction—especially among teams looking to reduce tool sprawl.

A Play for Emerging and Global Markets

Ndovesha AI is also positioning itself with a geographic lens, aiming to serve businesses across Africa and other growth markets.

In regions where access to design, development, and marketing resources can be uneven, an integrated AI platform could level the playing field—giving smaller teams the ability to launch and scale digital campaigns quickly.

That aligns with a broader democratization trend in AI, where advanced capabilities are becoming more accessible beyond traditional tech hubs.

The Bigger Picture: Agentic AI Goes Mainstream

Industry analysts have been pointing to autonomous and agentic AI as the next major wave of productivity tools. The shift is from tools that assist humans to systems that act on their behalf.

Ndovesha AI’s platform is a clear example of that transition in the marketing domain.

Instead of asking, “How can AI help me create this asset?” the question becomes, “Which agent should handle this task?”

It’s a subtle but important shift—one that could redefine how marketing teams operate over the next few years.

The Bottom Line

Ndovesha AI is stepping into the agentic AI race with an ambitious goal: to turn marketing production into a largely automated, AI-driven process.

By combining multiple specialized agents into a single platform, it’s aiming to streamline workflows, cut costs, and help businesses move faster from idea to execution.

Whether it can compete with more established players will depend on how well it balances breadth with quality. But the direction is clear—AI in marketing is moving beyond assistance and into full-scale execution.

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CallRail is pushing AI voice from reactive to contextual with a new upgrade to its HubSpot integration—one that lets its Voice Assist platform access CRM data before a conversation even begins.

The result: an AI voice assistant that doesn’t just answer calls, but recognizes who’s calling, recalls past interactions, and responds accordingly. It’s a small technical shift with big implications for how businesses handle inbound leads—especially in a world where customers expect continuity, not repetition.

From “Hello” to Context in Seconds

At the center of the update is real-time CRM access.

When a call comes in, CallRail’s Voice Assist checks for a match inside HubSpot. If it finds one, the assistant can immediately tailor the interaction—skipping basic questions, referencing past conversations, and delivering a more personalized greeting.

In practical terms, that means:

• Returning callers are recognized instantly
• Conversations pick up where they left off
• Known details don’t need to be repeated

If no match exists, the system defaults to a standard intake flow, capturing new lead data without interrupting the experience.

It’s a straightforward feature on paper, but one that addresses a persistent pain point in AI-driven customer interactions: the lack of memory.

Fixing AI’s “Memory Problem”

Despite rapid advances in conversational AI, most voice assistants still operate in isolation. Each call is treated as a fresh interaction, forcing customers to reintroduce themselves and restate their needs.

That disconnect—what CallRail describes as the “memory gap”—does more than annoy users. It slows down conversations, erodes trust, and ultimately hurts conversion rates.

By integrating CRM context at the start of every call, CallRail is attempting to close that gap. The assistant doesn’t just respond; it continues a relationship.

That aligns with a broader trend across MarTech: the push to make AI systems stateful, not stateless—aware of history, context, and customer identity across touchpoints.

More Than Just Personalization

While personalization is the headline feature, the integration also tightens the feedback loop between conversations and CRM data.

With the update, Voice Assist can automatically log:

• Caller intake details
• Conversation summaries
• Updated lead information

directly into HubSpot.

That reduces manual data entry and ensures records stay current—an ongoing challenge for sales and marketing teams relying on CRM accuracy to drive decisions.

It also reinforces the idea that AI voice isn’t just a front-end experience layer. It’s becoming a data collection and enrichment engine in its own right.

Why This Matters for B2C Marketers

The timing of this update reflects rising expectations from customers—and increasing pressure on businesses.

As CRM adoption grows, so does the expectation that companies will actually use that data in real time. Customers don’t just want to be stored in a database; they want to be recognized.

Failing to meet that expectation can have measurable consequences:

• Lower engagement during calls
• Increased drop-offs
• Reduced conversion rates

CallRail’s approach tackles that problem directly, especially for high-intent inbound calls where context can make or break the interaction.

AI Voice Moves Into Revenue Territory

There’s also a bigger shift at play.

AI voice assistants are evolving from cost-saving tools—handling overflow calls or after-hours support—into revenue-generating assets. By improving conversion rates and customer experience, they’re starting to influence the top line, not just operational efficiency.

CallRail is leaning into that narrative. Its updated Voice Assist is positioned not just as a support tool, but as a way to turn more conversations into conversions without adding headcount.

That’s particularly relevant for small and mid-sized businesses, where missed calls or inconsistent follow-ups can directly impact revenue.

Competitive Context

CallRail isn’t alone in this space. CRM-native AI and conversational platforms are racing to embed deeper context into customer interactions.

What differentiates this move is the timing of the data access—before the conversation begins, not midstream. That subtle distinction makes the interaction feel more natural, avoiding the awkward “let me look that up” moment that often breaks immersion in AI conversations.

It’s a step toward making AI voice feel less like a bot and more like a well-informed human agent.

The Bottom Line

With this HubSpot integration upgrade, CallRail is addressing one of the most noticeable gaps in AI voice: the inability to remember.

By bringing CRM context into the first second of a call, it’s turning voice interactions into continuous, relationship-driven experiences rather than isolated transactions.

For marketers, that could mean fewer dropped conversations, stronger customer trust, and ultimately, better conversion outcomes—all without adding strain on human teams.

As AI voice becomes more embedded in the customer journey, the winners won’t just be the ones who can talk—they’ll be the ones who can remember.

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ActiveProspect is sharpening its position in the evolving MarTech stack with a clear structural shift. The company has rebranded its recently acquired Verisk Marketing Solutions business as InfutorData, signaling a strategic split between compliance-driven lead generation and data-powered identity intelligence.

The move follows ActiveProspect’s December 2025 acquisition of Verisk Marketing Solutions from Verisk Analytics, backed by Five Elms Capital. It’s less a cosmetic rebrand and more a repositioning play aimed at clarifying how marketers navigate two increasingly intertwined—but operationally distinct—challenges: consent and identity.

The combined business now tops $100 million in annual recurring revenue, putting ActiveProspect in a stronger position to compete in a market where compliance, data quality, and identity resolution are converging fast.

Two Lanes, One Strategy

At the core of the announcement is a simplified operating model.

ActiveProspect will continue focusing on the opt-in lead generation ecosystem—its traditional stronghold. That includes tools for TCPA compliance, lead certification, filtering, and partner orchestration. In an era of tightening privacy regulations and litigation risk, that’s not a small niche; it’s a necessity.

InfutorData, meanwhile, becomes a standalone identity and data intelligence arm. Its remit is broader: identity resolution, data enrichment, and marketing intelligence for brands, data providers, and platforms.

The split reflects a growing reality in B2C marketing. Consent and identity may be connected, but they require different infrastructure, different data models, and often different buyers within the enterprise.

Why This Matters Now

The timing is notable.

Marketers are under pressure from multiple fronts: stricter privacy laws, signal loss from cookies and mobile identifiers, and rising customer acquisition costs. At the same time, expectations for personalization haven’t gone away.

That tension is pushing companies to invest in two parallel capabilities:

• Verified, compliant first-party data pipelines
• Robust identity resolution to unify fragmented customer signals

ActiveProspect is effectively aligning its business around those twin needs.

Its core platform ensures that leads are consented, compliant, and auditable—critical in a world shaped by TCPA enforcement and evolving privacy frameworks. InfutorData extends that value by helping marketers actually use that data more effectively once it’s captured.

Reviving the Infutor Brand

The Infutor name isn’t new—it has a two-decade history in the data and identity space. Bringing it back suggests ActiveProspect sees brand equity in separating its identity business from its compliance roots.

CEO Steve Rafferty framed the move as an expansion of the company’s founding vision: building a marketing ecosystem grounded in consent and transparency. The difference now is scope. Instead of focusing solely on lead generation, the company is positioning itself across the broader data lifecycle.

That’s a crowded space, with established players in identity resolution and data onboarding already competing for enterprise budgets. But ActiveProspect’s angle—linking compliance-grade data collection with downstream identity intelligence—could resonate with organizations trying to connect governance with growth.

What InfutorData Brings to the Table

InfutorData’s capabilities center on making customer data more usable and trustworthy.

Its platform links identities across channels, improving match rates and helping marketers build more complete customer profiles. That, in turn, supports better targeting, reduced fraud, and more efficient acquisition strategies.

In practical terms, this means:

• Connecting disparate identifiers across devices and platforms
• Enriching customer records with additional attributes
• Improving accuracy in audience segmentation
• Supporting omnichannel marketing efforts

For marketers navigating signal loss and fragmented data ecosystems, those capabilities are quickly becoming table stakes.

A Broader Industry Shift

ActiveProspect’s restructuring mirrors a wider trend in MarTech: specialization within integration.

Vendors are increasingly breaking out distinct capabilities—compliance, identity, analytics—into modular offerings while still promising interoperability. The goal is to give enterprises flexibility without sacrificing cohesion.

It’s also a response to buying behavior. Different stakeholders—legal, marketing, data teams—often control different parts of the stack. A dual-brand approach can make it easier to sell into those silos while maintaining a unified backend strategy.

The Bottom Line

With InfutorData, ActiveProspect is making a calculated bet: that the future of B2C marketing hinges on balancing two forces—strict compliance and sophisticated identity intelligence.

By separating those functions while keeping them strategically aligned, the company is aiming to serve both sides of that equation without forcing customers into a one-size-fits-all platform.

Whether that model scales will depend on execution—and on how well marketers adapt to a landscape where knowing your customer increasingly depends on both permission and precision.

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Sovereign AI has been a buzzword for years, but its traditional definition—focused largely on where data sits—is starting to look outdated. As enterprises embrace agentic AI systems that don’t just analyze data but act on it, the rules are changing fast.

The shift is subtle but significant: AI is no longer confined to dashboards and insights. It’s triggering workflows, moving data across environments, and interacting with systems that span jurisdictions. That evolution is exposing blind spots in how organizations think about control, compliance, and risk.

In short, knowing where your data lives is no longer enough. You also need to know what your AI is doing with it.

The Limits of “Data Residency First” Thinking

Most sovereign AI strategies today are built on a simple premise: control the environment, and you control the data. That assumption worked reasonably well when AI workloads were largely static or confined to a single cloud.

But modern enterprise workflows don’t behave that way.

They stretch across multiple clouds, on-prem systems, SaaS platforms, and geographic regions. Add regulatory fragmentation to the mix—three-quarters of countries now enforce some form of data localization—and the idea of a single, controlled environment starts to fall apart.

Many AI vendors haven’t caught up. Their platforms still push centralized architectures or cloud-only deployments, effectively asking enterprises to bend their operations to fit the technology. For global organizations, that’s increasingly unrealistic.

Agentic AI Changes the Stakes

The rise of agentic AI—systems that autonomously execute tasks—raises the stakes even further.

These systems don’t just access data; they move it, transform it, and act on it. They initiate workflows, call APIs, and interact with multiple systems in real time. Each of those actions introduces new exposure points.

That’s where traditional sovereign AI models struggle. Zero-copy architectures and data residency policies don’t account for how data behaves once AI starts operating on it.

The key question has shifted from “Where is the data stored?” to “What happens when AI uses it?”

A Shift Toward “Spectrum of Control”

Automation Anywhere is leaning into this shift with a reframing of sovereign AI—not as a fixed architecture, but as a “spectrum of control.”

The idea is straightforward: enterprises should define control across multiple dimensions, not just storage. That includes:

• Where data and metadata reside
• How data is processed, moved, or copied
• Who can access it and who owns encryption keys
• Where AI-driven work actually happens
• Which legal jurisdictions govern access

This broader view aligns more closely with how enterprises actually operate—distributed, hybrid, and regulated in different ways across regions.

It also reflects a growing realization: sovereignty isn’t just about infrastructure. It’s about governance across the entire lifecycle of data and execution.

Control Without Lock-In

One of the more interesting claims from Automation Anywhere is that this level of control doesn’t require centralization—or a single deployment model.

Its Agentic Process Automation (APA) platform is designed to let enterprises operate across cloud, multi-cloud, and on-prem environments without forcing them into a specific architecture. That flexibility matters in industries where regulatory requirements vary not just by country, but by data type.

Key capabilities include:

• Flexible deployment models: Supporting hybrid and multi-cloud strategies
• Granular data governance: Letting organizations define how and where data is processed
• Composable architecture: Integrating with existing tools and avoiding vendor lock-in
• Workflow-level controls: Governing how AI systems act across processes
• Jurisdiction-aware sovereignty controls: Aligning operations with legal requirements
• Built-in security and auditability: Enabling compliance and monitoring

This approach mirrors a broader industry trend: enterprises want modular, interoperable systems rather than all-in-one platforms that dictate architecture.

Operationalizing Sovereign AI

Of course, defining control is one thing. Enforcing it is another.

To operationalize sovereign AI, organizations need to rethink how they manage data and AI workflows end to end. That includes:

• Processing data where it resides instead of moving it unnecessarily
• Keeping AI workflows within defined boundaries across environments
• Retaining control over access, encryption, and key management
• Matching deployment models to regulatory and risk requirements
• Maintaining visibility with audit trails and governance controls

None of this is trivial. It requires coordination across IT, security, compliance, and business teams—areas that don’t always move in sync.

Why This Matters Now

The timing isn’t accidental.

As AI systems take on more operational responsibility, the consequences of losing control increase. A misconfigured workflow or an over-permissioned AI agent isn’t just a technical issue—it can quickly become a compliance or security problem.

At the same time, regulators are paying closer attention to how data is used, not just where it’s stored. That shift is pushing enterprises to adopt more nuanced approaches to sovereignty.

Vendors that can support this complexity—without locking customers into rigid architectures—are likely to have an edge.

The Bottom Line

Sovereign AI is evolving from a checkbox exercise into a strategic capability.

Enterprises can no longer rely on data residency alone. They need visibility and control over how AI systems operate across environments, workflows, and jurisdictions.

Agentic AI is accelerating that shift, forcing organizations to rethink not just their technology stacks, but their governance models as well.

The companies that get this right won’t just stay compliant—they’ll be better positioned to scale AI across borders without losing control in the process.

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Microsoft, Postel, and Audiencerate have launched a new data and AI-powered platform aimed at helping Italian small and midsize businesses modernize customer engagement. The solution combines Microsoft cloud and AI tools, Postel’s omnichannel communications network, and Audiencerate’s data intelligence capabilities to give SMEs access to enterprise-grade marketing technology.

Microsoft is expanding its push into Europe’s small business digital transformation market through a new collaboration with Postel, a company within the Poste Italiane Group, and marketing technology provider Audiencerate.

The three companies announced an integrated platform designed to help Italian SMEs manage customer relationships using AI, automation, and data-driven marketing tools that have traditionally been available mainly to larger enterprises.

The launch reflects a broader market trend: AI adoption is moving beyond global corporations and into the mid-market, where smaller businesses often lack the budgets or internal teams required to deploy complex customer data and marketing systems.

Why Italian SMEs Matter

Italy’s economy is heavily driven by SMEs, many of which operate in manufacturing, retail, tourism, logistics, and family-owned service sectors. While these businesses form the backbone of the national economy, they have historically lagged larger enterprises in digital maturity.

That creates a major opportunity for technology vendors.

Cloud-based AI platforms can lower barriers by replacing expensive custom infrastructure with subscription tools that are easier to deploy and scale.

Microsoft and its partners appear to be targeting that opportunity directly.

What the Platform Does

The new solution combines three core capabilities:

• Postel’s omnichannel communications infrastructure, spanning physical and digital outreach
• Audiencerate’s customer data intelligence and activation tools
• Microsoft Azure cloud and AI services

Together, the platform is designed to help SMEs centralize and activate first-party data across the full customer lifecycle.

That includes segmenting customers, automating campaigns, orchestrating communication across channels, and generating market insights based on competitive and sector trends.

For smaller businesses, this can mean moving from fragmented spreadsheets and manual email tools to coordinated lifecycle marketing systems.

Bringing Enterprise Martech to the Mid-Market

One of the more significant aspects of the launch is democratization.

Large enterprises have long used customer data platforms, predictive analytics, and marketing automation tools from vendors such as Salesforce, Adobe, HubSpot, and Oracle. Many SMEs, however, have been priced out of these ecosystems or lacked implementation resources.

By embedding Azure AI into Audiencerate’s infrastructure, the partners say they can make advanced segmentation, market intelligence, and automation more accessible.

That matters because smaller companies face many of the same customer acquisition challenges as enterprises: rising ad costs, changing buyer expectations, and increasing pressure to personalize engagement.

The difference is scale and resources.

AI as a Competitive Equalizer

Microsoft Italy’s leadership framed the partnership around accessibility, arguing that cloud and AI can help smaller organizations operate more intelligently and regain competitiveness.

That aligns with wider market research. According to IDC, European SMEs are increasing investment in cloud services and automation to improve productivity and resilience. Meanwhile, McKinsey & Company has noted that AI adoption among mid-sized firms can generate outsized gains when tied to sales, service, and operational efficiency.

For Italian SMEs, practical use cases may include:

• Automated re-engagement campaigns for dormant customers
• Dynamic segmentation based on purchase behavior
• Real-time trend monitoring in local markets
• Multichannel communication across email, SMS, print, and digital touchpoints
• Better retention and loyalty programs

Why Postel’s Role Is Strategic

Postel brings an important local advantage: trusted communication infrastructure.

As part of the Poste Italiane ecosystem, it has deep roots in physical and digital communications. That may be particularly relevant in Italy, where many SMEs still rely on hybrid engagement models combining traditional direct outreach with newer digital channels.

This hybrid capability could differentiate the platform from purely digital SaaS competitors.

What Comes Next

The companies said the platform already includes customer segmentation, market intelligence, and connectors to Postel channels. Upcoming additions include a marketing automation module and new communication channels.

They also signaled continued innovation through Microsoft AI capabilities and Audiencerate’s broader AdTech and MarTech partnerships.

That suggests the platform could evolve into a full-stack SME growth engine rather than a narrow CRM tool.

Bigger Industry Implication

The launch points to a larger shift in B2B software markets: AI-enabled enterprise functionality is cascading downward into the SME segment.

That creates new competition not only among global cloud vendors, but also among regional operators that combine local trust, data services, and vertical expertise.

For Italian SMEs, the question is no longer whether advanced customer intelligence tools are available.

It is whether they can adopt them quickly enough to compete.

Market Landscape

The SME martech market is becoming a key battleground for Microsoft, Google, Salesforce, HubSpot, Zoho, and regional providers. Demand is rising for affordable platforms that combine CRM, automation, analytics, and AI. In Europe, local trust, regulatory compliance, and multilingual support remain important buying factors alongside product capability.

Top Insights

• Microsoft, Postel, and Audiencerate launched an AI marketing platform for Italian SMEs.
• The solution combines cloud AI, customer data intelligence, and omnichannel communications.
• Smaller businesses gain access to tools once reserved for large enterprises.
• Upcoming features include marketing automation and expanded communication channels.
• The move reflects growing SME demand for AI-driven competitiveness in Europe.

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RAD Amplify has expanded its collaboration across the Omnicom network for a third consecutive year, signaling continued demand for AI-powered creator marketing strategies built on live audience intelligence. The latest phase of the relationship includes work with Ketchum, where the company is helping integrate audience data into creator planning and campaign execution earlier in the process.

RAD Amplify is deepening its relationship with Omnicom agencies as holding companies increasingly seek data-driven ways to improve creator marketing performance.

The company, part of RAD Intel, announced an expanded collaboration across the Omnicom network for a third straight year. Most recently, that work has included Ketchum, where RAD Amplify is being used to bring audience intelligence into creator strategy before campaigns launch.

The move highlights a broader shift in influencer and creator marketing: brands are moving away from static audience personas and follower-count metrics toward real-time behavioral insight.

Why This Matters

Creator marketing has become one of the fastest-growing segments of digital media, but many enterprise campaigns still rely on outdated planning models. Brands often choose creators based on demographics, historical performance, or broad audience categories rather than current consumer sentiment and emerging cultural signals.

That creates risk.

Audience preferences can change quickly across platforms such as TikTok, Instagram, YouTube, Reddit, and search ecosystems. A creator who fit a campaign brief weeks ago may no longer be the best match by launch day.

RAD Amplify is positioning itself as a solution to that lag.

Its platform analyzes language and engagement behavior across social, search, and digital environments, then converts those signals into strategic recommendations. The company says its proprietary RAD Score helps identify how audiences think, respond, and engage in real time.

That insight can be used to guide creator selection, messaging, and creative direction.

Omnicom’s Interest in Integrated Creator Intelligence

For agency networks such as Omnicom, creator marketing is becoming a more strategic discipline rather than a niche social activation channel.

Large clients increasingly want creator campaigns connected to broader brand strategy, PR, commerce, paid media, and measurement systems. That means agencies need tools that bridge strategy and execution rather than operate as isolated influencer programs.

Ketchum Chief Innovation Officer Rob Bernstein said clients are asking for greater connectivity across strategy, data, and execution. That reflects a common enterprise challenge: marketing teams often run creators, media, analytics, and communications in separate silos.

Platforms like RAD Amplify aim to create a shared operating view of audience behavior across those teams.

Language as a Predictive Signal

One of the more interesting elements of RAD Amplify’s positioning is the idea that “language is the new behavior.”

That thesis suggests what people say online — comments, searches, reactions, and conversational trends — may reveal intent earlier than traditional campaign metrics such as clicks or conversions.

If accurate, that could be valuable for brands trying to respond faster to shifts in consumer mood, category demand, or cultural relevance.

For example, spikes in search phrasing or sentiment around wellness, sustainability, or pricing concerns may help marketers refine creator briefs before assets are produced.

This mirrors a wider movement across martech where conversational signals, first-party data, and social listening are increasingly used for predictive planning.

Why Holding Companies Are Investing

The world’s largest agency holding companies face pressure from multiple sides: clients want measurable ROI, creators expect faster workflows, and platforms keep changing their algorithms.

AI-driven audience intelligence offers a possible efficiency layer.

Rather than manually researching communities or relying on post-campaign reporting, agencies can use real-time signals to plan smarter upfront. That can improve creator fit, reduce wasted spend, and increase campaign relevance.

According to Statista, influencer marketing budgets continue to rise globally, while marketers are demanding stronger attribution and brand safety controls. Tools that combine strategy, intelligence, and execution are likely to benefit from that trend.

What It Means for Brands

For enterprise brands, the practical value lies in reducing guesswork.

Instead of relying only on broad personas such as “Gen Z beauty fans” or “millennial parents,” teams can understand how those audiences are talking now, what language resonates, and which creators align authentically with current sentiment.

That can lead to sharper briefs, stronger content resonance, and faster optimization.

For agencies, it may also help protect margins by reducing inefficient creator selection cycles and disconnected planning processes.

Bigger Industry Signal

RAD Amplify’s third-year expansion with Omnicom suggests creator marketing is maturing into a data infrastructure category.

The next phase of competition may not be about who has the largest creator roster. It may be about who best understands audience behavior before campaigns go live.

If that proves true, AI-powered audience intelligence could become as essential to creator strategy as programmatic data became to media buying.

Market Landscape

Creator marketing platforms are evolving beyond influencer marketplaces into intelligence-led martech systems. Competitors include CreatorIQ, Sprout Social, Captiv8, Aspire, and agency-owned platforms. At the same time, holding companies such as Omnicom, WPP, and Publicis are integrating creator programs into broader commerce, PR, and performance media operations.

Top Insights

• RAD Amplify expanded its Omnicom collaboration for a third year, reflecting sustained enterprise demand.
• New work with Ketchum focuses on using audience intelligence earlier in creator campaign planning.
• The company’s RAD Score uses live language and engagement signals to guide decisions.
• Agencies want tighter integration between creator strategy, analytics, and execution.
• Creator marketing is shifting from influencer lists to real-time audience behavior models.

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A new global study from the CMO Council and WongDoody argues that artificial intelligence alone is not enough to transform marketing performance. Instead, organizations that combine AI with human creativity, judgment, and emotional intelligence are significantly more likely to generate ROI, build stronger customer relationships, and outperform slower-moving peers.

As generative AI adoption spreads across marketing departments, a new report suggests the real winners may not be the companies deploying the most AI tools — but those redesigning marketing operations around human-machine collaboration.

The CMO Council, in partnership with WongDoody, has released new research titled Marketing’s Power Partners: AI and the Human Essence, based on a survey of 371 senior marketing leaders worldwide. Its central conclusion is direct: AI creates the most business value when paired with human marketers rather than used as a standalone replacement strategy.

The report labels top-performing organizations as Power Partners — companies that intentionally combine machine intelligence with human decision-making, creativity, and emotional understanding.

A Clear Performance Gap

The data points to a widening divide between mature adopters and everyone else.

According to the study, 73% of Power Partners say they exceed ROI expectations or achieve measurable returns from AI investments, compared with only 22% of other organizations. Nearly 70% of Power Partners report consistently building strong emotional customer connections, versus 40% of peers.

The difference extends into campaign performance. 86% of Power Partners say AI has delivered moderate-to-major ROI impact, while only 43% of less advanced organizations report the same.

Those numbers reinforce a growing market reality: AI tools are widely available, but value creation is uneven.

That mirrors broader enterprise technology trends. According to Gartner, many AI initiatives fail not because of model quality, but because organizations lack operational readiness, trusted data, and clear ownership models.

Workflow Redesign Matters More Than Tool Adoption

One of the report’s strongest findings is that AI success depends less on software procurement and more on process redesign.

The study found 70% of Power Partners are prepared to redesign workflows for AI-human collaboration, compared with only 7% of peers. Meanwhile, 94% have clearly defined collaborative content processes, versus 42% of other respondents.

That suggests many marketing teams are still treating AI as a bolt-on productivity tool rather than a structural operating model change.

Examples of redesign may include using AI for research, audience clustering, media optimization, content drafts, and reporting, while human marketers focus on brand positioning, creative direction, emotional resonance, and strategic decisions.

In practice, that can turn AI from a tactical assistant into a multiplier.

Why Many Organizations Are Falling Behind

The report identifies several barriers slowing adoption:

• AI skills gaps and lack of training
• Low trust in AI-generated outputs
• Concerns around brand authenticity
• Weak or fragmented customer data
• Unclear division of labor between people and AI systems

These are less technical problems than organizational ones.

Many enterprises still operate with siloed teams, legacy approval processes, and disconnected martech stacks. Adding AI into those environments often produces isolated pilots rather than scaled transformation.

For CMOs, this is becoming a leadership challenge as much as a technology one.

Regional and Business Model Differences

The research also highlights geographic and sector-based divides.

In the United States, organizations appear further ahead in AI adoption and measurable returns, though maintaining emotional relevance at scale remains a challenge. Europe faces more structural constraints tied to fragmented data readiness. APAC shows strong investment momentum, but cultural resistance to change is slowing execution.

The gap is also visible across business models.

B2C and hybrid companies are more likely to achieve strong ROI and redesign workflows, likely because they have higher campaign velocity, larger customer datasets, and more pressure for personalization.

B2B organizations, by contrast, often use AI narrowly for productivity gains rather than end-to-end transformation.

That is notable because B2B marketing increasingly depends on account intelligence, predictive demand generation, sales alignment, and complex buying journeys — all areas where AI can create material advantage if integrated properly.

Marketing to Machines

One of the report’s more forward-looking ideas is that brands are increasingly marketing not only to people, but to machines that influence purchasing decisions.

AI assistants, recommendation engines, procurement algorithms, and autonomous buying systems are beginning to shape discovery and decision-making. That means future marketing strategies may need to optimize for both human emotion and machine evaluation.

This could reshape SEO, ecommerce merchandising, content strategy, and B2B buying experiences.

The Bigger Takeaway

The study’s core message is timely: AI does not eliminate the need for marketers. It changes where marketers create value.

If marketing teams are defined by repetitive tasks, automation may replace them. If they are defined by judgment, empathy, narrative building, and strategic interpretation, AI can amplify them.

For enterprise leaders, the implication is clear. Competitive advantage will not come from simply buying AI tools. It will come from redesigning teams, workflows, and decision systems around collaborative intelligence.

That divide is already forming — and according to the CMO Council, it is growing quickly.

Market Landscape

Marketing AI adoption is accelerating across platforms from Google, Microsoft, Adobe, Salesforce, HubSpot, and enterprise martech vendors. Yet many organizations remain stuck between experimentation and scaled ROI. The next phase of competition is shifting from tool access to workflow orchestration, trusted data, governance, and human-AI collaboration models.

Top Insights

• CMO Council found companies blending AI with human marketers are 3x more likely to achieve measurable ROI.
• Workflow redesign matters more than simply adding AI tools to legacy marketing processes.
• Power Partners outperform peers in emotional connection, speed, creativity, and adaptability.
• Skills gaps, trust issues, and weak data remain major blockers to AI success.
• B2B marketers risk falling behind if AI is used only for productivity instead of transformation.

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