As networks morph into AI-powered ecosystems, testing is no longer about checking boxes—it’s about validating behavior at scale.

That’s the message from VIAVI Solutions Inc., which has unveiled its demonstration lineup for Mobile World Congress Barcelona 2026, taking place March 2–5. At booth 5B18, the company plans to showcase more than 30 demonstrations spanning AI-RAN, quantum-safe communications, AIOps, AI data centers, and 6G readiness.

The theme: convergence.

From Isolated Domains to AI-First Systems

According to VIAVI’s CTO Sameh Yamany, previously siloed domains—networks, AI, wireless, photonics, security, and sensing—are collapsing into one tightly coupled system.

That shift has massive implications for operators and infrastructure providers. Validation now extends beyond components to encompass trust, resilience, and performance across AI-driven environments.

In practical terms, that means:

• Testing AI-RAN algorithms before live deployment

• Verifying performance across scale-up and scale-out AI data centers

• Securing communications against quantum-era threats

• Ensuring precision timing in GNSS-denied environments

In other words, infrastructure must be validated as an intelligent organism, not a collection of parts.

Digital Twins Take Center Stage

A standout feature at the booth will be VIAVI’s daily live digital twin demonstration, scheduled each day at 4 PM CET. The use case is designed to show how the company’s solutions integrate into a complete, end-to-end digital twin environment.

Digital twins are becoming essential in telecom as AI-RAN architectures mature. Instead of relying solely on physical lab testing, operators can simulate real-world conditions, train algorithms, and stress-test scenarios virtually.

VIAVI plans to dive deep into:

• Digital twin environments for training AI-RAN algorithms in 6G

• Ray-tracing-based lab testing to model real-world UE behavior

• Agentic AI-RAN digital twin scenarios

As 6G research accelerates globally, digital twins are expected to become foundational tools—not optional add-ons.

AI Data Centers and the Rise of AIOps

Beyond the radio network, VIAVI is targeting the AI data center—a rapidly expanding infrastructure layer driven by generative AI and hyperscale compute demands.

The company will demonstrate validation for scale-up and scale-out architectures, reflecting the growing complexity of AI clusters.

With hyperscalers like AWS and chip leaders like NVIDIA pushing AI compute boundaries, network performance inside and between data centers has become mission-critical.

Add AIOps into the mix, and testing extends beyond throughput and latency into predictive optimization and automated remediation.

Quantum-Safe and Assured Timing: Security Gets Physical

Security and timing technologies also feature prominently.

VIAVI will showcase optimization tools for PQC (post-quantum cryptography) and QKD (quantum key distribution), reflecting industry urgency around quantum-safe communications.

As governments and operators begin preparing for “harvest now, decrypt later” threats, validation frameworks for quantum resilience are becoming essential.

The company will also display its new ePRTC360+™, described as the only non-Cesium holdover clock capable of maintaining 100 ns accuracy in GNSS-denied environments.

In mission-critical communications—public safety networks, defense infrastructure, financial systems—assured Position, Navigation, and Timing (APNT) is no longer optional. GNSS vulnerabilities have elevated timing resilience to a board-level concern.

Non-Terrestrial Networks and 6G Horizons

The demo lineup includes testing and performance verification for Non-Terrestrial Networks (NTN), signaling the increasing importance of satellite integration in next-generation telecom.

As 6G visions expand to include integrated sensing and communications (ISAC) applications—such as disaster monitoring—the validation ecosystem must evolve accordingly.

VIAVI’s collaboration roster underscores this shift. The company is working with over 20 partner organizations, including the AI-RAN Alliance, Ericsson, Nokia, Rohde & Schwarz, and others across the telecom and AI value chain.

Partnerships are becoming essential as no single vendor controls the entire AI-first infrastructure stack.

The Bigger Picture: Testing for Trust in the AI Era

Telecom is entering an AI-native phase. Networks are being optimized by algorithms, data centers are built around GPU clusters, and wireless standards are embedding AI at the protocol level.

That convergence changes testing fundamentally.

It’s no longer enough to validate whether a component meets specification. Operators must understand how systems behave under AI-driven load, adversarial conditions, and quantum-era security constraints.

VIAVI’s MWC 2026 lineup positions the company as a validation layer across that complexity—spanning 6G research, AI-RAN deployment, quantum security, and mission-critical timing.

If the telecom industry’s next chapter is about intelligent infrastructure, the companies that validate that intelligence may become just as critical as those building it.

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Healthcare chatbots are good at answering questions. Acting on them? That’s where most still fall short.

Hyro, a Responsible AI Agent Platform purpose-built for healthcare, is aiming to close that gap through a new strategic partnership with WebMD Ignite. The collaboration is designed to help health systems deliver guided, clinically aligned conversational journeys that don’t end with information—but with outcomes.

The goal: move patients seamlessly from initial questions to meaningful next steps such as scheduling appointments, routing to the right specialty, or navigating care options, all within a single digital experience.

Beyond Q&A: Why Healthcare AI Needs to Act

Agentic AI is quickly becoming a primary engagement channel for health systems. Patients increasingly turn to digital front doors—chatbots, virtual assistants, and web agents—for answers about symptoms, services, and care options.

But there’s a problem.

Many of these experiences stop at static Q&A. Patients get information, but no clear direction on what to do next. In symptom-driven scenarios, that lack of clinical context and decisioning can create confusion, friction, or unnecessary calls to already overburdened staff.

Hyro and WebMD Ignite are positioning their partnership as a response to that limitation: conversational AI that not only informs patients, but actively guides them to the next best action.

Embedding Clinical Intelligence Into Conversations

At the core of the partnership is the integration of WebMD Ignite’s clinically validated content and decision logic into Hyro’s enterprise conversational AI engine.

The combined solution brings together two complementary strengths:

• Hyro’s healthcare-trained conversational AI, built for scale, security, and enterprise deployment

• WebMD Ignite’s clinical intelligence, including symptom understanding, education content, and decision pathways

Together, they enable more structured, context-aware interactions that help patients progress from discovery to action—without jumping between systems or restarting conversations.

This is less about making chatbots smarter in isolation, and more about embedding clinical-grade reasoning directly into conversational workflows.

Two Foundational Capabilities at Launch

The initial phase of the partnership focuses on two core capabilities aimed squarely at care navigation and symptom-driven journeys.

1. Healthcare Intelligence Layer
Hyro will integrate a WebMD Ignite–powered intelligence layer into its conversational platform. This adds deeper symptom understanding and improved triage support, allowing AI agents to deliver more clinically aligned guidance while maintaining consistency and safety.

2. Decision-Driven Clinical Education
WebMD Ignite’s clinical education content will be delivered directly within Hyro’s chat agents. Crucially, this content is paired with real-time decision logic that recommends the most appropriate next step—such as routing to a specialty, escalating to a care navigator, enrolling in a hospital class, or scheduling an appointment.

The emphasis is on conversations that consistently lead somewhere, rather than ending in informational dead ends.

A Shift Toward Outcome-Driven Digital Front Doors

The partnership reflects a broader shift in digital health engagement. Health systems are under pressure to:

• Reduce call center volume

• Expand self-service without compromising care quality

• Improve access and navigation across increasingly complex service lines

Conversational AI is well positioned to help—but only if it’s clinically grounded and operationally connected.

By embedding decisioning and execution into AI agents, Hyro and WebMD Ignite are effectively turning chatbots into digital care guides—capable of orchestrating next steps, not just answering FAQs.

That distinction matters in healthcare, where misdirected patients can lead to delays, dissatisfaction, or higher costs.

Executive Perspective: From Information to Action

WebMD Ignite frames the partnership as part of a broader push to make AI-powered patient engagement more actionable.

According to the company, agentic AI should play a dual role: educate patients and help them act on that information. Bringing clinical intelligence directly into conversational workflows is positioned as a way to deliver more connected digital care experiences without adding operational burden.

Hyro, meanwhile, sees the collaboration as closing a long-standing gap in healthcare AI—bridging trusted health knowledge with enterprise-grade execution.

The combined platform is designed to “navigate” patients, not just converse with them.

Availability and What Comes Next

The jointly powered solution will be available as part of Hyro’s enterprise conversational platform, with early deployments focused on:

• Guided symptom assessment

• Care-navigation journeys

• Next-best-action experiences

If successful, the model could expand into additional use cases where clinical context and operational follow-through are critical.

The Bigger Picture: Conversational AI Grows Up

Healthcare AI is entering a more mature phase. Early adoption focused on access and automation. The next phase is about orchestration—connecting clinical insight, decision logic, and operational systems into cohesive patient journeys.

Hyro and WebMD Ignite are betting that health systems are ready for conversational AI that doesn’t just talk—but acts.

And in an environment where patient expectations for digital experiences increasingly mirror those in retail and banking, that evolution may be less optional than inevitable.

Get in touch with our MarTech Experts.

Market research is getting its copilots—and now, its architects.

quantilope has rolled out a major update to its AI Research Partner, quinn, completing what it calls a fully integrated, end-to-end AI research workflow. The headline feature: quinn can now create and review comprehensive, methodologically sound research studies from scratch.

For a sector long dominated by manual setup, logic checks, and spreadsheet wrangling, that’s a bold claim.

From DIY to “Do-It-With-AI”

quantilope is positioning this release as more than incremental AI enhancement. The company says it marks a formal transition from traditional DIY research to what it calls “Do-It-With-AI” (DIA)—or, in branded shorthand, “Do-it-with-quinn.”

The shift reflects a broader trend across enterprise software: AI is no longer just summarizing outputs. It’s designing workflows.

With the update, quinn now supports the entire research lifecycle:

• Drafting studies from high-level objectives

• Structuring questionnaires using advanced methodologies

• Automatically validating logic and setup

• Conducting AI-powered analysis

• Generating automated reports

In other words, quinn moves from assistant to orchestrator.

AI as the Research “Nervous System”

At the core of the upgrade is what quantilope describes as advanced end-to-end AI integration. Quinn now maintains persistent context across the research journey—from initial study design through analysis and reporting.

That continuity is crucial.

Many AI tools in research today operate in silos: one for survey drafting, another for analysis, another for visualization. Context gets lost between steps. Errors creep in. Researchers spend time re-explaining objectives.

Quinn’s updated architecture aims to eliminate that fragmentation by acting as the platform’s “nervous system,” carrying intent and logic across stages.

The update also includes:

• Strengthened AI model performance

• Saved chat histories for contextual continuity

• Expanded dashboarding capabilities

• Direct integration within quantilope’s Editor

That Editor integration is particularly significant. Researchers can now convert high-level business objectives into structured questionnaires within minutes—using advanced methods—while quinn automatically reviews configurations to catch logic mistakes before launch.

For teams under tight timelines, that automation could cut hours—or days—of back-and-forth.

Real-Time Refinement Inside the Workflow

Beyond study creation, the update introduces real-time refinement tools.

New “quinn Action Buttons” allow one-click improvements to question phrasing, helping researchers fine-tune clarity and reduce bias. Meanwhile, persistent chat functionality lets users interrogate survey logic or request technical clarifications without leaving the build environment.

That conversational layer reflects a larger UX shift happening in enterprise platforms. Instead of navigating complex menus, users increasingly interact through dialogue—asking systems to explain, adjust, or optimize on demand.

In practical terms, it lowers the barrier to advanced methodologies. Researchers don’t need to manually configure every detail—they can collaborate with the AI to get there faster.

Productivity Gains—With a Human in Control

quantilope is careful to emphasize that quinn is “Human-Led, AI-Powered.” The positioning mirrors broader AI adoption narratives across enterprise software: augmentation over automation.

The company frames quinn as a master architect—handling structural rigor and execution—while researchers provide strategic context, brand nuance, and stakeholder considerations.

That balance matters in research, where methodological integrity and contextual understanding are critical.

According to quantilope’s leadership, the productivity shift is substantial. Instead of spending time on manual configuration and error-checking, researchers can focus on higher-level insight generation and strategic interpretation.

In a market where insights teams are often asked to do more with fewer resources, that productivity narrative is compelling.

Competitive Context: AI Arms Race in Insights

The consumer insights space has seen an AI surge over the past two years. Survey platforms, analytics vendors, and full-stack research solutions are racing to embed generative AI across their offerings.

But many tools still function as bolt-ons—AI summarizing findings after the fact, or suggesting edits without owning the process.

quantilope’s bet is that full lifecycle integration is the differentiator.

If quinn can reliably draft, validate, analyze, and report within one cohesive workflow, it could reduce the need for external scripting, manual QA, and third-party analysis tools.

The real test will be methodological depth. Enterprise research buyers won’t trade rigor for speed. If quinn consistently produces statistically sound studies while maintaining flexibility for customization, it could raise expectations for the category.

What This Means for Research Teams

For insights professionals, the implications are clear:

• Faster time from brief to field

• Fewer manual logic errors

• More iterative experimentation

• Greater focus on strategic storytelling

It also signals a philosophical shift. Research platforms are evolving from execution tools to collaborative intelligence systems.

If AI can shoulder structural complexity, researchers can concentrate on the harder part: asking better questions.

The Bottom Line

With this update, quantilope is aiming to redefine how enterprise research gets done. Quinn’s evolution from support tool to end-to-end workflow engine reflects a broader transformation across B2B tech—where AI is embedded deeply, not sprinkled on top.

The promise is ambitious: compress the research lifecycle without compromising methodological integrity.

If delivered consistently, “Do-It-With-AI” may not just be a slogan—it could become the default operating model for modern insights teams.

Get in touch with our MarTech Experts.

In a market flooded with AI claims and cloud slogans, Tata Communications is pressing reset on how it wants to be seen.

The global communications technology provider—serving 300 of the Fortune 500—has introduced a new brand identity and positioning: “Together, limitless.” The move marks a strategic milestone in the company’s 24-year evolution and signals a sharpened focus on integration, long-term value, and what it calls leadership in the “intelligent age.”

Brand refreshes are common. But timing is everything—and this one arrives as enterprises are rearchitecting their tech stacks amid escalating complexity.

Why This Rebrand Matters Now

Enterprises today aren’t just adopting new tools; they’re rewiring their operating models. Hybrid work, distributed cloud, cybersecurity pressures, AI deployment, and compliance demands have created what many CIOs describe as “stack sprawl.”

The result? More vendors, more dashboards, more noise.

Tata Communications says its research and customer listening surfaced a consistent tension: organizations don’t need more technology—they need clarity, integration, and trusted orchestration.

That message is at the core of “Together, limitless.” It’s less about flashy innovation and more about unifying platforms, expertise, and partnerships to deliver measurable outcomes.

For a company historically known for global connectivity and network infrastructure, this signals an ambition to move higher up the value chain.

From Connectivity Provider to Integration Partner

Managing Director and CEO A.S. Lakshminarayanan framed the moment as a shift toward becoming a “more integrated, future-ready company.”

The emphasis isn’t just semantic.

Tata Communications has been steadily expanding beyond core network services into cloud, security, collaboration, IoT, and managed services. The brand repositioning aligns these capabilities under a single narrative: simplification in a hyperconnected world.

At the heart of that strategy is its “Digital Fabric”—a platform approach designed to integrate networking, cloud, security, and edge capabilities into a cohesive environment. The pitch is straightforward: help enterprises simplify complexity and accelerate innovation without stitching together a dozen separate vendors.

In today’s environment, that integration story resonates. Enterprises are increasingly fatigued by multi-vendor fragmentation, especially as AI workloads demand tighter interoperability across infrastructure layers.

The Competitive Landscape: Integration Is the New Differentiator

Tata Communications’ repositioning reflects a broader industry shift. Major players across telecom and cloud are repositioning themselves as transformation partners rather than infrastructure providers.

Enterprises are asking harder questions:

• Can this vendor unify my stack?

• Will they reduce operational complexity?

• Can they scale securely across geographies?

• Are they accountable beyond deployment?

In that context, “Together, limitless” is less marketing flourish and more strategic posture. It underscores partnership, co-creation, and shared outcomes—language increasingly central to enterprise buying decisions.

Stephen Meade, EVP — Corporate and B2B at McCann, which developed the new campaign, distilled it neatly: companies don’t need more technology; they need better integration.

That sentiment captures the mood of a market moving from experimentation to consolidation.

A Campaign Built Around Clarity

The brand launch is backed by Tata Communications’ first major television and digital campaign, also created with McCann. The creative concept mirrors today’s tech landscape—crowded, busy, overwhelming—before pivoting to the calm and clarity enabled by thoughtful orchestration.

It’s a subtle but pointed commentary on the state of enterprise IT.

Rather than leading with product features or AI buzzwords, the campaign leans into emotional reassurance: trust, partnership, stability.

That’s notable. As generative AI dominates headlines, some enterprise buyers are prioritizing resilience and integration over novelty.

Strategic Signals Beneath the Surface

Beyond the tagline, the repositioning signals several deeper shifts:

1. Integrated Go-to-Market Alignment
The company is strengthening capabilities across products, sales, marketing, and operations. That internal alignment is critical if the “integration” promise is to hold externally.

2. Value Creation Over Volume
The emphasis on long-term momentum and differentiated competitiveness suggests a focus on higher-value enterprise engagements rather than commoditized connectivity deals.

3. Global Expansion with Local Relevance
Serving 300 Fortune 500 companies gives Tata Communications scale credibility. The new positioning reinforces its ambition to deepen those relationships rather than simply maintain them.

The Bigger Picture: The Intelligent Age and Enterprise Expectations

We’re entering what many analysts call the “intelligent enterprise” era—where AI, automation, and distributed infrastructure converge.

But intelligence without orchestration leads to chaos.

Enterprises now demand:

• Speed without sacrificing security

• Innovation without operational fragility

• Scalability without runaway complexity

Tata Communications’ rebrand is effectively a bet that integration—not invention—will define the next decade of enterprise technology leadership.

It’s a calculated pivot. In crowded markets, clarity can be a competitive advantage.

Will the Message Stick?

Brand positioning alone doesn’t transform a company. Execution does.

The real test will be whether Tata Communications can consistently demonstrate:

• Tangible simplification of complex environments

• Measurable acceleration of digital initiatives

• Deep, trust-based partnerships across global markets

If “Together, limitless” translates into operational excellence and platform cohesion, it could strengthen the company’s standing in an increasingly integration-driven market.

If not, it risks blending into the sea of aspirational enterprise taglines.

For now, the message is clear: in a noisy, hyperconnected world, Tata Communications wants to be the steady orchestrator.

And in today’s enterprise landscape, that might be exactly what customers are looking for.

Get in touch with our MarTech Experts.

Consumer goods giant Unilever is betting big on AI—and on Google Cloud—to reshape how its brands are discovered, marketed, and sold in a world increasingly shaped by conversational search and autonomous agents.

The companies today announced a five-year strategic partnership aimed at accelerating Unilever’s business transformation through Google Cloud’s AI, data infrastructure, and next-generation marketing tools. At the center of the effort: enterprise-scale AI, agentic workflows, and a complete rethink of how CPG brands win attention in the age of intelligent systems.

For an industry often criticized for slow digital evolution, this is a decisive move.

AI Moves From Experiment to Operating System

Unilever’s portfolio spans global heavyweights like Dove, Vaseline, and Hellmann's. Traditionally, growth for these brands has relied on mass media, retail dominance, and increasingly, e-commerce optimization. But the ground is shifting.

Consumers are no longer just browsing search results—they’re asking AI assistants what to buy. Discovery is becoming conversational. Shopping journeys are becoming agentic. And brand influence is increasingly mediated by algorithms rather than shelf placement.

Unilever’s answer? Build what it calls an “AI-first digital backbone” by migrating its integrated data and cloud platforms onto Google Cloud.

That backbone will power:

• Faster demand generation

• Real-time data-to-insight pipelines

• AI-augmented marketing workflows

• Agentic systems capable of executing multi-step business processes

In short, AI won’t sit on top of operations. It becomes the operating layer.

Vertex AI, Gemini, and the Rise of Agentic Commerce

The technical foundation rests on Google Cloud’s enterprise AI platform, Vertex AI, along with advanced models like Gemini.

Vertex AI gives enterprises the tooling to build, deploy, and scale machine learning and generative AI models. But this partnership goes beyond standard AI deployment. It focuses on enabling what both companies describe as “agentic workflows.”

In practical terms, that means intelligent systems that don’t just analyze data—they take action. These agents could:

• Optimize media spend dynamically

• Adjust pricing or promotions based on predictive demand

• Generate and test creative variations at scale

• Automate supply chain responses to real-time signals

For marketers, this marks a shift from dashboard-driven decision-making to semi-autonomous execution systems.

It also reflects a broader trend across the enterprise software landscape: generative AI is evolving from content co-pilot to decision-making infrastructure.

Marketing in the Age of AI Discovery

Perhaps the most strategically important pillar of the partnership is what the companies call “agentic commerce and marketing intelligence.”

As AI assistants increasingly influence product discovery, brands must ensure they’re visible not just in search results, but in AI-generated answers.

That’s a subtle but massive shift.

Instead of optimizing solely for keywords and ad placements, brands must now consider:

• How AI models interpret product attributes

• How brand data feeds into conversational systems

• How performance is measured in AI-mediated journeys

Measurement itself is changing. Traditional attribution models—already strained in a privacy-first world—face new complexity when AI agents act as intermediaries between consumer intent and purchase.

By combining Unilever’s first-party data and Google Cloud’s AI capabilities, the companies aim to build new models for brand discovery, conversion, and performance tracking in these conversational environments.

For CPG, that’s uncharted territory.

Rebuilding the Enterprise Core

The second major pillar is less flashy but arguably more important: migrating key enterprise applications and data platforms to Google Cloud.

For a company of Unilever’s scale, this isn’t a lift-and-shift IT project. It’s structural surgery.

The move is designed to create a unified data environment capable of:

• Scalable AI deployment across supply chain and marketing

• Faster cross-functional decision-making

• Real-time responsiveness to market shifts

Willem Uijen, Unilever’s chief supply chain and operations officer, framed the shift bluntly: technology has moved “to the core of value creation.”

That language signals something critical. This isn’t about digital optimization at the margins. It’s about embedding AI into every layer of operations—from manufacturing forecasts to campaign activation.

Competitive Context: Why This Matters Now

Unilever’s partnership comes amid a broader wave of enterprise AI alliances. Major CPG and retail players are racing to modernize their stacks as cloud hyperscalers aggressively position themselves as transformation partners rather than infrastructure vendors.

Google Cloud, in particular, has been pushing hard into vertical-specific AI solutions to compete with rivals. Strategic, long-term enterprise deals are key to that effort.

For Unilever, the stakes are equally high. The CPG sector faces:

• Margin pressure from inflation and supply chain volatility

• Fragmented consumer attention across digital channels

• Rising customer acquisition costs

• Intensifying private-label competition

In this environment, speed and intelligence become differentiators.

If AI can shorten the loop between insight and action—even by days—that translates directly into competitive advantage.

From Automation to Autonomy

There’s also a philosophical shift embedded in this deal.

Previous waves of digital transformation centered on automation—making processes faster and cheaper. The current wave aims for autonomy—systems that reason, learn, and act.

Google Cloud’s EMEA President Tara Brady emphasized this transition, describing the deployment of advanced models as building a “system of intelligence” rather than merely modernizing legacy systems.

That distinction matters.

Automation reduces friction. Intelligence changes behavior.

For marketers, that could mean AI systems continuously refining messaging based on live performance data. For supply chain teams, it could mean predictive systems that preempt disruptions before they escalate.

The Big Picture: CPG’s AI Inflection Point

CPG has historically lagged sectors like financial services and technology in advanced data integration. Complex distribution networks and reliance on third-party retailers have slowed unified data strategies.

But as retail media networks expand and direct-to-consumer models mature, CPG brands are regaining access to richer consumer data.

Pair that data with scalable generative AI, and you get something new: intelligent commerce ecosystems.

Unilever’s five-year commitment suggests it sees this as a once-in-a-decade inflection point. By locking in a long-term AI and cloud strategy now, it positions itself ahead of what may soon become table stakes.

What to Watch

Over the next 12 to 24 months, key signals will determine whether this partnership delivers on its promise:

• Are agentic marketing systems deployed at scale—or stuck in pilots?

• Does AI measurably improve media efficiency and ROI?

• Can integrated data platforms meaningfully accelerate decision cycles?

• Do competitors announce similar hyperscaler alliances?

If successful, this deal could serve as a blueprint for how CPG companies adapt to AI-native commerce.

If not, it risks becoming another ambitious transformation story swallowed by enterprise complexity.

For now, one thing is clear: in the AI era, brand equity alone isn’t enough. The companies that win will be those whose infrastructure thinks as fast as their consumers do.

Get in touch with our MarTech Experts.

Recurly, a subscription management and billing platform, has appointed Suzin Wold as Chief Marketing Officer. Wold brings more than 25 years of experience scaling high-growth technology companies and will oversee the company’s global marketing strategy, brand positioning, and go-to-market execution.

Driving Predictable Growth

Wold is recognized for building data-driven marketing organizations that align brand, demand generation, and product strategy to deliver consistent revenue growth. Her experience spans both B2C and B2B environments, with a focus on applying customer insights to strengthen market positioning and accelerate expansion.

“Suzin is a transformational leader who combines strategic vision with operational excellence,” said Joe Rohrlich, CEO of Recurly. “Her unique experience makes her the ideal partner to lead our marketing efforts as we help brands master the growth opportunities of the subscription economy.”

Track Record of Category Leadership

Wold previously co-founded Blackhawk Network, where she helped pioneer the modern retail gift card category, reshaping consumer purchasing behavior and driving large-scale adoption.

She has also held senior leadership roles at:

• Rithum

• Bazaarvoice

• Sama

Across these organizations, she focused on scaling high-performing teams and strengthening alignment between marketing, sales, and product functions.

Strengthening Subscription Leadership

“Recurly is at the forefront of the subscription revolution, providing the essential infrastructure that allows brands to grow and scale,” said Wold. “I am thrilled to join this talented team and focus on building a world-class marketing engine that empowers our customers to deliver incredible subscriber experiences.”

The appointment reflects Recurly’s continued investment in executive leadership as it aims to reinforce its position as an enterprise standard for subscription growth in an increasingly competitive market.

Get in touch with our MarTech Experts.

ACA Group (ACA), a governance, risk, and compliance (GRC) advisory firm focused on financial services, has launched Encore AI for Marketing Review, an artificial intelligence enhancement to its ComplianceAlpha® Marketing Review module. The new capability embeds AI-driven automation directly into existing compliance workflows, aiming to accelerate review cycles while preserving transparency, auditability, and human oversight.

Responding to Growing Regulatory Pressure

The launch comes as financial services firms face mounting regulatory scrutiny and increased marketing output across digital channels. Compliance teams must navigate evolving requirements under frameworks such as the SEC Marketing Rule and advertising standards set by the Financial Industry Regulatory Authority (FINRA), alongside global regulatory obligations.

Traditional manual review processes are increasingly strained by higher content volumes, complex disclosures, and tighter oversight expectations.

Building on an Established Compliance Platform

ACA’s Marketing Review module has already supported nearly 1,300 clients, processing more than 143,000 submissions and reviewing approximately 8.9 million pages of marketing and financial promotion materials.

Encore AI builds on that foundation by augmenting human compliance expertise with purpose-built AI. Embedded directly within the ComplianceAlpha workflow, the tool:

• Identifies potentially non-compliant language

• Flags missing disclosures

• Detects inconsistencies across marketing materials

• Preserves full visibility into review rationale

• Maintains human oversight at every stage

The system is designed for compliance, legal, and marketing teams at firms regulated by the SEC and FINRA, including registered investment advisers, private funds, broker-dealers, and other regulated financial institutions.

Flexible Deployment and Managed Services Integration

Encore AI can operate independently or alongside ACA’s Marketing Review Managed Services. This flexibility allows firms to tailor oversight processes based on internal workflows, risk tolerance, and jurisdictional requirements. Support for additional regulatory frameworks is planned as part of ACA’s broader ComplianceAlpha roadmap.

ACA’s fund launch and compliance division, ACA Foreside, along with its affiliated broker-dealers, are among the most active filers with FINRA, contributing operational regulatory insight to the platform’s design.

Key Capabilities

Encore AI for Marketing Review includes:

• AI-assisted analysis of PDFs and Microsoft Office documents

• Smart tagging and prioritization of higher-risk content

• Structured annotations with regulatory context

• Audit-ready reporting with traceable review history

• Workflow access for both compliance teams and marketing content creators

• Planned support for audio and visual marketing materials

Leadership Commentary

Jody Kochansky, Head of Product and Engineering at ACA, emphasized that governance and explainability are central to the platform’s design, noting that the tool combines auditable AI with embedded regulatory expertise.

Patrick Olson, CEO of ACA Group, highlighted the firm’s broader investment in engineering governance and quality assurance, referencing the company’s Quality Engineering Center of Excellence as foundational to delivering scalable, reliable enhancements such as Encore AI.

Get in touch with our MarTech Experts.

For decades, marketing ran on campaigns.

Define the audience. Map the journey. Set the rules. Launch. Optimize with A/B tests. Repeat.

That model is now under pressure.

Autonomous, agent-based AI systems—often referred to as agentic marketing—are beginning to replace traditional campaign structures with real-time decision engines that optimize for business outcomes instead of activity metrics. In an industry built on segmentation and scheduled workflows, this shift could prove as disruptive as the move from batch-and-blast email to marketing automation.

And this time, the workflow itself is what’s being automated.

From Campaigns to Continuous Decisioning

Classic campaign-based marketing relies on predefined logic: if a customer clicks, send X; if they abandon cart, trigger Y; if they belong to Segment A, place them in Journey B.

That approach worked in a relatively stable environment. Consumer behavior was more predictable, channels were fewer, and engagement patterns followed recognizable arcs.

Today, those assumptions no longer hold.

Customers bounce between WhatsApp, email, push notifications, RCS, in-app messaging, and paid social—often within the same day. Pricing sensitivity fluctuates. Inventory changes in real time. Competitive offers surface instantly. Intent shifts rapidly, and often invisibly.

In this context, linear journeys and static segmentation begin to look blunt.

Agentic systems take a different approach. Instead of asking, “Which campaign should this customer enter?” they evaluate a more granular question: “What is the next best action for this customer right now?”

That action could be a message. It could be an offer. It could be silence.

Autonomous agents continuously ingest behavioral signals and adjust message, channel, timing, and frequency without waiting for human intervention. Crucially, they can also decide restraint—pausing outreach when additional communication would create friction rather than value.

This is less about optimizing a flow and more about governing a living system.

Why Rule-Based Marketing Is Hitting Its Limits

Segmentation-based personalization has long been marketed as precision. In practice, it has often been approximation at scale.

Customers are grouped by shared attributes—demographics, last purchase, engagement recency—and treated as statistically similar. Everyone in the segment receives the same message, delivered on a predetermined schedule.

Even advanced techniques such as predictive scoring and dynamic content typically operate within predefined logic. A/B testing improves outcomes, but usually for the median customer rather than the individual.

The result? Campaigns optimized for averages.

Vanity metrics—opens, clicks, short-term conversions—become proxies for success. Meanwhile, over-messaging, repetitive offers, and unnecessary discounting chip away at long-term customer lifetime value (CLTV).

Agentic marketing challenges that foundation.

Instead of designing journeys in advance, brands define business goals—revenue, retention, churn reduction—and let autonomous systems determine how to achieve them at the individual level.

The emphasis shifts from managing flows to maximizing outcomes.

Personalization, Rebuilt in Real Time

In the pre-agentic era, data was largely backward-looking. Marketers relied on historical indicators—last click, last purchase, demographic profiles—to infer intent.

But intent is not static.

Agentic systems treat each interaction as a fresh decision point, recalculated in real time. Signals such as browsing velocity, price changes, stock levels, time of day, and competitive context can influence the system’s choice of action.

Rather than moving customers through fixed journeys, the system adapts dynamically. There is no “step three.” There is only the next best action.

This architecture allows for course correction on the fly—something traditional campaigns struggle to do once deployed.

For brands, that means fewer wasted impressions, reduced budget leakage, and more precise allocation of attention.

The CMO’s New Mandate: Governance Over Execution

As execution shifts to autonomous systems, the role of the Chief Marketing Officer evolves.

CMOs no longer manage campaign calendars as the primary lever of performance. Instead, they set objectives, define guardrails, and oversee governance frameworks for AI-driven decision-making.

In agentic marketing models, each customer can effectively be assigned a decisioning agent that learns in real time and determines optimal engagement parameters. Leadership focus moves upstream: from designing journeys to defining outcomes.

The questions change:

• Not “What campaign are we launching next quarter?”

• But “What revenue or retention goal are we optimizing toward—and under what constraints?”

This also reshapes how performance is measured. Outcome metrics such as CLTV, churn reduction, and incremental revenue take precedence over surface-level engagement stats.

Execution becomes automated. Accountability becomes strategic.

Martech Pricing in the Outcome Era

The ripple effects extend beyond workflow into commercial models.

Traditional martech pricing is consumption-based: licenses, feature tiers, message volumes, dashboard access.

In the agentic era, vendors are beginning to experiment with outcome-based pricing. Systems are evaluated—and in some cases compensated—based on measurable business impact.

That reframes procurement conversations.

Instead of asking, “What features does this platform include?” brands increasingly ask, “What lift can it deliver?”

Budgets may shift away from sprawling stacks of specialized tools toward consolidated, accountable systems designed to prove performance.

For martech vendors, this represents both an opportunity and a threat. Platforms that cannot tie activity to outcomes may struggle to justify premium pricing.

Early Use Cases: Focused, Not Flashy

Agentic marketing is still emerging. Most early adopters are starting with contained, high-impact use cases:

• Churn prevention

• Pricing optimization

• Inventory-driven offers

• Retention and upsell moments

These are domains where real-time decisioning can deliver measurable lift quickly.

The disciplined approach appears to be working. Rather than automating every touchpoint at once, leading brands are proving value in narrow lanes before expanding autonomy across the customer lifecycle.

Agentic thinking is less about flipping a switch and more about re-architecting engagement logic.

The Road Ahead: Marketing at the Speed of Intent

If this shift holds, marketing’s defining capability will no longer be creativity alone—or data alone—but the ability to translate intelligence into action instantly.

Campaigns won’t disappear overnight. But their dominance as the primary operating model is eroding.

In their place: autonomous systems that treat every interaction as a decision point, every customer as a dynamic context, and every message as accountable to business outcomes.

Agentic marketing has moved beyond proof-of-concept. It is emerging as the operating logic for brands that want to move at the speed of customer intent rather than the speed of campaign calendars.

For martech leaders, the message is clear: the future isn’t more journeys.

It’s better decisions.

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