As outbound phone calls become less effective in customer engagement strategies, enterprise communication platforms are increasingly shifting toward asynchronous, mobile-first outreach models. Verse.ai, a customer texting platform owned by NiCE, is expanding that strategy through a new integration with VoApps that combines AI-powered text conversations with ringless voicemail delivery technology.

The traditional outbound sales call is losing relevance.

Consumers increasingly ignore unknown phone numbers, silence calls during work hours, or avoid voice conversations entirely. For enterprise sales and customer engagement teams, that behavioral shift is creating mounting pressure to rethink outreach strategies built around manual dialing and call-first workflows.

Verse.ai’s latest integration with VoApps reflects how quickly the market is adapting.

The partnership combines Verse.ai’s AI-powered conversational texting platform with VoApps’ DirectDrop Voicemail technology, enabling businesses to send ringless voicemail messages alongside automated text-based engagement campaigns.

The broader goal is to create a less disruptive, more responsive communication model centered around customer-controlled interactions.

Unlike conventional outbound calls, ringless voicemail technology delivers prerecorded messages directly into voicemail inboxes without causing the recipient’s phone to ring. Supporters of the approach argue that it allows businesses to maintain outreach while reducing interruption fatigue and improving response quality.

The integration arrives as enterprise communication strategies increasingly move toward asynchronous engagement — interactions where customers respond on their own schedule rather than in real time.

That trend has accelerated across industries including financial services, healthcare, retail, real estate, and SaaS, where customer expectations around convenience and personalization continue evolving.

Verse.ai cited industry data showing that 78% of consumers prefer businesses to communicate through text rather than phone calls. At the same time, the company claims only 13% of outbound calls are answered, highlighting growing inefficiencies tied to traditional outbound sales operations.

Those numbers are forcing sales and marketing teams to reassess the economics of outbound engagement.

Enterprise organizations have historically relied on large call-center operations, outbound dialing systems, and sales development teams focused on high-volume calling activity. But declining answer rates, rising labor costs, and stricter compliance regulations are making those models harder to sustain at scale.

Text messaging and AI-assisted conversational platforms are increasingly emerging as alternatives.

Verse.ai’s platform uses AI-powered messaging workflows to respond to leads and customers automatically, nurture conversations across SMS and email channels, and escalate interactions when prospects are ready for live conversations or appointment scheduling.

The addition of VoApps’ voicemail delivery system extends that engagement strategy into voice messaging without requiring live outbound calls.

According to the companies, the combined workflow can significantly reduce manual dialing requirements while increasing engagement rates through multichannel communication.

The shift also aligns with broader customer experience trends occurring across enterprise communications infrastructure.

Major technology providers including Salesforce, Twilio, Microsoft Dynamics 365, and Adobe Experience Cloud are investing heavily in omnichannel engagement systems designed to unify messaging, voice, automation, analytics, and AI-driven personalization.

The communications industry is increasingly prioritizing customer-controlled engagement over interruption-based outreach.

Research from Gartner suggests that conversational AI and asynchronous messaging are becoming central components of customer engagement infrastructure, particularly as enterprises seek scalable ways to improve responsiveness while controlling operational costs.

Meanwhile, Forrester has noted that customers increasingly expect interactions to occur through the communication channels they already use daily, including SMS, messaging apps, and mobile notifications.

Compliance is also becoming a major factor in communication platform design.

Outbound calling regulations tied to the Telephone Consumer Protection Act (TCPA) and related consumer privacy laws have increased operational complexity for organizations conducting large-scale outreach campaigns. Companies are under growing pressure to ensure messaging workflows remain compliant while still delivering measurable engagement performance.

Verse.ai and VoApps both emphasized TCPA-compliant communication processes as part of the integration announcement.

That focus is important because ringless voicemail technology itself has faced regulatory scrutiny in certain jurisdictions over whether it should be treated similarly to robocalling systems.

The companies position their combined platform as a compliant, permission-based communication system designed to support modern customer engagement preferences rather than aggressive outbound marketing practices.

The larger industry implication is that customer communication infrastructure is evolving away from single-channel outreach toward integrated conversational ecosystems.

AI-driven texting, automated scheduling, conversational commerce, and asynchronous voice messaging are increasingly converging into unified engagement platforms capable of orchestrating customer journeys across multiple mobile-first touchpoints.

For enterprise sales and customer experience teams, success may increasingly depend less on how many outbound calls are placed and more on how effectively brands create low-friction, responsive conversations customers are willing to engage with voluntarily.

That transition is reshaping the economics — and expectations — of digital customer communication.

Market Landscape

Enterprise customer engagement platforms are rapidly evolving toward AI-driven omnichannel communication systems that combine messaging, voice, automation, and personalization into unified workflows.

Organizations are investing heavily in conversational AI, asynchronous communication, and mobile-first engagement strategies as traditional outbound calling effectiveness declines.

Technology ecosystems from Google Cloud Contact Center AI, Amazon Connect, Twilio, and Microsoft Azure Communication Services are expanding AI-powered communication infrastructure for enterprises managing customer interactions at scale.

Industry analysts expect SMS engagement, conversational AI, and automated customer journey orchestration to remain major investment areas as enterprises modernize customer experience operations.

Top Insights

• Verse.ai integrated with VoApps to combine AI-powered text messaging with ringless voicemail delivery for asynchronous customer engagement.
• Enterprises are shifting away from traditional outbound calling as answer rates decline and consumers increasingly prefer text-based communication.
• The combined platform enables businesses to automate lead nurturing, voicemail outreach, appointment booking, and live call escalation workflows.
• AI-driven conversational engagement is becoming central to enterprise customer experience strategies across sales, support, and marketing operations.
• Compliance and customer-controlled communication are emerging as major priorities in modern omnichannel outreach infrastructure.

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Supply chain resilience has become a boardroom priority as enterprises navigate geopolitical instability, regulatory pressure, cybersecurity threats, and climate-related disruptions. Against that backdrop, supply chain intelligence company Exiger is positioning artificial intelligence as the next operational layer for supplier risk management after being named a Leader in the 2026 Gartner Magic Quadrant for Supplier Risk Management Solutions for the second consecutive year.

The global supply chain industry is moving beyond static risk assessments and periodic compliance checks toward something more dynamic: autonomous risk intelligence systems capable of continuously monitoring supplier ecosystems in real time.

That shift is at the center of Exiger’s latest positioning following its recognition in the 2026 Gartner Magic Quadrant for Supplier Risk Management Solutions, where the company said it ranked highest in execution and furthest in completeness of vision.

The announcement reflects a broader evolution occurring across enterprise procurement, logistics, and supply chain technology markets as organizations attempt to modernize supplier oversight amid increasingly volatile global conditions.

Supplier risk management software has historically focused on compliance workflows, vendor onboarding, and periodic assessments. Today, however, enterprises are demanding systems capable of continuously analyzing geopolitical events, financial exposure, cybersecurity threats, ESG risks, sanctions, trade restrictions, and sub-tier supplier vulnerabilities simultaneously.

Exiger is among a growing group of enterprise technology providers attempting to address those demands using AI-native infrastructure.

The company’s 1Exiger platform combines supplier intelligence, procurement workflows, compliance monitoring, and supply chain analytics into a unified operational environment designed to automate risk detection and response processes across supplier ecosystems.

According to Exiger, the platform maps supplier networks down to individual parts and material levels while embedding risk intelligence directly into enterprise systems already used by procurement and operations teams.

The company’s emphasis on “agentic” AI mirrors a broader trend across enterprise software markets where vendors are evolving from workflow automation toward autonomous systems capable of independently identifying issues, recommending actions, and initiating responses.

In supply chain environments, that capability is becoming increasingly important.

Over the past several years, global enterprises have faced repeated disruptions tied to geopolitical conflicts, semiconductor shortages, shipping bottlenecks, cyberattacks, sanctions enforcement, and climate-related events. Those pressures have exposed the limitations of fragmented supplier oversight systems and manual risk management processes.

Research from McKinsey & Company has shown that supply chain disruptions can erase significant annual earnings for large organizations, while analysts at IDC project continued enterprise investment in AI-driven operational resilience platforms.

Exiger argues that autonomous supplier risk management systems are becoming necessary rather than optional.

According to Chief Product Officer Brendan Galla, enterprises are shifting from traditional assessment models toward continuously operating intelligence systems capable of monitoring, triaging, and responding to risk events in real time.

That operational model differs significantly from legacy procurement software architectures, which often depended on periodic supplier reviews and manually updated data.

Exiger says its AI-native infrastructure was designed from the outset to support autonomous workflows rather than layering generative AI capabilities onto older enterprise systems retroactively.

The company claims its architecture automates screening, monitoring, reporting, and recommended courses of action while integrating directly into enterprise procurement and compliance environments.

That positioning places Exiger within a rapidly expanding market category where supply chain resilience, AI governance, and operational intelligence increasingly intersect.

Major enterprise ecosystems including Microsoft Azure AI, Google Cloud Supply Chain Solutions, Amazon Web Services, and SAP are also investing heavily in predictive supply chain analytics, AI orchestration, and operational automation technologies.

The competitive landscape is evolving quickly as procurement organizations seek deeper visibility into supplier ecosystems extending beyond direct vendors into sub-tier manufacturing, sourcing, logistics, and raw material networks.

That visibility challenge has intensified due to tightening global regulations tied to ESG reporting, forced labor compliance, sanctions enforcement, and cybersecurity risk disclosure requirements.

Supplier risk management platforms are increasingly expected to support sustainability tracking, financial health analysis, trade compliance, and operational continuity simultaneously.

Gartner’s market definition for supplier risk management reflects that expansion, emphasizing capabilities tied to disruption management, compliance monitoring, supplier performance optimization, and AI-powered analytics.

Exiger’s recognition in Gartner’s accompanying Critical Capabilities report for the Supply Ecosystem Risk Management use case further highlights how the market is prioritizing broader ecosystem intelligence rather than isolated vendor monitoring.

The larger industry implication is clear: supplier risk management is evolving into a continuous intelligence discipline rather than a procurement back-office function.

For enterprise organizations, that transition could reshape how supply chains are managed operationally.

AI-driven supplier ecosystems may eventually enable procurement teams to detect disruptions before they escalate, model alternative sourcing strategies automatically, and dynamically adjust operational decisions based on real-time global conditions.

The concept aligns with a growing enterprise technology narrative around autonomous operations — systems that not only analyze risk but actively coordinate mitigation responses across interconnected business environments.

Whether enterprises fully embrace that vision will depend on factors including data quality, regulatory oversight, AI governance, and integration complexity. Still, the direction of travel across the supply chain software market is becoming increasingly clear.

Risk intelligence is moving closer to real-time autonomous decisioning.

Market Landscape

The supplier risk management market is rapidly evolving as enterprises confront increasingly complex global supply chain disruptions, regulatory requirements, and geopolitical uncertainty.

Organizations are investing in AI-powered supply chain intelligence platforms capable of monitoring supplier ecosystems continuously across financial, operational, cybersecurity, ESG, and compliance dimensions.

Technology vendors including Microsoft, Google Cloud, SAP, and Oracle are embedding predictive analytics, machine learning, and automation into procurement and logistics systems to improve operational resilience.

Industry analysts expect AI-native supplier intelligence platforms to become increasingly central to enterprise procurement, sustainability reporting, and business continuity strategies as organizations seek deeper visibility into extended supplier networks.

Top Insights

• Exiger was named a Leader in the 2026 Gartner Magic Quadrant for Supplier Risk Management Solutions for the second consecutive year.
• The company is positioning agentic AI and autonomous operational intelligence as the future of supplier risk management and supply chain resilience.
• Exiger’s AI-native platform combines procurement, compliance, analytics, and supplier intelligence into a unified risk management environment.
• Enterprises are increasingly demanding real-time visibility into sub-tier suppliers, geopolitical exposure, ESG risks, and operational disruptions.
• The broader supplier risk management market is shifting from static assessments toward continuous AI-powered monitoring and autonomous response systems.

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As banks and credit unions modernize digital customer engagement, many are confronting a persistent problem: disconnected technology systems that limit personalization and slow service delivery. Alkami Technology is using its upcoming industry webinar to spotlight how financial institutions can move beyond reactive digital banking models toward what it describes as “anticipatory banking” — a strategy centered on connected customer experiences, unified data infrastructure, and proactive engagement.

Digital banking transformation has entered a new phase.

For years, financial institutions focused heavily on digitizing basic customer interactions such as account opening, mobile banking, and online service requests. But as digital experiences become standard across the banking sector, competitive differentiation is increasingly shifting toward personalization, predictive engagement, and frictionless customer journeys.

That transition is driving renewed interest in what the banking industry is beginning to call anticipatory banking — systems designed to predict customer needs and proactively guide financial interactions before account holders take action themselves.

Against that backdrop, Alkami Technology announced it will host a webinar with Credit Union Times on May 14, 2026, focused on helping banks and credit unions create more connected digital ecosystems.

The webinar, titled “Anticipatory Banking Starts Here: A Practical Path Forward,” comes as financial institutions across the U.S. face mounting pressure to modernize legacy technology infrastructure while improving customer retention and digital engagement.

Many banks and credit unions already operate multiple digital systems spanning account opening, mobile banking, analytics, CRM, and marketing automation. The challenge, according to industry analysts, is that those platforms often operate independently, creating fragmented customer experiences and limiting real-time visibility into customer behavior.

The result is a digital environment where financial institutions can collect large amounts of data but struggle to operationalize it effectively.

Alkami’s webinar aims to address that gap by focusing on how institutions can integrate digital banking systems to support more proactive customer engagement strategies.

The discussion will include executives from Tri City National Bank, Raiz Federal Credit Union, and Educators Credit Union alongside Alkami executives responsible for solution architecture and digital transformation strategy.

One of the core themes emerging from the announcement is the growing convergence between digital banking platforms, customer data systems, and marketing technology infrastructure.

Historically, many financial institutions treated these functions separately. Digital banking focused on transactions and service delivery, while marketing platforms managed communications and cross-sell campaigns. Increasingly, however, institutions are attempting to unify those systems to create a continuous account holder journey.

That mirrors broader enterprise technology trends seen across industries where organizations are integrating customer data platforms, analytics engines, and AI-driven engagement systems to improve personalization and retention.

Research from Gartner has shown that customer experience remains a primary competitive differentiator in financial services, while McKinsey & Company has reported that banks adopting advanced personalization strategies can significantly improve customer satisfaction and revenue growth.

In the webinar announcement, Raiz Federal Credit Union highlighted operational improvements achieved after implementing MANTL Account Opening & Onboarding technology. According to Amy Krasikov, vice president of digital experience at the organization, online account opening times were reduced to approximately 4.5 minutes.

The institution now plans to expand its use of data and marketing tools to improve personalization and strengthen long-term member relationships.

That reflects a broader shift occurring in financial services technology.

Digital onboarding is no longer viewed simply as a convenience feature. Instead, it is increasingly treated as the starting point for long-term relationship management powered by behavioral analytics, lifecycle marketing, and predictive engagement.

Large enterprise technology vendors including Microsoft Cloud for Financial Services, Salesforce Financial Services Cloud, and Adobe Experience Cloud are also investing heavily in AI-powered personalization and customer journey orchestration tools tailored for banks and financial institutions.

Alkami’s positioning differs slightly by emphasizing operational connectivity between systems rather than focusing exclusively on AI-driven personalization.

According to George Dow, senior director of solution architecture at Alkami, many financial institutions already possess the foundational technologies needed to support proactive engagement. The larger challenge is that those systems were not originally designed to work together.

Connecting those environments allows institutions to identify behavioral patterns earlier, surface actionable insights, and deliver more relevant customer interactions in real time.

For banks and credit unions, the implications extend beyond customer experience alone.

Fragmented digital ecosystems often create operational inefficiencies, duplicated workflows, inconsistent customer records, and compliance challenges. Integrating data and engagement infrastructure can improve visibility across the account holder lifecycle while helping institutions respond more effectively to changing customer expectations.

The timing is especially important as regional banks and credit unions compete against both national financial institutions and digital-first fintech platforms that increasingly prioritize speed, personalization, and seamless onboarding experiences.

The webinar also reflects how financial services organizations are approaching digital transformation more cautiously than some other sectors.

Rather than replacing core systems entirely, many institutions are pursuing phased modernization strategies designed to minimize operational disruption while gradually improving digital capabilities.

That practical approach could define the next stage of banking modernization, particularly among mid-sized financial institutions balancing innovation priorities with regulatory and operational constraints.

As consumer expectations continue evolving, anticipatory banking may become less of a competitive advantage and more of a baseline expectation for digital financial services.

Market Landscape

The banking industry is rapidly shifting from transactional digital experiences toward predictive, personalized engagement models powered by connected data infrastructure and AI-driven insights.

Financial institutions are investing heavily in customer data integration, digital onboarding, analytics platforms, and marketing automation tools to compete with fintech challengers and digitally native banking experiences.

Technology ecosystems from Google Cloud for Financial Services, Amazon Web Services, and Microsoft Azure are accelerating innovation in cloud banking infrastructure, AI personalization, and customer engagement orchestration.

Industry analysts expect banks and credit unions to increasingly prioritize unified customer journeys, predictive engagement systems, and real-time analytics as digital banking competition intensifies.

Top Insights

• Alkami’s upcoming webinar focuses on helping banks and credit unions transition from reactive digital banking toward anticipatory, data-driven customer engagement models.
• Financial institutions are increasingly integrating digital banking, analytics, onboarding, and marketing systems to reduce fragmentation and improve personalization capabilities.
• Raiz Federal Credit Union reported reducing digital account opening times to approximately 4.5 minutes through onboarding modernization initiatives.
• The banking sector is shifting toward connected customer journeys powered by unified data infrastructure, predictive analytics, and AI-enhanced engagement platforms.
• Regional banks and credit unions are pursuing phased digital transformation strategies to modernize operations without disrupting core banking infrastructure.

Get in touch with our MarTech Experts

As generative AI platforms increasingly shape how consumers, buyers, and enterprise decision-makers discover information, PR and digital marketing agencies are beginning to rethink visibility beyond traditional search engines. Brandi AI, a platform focused on AI visibility and Generative Engine Optimization (GEO), is expanding its global agency network through a new partnership with Finnish communications agency Medita Communication, signaling how rapidly AI-driven discovery is becoming part of modern brand strategy.

The rise of generative AI platforms such as ChatGPT, Google Gemini, Claude, and Perplexity is creating a new competitive battleground for enterprise brands: AI-generated answers.

Unlike traditional search rankings, where SEO teams optimize webpages for visibility on search engine results pages, generative AI systems synthesize information from multiple sources to produce direct responses. That shift is forcing PR agencies, communications teams, and marketers to rethink how authority, trust, and discoverability are measured online.

Against that backdrop, Medita Communication has joined Brandi AI’s Global Agency Partnership Program, becoming the first Finnish agency to participate in the initiative. The partnership expands Brandi AI’s reach into Finland and the broader Nordic region while highlighting growing demand for services tied to Generative Engine Optimization, or GEO.

GEO refers to the practice of improving how brands, executives, products, and organizations appear within AI-generated responses. The discipline combines elements of SEO, digital PR, entity optimization, structured content strategy, and reputation management to strengthen how AI systems interpret and reference brands.

The category has gained momentum over the past year as enterprise marketing teams recognize that AI assistants are increasingly functioning as recommendation engines, research tools, and discovery platforms for both consumers and B2B buyers.

“Generative AI is changing PR, communications and marketing by shifting discovery from traditional search results to AI-generated answers,” the companies said in a joint announcement. “This creates a new responsibility for agencies and marketing teams: ensuring that brands are accurately described, credibly sourced and visible in answer environments.”

For agencies, the shift represents both a challenge and a potential new revenue stream.

Traditional SEO strategies were largely built around optimizing websites for search crawlers and keyword rankings. GEO, by contrast, focuses on strengthening broader trust signals that influence AI-generated outputs. That includes authoritative media coverage, entity consistency across platforms, expert citations, structured knowledge signals, and answer-ready content that AI systems can easily interpret.

Medita Communication says the partnership will help clients understand why their brands appear — or fail to appear — inside AI-generated responses across major AI ecosystems.

According to Mika Särkijärvi, Senior Advisor and co-founder of Medita Communication, one of the key advantages of the platform is visibility into the underlying factors influencing AI-generated brand representation.

The company says Brandi AI identifies issues such as weak trust signals, missing contextual relevance, inconsistent entity information, and limited authoritative citations that may reduce AI visibility.

That capability arrives as marketing organizations increasingly prioritize AI search visibility alongside conventional search performance metrics.

Research from Gartner suggests generative AI is rapidly reshaping digital discovery and customer engagement workflows. Meanwhile, analysts at Forrester have noted that AI-assisted search behavior is likely to reduce dependence on traditional search engine navigation over time, particularly for informational and research-oriented queries.

The emergence of GEO also reflects broader changes occurring across enterprise MarTech stacks.

Large technology vendors including Google Cloud, Microsoft, Salesforce, and Adobe are integrating generative AI capabilities into search, customer engagement, analytics, and content infrastructure. As those systems become more deeply embedded into enterprise workflows, brand visibility within AI-generated outputs could become as strategically important as search rankings were during the SEO boom of the early 2000s.

Brandi AI is attempting to position itself within that transition.

The company describes its platform as an enterprise AI visibility intelligence system that helps communications, SEO, and digital marketing teams understand how brands are discovered, described, cited, and trusted by AI systems.

That intelligence layer could become increasingly valuable for industries where reputation and authority directly influence buying decisions, including B2B technology, financial services, healthcare, consulting, and enterprise software.

For PR agencies specifically, GEO creates a measurable framework connecting earned media coverage with downstream AI visibility outcomes. Historically, demonstrating the business impact of PR campaigns has often been difficult beyond impressions, share of voice, or media reach metrics.

AI-generated citations may introduce a new performance category altogether.

If AI assistants consistently reference certain brands, experts, or publications in response to buyer questions, those mentions could influence market perception, purchasing decisions, and category authority in ways similar to high-ranking search results.

The Medita partnership illustrates how agencies are beginning to adapt.

Rather than positioning AI visibility as a standalone technical service, agencies are increasingly integrating GEO into broader communications, SEO, and content strategy offerings. The goal is not simply to rank in search engines but to ensure brands remain visible inside AI-powered recommendation and information ecosystems.

As generative AI becomes a primary interface for discovery, the competitive landscape for digital visibility is expanding beyond Google rankings into something more complex: machine-interpreted trust.

That transition may redefine how enterprise brands approach communications strategy over the next decade.

Market Landscape

The emergence of Generative Engine Optimization marks a new phase in digital marketing evolution as AI assistants increasingly replace traditional search navigation for research and discovery tasks.

Enterprise brands are now optimizing not only for search engines but also for AI-generated recommendation systems capable of synthesizing information from websites, media coverage, analyst reports, forums, and structured data sources.

This trend is accelerating investment in AI visibility monitoring, entity SEO, structured content systems, and digital authority management. Technology ecosystems led by Google, Microsoft Azure AI, and Amazon Web Services are embedding generative AI into enterprise search, productivity, and customer engagement platforms, creating new opportunities for GEO-focused vendors and agencies.

Industry analysts expect AI-driven discovery behavior to reshape SEO, PR, and digital marketing strategies over the next several years, particularly in sectors where trust, expertise, and category authority strongly influence purchasing decisions.

Top Insights

• Medita Communication became the first Finnish agency to join Brandi AI’s global partnership program focused on AI visibility and Generative Engine Optimization services.
• GEO is emerging as a new digital marketing discipline aimed at improving how brands appear inside AI-generated answers across platforms like ChatGPT, Gemini, and Claude.
• The partnership reflects growing enterprise demand for tools that measure AI-generated citations, authority signals, and brand trust within generative AI ecosystems.
• Brandi AI positions AI visibility as a strategic layer connecting PR, SEO, content marketing, and entity optimization for enterprise communications teams.
• The rise of AI-driven discovery is pushing agencies to expand beyond traditional search optimization toward machine-interpreted trust and authority management.

Get in touch with our MarTech Experts

As enterprise marketers race to operationalize artificial intelligence beyond basic automation, Netcore Cloud is positioning itself at the center of what it calls the next phase of digital marketing infrastructure: Agentic Marketing. At its Agentic Marketing 2025 summit in Mumbai, the company outlined a long-term AI strategy that moves marketing systems from rule-based execution toward autonomous decision-making engines capable of optimizing customer engagement in real time.

The marketing technology industry has spent the past decade refining automation. Email journeys became more sophisticated, customer data platforms improved targeting precision, and AI-powered analytics helped teams predict outcomes with greater accuracy. Yet much of the enterprise marketing stack still relies heavily on human configuration, static workflows, and manual campaign optimization.

Netcore Cloud believes that model is reaching its limit.

At the company’s Agentic Marketing 2025 summit held at Taj Lands End in Mumbai, executives presented a vision for what they describe as “Agentic Marketing” — an operational framework where AI agents independently analyze signals, make decisions, execute campaigns, and optimize business outcomes continuously.

The event, organized alongside Google Cloud and ETBrandEquity, reflects a broader shift occurring across the MarTech ecosystem as vendors attempt to evolve from automation providers into autonomous intelligence platforms.

The concept arrives at a time when enterprise marketing teams face mounting pressure to improve efficiency while managing increasingly fragmented customer journeys across email, mobile apps, commerce channels, paid media, and conversational interfaces.

According to industry estimates cited by Netcore, nearly 70% of digital marketing budgets are spent reacquiring existing or churned customers. That inefficiency has become a growing concern for brands operating large-scale retention programs, especially as acquisition costs continue rising across platforms owned by Google, Amazon, and social advertising ecosystems.

Netcore’s argument is that traditional marketing automation systems were designed to execute instructions, not independently pursue business goals.

“Marketing is moving from execution systems to decision systems,” the company said during the summit keynote. The distinction is important because it reframes AI from a support capability into an operational layer capable of acting autonomously.

The foundation for that strategy dates back to 2018, when Netcore launched Raman AI’s Send Time Optimization engine. The system used behavioral data to determine the ideal engagement time for individual users rather than relying on broad audience segmentation. According to the company, brands including FBB and several Southeast Asian travel platforms reported email open-rate improvements between 36% and 39%.

Over time, the platform expanded into predictive audience segmentation, channel preference modeling, churn forecasting, and personalized subject-line optimization. Those capabilities now form the intelligence backbone of Netcore’s broader agentic framework.

The company accelerated its ambitions further in 2023 after acquiring a majority stake in Unbxd, a California-based AI-powered product discovery platform. The acquisition extended Netcore’s capabilities beyond engagement automation into on-site commerce experiences, giving the company visibility across the full digital customer lifecycle.

That move mirrors a larger trend across the enterprise software market, where vendors are consolidating engagement, commerce intelligence, analytics, and customer data infrastructure into unified AI-driven ecosystems. Competitors including Salesforce, Adobe, and Microsoft have also accelerated investments in generative AI copilots, predictive analytics, and autonomous workflow orchestration over the past two years.

What differentiates Netcore’s approach is its emphasis on multi-agent collaboration.

The company’s platform includes specialized AI agents designed for distinct operational functions. An Insight Agent identifies performance anomalies and root causes. An Audience Agent dynamically refreshes micro-segments based on live behavioral signals. Scheduler Agents optimize timing and communication channels at the individual level, while Content Agents generate personalized campaign assets automatically.

The platform also includes a Decisioning Agent that determines next-best actions in real time and a Shopping Agent focused on conversational commerce experiences.

These systems are orchestrated through Co-Marketer, Netcore’s centralized intelligence layer designed to align AI-driven actions with broader business objectives and governance controls.

The practical implication for enterprise marketing teams is significant. Rather than manually building campaign flows inside traditional marketing automation platforms, teams increasingly supervise AI systems that independently adapt journeys based on evolving customer behavior.

That operational shift could reshape how enterprise marketing departments are structured over the next several years.

Research from Gartner has projected that generative AI will influence the majority of customer engagement workflows by the end of the decade, while McKinsey & Company estimates AI-enabled personalization can increase marketing ROI by up to 20% in certain sectors.

Netcore claims its own deployments are already producing measurable business results. Early enterprise users reportedly achieved campaign deployment speeds up to 25 times faster, segmentation improvements reaching 50 times deeper granularity, and conversion gains of up to 10 times in select scenarios.

Brands including Crocs India, Shriram Finance, Camper, Navia Markets, and New York & Company are among the companies using the platform, according to Netcore.

The broader significance of the announcement extends beyond a single product launch. Agentic systems represent a growing category across enterprise software, where AI is evolving from reactive assistance toward autonomous orchestration.

For marketing leaders, that creates both opportunity and risk.

Organizations adopting agentic systems may reduce operational bottlenecks, improve personalization accuracy, and respond faster to behavioral changes. At the same time, enterprises will face new governance challenges around transparency, AI accountability, and decision oversight.

The next competitive divide in MarTech may no longer be about who offers the most automation features. It may instead center on which platforms can independently optimize outcomes at enterprise scale while maintaining trust, compliance, and measurable performance.

Netcore is betting that transition has already begun.

Market Landscape

The rise of agentic AI reflects a broader transformation across the MarTech and enterprise SaaS landscape. Vendors traditionally focused on campaign automation are now evolving toward autonomous systems capable of real-time orchestration, predictive decisioning, and cross-channel optimization.

Large enterprise ecosystems including Adobe Experience Cloud, Salesforce Marketing Cloud, and Microsoft Dynamics 365 are increasingly embedding generative AI into customer engagement infrastructure. Meanwhile, AI-native vendors are attempting to redefine the category around autonomous execution rather than assisted workflows.

The market opportunity is substantial. IDC estimates global AI software spending will surpass hundreds of billions of dollars by the end of the decade, with marketing, customer experience, and commerce personalization representing major investment areas. As enterprise brands pursue unified customer journeys, platforms capable of combining customer data, predictive analytics, and AI-driven orchestration are likely to gain strategic importance.

Top Insights

• Netcore formally introduced its Agentic Marketing vision, positioning AI agents as autonomous decision-making systems for enterprise customer engagement, personalization, and real-time campaign optimization.
• The company’s AI strategy builds on seven years of Raman AI development, including predictive segmentation, send-time optimization, churn forecasting, and behavioral intelligence capabilities.
• Netcore’s acquisition of Unbxd expanded its platform into AI-powered commerce discovery, helping unify engagement, conversion, and retention across digital customer journeys.
• Enterprise brands using the platform reportedly achieved faster campaign deployment, deeper audience segmentation, improved ROAS, and stronger engagement through AI-driven orchestration.
• The announcement highlights a wider MarTech shift as vendors compete to move beyond automation into autonomous AI systems capable of managing marketing outcomes independently.

Get in touch with our MarTech Experts

Clutch has introduced an AI Visibility Dashboard designed to help agencies measure and improve how they appear in AI-generated search results. As platforms like ChatGPT and Google Gemini reshape discovery, the tool reflects a growing need for B2B firms to optimize not just for search engines—but for AI answers.

Search is changing—rapidly. As generative AI platforms become primary discovery channels for business buyers, traditional SEO strategies are being redefined. With the launch of its AI Visibility Dashboard, Clutch is stepping into this emerging space, offering agencies a way to understand how they are represented across AI-powered search environments.

The premise behind the product is straightforward: as buyers increasingly rely on AI assistants to research vendors, the sources these systems cite—and how often—are becoming critical indicators of brand visibility. Clutch, long known as a marketplace for B2B service providers, is positioning itself as a key data source in this new ecosystem.

The dashboard, developed in partnership with Conductor, aggregates performance data across multiple AI platforms, including ChatGPT, Claude, Perplexity AI, and Google Gemini. It simulates real-world buyer queries and measures how frequently agencies appear in generated responses, producing an AI Visibility Score that ranges from “Needs Work” to “Excellent.”

This scoring model represents a shift toward what many in the industry are calling “Generative Engine Optimization” (GEO)—a discipline focused on influencing how AI systems retrieve and present information. Unlike traditional SEO, which prioritizes rankings on search engine results pages, GEO emphasizes inclusion in AI-generated answers and recommendations.

For B2B agencies, the stakes are high. AI platforms are increasingly acting as intermediaries in the buyer journey, summarizing options and recommending vendors without users ever visiting a traditional search results page. In this context, being cited—or omitted—can directly impact lead generation and pipeline growth.

The AI Visibility Dashboard attempts to bring transparency to this process. In addition to scoring visibility, it provides competitive benchmarking, allowing agencies to compare their performance against peers within the same category. This includes ranking positions, percentile placement, and insights into top-performing profiles.

Actionability is another key focus. The platform offers recommendations to improve visibility, such as completing profile information, gathering verified reviews, and achieving Clutch Verified status. According to the company, verified profiles receive significantly higher citation rates in AI-generated results—highlighting the importance of structured, trustworthy data.

This aligns with broader trends across the MarTech ecosystem. Platforms like Google and Microsoft are integrating generative AI into search experiences, fundamentally changing how information is surfaced. At the same time, enterprise tools from Adobe and Salesforce are evolving to support AI-driven content and customer engagement strategies.

The emergence of AI visibility as a metric reflects a deeper shift in digital marketing. Instead of optimizing for clicks, brands must now optimize for inclusion within AI-generated narratives. This requires high-quality data, strong authority signals, and consistent representation across trusted platforms.

For Clutch, the move is also strategic. By positioning its marketplace data as a key input for AI systems, the company strengthens its role in the B2B discovery ecosystem. If AI assistants increasingly rely on Clutch data to recommend agencies, the platform becomes not just a directory, but a foundational layer in vendor selection.

Industry data supports the urgency of this shift. According to Gartner, by 2027, more than 50% of B2B buyer research will occur through AI-driven interfaces rather than traditional search engines. Meanwhile, Forrester notes that buyers are placing greater trust in aggregated, AI-curated insights over individual vendor claims.

However, measuring AI visibility is not without challenges. AI systems are inherently probabilistic, meaning results can vary based on query phrasing, context, and model updates. Ensuring consistent visibility across platforms requires ongoing optimization and monitoring.

The Clutch dashboard addresses this by running multiple query variations and aggregating results, providing a more stable view of performance. Still, agencies will need to adapt their strategies continuously as AI models evolve.

For enterprise marketing teams and agency leaders, the implications are clear. Visibility in AI search is becoming a competitive differentiator, influencing how brands are discovered, evaluated, and selected. Tools that provide insight into this process will likely become essential components of modern marketing stacks.

Ultimately, the AI Visibility Dashboard signals the next phase of digital discovery. As AI systems increasingly mediate the relationship between buyers and vendors, understanding—and influencing—how those systems represent your brand may be just as important as traditional search rankings.

Market Landscape

The rise of AI-driven search is reshaping digital discovery. According to Gartner, generative AI will influence the majority of B2B buying decisions by 2027, while Forrester reports that over 60% of buyers now rely on third-party platforms and aggregated insights during vendor evaluation.

As AI assistants become central to this process, the ability to measure and optimize visibility within these systems is emerging as a critical capability for B2B organizations.

Top Insights

• Clutch has launched an AI Visibility Dashboard that measures how often B2B agencies appear in AI-generated search results across platforms like ChatGPT, Claude, and Gemini.
• The tool introduces an AI Visibility Score, reflecting a shift toward Generative Engine Optimization as AI becomes a primary channel for buyer research and vendor discovery.
• Competitive benchmarking and actionable recommendations help agencies improve their presence, with verified profiles showing significantly higher citation rates in AI outputs.
• The launch highlights a broader industry trend where AI systems increasingly mediate the buyer journey, reducing reliance on traditional search engine rankings.
• For B2B marketers, optimizing for AI visibility is becoming essential as generative platforms reshape how brands are discovered and evaluated.

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KERV.ai has introduced Moment Match Engine™, an AI-powered platform designed to transform how brands engage audiences in video. Instead of relying on traditional ad placements, the system identifies high-attention moments within content and aligns them with commerce and advertising opportunities in real time.

The future of video advertising may no longer revolve around where ads appear—but when they appear. With the launch of its Moment Match Engine™, KERV.ai is pushing a shift toward moment-based targeting, where AI identifies peak engagement points within content and activates brand experiences at those precise instances.

At a technical level, the platform uses proprietary image recognition and contextual AI to analyze both live and on-demand video. It detects objects, scenes, and engagement signals down to the pixel level, generating structured data that identifies when viewer attention and intent are highest. These “moments” then become activation points for interactive advertising and commerce experiences.

This approach marks a departure from conventional contextual targeting, which typically evaluates entire pieces of content rather than specific scenes. By narrowing focus to moment-level signals, KERV.ai aims to deliver more precise alignment between content, audience intent, and brand messaging.

The implications for advertisers are significant. In traditional digital advertising, placements are often bought based on audience segments or contextual categories. While effective to a degree, these methods can miss the nuance of user engagement within a specific piece of content. Moment Match Engine attempts to bridge that gap, enabling brands to appear during the exact scenes where users are most receptive.

For example, a product featured within a scene—whether explicitly or implicitly—can trigger an interactive overlay or commerce opportunity. This creates a more seamless connection between discovery and action, effectively compressing the traditional marketing funnel into a single experience.

The platform is designed to operate across multiple channels, including connected TV (CTV), online video, and programmatic environments. This cross-channel flexibility aligns with broader trends in advertising, where marketers are seeking unified strategies across fragmented media ecosystems.

Major media organizations are already experimenting with similar models. Collaborations with NBCUniversal and Warner Bros. Discovery highlight growing interest in integrating commerce directly into premium content. These partnerships suggest that moment-driven advertising could become a key component of next-generation streaming monetization strategies.

Brands and agencies are also testing the approach. Campaigns involving IKEA and Carat have shown early performance gains. According to KERV.ai, one campaign achieved interaction rates 129% higher than traditional third-party behavioral targeting—a metric that underscores the potential of context-driven engagement.

From a publisher perspective, the technology introduces a new monetization model. Rather than inserting disruptive ad breaks, publishers can embed commerce experiences directly within content. This not only preserves the viewing experience but also creates additional revenue streams tied to engagement and conversion.

Control and compliance remain central concerns. KERV.ai addresses this through metadata validation layers and brand safety frameworks, ensuring that ads appear in suitable contexts. This is particularly important as advertisers demand greater transparency and control over where their messages appear.

The launch also reflects a broader evolution in AdTech. Platforms from Google, Amazon, and Adobe are increasingly incorporating AI-driven contextual intelligence into their advertising ecosystems. However, KERV.ai’s focus on scene-level precision represents a more granular approach to targeting.

For viewers, the experience is designed to feel less intrusive. Interactive elements—such as overlays or pause-based prompts—are triggered organically based on content, rather than interrupting it. This aligns with changing consumer expectations, where relevance and seamlessness are key to engagement.

The concept of “commerce video” is gaining traction as streaming platforms and advertisers look for ways to monetize content without degrading user experience. By connecting storytelling with product discovery, platforms like Moment Match Engine aim to create a more integrated form of digital commerce.

For enterprise marketing teams, the implications are clear. As attention becomes more fragmented and traditional targeting methods face limitations due to privacy changes, contextual and moment-based strategies are emerging as viable alternatives. The ability to align messaging with real-time engagement signals could redefine how campaigns are planned and measured.

Still, challenges remain. Scaling moment-level targeting across diverse content libraries requires significant computational resources and data accuracy. Additionally, adoption will depend on integration with existing ad tech stacks and measurement frameworks.

Even so, early results suggest that the model resonates with both advertisers and publishers. By focusing on moments rather than placements, KERV.ai is attempting to redefine the fundamentals of video advertising—shifting from interruption to integration.

Market Landscape

The rise of commerce-enabled video aligns with broader shifts in digital advertising. According to Statista, global video advertising spend is expected to exceed $200 billion by 2026, driven largely by CTV and streaming platforms. Meanwhile, Gartner notes that contextual targeting is regaining importance as privacy regulations limit the use of third-party data.

Moment-based advertising represents the next evolution of contextual strategies, offering a more granular and engagement-driven approach to targeting.

Top Insights

• KERV.ai has launched Moment Match Engine™, an AI-powered platform that identifies high-attention moments in video and aligns them with real-time advertising and commerce opportunities.
• The technology uses pixel-level image recognition and contextual AI to enable scene-specific targeting, moving beyond traditional content-level contextual advertising.
• Early campaigns with brands like IKEA and Carat show significantly higher engagement rates, highlighting the effectiveness of moment-driven activation strategies.
• Partnerships with NBCUniversal and Warner Bros. Discovery signal growing adoption of commerce video models within premium streaming ecosystems.
• The platform reflects a broader shift in AdTech toward contextual intelligence and privacy-compliant targeting, particularly in CTV and video advertising environments.

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WRITER has unveiled the AI CMO Council, a new executive forum designed to help enterprise marketing leaders operationalize AI at scale. The initiative reflects a growing industry shift from experimentation toward “agentic” AI systems that actively execute workflows across marketing organizations.

Enterprise marketing is entering a new phase—one where artificial intelligence is no longer confined to pilots or productivity tools, but embedded into core operations. With the launch of its AI CMO Council, WRITER is positioning itself at the center of this transition, bringing together senior executives tasked with turning AI ambition into measurable business outcomes.

The Council, co-chaired by WRITER’s CMO Diego Lomanto, CXO network leader Elizabeth van den Berg, and former Vanguard Global CMO Colin Kelton, is structured as a peer learning community for senior marketing leaders. Its purpose is straightforward: provide a confidential environment where executives can exchange practical insights on deploying AI across large, complex organizations.

This is not a theoretical exercise. The founding cohort includes CMOs from major enterprises such as Barclays, The Clorox Company, and KPMG—organizations already navigating the operational realities of AI adoption.

The timing is significant. Marketing departments have emerged as early adopters of enterprise AI, largely because they sit at the intersection of data, customer experience, and revenue. From content generation to campaign optimization and personalization, marketing functions offer immediate use cases for AI deployment.

Yet the transition from experimentation to execution remains uneven. According to Deloitte, while 74% of organizations aim to use AI to drive revenue growth, only 21% are achieving that outcome. Even more telling, 84% have not redesigned workflows or job roles to fully integrate AI into their operations.

This gap between ambition and execution is where initiatives like the AI CMO Council aim to create value. By facilitating knowledge sharing among leaders actively implementing AI, the Council seeks to accelerate the development of practical frameworks for AI-native marketing.

The concept of “agentic marketing” is central to this effort. Unlike traditional AI tools that assist with specific tasks, agentic systems are designed to autonomously plan and execute workflows within defined parameters. In marketing, this could mean AI systems that not only generate content but also orchestrate campaigns, manage budgets, and optimize performance in real time.

Platforms from Salesforce, Adobe, and Google are increasingly incorporating these capabilities, signaling a broader industry shift toward automation at scale. WRITER’s positioning as an “enterprise AI agent platform” aligns with this trajectory, emphasizing execution over assistance.

For CMOs, the implications are both strategic and operational. On one hand, AI offers the potential to dramatically increase efficiency and campaign effectiveness. On the other, it introduces new challenges around governance, brand differentiation, and organizational design.

One of the more pressing concerns is maintaining brand integrity in an AI-driven environment. As generative AI tools become more widely adopted, there is a risk that brands could converge in tone and messaging. Ensuring distinctiveness requires not only technical controls but also clear governance frameworks—a topic the Council is expected to address.

Another challenge is workflow integration. Many organizations have adopted AI tools in isolation, leading to fragmented processes and limited impact. Moving to an AI-native model requires rethinking how teams operate, how data flows across systems, and how decisions are made.

The Council’s agenda reflects these priorities. Topics include AI-powered content creation, marketing technology orchestration, data strategy and privacy, hyper-personalization, and account-based marketing. These areas represent some of the most complex—and potentially transformative—applications of AI in marketing.

The format of the Council is designed to encourage ongoing collaboration. Members will participate in monthly virtual roundtables and quarterly in-person sessions in key global hubs such as New York, San Francisco, and London. This hybrid approach mirrors the distributed nature of modern enterprise teams.

From a broader MarTech perspective, the launch underscores the growing importance of executive-level collaboration in navigating technological change. As AI reshapes marketing, the role of the CMO is evolving from campaign oversight to system orchestration—managing a network of tools, data sources, and automated processes.

For enterprise organizations, the stakes are high. AI has the potential to redefine competitive dynamics, enabling faster execution, deeper personalization, and more efficient resource allocation. However, realizing these benefits requires more than technology—it demands new operating models, skill sets, and leadership approaches.

The AI CMO Council represents an attempt to address these challenges collectively. By pooling insights from leaders at the forefront of AI transformation, it aims to accelerate the development of best practices that can be scaled across industries.

In that sense, the initiative is as much about organizational change as it is about technology. As marketing becomes increasingly agent-driven, the ability to learn, adapt, and collaborate may prove to be one of the most valuable assets for enterprise leaders.

Market Landscape

The rise of AI-native marketing is reshaping enterprise strategies. According to Gartner, over 80% of marketing organizations are expected to adopt AI-driven automation in core workflows by 2027. Meanwhile, McKinsey & Company estimates that AI could generate up to $2.6 trillion in annual value across marketing and sales functions globally.

Despite this potential, execution challenges persist, particularly in integrating AI into existing workflows and aligning it with business objectives. Initiatives like the AI CMO Council highlight the growing need for structured collaboration and knowledge sharing at the executive level.

Top Insights

• WRITER has launched the AI CMO Council to help enterprise marketing leaders operationalize AI and transition from experimentation to scalable, AI-native marketing execution models.
• The initiative focuses on “agentic marketing,” where AI systems autonomously execute workflows, signaling a shift beyond productivity tools toward full operational automation.
• Founding members from Barclays, Clorox, and KPMG highlight strong enterprise interest in collaborative frameworks for AI adoption and governance.
• Deloitte data reveals a significant execution gap, with only 21% of organizations achieving AI-driven revenue growth despite widespread investment.
• The Council aims to address challenges such as brand governance, workflow integration, and martech orchestration through peer-driven insights and best practices.

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