As manufacturers scale investments in robotics, autonomous systems, and industrial automation, a new challenge is emerging: coordinating increasingly complex operations across the factory floor. Rockwell Automation is aiming to address that challenge with the launch of FactoryTalk Orchestration software, a platform designed to synchronize production processes, material movement, and automated equipment through a unified operational framework.

Rockwell Automation has introduced FactoryTalk Orchestration software, a new production logistics solution designed to help manufacturers coordinate material flow and production activities across industrial operations. The company unveiled the platform at the Automate trade show in Chicago, where it is demonstrating the technology as part of its broader vision for connected and autonomous manufacturing.

The launch reflects a significant shift occurring across the industrial automation sector. While manufacturers have spent years deploying robots, automated guided vehicles, autonomous mobile robots (AMRs), manufacturing execution systems, and industrial IoT technologies, many organizations continue to operate these systems in isolation.

As a result, production environments often struggle with bottlenecks, inefficient material movement, disconnected workflows, and limited visibility across operations.

FactoryTalk Orchestration is designed to address those challenges by acting as a coordination layer between automated equipment, production systems, and enterprise applications. Built on Rockwell's FactoryTalk Optix platform, the software leverages real-time production signals to orchestrate workflows and align operations across the factory floor.

Rather than focusing solely on individual automation assets, the platform is intended to help manufacturers manage how machines, robots, materials, and people interact throughout production processes.

This reflects a broader industry evolution from automation toward orchestration.

Historically, manufacturers measured automation success by deploying individual technologies that improved specific tasks. Today, competitive advantage increasingly depends on how effectively those technologies work together. Coordinating production resources in real time can have a significant impact on throughput, operational efficiency, and responsiveness to changing demand conditions.

FactoryTalk Orchestration is positioned as a central component of Rockwell's production logistics strategy, providing a standardized framework for connectivity across automation systems. The software currently integrates with OTTO autonomous mobile robots and is expected to expand support for additional ecosystem technologies over time.

The growing importance of production orchestration coincides with increasing adoption of Industry 4.0 initiatives worldwide.

Manufacturers are under pressure to improve productivity while navigating labor shortages, supply chain disruptions, rising operational costs, and fluctuating customer demand. These challenges have accelerated investments in digital transformation technologies that provide greater visibility and control over production environments.

According to IDC, worldwide spending on digital transformation technologies continues to rise as manufacturers seek to improve operational resilience and increase automation capabilities. Gartner has similarly identified intelligent automation and connected operations as key priorities for industrial organizations pursuing smart factory strategies.

One of the primary advantages of orchestration platforms is their ability to respond dynamically to changing production conditions.

Traditional manufacturing environments often rely on static workflows and manual intervention when disruptions occur. In contrast, orchestration software can use real-time operational data to adjust workflows, redirect materials, and optimize resource utilization as conditions change.

Rockwell says the software can help manufacturers improve throughput, reduce bottlenecks, respond more quickly to production disruptions, and simplify operations through real-time coordination.

The company has already implemented the technology within its own manufacturing operations.

At Rockwell's facility in Twinsburg, Ohio, FactoryTalk Orchestration reportedly enabled autonomous production workflows across key manufacturing processes. According to the company, the deployment improved drop-off zone space utilization by 70% while reducing overall material handling space requirements by 50%.

While these results represent an internal deployment, they provide insight into how manufacturers are increasingly looking beyond isolated automation projects and toward integrated operational ecosystems.

The launch also highlights the growing convergence between physical automation and digital technologies such as digital twins.

During demonstrations at Automate, Rockwell is showcasing how FactoryTalk Orchestration works alongside OTTO AMRs and Emulate3D digital twin software. Digital twin technology enables manufacturers to simulate and optimize production environments before implementing changes in physical facilities, reducing risk and improving decision-making.

This combination of orchestration software, autonomous robotics, and digital twin modeling reflects broader trends shaping the future of industrial operations.

Major industrial technology providers including Siemens, Schneider Electric, Honeywell, and ABB are also investing heavily in software platforms that connect production systems, analytics, and automation technologies.

As artificial intelligence and machine learning capabilities become more deeply embedded within industrial environments, orchestration platforms may play an increasingly important role in enabling autonomous manufacturing operations.

The long-term vision extends beyond simply automating tasks. Instead, manufacturers are working toward environments where machines, robots, software systems, and logistics processes continuously coordinate activities with minimal human intervention.

For industrial organizations pursuing smart factory initiatives, FactoryTalk Orchestration represents another step toward that objective. By connecting production assets, material handling systems, and enterprise applications into a coordinated operational framework, manufacturers may be better positioned to improve efficiency, reduce operational complexity, and adapt more quickly to changing market demands.

As automation investments continue to grow, the ability to orchestrate entire production ecosystems could become just as important as the technologies performing the work.

Market Landscape

Manufacturing organizations are accelerating investments in smart factory technologies as they seek greater operational efficiency, resilience, and flexibility. According to IDC, industrial digital transformation spending continues to increase globally, driven by automation, industrial IoT, robotics, AI, and advanced analytics initiatives.

At the same time, manufacturers are moving beyond isolated automation projects toward integrated operational ecosystems. Software platforms that coordinate robots, machines, material handling systems, and enterprise applications are becoming increasingly important as organizations pursue autonomous manufacturing and Industry 4.0 objectives.

Companies such as Rockwell Automation, Siemens, Schneider Electric, ABB, and Honeywell are expanding software-driven automation portfolios that combine orchestration, analytics, digital twins, and industrial AI capabilities.

Top Insights

• Rockwell Automation launched FactoryTalk Orchestration software to coordinate production workflows, material movement, and automated systems across manufacturing operations.
• The platform serves as an orchestration layer connecting enterprise systems, production equipment, and autonomous mobile robots using real-time operational data.
• Manufacturers are increasingly shifting from standalone automation deployments toward integrated and autonomous production ecosystems.
• Internal deployments at Rockwell demonstrated significant improvements in material handling efficiency and production space utilization.
• Production orchestration, digital twins, robotics, and industrial AI are emerging as foundational technologies for next-generation smart factories.

Get in touch with our MarTech Experts

The race to build infrastructure capable of supporting next-generation artificial intelligence workloads continues to accelerate. Against this backdrop, EdgeCore Digital Infrastructure Chief Commercial Officer Clint Heiden has been confirmed as a featured speaker at TCW LIVE!, where industry leaders will discuss how surging demand for AI compute is transforming the data center, cloud, and connectivity sectors.

As enterprises, cloud providers, and AI developers invest heavily in large-scale computing environments, digital infrastructure providers are facing unprecedented pressure to expand capacity. The growing demand for AI training and inference workloads is driving a new wave of investment across data centers, power infrastructure, networking, and cloud ecosystems.

This industry transformation will take center stage at TCW LIVE! in September 2026, where Clint Heiden, Chief Commercial Officer of EdgeCore Digital Infrastructure, will join a panel examining the future of AI infrastructure and the rapidly evolving competitive landscape surrounding AI compute.

The session, titled "The New Digital Infrastructure Arms Race for AI Compute: Data Center Providers, Hyperscalers, and Neocloud Providers," will bring together executives from across the digital infrastructure ecosystem, including Oracle's Dr. Sanjay Basu and IG Group CEO Vinay Nagpal.

The discussion reflects one of the most significant technology infrastructure shifts in recent decades. The explosive growth of generative AI, large language models, and advanced machine learning applications has created demand for specialized infrastructure capable of supporting high-density GPU deployments, low-latency connectivity, and massive power requirements.

For data center operators, the challenge extends far beyond simply adding capacity.

Modern AI workloads require facilities specifically designed for dense computing environments, advanced cooling systems, resilient power delivery, and high-speed network connectivity. These requirements are reshaping how infrastructure providers select sites, design facilities, and engage with customers.

EdgeCore has positioned itself within this rapidly expanding segment of the market by focusing on large-scale infrastructure solutions for hyperscale cloud providers and enterprise customers. As Chief Commercial Officer, Heiden oversees the company's go-to-market strategy around delivering high-density infrastructure designed to support next-generation compute demands.

His participation in the panel comes at a time when AI infrastructure has become one of the most competitive areas within the technology sector.

Hyperscale providers such as Amazon, Microsoft, and Google continue to invest billions of dollars in expanding AI-ready infrastructure. At the same time, a growing ecosystem of specialized cloud providers, often referred to as "neoclouds," is emerging to serve organizations seeking dedicated AI compute resources.

These market dynamics have created what many industry observers describe as an infrastructure arms race.

According to IDC, global spending on AI infrastructure is expected to grow significantly over the next several years as enterprises move AI projects from experimentation to production environments. Gartner similarly identifies AI infrastructure modernization as a top priority for organizations seeking to scale advanced analytics and generative AI initiatives.

Power availability has become one of the most critical factors influencing infrastructure development.

Unlike traditional enterprise workloads, AI clusters require enormous amounts of electricity. As a result, data center operators are increasingly evaluating locations based on power accessibility, utility partnerships, renewable energy availability, and long-term capacity planning.

Cooling technology is also emerging as a strategic differentiator. High-performance GPU deployments generate substantially more heat than conventional computing environments, driving increased adoption of liquid cooling and other advanced thermal management approaches.

Beyond physical infrastructure, connectivity is becoming equally important.

AI applications depend on rapid data movement between cloud environments, enterprise systems, and distributed users. This has elevated the importance of fiber networks, interconnection ecosystems, edge computing infrastructure, and low-latency network architectures capable of supporting real-time AI services.

The TCW LIVE! discussion is expected to explore how these interconnected factors are influencing investment decisions across the digital infrastructure landscape. Industry leaders will examine how data center providers, cloud platforms, network operators, and emerging infrastructure specialists are adapting to meet growing demand.

Another area likely to receive significant attention is the evolving relationship between hyperscalers and infrastructure partners.

As AI deployments grow larger and more complex, cloud providers increasingly rely on ecosystem partnerships to accelerate expansion timelines and secure access to power, land, and connectivity resources. This trend is creating new business opportunities for infrastructure developers while simultaneously intensifying competition for strategic assets.

Heiden's extensive background across telecommunications and data center organizations provides a unique perspective on these developments. Over nearly three decades, he has held leadership roles across major infrastructure companies and has witnessed multiple technology transitions, from internet expansion and cloud adoption to today's AI-driven transformation.

The broader significance of the TCW LIVE! panel extends beyond infrastructure providers alone.

Enterprise technology leaders, SaaS providers, AI platform vendors, investors, and cloud operators all have a stake in how the next generation of digital infrastructure evolves. The availability of compute resources, networking capacity, and energy infrastructure will play a critical role in determining how quickly organizations can deploy and scale AI applications.

As AI adoption accelerates globally, infrastructure is increasingly becoming a strategic business issue rather than a purely technical consideration. Organizations seeking competitive advantages through artificial intelligence may ultimately find that access to scalable, reliable, and efficient infrastructure becomes just as important as the AI models themselves.

The discussions at TCW LIVE! highlight a reality facing the technology industry: the future of AI will depend not only on advances in algorithms and software but also on the physical and digital infrastructure capable of powering them.

Market Landscape

The AI infrastructure market is experiencing unprecedented growth as enterprises increase investments in generative AI, machine learning, and advanced analytics. According to IDC, spending on AI infrastructure continues to rise as organizations expand production-scale deployments. Gartner has similarly identified AI-ready infrastructure as a critical enabler of enterprise digital transformation.

The market is increasingly shaped by hyperscale cloud providers, specialized AI cloud platforms, colocation operators, and digital infrastructure companies competing to secure power resources, strategic locations, and connectivity assets. Data center modernization, liquid cooling technologies, edge computing, and GPU-optimized environments are emerging as major investment priorities across the sector.

Top Insights

• EdgeCore's Clint Heiden will join industry leaders at TCW LIVE! to discuss the growing demand for AI compute infrastructure and its impact on global digital ecosystems.
• Rising AI adoption is driving significant investments in data centers, power infrastructure, connectivity networks, and specialized GPU environments.
• Hyperscalers and emerging neocloud providers are competing to secure capacity needed to support next-generation AI workloads.
• Power availability, advanced cooling technologies, and network connectivity have become critical factors in AI infrastructure planning and deployment.
• Enterprise AI growth is increasingly dependent on scalable infrastructure capable of supporting high-density computing and low-latency data movement.

Get in touch with our MarTech Experts

The inaugural Festival of Marketing Asia has unveiled its opening keynote speakers and first wave of industry leaders, signaling its ambition to become a major gathering for marketing professionals across the Asia-Pacific region. Scheduled for September 2026 in Kuala Lumpur, the event will bring together B2B and B2C marketers at a time when artificial intelligence, customer experience, and revenue accountability are reshaping marketing leadership.

Haymarket Media Asia has announced the opening keynote lineup for the first-ever Festival of Marketing Asia (FoM Asia), a new regional edition of the long-running UK marketing event that has traditionally attracted more than 1,000 marketing professionals annually.

Taking place on 3 September 2026 at PARKROYAL COLLECTION Kuala Lumpur, the event is expected to bring together more than 300 marketers from across Asia for a day focused on modern marketing strategy, customer experience, brand growth, and AI-driven transformation.

The launch comes as marketing leaders face mounting pressure to deliver measurable business outcomes while adapting to rapidly changing consumer behavior, evolving technology platforms, and the growing influence of artificial intelligence across the customer journey.

Unlike many industry conferences that focus heavily on thought leadership presentations, Festival of Marketing Asia is positioning itself around case study-led sessions designed to showcase practical applications and real-world results. Organizers say the approach is intended to provide actionable insights for marketing leaders navigating increasingly complex business environments.

The event's opening keynote will feature executives from Disney Cruise Line, which recently expanded its presence in Asia through the launch of Disney Adventure, the first vessel in the company's fleet to homeport in the region.

The keynote, titled "Logic in marketing makes you right, but magic makes you remembered," will explore how storytelling remains a competitive advantage in an era increasingly dominated by data and automation. The session is expected to highlight how Disney combines brand experiences, regional partnerships, and immersive marketing activations to build awareness and customer engagement across diverse Asian markets.

The focus on storytelling reflects a broader industry trend. While enterprises continue investing heavily in analytics, AI-powered marketing tools, and customer data platforms, many brands are simultaneously seeking ways to create emotionally resonant experiences that differentiate them from competitors.

The event's B2C track will open with Bruno Bechtlufft, Head of Marketing, Customer Service and Commercial Excellence – Asia at Petronas Lubricants International. His keynote, "Designing for human states: beyond the customer journey," challenges traditional funnel-based marketing frameworks that have long dominated customer experience strategies.

Instead of viewing customers as progressing through predictable stages, Bechtlufft will advocate for designing experiences around changing emotional states, behavioral signals, and contextual factors. The concept aligns with a growing movement in customer experience management that prioritizes adaptability over rigid journey mapping.

For consumer brands, this shift is becoming increasingly relevant as customers engage across multiple digital and physical touchpoints. Research from Gartner has repeatedly highlighted the need for organizations to move beyond linear customer journeys and adopt more dynamic engagement models that reflect modern buying behavior.

On the B2B side, the opening keynote will be delivered by Mansi Chopra, Chief Marketing Officer at Finmo. Her session, "From marketing to growth architecture: the new mandate for B2B leaders," addresses one of the most significant transformations occurring within enterprise marketing teams.

Historically, marketing departments were primarily responsible for brand awareness, communications, and lead generation. Today's B2B organizations increasingly expect marketing leaders to influence revenue growth, pipeline development, customer retention, and overall business performance.

The concept of marketers acting as "growth architects" reflects a broader shift toward cross-functional collaboration between marketing, sales, customer success, and product teams. As SaaS companies, fintech firms, and enterprise technology providers face greater pressure to demonstrate return on investment, marketing leadership roles continue expanding beyond traditional brand responsibilities.

The conference agenda also reflects the growing convergence between B2B and B2C marketing disciplines. Organizers argue that many of the challenges facing marketers today—including personalization, customer engagement, AI adoption, and measurement—transcend industry categories.

That crossover is evident in the broader speaker lineup, which includes marketing leaders from Agoda, Kyndryl, Manulife, Muslim Pro, Plaza Premium Group, Adyen, Kyriba, Yubico, Uber Eats, GoPro, Volvo Cars, and Income Insurance.

Another notable addition is the event's invitation-only CMO Boardroom, developed in partnership with mediasense. The closed-door session is designed to facilitate candid discussions among senior marketing executives on issues ranging from AI implementation and organizational transformation to growth strategy and customer experience innovation.

The timing of Festival of Marketing Asia coincides with significant changes across the marketing technology landscape. According to IDC, worldwide spending on AI-enabled business applications continues to accelerate as organizations seek automation, predictive analytics, and personalization capabilities. Meanwhile, Forrester research suggests that customer experience remains one of the most important competitive differentiators for brands operating in increasingly crowded markets.

These trends are reshaping the responsibilities of marketing leaders throughout Asia. Success is no longer measured solely by campaign performance or brand awareness metrics. Instead, executives are being asked to connect marketing initiatives directly to revenue growth, customer lifetime value, and business outcomes.

Festival of Marketing Asia appears designed to address those evolving expectations. By bringing together B2B and B2C leaders under one roof, the event aims to create a forum where marketers can learn from adjacent industries, explore emerging technologies, and examine how the role of marketing continues to evolve in an AI-driven business environment.

As the region's marketing ecosystem becomes increasingly sophisticated, events that blend strategic leadership, customer experience innovation, and practical execution may become essential gathering points for the next generation of marketing decision-makers.

Market Landscape

Marketing leaders across Asia are facing a rapidly evolving environment shaped by artificial intelligence, changing customer expectations, and increasing pressure to demonstrate measurable business outcomes. According to Gartner and Forrester, organizations are prioritizing customer experience, personalization, and AI-powered engagement as key competitive differentiators.

At the same time, B2B and B2C marketing disciplines are becoming more interconnected. Concepts such as customer-centric design, growth marketing, predictive analytics, and customer journey orchestration are increasingly influencing strategies across industries. Events like Festival of Marketing Asia reflect this convergence by bringing together diverse perspectives from enterprise technology, consumer brands, fintech, travel, and digital commerce.

Top Insights

• Festival of Marketing Asia will debut in Kuala Lumpur, bringing together more than 300 B2B and B2C marketers focused on growth, customer experience, and AI-driven transformation.
• Disney Cruise Line will headline the main stage, highlighting how storytelling and experiential marketing remain powerful differentiators in a data-driven marketing environment.
• Petronas Lubricants International will challenge traditional customer journey models by exploring adaptive experiences built around emotions, intent, and behavioral signals.
• Finmo's keynote will examine how modern B2B marketers are evolving into growth leaders responsible for revenue, pipeline development, and customer retention.
• The event reflects growing convergence between B2B and B2C marketing strategies as personalization, AI adoption, and customer experience become universal priorities.

Get in touch with our MarTech Experts

As enterprises race to operationalize AI-driven customer engagement, one challenge continues to stand in the way of effective personalization and automation: identity. Verato, an identity intelligence provider focused on creating trusted customer and patient records, has been recognized by Snowflake as an Integration and Data Modeling “One to Watch” in its latest Modern Marketing Data Stack report, underscoring the growing importance of identity infrastructure in AI-powered marketing ecosystems.

Verato has been named an Integration and Data Modeling “One to Watch” in Snowflake’s The Modern Marketing Data Stack: Governing the Agentic Enterprise report, a recognition that highlights the increasing role of identity intelligence in modern marketing and customer experience strategies.

The announcement comes as organizations across industries invest heavily in AI, marketing automation, customer data platforms, and analytics initiatives. While these technologies promise more personalized customer experiences and operational efficiency, many enterprises continue to struggle with fragmented customer data spread across multiple systems.

Snowflake’s annual report examines how organizations are transforming marketing operations through AI-driven and agentic architectures built on governed data foundations. Now in its fifth year, the report draws insights from more than 11,500 customers and ecosystem partners, reflecting broader trends shaping the future of enterprise marketing technology.

Verato was recognized for its work in helping organizations establish trusted identity foundations within the Snowflake ecosystem. The company’s technology focuses on solving a fundamental challenge that affects nearly every customer-facing initiative: accurately identifying individuals across disconnected data sources.

For enterprise marketers, identity resolution has become a critical capability. Customer data often exists across CRM systems, marketing automation platforms, analytics tools, healthcare records, loyalty programs, e-commerce systems, and advertising platforms. Without a reliable way to connect those records, organizations risk delivering inconsistent experiences, inaccurate analytics, and ineffective AI outcomes.

Verato addresses this challenge through its identity intelligence platform and Verato MDM Cloud™, which enables organizations to create a unified view of customers, consumers, patients, and members. Through its Snowflake Native App, available on Snowflake Marketplace, enterprises can synchronize identity data directly within their Snowflake environments without introducing additional data complexity.

The approach aligns with a broader industry movement toward cloud-native architectures that bring applications closer to enterprise data rather than moving sensitive information across multiple systems. As privacy regulations become stricter and governance requirements increase, organizations are increasingly prioritizing technologies that support data accuracy while maintaining compliance controls.

Identity intelligence is becoming particularly important as enterprises adopt agentic AI systems. Unlike traditional marketing automation workflows, AI-powered agents rely on accurate, connected customer profiles to make decisions, personalize interactions, and generate insights. Poor identity resolution can lead to duplicate records, fragmented customer journeys, and unreliable AI outputs.

This challenge extends beyond marketing. Industries such as healthcare, financial services, retail, and telecommunications increasingly depend on trusted identity data to improve customer engagement, streamline operations, and support regulatory compliance.

The recognition from Snowflake also reflects a growing market emphasis on Customer 360 strategies. Many organizations have invested significantly in customer data platforms and analytics infrastructure over the past decade, yet continue to face obstacles in achieving a complete and trustworthy view of individual customers.

Verato’s integration with Snowflake is designed to address those challenges by enabling organizations to unify identity data at scale while leveraging the broader capabilities of the AI Data Cloud. By establishing trusted identity foundations, enterprises can improve customer segmentation, personalization, measurement, analytics, and AI-driven decision-making.

Industry research continues to reinforce the value of unified customer data strategies. According to Gartner, organizations that successfully establish integrated customer data ecosystems are better positioned to improve customer experience outcomes and maximize the value of AI investments. IDC research similarly highlights trusted data foundations as a key requirement for enterprise AI adoption and digital transformation success.

The recognition also positions Verato within a rapidly evolving competitive landscape where major technology vendors are increasingly investing in identity and data intelligence capabilities. Companies including Salesforce, Adobe, Microsoft, and Google are expanding customer data and identity offerings as organizations seek more reliable methods for managing customer relationships across digital channels.

As AI adoption accelerates, the importance of identity infrastructure is expected to grow. Agentic marketing systems, predictive analytics engines, customer journey orchestration platforms, and generative AI applications all depend on accurate identity resolution to function effectively.

For enterprise marketing leaders, the message is becoming increasingly clear: before organizations can unlock the full value of AI-powered engagement, they must first establish a trusted understanding of who their customers are.

Snowflake’s recognition of Verato as a company to watch reflects that reality. As enterprises modernize their marketing technology stacks and pursue AI-driven transformation, identity intelligence is emerging as a foundational capability rather than a supporting function. Organizations that successfully unify customer identity data may ultimately gain a significant advantage in delivering personalized experiences, improving measurement accuracy, and driving stronger business outcomes in the age of agentic AI.

Market Landscape

The shift toward AI-powered marketing is increasing demand for identity intelligence, customer data infrastructure, and governed analytics environments. Gartner and IDC research consistently identify trusted customer data as a prerequisite for successful AI initiatives, while enterprises continue investing in Customer 360 strategies to improve personalization and measurement.

Cloud platforms such as Snowflake, Salesforce Data Cloud, Adobe Experience Platform, Microsoft Fabric, and Google Cloud are increasingly positioning identity and data governance as critical components of modern AI ecosystems. As organizations deploy agentic AI systems, accurate customer identity data is becoming essential for personalization, analytics, automation, and customer engagement.

Top Insights

• Snowflake recognized Verato as an Integration and Data Modeling “One to Watch,” highlighting the growing strategic importance of identity intelligence in AI-driven marketing environments.
• Verato’s Snowflake Native App enables organizations to synchronize identity data directly within Snowflake, supporting analytics, Customer 360 initiatives, and AI-powered engagement.
• Identity resolution is emerging as a foundational requirement for agentic AI systems that depend on accurate and connected customer profiles.
• Enterprises continue investing in Customer 360 strategies to improve personalization, analytics, measurement, and customer experience outcomes.
• Trusted identity data is becoming increasingly important across industries including healthcare, financial services, retail, and digital marketing.

Get in touch with our MarTech Experts

As enterprises accelerate investments in AI-powered marketing operations, the underlying data infrastructure is becoming a critical competitive differentiator. Narrative, a provider of data collaboration and identity infrastructure solutions, has been named a Data & Identity “One to Watch” in Snowflake’s latest Modern Marketing Data Stack report, highlighting the growing importance of governed, interoperable data foundations for agentic marketing systems.

Narrative has been recognized by Snowflake as a Data & Identity “One to Watch” in the latest edition of The Modern Marketing Data Stack: Governing the Agentic Enterprise, a report that examines how organizations are modernizing marketing operations around AI, automation, and trusted data environments.

The recognition reflects a broader industry shift taking place across marketing technology. As enterprises deploy AI agents, predictive analytics platforms, customer data solutions, and automated decisioning systems, marketers are increasingly discovering that AI effectiveness depends heavily on data quality, identity resolution, and governance frameworks.

According to Snowflake, the modern marketing ecosystem is moving away from fragmented application environments toward agentic systems that operate directly on governed enterprise data. The report draws insights from more than 11,500 customers and ecosystem partners, offering a snapshot of how organizations are rethinking marketing infrastructure to support AI-driven execution.

Narrative was highlighted for its approach to data normalization, identity resolution, and secure data collaboration delivered natively within the Snowflake ecosystem. Rather than moving large volumes of customer and marketing data across disconnected platforms, the company's architecture enables enterprises to connect, enrich, and activate information while maintaining governance controls within their existing cloud environment.

This approach addresses one of the most persistent challenges facing enterprise marketing teams: data fragmentation.

Modern marketing organizations often manage customer information across multiple systems, including customer data platforms, advertising platforms, analytics environments, CRM systems, and retail media networks. These disconnected datasets can create operational inefficiencies, inconsistent customer profiles, and increased compliance risks.

Narrative's technology stack aims to solve those challenges through three core components: its Rosetta Stone Normalization Engine, Identity Orchestrator, and Marketplace infrastructure. Integrated through a Snowflake Native App, these capabilities allow organizations to perform identity resolution, audience creation, campaign activation, and measurement workflows directly inside the Snowflake AI Data Cloud.

For MarTech and AdTech teams, the significance lies in minimizing data movement.

As privacy regulations continue to evolve globally and enterprise data governance requirements become more stringent, organizations are looking for architectures that reduce duplication while preserving access controls. Running identity and activation processes within a governed cloud environment can help reduce security concerns while improving operational efficiency.

Industry analysts have increasingly identified data governance as a foundational requirement for enterprise AI adoption. Gartner has projected that organizations with mature data governance frameworks are significantly more likely to achieve measurable business outcomes from AI initiatives. Similarly, IDC research continues to highlight the growing importance of unified data ecosystems as enterprises scale machine learning and automation programs.

Narrative's recognition also reflects the expanding role of identity infrastructure within modern marketing stacks. While identity resolution has traditionally been associated with advertising and audience targeting, AI-powered marketing systems increasingly rely on accurate customer identity graphs to support personalization, attribution, journey orchestration, and predictive analytics.

The company's partnership with Snowflake positions it within a growing ecosystem of vendors building applications directly on cloud-native data platforms. This trend mirrors broader market movements seen across enterprise technology providers such as Google Cloud, Microsoft Azure, Salesforce Data Cloud, and Adobe Experience Platform, all of which are investing heavily in bringing analytics, AI, and activation capabilities closer to governed data environments.

A recent enterprise deployment highlighted by Narrative demonstrates the potential operational impact of this model. According to the company, a leading consumer brand was able to launch an end-to-end media network entirely within its Snowflake environment. By combining first-party customer data with third-party enrichment sources, the organization executed identity resolution, audience creation, and activation workflows without moving sensitive data outside its governance framework.

Perhaps more notable was the reported implementation timeline. Processes that traditionally required multiple quarters of integration work were reportedly completed within weeks, reflecting growing demand for composable marketing infrastructure that can accelerate deployment without introducing additional data complexity.

The recognition arrives at a time when AI-driven marketing has moved beyond experimentation and into operational execution. McKinsey research suggests that organizations successfully integrating AI into customer engagement and marketing workflows are seeing measurable gains in productivity and decision-making speed. However, those benefits often depend on access to trusted, connected, and governed data assets.

As marketing organizations continue building agentic AI environments, vendors that simplify data interoperability while maintaining governance controls are likely to play an increasingly important role. Snowflake's designation of Narrative as a company to watch suggests that data normalization, identity orchestration, and secure collaboration are becoming strategic pillars of next-generation marketing infrastructure rather than back-office operational functions.

For enterprise marketing leaders, the message is becoming clear: AI success may ultimately depend less on the intelligence layer itself and more on the quality, accessibility, and governance of the data powering it.

Market Landscape

The marketing technology industry is rapidly evolving toward AI-native architectures. Gartner estimates that organizations continue increasing investments in AI-enabled marketing platforms, while IDC research highlights unified data ecosystems as a key driver of successful digital transformation initiatives.

At the same time, enterprises are under pressure to comply with stricter privacy regulations and governance requirements. This has accelerated demand for cloud-native data collaboration platforms, identity resolution technologies, customer data infrastructure, and clean-room environments that support secure data activation.

Companies including Snowflake, Salesforce, Adobe, Google Cloud, and Microsoft are all expanding capabilities that bring AI applications closer to governed enterprise data, signaling a broader shift toward composable and agentic marketing ecosystems.

Top Insights

• Snowflake recognized Narrative as a Data & Identity “One to Watch,” highlighting the growing importance of governed data infrastructure for AI-driven marketing operations.
• Narrative enables identity resolution, data normalization, and audience activation directly within Snowflake environments, reducing data movement and governance risks.
• Enterprise marketers increasingly require unified customer identity frameworks to support personalization, analytics, attribution, and AI-powered decision making.
• Cloud-native architectures are reshaping MarTech stacks by bringing applications closer to governed enterprise data rather than moving data between platforms.
• The rise of agentic AI systems is increasing demand for trusted data collaboration technologies that improve interoperability, privacy, and operational efficiency.

Get in touch with our MarTech Experts

As enterprises increasingly deploy AI agents across sales, marketing, and revenue operations, access to trusted business data is becoming as important as the AI models themselves. ZoomInfo is addressing that challenge through a new native integration with Amazon Quick Suite, bringing its GTM.AI platform and verified go-to-market intelligence directly into AWS's agentic AI workspace. The move positions ZoomInfo at the center of a growing market focused on providing AI agents with reliable business context rather than simply access to raw data.

ZoomInfo has announced a native integration between its GTM.AI platform and Amazon Quick Suite, enabling sales, marketing, and revenue teams to access ZoomInfo's business intelligence directly within Amazon's AI-powered workspace.

The integration allows users to perform company research, account scoring, contact discovery, buying committee analysis, lead enrichment, and other go-to-market tasks through natural language interactions. Rather than switching between multiple applications, teams can execute these workflows inside Quick Suite across web, desktop, and mobile environments.

At the heart of the integration is GTM.AI, ZoomInfo's headless go-to-market context layer that exposes the company's proprietary business intelligence through APIs and Model Context Protocol (MCP). Through a custom MCP server connection, Amazon Quick Suite can access ZoomInfo's extensive dataset, which includes information on approximately 100 million companies, 500 million professional contacts, and billions of intent and buying signals.

The announcement highlights an emerging challenge in enterprise AI adoption: context quality.

While many organizations have successfully connected AI assistants to enterprise systems, access alone does not guarantee accuracy. AI agents often struggle when working with outdated, incomplete, or unverified information. In revenue-generating functions such as sales and marketing, poor-quality data can quickly lead to ineffective prospecting, inaccurate targeting, and lost opportunities.

ZoomInfo is positioning GTM.AI as a solution to that problem.

The company argues that successful AI agents require more than connectivity. They need structured, continuously refreshed, and verified business intelligence that can be trusted for operational decisions. This distinction is becoming increasingly important as enterprises move from AI experimentation toward autonomous workflows that directly influence revenue generation.

In practice, the integration allows users to perform complex go-to-market tasks through conversational requests.

For example, a user can ask Amazon Quick Suite to identify marketing executives in a specific market, analyze buying intent signals, enrich contact records, and generate prospect lists containing professional details such as titles, emails, phone numbers, and company information. The workflow is executed through ZoomInfo's data infrastructure while remaining accessible through the Quick Suite interface.

The integration also extends support for a broad range of ZoomInfo capabilities, including account research, total addressable market analysis, competitor intelligence, technology stack identification, meeting preparation, and lead scoring.

This reflects a broader trend across enterprise software.

Organizations are increasingly adopting AI agents that function as operational assistants capable of executing multi-step business workflows rather than simply answering questions. As a result, technology providers are investing heavily in context layers, orchestration platforms, and knowledge graphs that can supply AI systems with accurate business information.

Major technology companies including Amazon Web Services, Salesforce, Microsoft, Google, and HubSpot are all developing agentic AI ecosystems designed to automate business processes across sales, marketing, customer service, and operations.

ZoomInfo's strategy differs by focusing on the data layer that powers these agents.

The company describes GTM.AI as a unified context graph that serves as a consistent source of truth across multiple AI environments. In addition to Amazon Quick Suite, the platform already supports integrations with Salesforce Agentforce, HubSpot Breeze, Microsoft Copilot, Gong, Glean, Claude, ChatGPT, and Google Workspace.

This approach addresses another growing enterprise concern: governance.

As AI agents gain access to sensitive business information, organizations are demanding stronger controls around permissions, compliance, auditability, and data lineage. According to ZoomInfo, GTM.AI applies consistent governance policies across every connected environment, ensuring that access controls and compliance standards remain intact regardless of where AI interactions occur.

The company notes that enterprise protections include support for ISO 27001, ISO 27701, SOC 2 Type II, and GDPR-related compliance frameworks.

Industry analysts increasingly view data quality as one of the most significant factors influencing AI success. Gartner has identified trusted data foundations as a critical requirement for enterprise AI initiatives, while IDC reports that organizations are prioritizing data governance and contextual intelligence as they scale AI-powered business operations.

The timing is notable. As agentic AI adoption accelerates, organizations are discovering that model sophistication alone does not guarantee business value. Even advanced AI systems can produce poor outcomes when operating on outdated or fragmented information.

By integrating GTM.AI into Amazon Quick Suite, ZoomInfo is betting that the next phase of enterprise AI competition will be defined not only by reasoning capabilities but by the quality, freshness, and reliability of the business context available to those agents.

Market Landscape

The rise of agentic AI is creating new demand for enterprise-grade data infrastructure. Gartner forecasts growing investment in AI agents capable of executing business workflows autonomously, while IDC reports that data quality and governance remain among the top barriers to enterprise AI success.

At the same time, go-to-market teams are increasingly adopting AI-powered sales and marketing tools to improve prospecting, lead qualification, account intelligence, and revenue operations. This trend is driving demand for context layers that provide AI systems with verified, continuously updated business intelligence rather than static datasets.

Top Insights

•  ZoomInfo has integrated GTM.AI with Amazon Quick Suite, enabling AI-powered sales and marketing workflows inside AWS's agentic workspace.
• The integration provides access to business intelligence covering 100 million companies, 500 million contacts, and billions of buying signals.
• GTM.AI acts as a context layer that supplies AI agents with verified, continuously refreshed go-to-market data.
• Organizations can perform account research, lead enrichment, prospecting, and buying committee analysis through natural language requests.
• The launch reflects growing enterprise demand for trusted data foundations that support agentic AI and autonomous revenue workflows.

Get in touch with our MarTech Experts

As enterprises scale generative AI applications, managing vector data across multiple systems has emerged as a growing operational challenge. Zilliz is aiming to simplify that complexity with the launch of Vector Lakebase, a new platform that combines vector search, analytics, and lake-native storage into a unified AI data foundation. The release expands the capabilities of Zilliz Cloud beyond vector database services, positioning the company to compete in the rapidly evolving market for AI-native data infrastructure.

Zilliz, the company behind the open-source vector database Milvus, has announced the public preview of Vector Lakebase, a new platform designed to consolidate vector search, analytics, and AI data management into a single architecture.

The launch represents one of the company's most significant product expansions since introducing Zilliz Cloud and reflects a broader industry shift toward unified data platforms capable of supporting the full lifecycle of artificial intelligence applications.

At its core, Vector Lakebase combines Zilliz's production-grade vector database technology with a shared lake-native storage layer, enabling multiple workloads to operate against a single logical copy of data. The company says this eliminates the need for organizations to maintain separate systems for retrieval, analytics, data preparation, and AI model development.

The challenge is becoming increasingly relevant as enterprises deploy retrieval-augmented generation (RAG), AI agents, recommendation engines, semantic search platforms, and multimodal AI applications at scale.

While vector databases have become essential components of modern AI infrastructure, many organizations continue to manage fragmented architectures where real-time search, batch analytics, and data engineering workflows operate in separate environments. This often results in duplicated data, higher storage costs, increased complexity, and slower iteration cycles.

According to Zilliz, Vector Lakebase addresses these limitations through what it describes as a zero-copy semantic data plane, allowing real-time serving, interactive discovery, and large-scale analytics to operate from the same data foundation.

The platform extends the capabilities of Zilliz Cloud, which is already used by organizations including Zillow, OpenEvidence, Exa, and Filevine, alongside thousands of enterprise AI teams worldwide.

The announcement arrives as vector databases become increasingly important within the AI ecosystem.

Large language models and generative AI systems rely heavily on vector embeddings to represent unstructured information such as documents, images, audio files, and user interactions. Vector databases store and retrieve these embeddings, enabling applications to deliver contextually relevant responses, recommendations, and search results.

However, AI workflows are evolving beyond simple retrieval tasks.

Modern AI systems often operate in continuous cycles that involve serving production queries, collecting feedback, analyzing usage patterns, refining training datasets, and updating models. Each stage frequently requires different tools and storage systems, creating operational bottlenecks.

Vector Lakebase is designed to address these workflows through five primary capabilities.

The platform introduces tiered real-time serving options optimized for different performance and cost requirements, allowing organizations to choose configurations based on latency and throughput needs.

It also adds on-demand search functionality, enabling teams to scale compute resources dynamically rather than maintaining always-on infrastructure. This approach aligns with growing enterprise demand for cost-efficient AI infrastructure, particularly for workloads that experience irregular usage patterns.

Another notable feature is support for external data lake search. Organizations can perform vector and semantic search directly on data stored in formats such as Iceberg, Parquet, Lance, and Vortex without moving information into a separate database environment.

This capability reflects the growing convergence between AI infrastructure and data lake architectures.

Major cloud and data platform providers including Snowflake, Databricks, Google Cloud, Microsoft Azure, and Amazon Web Services are increasingly investing in architectures that unify analytics, machine learning, and AI workloads on shared data foundations.

Vector Lakebase also supports hybrid search across vectors, structured data, text, JSON objects, and geospatial datasets. This capability is becoming increasingly important as enterprises seek to combine semantic search with traditional database queries and business intelligence workflows.

Underlying the platform is a new storage layer built on Vortex, an open columnar data format designed to optimize random-read performance for AI workloads. According to Zilliz, the architecture reduces storage inefficiencies while supporting large-scale datasets ranging from gigabytes to petabytes.

Industry analysts have increasingly highlighted the importance of unified AI infrastructure. Gartner has identified vector databases and AI-ready data architectures as critical enablers of enterprise generative AI deployments. Meanwhile, IDC projects continued growth in investments related to AI data platforms as organizations seek to operationalize machine learning and foundation model initiatives.

For enterprises building AI applications, the value proposition centers on simplification. Rather than managing separate vector databases, analytics platforms, data lakes, and search systems, organizations can potentially consolidate these functions into a single environment.

The launch also signals broader competition within the AI infrastructure market. As vector databases evolve beyond retrieval engines and expand into comprehensive AI data platforms, vendors are increasingly competing on integration, scalability, operational efficiency, and support for end-to-end AI workflows.

With Vector Lakebase, Zilliz is positioning itself at the center of that transition, aiming to provide enterprises with a unified platform capable of supporting the next generation of AI-powered applications.

Market Landscape

The vector database market has emerged as a critical layer of enterprise AI infrastructure. Gartner identifies vector search and semantic retrieval technologies as foundational components for generative AI, retrieval-augmented generation (RAG), and AI agent architectures.

At the same time, enterprises are increasingly adopting lakehouse and unified data architectures that combine analytics, machine learning, and operational workloads on shared data foundations. IDC forecasts continued growth in AI infrastructure spending as organizations seek scalable platforms capable of managing both real-time AI applications and large-scale data processing environments.

Top Insights

•  Zilliz has launched Vector Lakebase, a unified AI data platform that combines vector search, analytics, and lake-native storage architecture.
• The platform enables real-time serving, interactive discovery, and batch analytics to operate on a single logical copy of data.
• Vector Lakebase introduces zero-copy search capabilities across external data lakes including Iceberg, Parquet, Lance, and Vortex formats.
• Enterprises can consolidate vector databases, analytics systems, and AI data workflows into a unified infrastructure layer.
• The launch reflects growing demand for scalable AI-native data platforms supporting generative AI, RAG applications, and AI agents.

Get in touch with our MarTech Experts

As connected TV advertising continues to mature, marketers are increasingly looking beyond audience targeting alone and focusing on content context as a key driver of campaign performance. Xumo, the streaming joint venture between Comcast and Charter, is expanding its contextual advertising capabilities through new integrations with Gracenote and IRIS.TV, bringing deeper content intelligence to its growing FAST (Free Ad-Supported Streaming Television) ecosystem.

Xumo has announced expanded integrations with Gracenote and IRIS.TV aimed at enhancing contextual targeting across its FAST streaming inventory, providing advertisers with richer content signals to improve campaign relevance, brand suitability, and performance.

The move comes as advertisers face growing challenges navigating an increasingly fragmented streaming landscape. Consumers are now spread across dozens of streaming platforms, channels, and devices, making it more difficult for brands to identify the most relevant content environments for ad placements.

While audience-based targeting remains a cornerstone of digital advertising, contextual intelligence is becoming a critical complement, particularly in privacy-conscious environments where access to user-level identifiers continues to decline.

Xumo's latest initiative seeks to address this challenge by expanding the depth and quality of content metadata available across its streaming inventory. The company operates one of the largest FAST ecosystems in the market, with more than 2,000 channels distributed across over 30 platforms.

The integration with Gracenote introduces standardized content metadata, program-level identifiers, and classification taxonomies that help advertisers better understand the nature of programming available within Xumo's FAST inventory.

For marketers, this creates a more consistent framework for evaluating content opportunities and activating campaigns with greater transparency and precision. Program-level metadata can help buyers align campaigns with specific genres, themes, audiences, and brand suitability requirements.

The addition of IRIS.TV extends those capabilities further through video-level contextual analysis.

Using artificial intelligence and computer vision technologies, IRIS.TV enables content to be analyzed frame by frame, generating contextual signals that can identify themes, objects, emotions, scenes, and other content characteristics. These insights allow advertisers to move beyond broad channel categories and target specific content moments that may be more relevant to their campaigns.

The significance of the announcement lies in the application of these capabilities to FAST environments.

Historically, many advanced contextual targeting tools have been associated primarily with video-on-demand and digital content libraries where metadata and content analysis are more easily managed. FAST channels, which replicate traditional linear television experiences within streaming environments, have presented greater challenges due to the continuous nature of programming streams.

Xumo is positioning itself among the first FAST providers to bring both program-level and video-level contextual intelligence into linear streaming inventory at scale.

This capability may become increasingly valuable as advertisers seek alternatives to identity-based targeting approaches. Regulatory changes, privacy legislation, and the ongoing deprecation of third-party identifiers have accelerated investment in contextual advertising strategies that focus on content rather than individual user profiles.

Industry research continues to support this shift. According to Gartner, contextual targeting is gaining renewed importance as marketers prioritize privacy-compliant advertising approaches. Similarly, Forrester has highlighted contextual intelligence as a critical component of next-generation media buying strategies, particularly across connected TV and streaming platforms.

The partnership also reflects broader changes occurring within the connected TV advertising market.

Major media companies and streaming providers including Comcast, Charter Communications, Netflix, Amazon, and Google are increasingly investing in contextual intelligence, AI-powered media planning, and content-level advertising solutions.

For advertisers, the ability to align messaging with specific content environments can improve both efficiency and effectiveness. Contextually relevant advertising has been shown to increase engagement, improve recall, and strengthen purchase intent compared with less relevant placements.

The announcement follows a period of strong growth for Xumo Play. According to the company, the platform experienced approximately 40% year-over-year growth in monthly active users, a 64% increase in total viewing hours, and a 22% rise in time spent per user during the past year.

As FAST continues to emerge as one of the fastest-growing segments within connected TV advertising, these audience gains create additional incentives for marketers to invest in smarter targeting capabilities.

By combining Gracenote's metadata infrastructure with IRIS.TV's AI-driven contextual analysis, Xumo is building a more sophisticated advertising framework designed to help brands identify high-value content opportunities across both linear and on-demand streaming experiences.

The initiative highlights a broader evolution in streaming advertising where contextual intelligence, AI-powered content analysis, and premium inventory access are becoming central to campaign optimization strategies. For marketers seeking greater relevance and performance in connected TV, content context is increasingly becoming as important as audience data itself.

Market Landscape

The FAST advertising market continues to experience rapid growth as consumers increasingly embrace free, ad-supported streaming services. According to industry forecasts from eMarketer and IDC, connected TV advertising spending is expected to continue expanding as brands shift budgets away from traditional linear television.

At the same time, contextual targeting is gaining momentum as advertisers seek privacy-friendly alternatives to identity-based targeting. Gartner identifies contextual intelligence as a growing area of investment within digital advertising, while Forrester notes that AI-powered content analysis is becoming a critical capability for streaming media monetization and programmatic advertising optimization.

Top Insights

•  Xumo has expanded contextual advertising capabilities through new integrations with Gracenote and IRIS.TV across its FAST streaming inventory.
• Gracenote provides standardized program-level metadata, while IRIS.TV delivers AI-powered video-level content analysis for more precise ad targeting.
• The initiative brings advanced contextual intelligence to FAST channels, an area traditionally more challenging than on-demand streaming environments.
• Advertisers can improve brand suitability, campaign relevance, and content alignment while reducing reliance on identity-based targeting approaches.
• The announcement supports Xumo's broader strategy of enhancing monetization opportunities across its growing FAST ecosystem.

Get in touch with our MarTech Experts