B2B marketing teams have more measurement data than ever, yet proving that communications activity contributes to revenue remains difficult. New research from 10Fold and Sapio Research finds that marketing and communications leaders are expanding measurement across digital, social, content, PR and AI visibility, but many lack the integrated systems needed to connect those signals to pipeline and revenue.

B2B marketing measurement is entering an awkward phase: the industry has become better at collecting data, but not necessarily better at explaining what that data means for the business.

That is the central finding of a new study from 10Fold, conducted by Sapio Research, which surveyed 400 B2B technology marketing and communications leaders. The research, titled The Communications ROI Reset: What B2B Leaders Measure, Trust and Act On, examines how companies are evaluating communications performance as AI search, digital channels and traditional media increasingly converge.

More than half of respondents now track a broader set of business-impact signals, including website traffic, AI visibility, AI referral traffic and downstream actions. Yet only 38% measure pipeline or revenue influence correlated with those metrics, while just 35% report having fully integrated reporting across earned, paid social, content and digital channels.

The problem, therefore, is not a shortage of dashboards. It is the absence of connections between them.

For B2B marketing leaders, that distinction matters. A PR placement may increase brand awareness, a social campaign may generate engagement and an AI search result may introduce a prospect to a company. But without a way to connect those activities to buyer behavior and commercial outcomes, proving their contribution to growth becomes difficult.

AI Visibility Is Becoming an Executive Metric

One of the most notable changes in the research is the emergence of AI visibility as part of mainstream communications measurement.

More than half of respondents measure AI search visibility or brand citations within AI-generated content. The same proportion tracks referral traffic from AI services such as ChatGPT, Perplexity and Google Gemini.

The shift changes the definition of search visibility.

For years, B2B marketers largely optimized for rankings, clicks and organic traffic. Generative AI introduces another layer: whether a company's brand, products or executives are mentioned, summarized or cited when potential buyers ask AI systems questions.

That means Generative Engine Optimization (GEO) and AI search visibility are moving closer to corporate communications and brand strategy.

The research says 58% of respondents include an AI search or LLM visibility platform in integrated reporting. That is a significant signal that AI discovery is moving from an experimental SEO initiative toward an executive-level marketing concern.

The challenge is attribution. A brand citation in an AI-generated response may influence a buyer long before a measurable website visit occurs. Marketing teams therefore need measurement frameworks capable of capturing influence across increasingly nonlinear customer journeys.

Executives Trust Business Outcomes More Than Activity

The research also highlights a persistent gap between what marketing teams measure and what senior executives trust.

According to the study, 87% of marketing leaders agree that their CEO or board primarily trusts metrics aligned with business outcomes.

Revenue impact ranks as the most trusted metric, cited by 34% of respondents. Website traffic follows at 27%, while social engagement ranks at 25%. Leads generated, SEO and organic visibility, and AI visibility each receive 24%.

Pipeline influence, however, ranks at just 16%, while share of voice is lowest at 11%.

The findings do not suggest that traditional communications metrics have become irrelevant. Rather, their value increases when they can be connected to measurable buyer behavior.

A media placement becomes more meaningful when it contributes to branded search, website engagement, qualified leads or pipeline. Likewise, AI visibility becomes more commercially relevant when marketers can demonstrate that increased discoverability influences consideration or downstream action.

Measurement Is Influencing Budgets Despite Data-Quality Concerns

Measurement is already affecting marketing investment decisions.

More than 80% of respondents say measurement is changing strategy and budget decisions across paid social, paid media, digital, owned content, earned media, earned content and organic social.

Yet only 49% say they are very confident in the accuracy and completeness of their communications data.

That creates a potentially uncomfortable situation for CMOs.

Organizations may be reallocating budgets based on measurement systems that their own marketing leaders do not fully trust. The problem can become especially complicated when data is distributed across PR platforms, social networks, advertising systems, web analytics, CRM platforms, marketing automation tools and emerging AI visibility products.

A modern enterprise MarTech stack can contain dozens of systems, but integration does not automatically create reliable attribution.

The next phase of marketing analytics will therefore be less about adding another metric and more about creating a consistent measurement architecture across the customer journey.

Different B2B Industries Need Different Proof

The study's vertical analysis also suggests that communications ROI cannot be reduced to one universal scorecard.

Enterprise software companies may need stronger connections between communications, revenue, buyer behavior and analyst influence. AI and data companies, meanwhile, face a growing need to demonstrate how discoverability translates into authority and market preference.

Cybersecurity companies may need defensible search and AI visibility metrics, while fintech and health-tech organizations may need to demonstrate visibility and trust to increasingly demanding executive stakeholders.

Company size matters as well.

Smaller organizations may benefit from concentrating on a limited number of reliable growth indicators rather than building complicated reporting systems. Mid-market businesses increasingly need to integrate digital and AI signals into an executive narrative. At the largest enterprises, the challenge may be simplifying massive quantities of marketing data into a scorecard that senior leadership can actually use.

A Connected Communications Scorecard

10Fold recommends organizing measurement around five layers: visibility, engagement, authority, action and business impact.

Visibility can include earned media, social audience growth, AI search visibility and share of voice. Engagement encompasses content interaction, downloads, social amplification and website behavior.

Authority adds analyst and influencer inclusion, message pull-through and AI-generated citations. Action moves closer to conversion through clicks, forms, demos, registrations and subscriptions.

At the final layer are leads, pipeline influence, revenue impact and multi-touch attribution.

The structure is useful because it recognizes that communications rarely create revenue in a single step.

A prospect might first encounter a company through earned media, later see an executive post on LinkedIn, encounter the company in an AI-generated answer, visit its website and eventually request a demo. Treating only the final interaction as responsible for the conversion can obscure the cumulative effect of marketing and communications.

Market Landscape

The B2B marketing measurement market is shifting from channel-specific reporting toward connected measurement and revenue attribution.

Traditional platforms such as Google Analytics, CRM systems and marketing automation platforms remain central, but marketers increasingly need to incorporate AI-search visibility, generative AI referrals, social signals and earned media into the same business narrative.

This is creating opportunities for customer data platforms, marketing analytics platforms and AI visibility tools that can connect previously isolated signals.

The bigger challenge is organizational. Marketing, communications, sales and finance often use different definitions of success. Without shared data models and attribution rules, even sophisticated technology can produce conflicting answers.

The 10Fold findings indicate that the next competitive advantage may therefore come from measurement governance rather than simply measurement volume.

Strategic Outlook

The rise of AI search makes the traditional marketing funnel harder to measure but potentially more important to understand.

A company can influence a buyer before that buyer ever visits its website. An AI-generated recommendation, analyst reference, social conversation or editorial article can shape consideration without producing an immediate attributable click.

That means B2B organizations will increasingly need to measure visibility, influence and business outcomes together.

For marketing leaders, the strategic goal should not be to eliminate every attribution challenge. It should be to establish a credible measurement framework that distinguishes correlation from causation, makes assumptions explicit and gives executives enough evidence to make investment decisions.

As AI becomes embedded across search, content and customer engagement, the organizations that can connect these new signals to established revenue systems will have a stronger case for marketing's contribution to growth.

Top Insights

• B2B marketers are expanding measurement into AI visibility and referral traffic, but only 38% connect these signals to pipeline or revenue influence.
• AI-generated citations are becoming executive-level visibility metrics as brands compete for recommendations and references across ChatGPT, Gemini and other AI systems.
• More than 80% of respondents say measurement influences marketing budgets, despite only 49% expressing high confidence in communications data accuracy and completeness.
• Integrated reporting remains a major weakness, with just 35% reporting unified measurement across earned, paid social, content and digital channels.
• The emerging communications scorecard connects visibility, engagement, authority, action and business impact rather than treating media activity as an isolated outcome.

Get in touch with our MarTech Experts

Globant has appointed Sarab Narang as CEO of Glob.AI, a new AI-native technology services model that aims to change how enterprises purchase, deploy, and manage artificial intelligence services.

The initiative represents a significant change in the traditional IT services model. Instead of pricing technology work primarily around employee hours, project teams, or software seats, Glob.AI is designed around AI Pods—service units operated by groups of AI agents under human supervision—with pricing tied to output or consumption.

The approach reflects a broader transformation taking place across enterprise technology services. Generative AI and agentic AI are increasingly capable of handling software development, research, data analysis, customer support, content creation, and other workflows that previously required large teams of specialists.

The challenge for services companies is no longer simply adding AI tools to existing delivery models. It is determining whether AI can fundamentally change how technology work is organized, delivered, governed, and priced.

Glob.AI is Globant's answer to that question.

The company says its AI Pods combine AI-native execution with the governance and enterprise delivery experience developed through Globant's 23 years in the technology services market. Humans remain involved in supervision and governance, while AI agents perform defined tasks within the service model.

That distinction is important for enterprise buyers. Fully autonomous AI services remain difficult to deploy across complex organizations because of concerns surrounding security, accountability, accuracy, compliance, and integration with existing systems.

A human-supervised agentic model attempts to balance automation with organizational controls.

Narang brings more than two decades of experience across agentic AI, generative AI, machine learning, enterprise software, and management consulting. Before joining Globant, he served as vice president of central product management at ServiceNow, where he worked on agentic and generative AI products and go-to-market strategies.

His previous experience includes more than five years at Amazon Web Services (AWS), where he ultimately served as general manager and global head of generative AI and machine learning go-to-market. During his AWS tenure, he was involved with enterprise AI capabilities including Amazon SageMaker and Amazon Bedrock.

Earlier, Narang spent 12 years at KPMG, leading AI, machine learning, and technology engagements while developing new offerings and global delivery organizations.

That background gives Glob.AI an executive who has worked across several layers of enterprise AI: consulting, cloud infrastructure, software products, go-to-market strategy, and now AI-native services.

Globant says Glob.AI is already showing commercial momentum. As of June, the initiative's annual recurring revenue had increased approximately 60% in a single quarter, while its pipeline reached $436 million. Adoption had also extended to 45% of Globant's top 20 accounts.

Those figures are company-reported rather than independent market measurements, but they indicate that Globant is testing the AI-native services model with existing enterprise customers rather than positioning it solely as a future concept.

The strategy puts Globant into a competitive market that includes traditional IT services companies such as Accenture, IBM, Capgemini, and Tata Consultancy Services, as well as cloud providers and enterprise software vendors increasingly building AI agents into their platforms.

The competitive landscape is changing because AI can potentially reduce the labor intensity of some technology services. Traditional services companies have historically generated revenue by combining skilled personnel with consulting expertise and delivery capacity. If AI agents can perform portions of that work at significantly lower marginal cost, services providers may need to rethink pricing and revenue models.

Glob.AI's output- and consumption-based pricing is therefore one of the more consequential elements of the announcement.

Pricing based on outcomes rather than hours could align vendor incentives more closely with customer results. But it also introduces new questions around how output is defined, measured, verified, and governed. Enterprise buyers will need transparency into what AI agents are doing, how performance is evaluated, and where human oversight remains necessary.

The model also has implications for enterprise marketing and customer experience teams.

AI Pods could eventually support functions such as marketing analytics, content operations, campaign optimization, customer research, software development, personalization, and customer engagement. For organizations already building AI into their MarTech stacks, an AI-native services layer could potentially sit between internal teams and the underlying technology platforms.

The larger trend is clear: enterprises are moving from experimenting with individual AI copilots toward designing agentic workflows that can execute multi-step business processes.

Glob.AI is attempting to package that shift as a service.

Market Landscape

The enterprise AI services market is becoming increasingly crowded as traditional consultancies, cloud providers, software companies, and technology integrators build AI capabilities.

Microsoft, Google, AWS, Salesforce, ServiceNow, and other enterprise technology vendors are embedding AI agents into their platforms. At the same time, consultancies such as Accenture and IBM are helping customers redesign business processes around generative and agentic AI.

Glob.AI's differentiation is its attempt to make AI agents the fundamental delivery unit of a technology services model rather than simply an additional tool used by conventional consultants and developers.

Its success will depend on whether enterprises are willing to purchase AI-generated outputs at scale, and whether Globant can maintain quality, governance, and accountability as the number of AI Pods grows.

Strategic Outlook

Glob.AI represents a bet that AI will change not only how technology work is performed but also how technology services are purchased.

If the model succeeds, enterprises could increasingly buy defined AI-driven outcomes rather than staffing projects around fixed teams or hours. That could alter the economics of consulting, software development, marketing operations, analytics, and managed services.

The appointment of Narang strengthens that strategy by bringing experience from AWS, ServiceNow, and KPMG. His immediate challenge will be turning Glob.AI's early customer adoption and pipeline into a scalable operating model while maintaining enterprise-grade governance.

The broader industry will be watching the economics closely. AI-native services promise faster execution and potentially lower costs, but enterprises will ultimately judge the model on measurable business outcomes—not the number of agents deployed.

Top Insights

• Glob.AI shifts technology services toward AI Pods, combining autonomous agents with human oversight and potentially changing how enterprises purchase technology work.
• Output-based pricing challenges hourly consulting models, creating opportunities for efficiency while raising new questions about performance measurement, quality, and accountability.
• Sarab Narang brings enterprise AI experience, having held leadership roles at ServiceNow, AWS, and KPMG across AI products and go-to-market strategies.
• Glob.AI is showing early commercial traction, with Globant reporting 60% quarterly ARR growth and a $436 million pipeline as of June.
• AI-native services could reshape enterprise operations, particularly software development, analytics, marketing, customer engagement, and other knowledge-intensive workflows.

Get in touch with our MarTech Experts

The AI infrastructure buildout shows little sign of slowing, even as technology companies and enterprises face growing questions about power availability, supply chains, hardware costs, and the economic return from artificial intelligence.

Worldwide data center capital expenditure is forecast to exceed $3 trillion by 2030, according to Dell'Oro Group's Data Center IT Capex 5-Year July 2026 Forecast Report. The research firm says the outlook has increased sharply from its January 2026 forecast, driven by higher hyperscaler spending guidance, stronger expectations for global data center power capacity, and rising commodity costs.

At the center of that spending wave are high-end accelerators used in AI-optimized servers.

Unlike conventional enterprise computing, modern AI workloads require large quantities of specialized processors for model training and inference. NVIDIA's GPUs have become the most visible example, but the broader accelerator market also includes alternatives from AMD, Google, Amazon, and other semiconductor vendors.

Dell'Oro expects these high-end accelerators to represent the largest portion of data center capital expenditure through 2030. That makes AI hardware a key determinant not only of data center construction but also of the economics of cloud computing and enterprise AI adoption.

The scale of the projected investment is significant. The largest U.S. hyperscalers alone could account for roughly half of global data center capex, according to Dell'Oro Vice President of Research Baron Fung.

That concentration highlights the growing influence of companies such as Amazon, Microsoft, Google, and Meta on the global AI infrastructure market. Their expanding AI services require enormous quantities of compute, networking, storage, and power, creating a feedback loop between AI demand and data center investment.

Yet the next stage of the buildout may look different from the first.

The initial AI infrastructure surge has largely been associated with training increasingly sophisticated models. As generative AI applications mature, inference is expected to become a much larger component of workloads. AI agents, enterprise copilots, recommendation systems, search experiences, and real-time applications can all generate sustained inference demand.

Dell'Oro expects general-purpose servers to benefit from that expansion, along with growing storage and agentic AI workloads. That suggests data center spending will increasingly extend beyond specialized GPU clusters.

The change matters for enterprise technology buyers. AI infrastructure is not simply about purchasing accelerators. Production AI systems require conventional CPUs, storage, networking, virtualization, orchestration, databases, cybersecurity, observability, and increasingly sophisticated cooling and power systems.

The result is a more heterogeneous computing environment.

Dell'Oro says accelerated and heterogeneous computing, combined with improvements in server efficiency, could help offset some of the rising costs and infrastructure requirements associated with AI. In other words, increasing AI demand does not necessarily mean every workload will run on the most expensive accelerator available.

The industry is also seeing a new customer segment emerge: AI-specialized cloud providers, or neoclouds.

Dell'Oro projects this segment, which includes AI model builders and neocloud service providers, to grow at nearly 60% compound annual growth through 2030. These providers are building infrastructure specifically around accelerated computing and AI workloads, creating an alternative to conventional hyperscale cloud services.

That growth could reshape the competitive landscape.

Companies that need AI capacity but cannot justify building large private clusters can increasingly rent specialized infrastructure from providers optimized for GPU workloads. At the same time, hyperscalers are investing heavily in their own AI infrastructure and developing custom silicon to reduce dependence on third-party accelerators.

The result is likely to be a highly competitive market spanning hyperscale clouds, neocloud providers, semiconductor companies, server manufacturers, networking vendors, and data center operators.

Power, however, may become the industry's most significant physical constraint.

AI accelerators consume substantial amounts of electricity, and high-density AI clusters can require different power and cooling architectures than conventional data center deployments. Building new capacity can also take years because of grid interconnection requirements, permitting, equipment availability, and local infrastructure constraints.

Dell'Oro's forecast specifically identifies power availability and supply-chain conditions as factors that could influence the pace of future investment.

For technology companies and enterprise buyers, the implication is straightforward: AI infrastructure decisions are becoming long-term capital allocation decisions rather than short-term IT purchases.

The question is no longer whether AI will require more compute. It is how much compute will be economically sustainable, where that capacity will be located, which workloads deserve specialized acceleration, and how quickly the industry can convert infrastructure investment into measurable business value.

Market Landscape

The AI infrastructure market is expanding across several layers simultaneously.

At the hardware level, NVIDIA, AMD, Google, Amazon, and other chipmakers are competing to supply accelerators and custom AI silicon. At the infrastructure level, hyperscalers such as Microsoft Azure, AWS, and Google Cloud are expanding data center capacity while specialized neocloud providers target customers with demanding GPU workloads.

Meanwhile, enterprise organizations are beginning to invest in AI infrastructure for private and hybrid environments. This creates demand for platforms capable of managing both traditional workloads and accelerated AI computing.

Dell'Oro's forecast suggests that high-end accelerators will remain the largest spending category through 2030, but growth in inference, agentic AI, storage, and general-purpose computing indicates that the AI data center will become increasingly heterogeneous.

Strategic Outlook

The $3 trillion capex projection illustrates both the opportunity and the risk surrounding the AI infrastructure boom.

Investment can create the capacity needed to support a new generation of AI applications, but infrastructure spending must eventually be justified by utilization and economic returns. Dell'Oro notes that enterprise investment remains constrained by uncertainty around AI returns, while hyperscalers continue to drive a disproportionate share of global spending.

The next phase of the market will therefore be defined by efficiency as much as scale. Better accelerators, custom silicon, advanced cooling, optimized networking, workload scheduling, and efficient inference could determine which AI infrastructure investments deliver sustainable returns.

For enterprise technology leaders, that means AI strategy increasingly needs to account for infrastructure economics. Model selection, cloud architecture, data governance, workload placement, and compute utilization are becoming interconnected decisions.

Top Insights

• Global data center capex could exceed $3 trillion by 2030, underscoring the extraordinary infrastructure investment required to support expanding AI workloads.
• High-end AI accelerators remain the primary spending driver, making GPU availability, pricing, efficiency, and utilization critical concerns for cloud providers and enterprises.
• AI-specialized neoclouds are expanding rapidly, with Dell'Oro forecasting nearly 60% CAGR as organizations seek flexible access to accelerated computing.
• Inference and agentic AI will broaden infrastructure demand, increasing the importance of general-purpose servers, storage, networking, and orchestration alongside accelerators.
• Power availability could constrain AI growth, forcing data center operators to prioritize energy efficiency, advanced cooling, and strategic infrastructure locations.

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Nutanix and ChronoScale Holdings have entered a strategic partnership to deliver what the companies describe as an enterprise-ready infrastructure stack for organizations deploying artificial intelligence at scale.

The agreement combines Nutanix's hybrid cloud and agentic AI software portfolio with ChronoScale's accelerated computing platform, enterprise AI foundry, and GPU-as-a-Service (GPUaaS) capabilities. The companies say the objective is to simplify the infrastructure required to move AI applications from proof-of-concept environments into production.

The partnership comes at a time when enterprises are discovering that deploying AI is not simply a model-selection exercise. Organizations need compute capacity, virtualization, Kubernetes environments, data governance, security controls, inference infrastructure, and operational tooling that can work together.

ChronoScale plans to use Nutanix software within its accelerated computing platform for functions including customer onboarding, tenant management, service automation, virtualized infrastructure, managed Kubernetes, and AI services. The companies also intend to maintain demonstration and proof-of-concept environments where customers can evaluate agentic AI workloads before committing to production deployments.

The infrastructure strategy is particularly focused on the growing demand for GPU capacity.

AI training and inference workloads can require substantially more compute resources than conventional enterprise applications. Yet many organizations do not want to purchase enough accelerators to cover occasional demand, particularly as hardware generations and AI architectures evolve rapidly.

ChronoScale plans to address that issue through two consumption models. Its GPU-as-a-Service offering is intended to provide reserved capacity for predictable workloads, while its Token Factory uses prepaid inference tokens backed by open-source models for burst and experimental workloads.

The companies plan to connect those services with Nutanix's enterprise AI capabilities, including Nutanix Agent Gateway and Private Inferencing. The stated goal is to provide a unified control plane that can span infrastructure deployed inside an enterprise and additional GPU capacity accessed through ChronoScale.

That hybrid model could become increasingly important as enterprises try to balance performance with control over sensitive data.

For regulated industries and organizations handling proprietary information, sending every AI workload to a public cloud is not always desirable. Keeping agents, enterprise data, and workflow state within an organization's own environment can provide greater control over security, governance, compliance, and data sovereignty.

The partnership takes that concept further through ChronoScale Foundry, which Nutanix plans to make deployable inside customer environments. Foundry is positioned as an enterprise AI platform for building, operating, and governing agentic workflows locally.

Customers are expected to be able to deploy ChronoScale Foundry through the Nutanix Kubernetes Platform Catalog. That approach potentially reduces one of the operational barriers associated with deploying AI agents: assembling multiple infrastructure and software components before developers can begin building applications.

Agentic AI is also changing infrastructure requirements. Conventional generative AI applications often involve a user submitting a prompt and receiving an answer. Agentic systems can instead execute multi-step tasks, interact with enterprise systems, retain workflow state, and make decisions within defined boundaries.

That creates a need for infrastructure capable of supporting persistent workflows, orchestration, security policies, model access, and enterprise data integration.

Nutanix is positioning its broader AI portfolio around that transition. Its competition includes infrastructure and cloud platforms from Microsoft Azure, Amazon Web Services, Google Cloud, VMware by Broadcom, and Red Hat, as well as specialized GPU cloud providers. NVIDIA also occupies a critical position in the ecosystem through its GPU hardware, networking, software, and AI development stack.

The Nutanix-ChronoScale relationship is closely tied to NVIDIA. ChronoScale is an NVIDIA Cloud Partner, while Nutanix is an NVIDIA technology partner and independent software vendor with NVIDIA-validated software for enterprise AI infrastructure.

ChronoScale says it plans to deploy NVIDIA HGX B300 systems, NVIDIA Spectrum-X networking, and NVIDIA AI Enterprise software, including NVIDIA NIM microservices and NVIDIA NeMo. This creates a vertically integrated approach spanning accelerated compute, networking, AI software, enterprise virtualization, and managed services.

The competitive significance is not simply access to NVIDIA hardware. Many cloud and infrastructure providers can offer NVIDIA accelerators. The differentiator is increasingly the layer surrounding those GPUs: provisioning, orchestration, security, workload management, model deployment, governance, and the ability to move workloads between environments.

For enterprise IT and marketing technology leaders, that infrastructure layer matters because AI applications are increasingly becoming embedded in customer engagement, analytics, automation, content generation, sales operations, and decision support. The underlying AI infrastructure can determine how quickly those applications can scale and how effectively organizations can control their data.

Market Landscape

The enterprise AI infrastructure market is moving toward a hybrid model in which organizations combine private infrastructure, public cloud resources, and specialized accelerated-compute providers.

Nutanix's strength lies in its hybrid multicloud management and enterprise infrastructure software, while ChronoScale is positioning itself around high-performance AI compute and managed AI services. Together, the companies are targeting enterprises that want more flexibility than a conventional on-premises deployment but greater control than a fully public-cloud AI architecture may provide.

NVIDIA's expanding software ecosystem also changes the competitive landscape. Components such as NIM and NeMo can shorten the path from AI models to production applications, while validated infrastructure designs can reduce integration uncertainty.

The market is consequently moving beyond the question of which AI model to use toward a broader question: what infrastructure architecture can run AI securely, economically, and at production scale?

Strategic Outlook

The Nutanix-ChronoScale partnership reflects a broader industry shift from AI pilots toward AI factories and production-grade agentic infrastructure.

The companies' combined proposition is designed around three requirements: access to scalable GPU capacity, enterprise control over AI workloads, and simplified deployment. Whether that translates into a durable competitive advantage will depend on pricing, workload performance, ecosystem breadth, and how easily enterprises can migrate between on-premises and external compute.

For CIOs and enterprise technology teams, the more significant trend is the emergence of AI infrastructure as a strategic layer of the enterprise technology stack. As AI moves into everyday business processes, organizations will need infrastructure that is not only powerful but also governable, observable, and economically sustainable.

Top Insights

• Nutanix and ChronoScale are combining hybrid cloud software with accelerated compute, targeting enterprises that need scalable AI infrastructure without surrendering control over workloads.
• GPU-as-a-Service expands AI capacity, allowing enterprises to access external accelerators for predictable or burst workloads rather than relying entirely on owned hardware.
• ChronoScale Foundry extends agentic AI into private environments, keeping enterprise data, agents, and workflow state within customer-controlled infrastructure.
• NVIDIA's ecosystem anchors the architecture, combining HGX B300 systems, Spectrum-X networking, NIM, and NeMo with Nutanix enterprise infrastructure software.
• AI infrastructure is becoming a strategic enterprise layer, as production AI requires orchestration, governance, security, compute economics, and workload portability.

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Brick Marketing will exhibit at The MedTech Conference 2026, hosted by AdvaMed, from October 18 through October 21 at the Thomas M. Menino Convention and Exhibition Center in Boston. The agency will be located at Booth 637, where representatives are expected to discuss digital marketing strategies aimed at medical device companies and other organizations operating across the medtech ecosystem.

AdvaMed describes The MedTech Conference as a global gathering for companies shaping the future of medical technology. The 2026 event is scheduled for October 18–21 in Boston and is expected to bring together executives, investors, clinicians, innovators, policymakers, and industry partners.

For Brick Marketing, the event is less about introducing a new software product and more about positioning marketing execution as an important part of how specialized technology companies compete for attention.

The agency says it will use the conference to discuss SEO strategy, AI SEO, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), content planning, paid advertising, social media, email marketing, website development, marketing consulting, and fractional CMO services.

That combination reflects a broader change in B2B marketing. Search is no longer limited to conventional results pages. Prospective customers increasingly use conversational AI tools to research vendors, compare technologies, understand technical subjects, and identify potential partners before speaking with a sales representative.

Forrester reported in 2026 that 87% of B2B buyers identified generative AI conversational search tools as a meaningful interaction in the buying process. Gartner, meanwhile, found that 45% of surveyed B2B buyers used generative AI to gather information about vendors and products, while 69% preferred to validate AI-generated insights with sales representatives.

The figures point to an important distinction for medtech marketers: AI search does not eliminate traditional marketing channels. Instead, it adds another layer to an already complicated buying journey.

Medical technology companies often sell products and services that require substantial explanation. Engineers may care about technical specifications, clinicians about clinical relevance, procurement teams about cost and implementation, executives about business outcomes, and investors about market opportunity. A single product page is unlikely to address all of those information needs effectively.

This creates a role for structured content strategies spanning technical articles, case studies, executive thought leadership, service pages, frequently asked questions, company background information, and other authoritative resources.

Brick Marketing says its approach combines strategic planning with execution. Rather than treating SEO, content, advertising, email, social media, and website development as isolated activities, the agency says it assesses business goals, sales objectives, existing marketing performance, internal resources, and target audiences before establishing priorities.

That model places the agency within a crowded digital marketing services market. Large enterprise platforms such as Salesforce, Adobe, and Microsoft offer increasingly broad marketing and customer engagement capabilities, while specialist agencies and consultancies compete on strategy, implementation, and domain expertise. For medtech companies, the distinction is often less about owning another marketing platform and more about connecting existing technologies and content into a coherent customer journey.

The same challenge exists across enterprise marketing stacks. A company might use a CRM, marketing automation platform, analytics system, advertising platforms, content management system, and customer data infrastructure from different vendors. Google remains central to traditional search discovery, while AI-driven experiences are changing how information is surfaced and summarized. Amazon and other large digital ecosystems also demonstrate how quickly search, recommendations, commerce, and personalization can converge.

Brick Marketing's emphasis on AI SEO, GEO, and AEO therefore reflects a wider transition from keyword-centric optimization toward entity visibility, authoritative content, structured information, and answer-oriented publishing.

The agency's presence at The MedTech Conference also highlights a practical challenge for specialized B2B marketers: visibility alone is not enough. A company can attract search impressions without generating qualified conversations if its messaging does not explain who it serves, what it offers, why its expertise matters, and what prospective customers should do next.

For enterprise marketing teams, that means AI search should be treated as part of a broader digital discovery strategy rather than as a replacement for SEO or paid acquisition. The strongest approach is likely to connect technical SEO, authoritative content, paid media, social distribution, email nurturing, analytics, and sales enablement.

The conference could give Brick Marketing an opportunity to test that proposition directly with medtech companies. More importantly, it illustrates how marketing agencies are adapting their services as AI changes the way B2B buyers discover and evaluate suppliers.

Market Landscape

The medtech sector operates in a particularly information-intensive environment. Buyers often need evidence, technical detail, credibility, and multiple forms of validation before moving toward a commercial discussion.

That makes the shift toward AI-assisted discovery significant. Forrester says 95% of B2B buyers plan to use generative AI in at least one part of a future purchase, with more than half reporting that AI helped them consider additional or different vendors while saving time.

McKinsey's latest B2B research also points toward increasingly complex buyer journeys. Respondents reported using an average of 10 channels throughout the buying process, reinforcing the need for consistent messaging across digital, remote, and in-person interactions.

For medtech marketers, the competitive advantage is therefore shifting from simply publishing more content to building a credible information ecosystem that works across Google search, AI answer engines, websites, social channels, sales interactions, and industry events.

Strategic Outlook

Brick Marketing's appearance at The MedTech Conference reflects a broader evolution in digital marketing services: agencies are increasingly being asked to help companies become discoverable across both conventional search and generative AI environments.

The long-term opportunity will depend on execution. AI optimization cannot compensate for weak technical foundations, thin content, unclear positioning, or fragmented customer journeys. Medtech companies that combine subject-matter expertise with structured digital content, strong brand authority, measurable campaigns, and coordinated sales and marketing operations are better positioned to benefit as discovery becomes increasingly AI-mediated.

Top Insights

• Brick Marketing's MedTech Conference presence highlights how specialized B2B agencies are adapting SEO and content services for AI-driven buyer discovery.
• AI search is becoming part of B2B research, forcing medtech marketers to create authoritative information that both humans and answer engines can understand.
• Medtech buying journeys involve multiple audiences, making coordinated content, technical SEO, thought leadership, and sales enablement increasingly important.
• Enterprise marketing teams face growing channel complexity, strengthening the case for integrated strategies across search, advertising, CRM, email, analytics, and content.
• AI does not replace human validation: Gartner's research shows buyers still rely on sales representatives to assess and confirm AI-generated information.

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Connected TV advertising has moved beyond simple genre and keyword targeting, but advertisers still face a basic challenge: knowing what is actually happening inside the video content surrounding their ads. Anoki AI is addressing that gap with ContextIQ, an AI-powered CTV planning platform that analyzes streaming video at the scene level. The platform has now been named AI Marketing Copilot of the Year in the 9th annual MarTech Breakthrough Awards.

Anoki AI, a video artificial intelligence company, has received the AI Marketing Copilot of the Year award for its ContextIQ platform, highlighting the growing role of multimodal AI in connected TV advertising and media planning.

ContextIQ is designed to analyze streaming video beyond conventional metadata such as genre labels, keywords and program categories. Its underlying AI technology examines visual elements, audio, dialogue, objects, emotional tone and narrative context within individual scenes.

The company's central proposition is straightforward: advertisers should be able to understand the actual content surrounding an advertisement rather than relying primarily on broad program-level classifications.

That distinction is becoming increasingly important as CTV advertising expands and marketers demand greater transparency, targeting precision and brand-safety controls across fragmented streaming environments.

According to Anoki, ContextIQ approaches video as a multimodal understanding problem. Its Copilot interface then turns that analysis into actionable media-planning recommendations.

From Genre Targeting to Scene-Level Context

Traditional CTV targeting often relies on information such as program genre, channel, audience demographics or keyword-level contextual signals.

Anoki is attempting to make the individual scene the unit of contextual analysis.

The company's AI evaluates multiple dimensions of video content, allowing advertisers to identify specific moments that align with their brand positioning or campaign objectives.

That could change how contextual CTV planning works.

Consider a sportswear brand promoting a new running shoe. A conventional contextual strategy might target sports or fitness programming. A scene-level system could potentially identify moments featuring running, outdoor activity, athletic performance or other relevant visual and narrative signals, creating a more granular connection between creative and content.

The result is closer to creative-to-context matching than conventional audience targeting.

ContextIQ Copilot Turns Briefs Into Media Plans

The Copilot component is where Anoki's video understanding becomes a media-planning workflow.

Media buyers can enter natural-language requests, campaign briefs, product information or full RFPs. The system then identifies contextually relevant scenes and produces recommendations aligned with brand objectives and suitability requirements.

Advertisers can refine the plan through a conversational interface rather than repeatedly rebuilding targeting parameters.

The platform also provides explanations for its recommendations, scene previews and information about content filtered out under brand-safety requirements.

That transparency is important.

AI-powered targeting can create a new layer of complexity if advertisers receive recommendations without understanding why a particular environment was selected. By exposing the underlying scene, Anoki is attempting to make AI recommendations more inspectable for media buyers.

Creative Becomes a Targeting Signal

One of ContextIQ's more interesting capabilities is Creative Analyzer, which analyzes an advertiser's own video creative.

The system evaluates elements such as visuals, audio, dialogue, objects, emotional tone and narrative structure. Anoki says those signals can then be used to generate CTV recommendations that correspond with the brand's creative.

This creates a two-sided contextual model.

Instead of asking only, "What content is this?" advertisers can also ask, "What does my creative communicate, and where does that meaning fit?"

That could be particularly useful for large campaigns where creative variants are designed for different audiences, products or stages of the customer journey.

It also fits a wider trend in advertising technology toward multimodal AI, where systems analyze text, images, video and audio together rather than treating each format independently.

Why Scene-Level CTV Measurement Matters

CTV has grown into a major component of the digital advertising ecosystem, but measurement and transparency remain persistent challenges.

Unlike web advertising, where marketers can often access granular page-level information, streaming environments can provide less visibility into the precise content surrounding an ad. Multiple platforms, publishers, applications and supply-side technologies can further complicate the media supply chain.

Anoki's approach attempts to address part of that problem through contextual intelligence.

The company says Copilot allows buyers to see actual scene previews before committing media dollars, understand the reasoning behind recommendations, assess brand suitability and forecast available scale.

Those capabilities could help shift contextual CTV from a broad targeting technique into a more transparent planning discipline.

Competing With the Broader CTV Technology Stack

Anoki operates in a market that includes CTV measurement companies, contextual advertising platforms, demand-side platforms and supply-side technology providers.

Companies such as DoubleVerify, Integral Ad Science and Comscore provide various forms of advertising verification, measurement, audience intelligence and media-quality analysis. Meanwhile, major advertising platforms are increasingly incorporating AI into campaign planning and optimization.

Anoki's differentiation is its focus on video understanding at the scene level.

That does not necessarily make ContextIQ a replacement for traditional verification or DSP technologies. Instead, it can sit earlier in the media-planning process, helping marketers determine which video environments are contextually appropriate before activation.

The ability to connect those recommendations with premium CTV supply and supply-side platforms could determine how valuable the technology becomes in practice.

AI Is Becoming a Planning Interface

The award also points to a broader change in marketing technology.

Natural-language interfaces are increasingly becoming the front door to complex advertising systems. Instead of navigating dozens of targeting settings, media buyers can increasingly describe the outcome they want and allow AI to translate that intent into campaign parameters.

The important question is whether the AI has enough underlying intelligence to produce reliable recommendations.

Anoki's approach is based on its video-understanding layer. The Copilot is essentially an interface sitting on top of that contextual intelligence.

That architecture resembles a broader enterprise AI pattern: specialized models and data provide domain expertise, while conversational agents make that capability accessible to users who do not need to understand the underlying technical system.

For CTV marketers, that could reduce the complexity of contextual planning while increasing the amount of evidence available before a campaign launches.

Market Landscape

CTV advertising is evolving from broad audience and program-level targeting toward more granular contextual, content and supply-path intelligence.

The market includes DSPs, SSPs, measurement companies, identity platforms and specialized contextual providers. As streaming inventory expands, marketers are increasingly looking for ways to understand not just who is watching, but what the viewer is watching at the moment an ad appears.

Anoki's scene-level approach addresses that second question.

The competitive advantage will ultimately depend on the depth and accuracy of video analysis, the breadth of inventory that can be classified and whether media buyers can activate contextual recommendations efficiently across major CTV supply partners.

Strategic Outlook

Multimodal AI could become one of the more important technologies shaping CTV advertising over the next several years.

Video contains far more information than a genre label can capture. Scene composition, dialogue, objects, sentiment, audio and narrative context can all influence whether an environment is appropriate for a particular brand.

As AI becomes capable of interpreting those signals at scale, contextual advertising can become more precise without necessarily depending on individual-level identity data.

That creates an attractive proposition in a privacy-conscious advertising environment.

For enterprise marketers, the longer-term opportunity is a CTV planning stack in which AI can translate a campaign brief into contextual environments, explain each recommendation, forecast scale and pass activation-ready signals into media-buying systems.

ContextIQ is positioning itself toward that future.

Top Insights

• Anoki's ContextIQ uses multimodal AI to analyze streaming video scene by scene, giving CTV advertisers more granular contextual intelligence than traditional genre-based targeting.
• The platform's Copilot translates natural-language briefs into contextual CTV plans, reducing manual planning while providing explanations and previews for recommended environments.
• Creative Analyzer connects an advertiser's own video content with relevant CTV scenes, creating a more direct relationship between campaign creative and contextual media placement.
• Scene-level video understanding could strengthen brand suitability and transparency as advertisers seek alternatives to increasingly fragmented audience targeting and identity-based signals.
• Anoki's award highlights a broader shift toward AI interfaces that turn complex advertising data and planning systems into conversational workflows for media buyers.

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AI marketing tools are becoming easier to build, but access to reliable shopper data and distribution remains difficult. Checkmate is betting that the next generation of marketing automation will combine both. The company has brought mate, its AI growth platform for ecommerce teams, out of stealth after signing more than 700 brands and reaching a reported $15 million annualized run rate.

Checkmate has publicly launched mate, an AI-powered growth platform designed to help ecommerce marketing teams analyze customers, identify high-intent shoppers, create campaigns and drive purchases.

The company says mate had been operating quietly on the business side while Checkmate's consumer shopping platform expanded. More than 700 brands, including Everlane, Billabong, Brooklinen, Malbon and JD Sports, are now using the platform, according to the company.

Checkmate also says the business is profitable and on track to exit the year at an approximately $15 million annualized run rate, without spending on product marketing or announcing a new financing round.

The more important part of the launch, however, is the architecture behind mate.

Rather than presenting another AI-powered marketing dashboard, Checkmate describes mate as a suite of AI agents that can execute tasks across the marketing lifecycle. The platform operates on top of a network of more than 100 million shoppers and over 12 billion shopper-intent signals, according to the company.

That combination of intelligence and distribution is becoming increasingly important as marketers face a fragmented customer-acquisition environment.

From Marketing Dashboard to AI Growth Teammate

Most marketing technology platforms still require marketers to interpret data, decide what action to take and then execute campaigns through separate systems.

Mate is designed around a different model.

Its six initial agents cover customer intelligence, competitive benchmarking, campaign creation and delivery, customer enrichment, AI-search visibility and reporting. The stated goal is to move from analytics and recommendations toward execution.

That places mate within the rapidly expanding market for AI marketing agents, where platforms from Salesforce, Adobe, HubSpot and other vendors are increasingly automating parts of campaign planning, content production and customer engagement.

The competitive challenge is that AI agents themselves are becoming less differentiated.

Large language models have made campaign generation, copywriting and analysis increasingly accessible. What becomes harder to replicate is the data and distribution infrastructure connected to those agents.

Checkmate is making that infrastructure the centerpiece of its positioning.

First-Party Shopper Data Becomes the Moat

The company says its network spans more than 100 million consumers across its app, browser extension, email, SMS, desktop products and publisher relationships, including NBCUniversal.

That gives mate something many standalone AI marketing tools lack: an external source of shopper behavior and a mechanism for reaching those consumers.

The distinction matters as marketers navigate the continuing decline of traditional third-party tracking.

Apple's Safari and Mozilla's Firefox have long restricted third-party cookies, while Google's Chrome strategy has evolved around user choice and alternative privacy technologies. European regulators have also increased scrutiny of cookie-consent practices.

The result is a more fragmented advertising ecosystem in which brands have less visibility into consumers outside their own properties.

Forrester and other research firms have consequently emphasized the growing importance of first-party data and identity strategies as marketers adapt to privacy changes.

Mate's proposition is to combine customer intelligence with activation. Instead of simply telling a marketer which shoppers appear likely to buy, the platform aims to identify those shoppers and place campaigns in front of them.

AI Search Adds Another Acquisition Channel

The launch also comes as AI changes how consumers discover products.

Checkmate cites Adobe Analytics data showing that traffic from AI sources to U.S. retail sites increased 393% year over year in the first quarter of 2026.

The shift is strategically significant because consumers increasingly use AI systems to research products, compare alternatives and discover brands. Those journeys do not necessarily begin on Google Search or a social platform where a conventional advertiser can bid for attention.

That creates a new category of marketing work: AI-search visibility.

Mate includes an agent specifically focused on this area, suggesting Checkmate sees AI discovery as part of the growth stack rather than simply an SEO problem.

The distinction is increasingly relevant to ecommerce marketers. Traditional search optimization is centered on ranking webpages, while AI-driven discovery can involve product feeds, structured information, brand authority, reviews and the likelihood that an AI system recommends a particular product.

Performance Pricing Changes the Equation

Mate's pricing starts at $199 per month, with individual agents available separately and growth services priced according to performance.

That pricing structure could make the platform accessible to smaller ecommerce teams while giving larger brands a way to align some marketing costs with measurable outcomes.

The company cites several early customer results, including $480,000 in net-new revenue and 2,450 orders for Everlane over 30 days, as well as a 40% revenue increase for Kind Patches.

Those figures are company-reported results rather than independently verified performance benchmarks, so marketers should evaluate the underlying methodology, attribution model and campaign conditions before comparing them with other platforms.

That caveat is important in an AI marketing market where vendors increasingly promote revenue outcomes rather than traditional engagement metrics.

The Real Competitive Advantage May Be Distribution

The broader strategic bet behind mate is that AI capability is becoming commoditized while access to quality data and customers is becoming more valuable.

A marketing agent can generate a campaign in seconds. That does not necessarily mean it can identify the right consumers, obtain permission to reach them, place the campaign in an appropriate environment and connect exposure to a transaction.

Checkmate's consumer and business products create a closed loop: shopper signals inform marketing decisions, the company's network provides distribution, and purchase behavior feeds back into the system.

That model could give mate an advantage over AI marketing assistants that sit entirely inside a brand's existing data stack.

It also creates a dependency on the quality, scale and consent framework surrounding Checkmate's shopper network.

What It Means for Ecommerce Marketing Teams

For enterprise marketers, mate represents a broader direction in marketing technology: AI agents connected to proprietary data and activation channels.

The winning platforms may not be those with the most impressive generative AI demonstrations. They may be the ones capable of connecting intelligence to execution while maintaining privacy, attribution and measurable commercial outcomes.

Checkmate's early traction gives it an interesting position in that race.

The company's challenge now is scaling beyond its initial network and proving that its performance model can deliver consistently across industries, customer segments and economic conditions.

If it succeeds, mate could become less like another marketing SaaS application and more like an AI-powered growth layer sitting between shopper intelligence and media distribution.

Market Landscape

The AI marketing platform market is moving rapidly toward autonomous agents that can analyze data, create campaigns and execute workflows. Salesforce's Agentforce, Adobe's AI capabilities and HubSpot's AI tools are examples of the broader shift toward AI-assisted and agent-driven marketing operations.

The key differentiator is increasingly access to proprietary data and distribution.

Marketing teams can obtain AI-generated content from many vendors. They have fewer options for acquiring consented shopper signals and connecting those signals directly to customer acquisition.

That is where Checkmate is positioning mate differently from conventional marketing automation platforms. Its competitive proposition is not simply the intelligence layer; it is the combination of AI agents, shopper data and owned distribution.

Strategic Outlook

The next generation of ecommerce marketing technology will likely be defined by the integration of AI agents, first-party data, media activation and AI-search visibility.

Marketing teams will still need traditional CRM, CDP, analytics and advertising infrastructure. But AI agents could increasingly sit above those systems, translating customer signals into decisions and executing workflows automatically.

Mate's model suggests another possibility: platforms may increasingly bring their own audience networks rather than relying entirely on brands to supply data and media infrastructure.

That could become particularly valuable as privacy restrictions and fragmented discovery channels make traditional customer acquisition more difficult.

The question for Checkmate will be whether its shopper network creates durable performance advantages and whether marketers trust an AI system to make increasingly consequential decisions about customer acquisition.

Top Insights

• Mate combines AI marketing agents with Checkmate's shopper network, giving ecommerce teams access to customer intelligence and distribution within one growth platform.
• More than 700 brands reportedly use mate, giving Checkmate early validation as marketers search for AI systems that execute rather than simply recommend.
• The platform's first-party shopper signals could become a competitive advantage as privacy restrictions make traditional third-party tracking and audience targeting harder.
• Mate's AI-search visibility agent reflects a changing discovery environment where consumers increasingly research products through generative AI instead of conventional search.
• Performance-based growth pricing shifts attention from AI-generated campaigns toward measurable commercial outcomes, although customer-reported results require independent validation.

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Enterprise digital experience platforms are becoming more complex as companies adopt headless architectures, composable systems and AI-driven applications. Dataweavers is expanding into EMEA with an Azure-native platform operations model designed to address a related challenge: how enterprises can modernize their digital experience infrastructure without giving up control over data, security or deployment environments.

Dataweavers, a Microsoft-first and Azure-native platform company, is expanding its operations into Europe, the Middle East and Africa (EMEA) as enterprise demand grows for secure and sovereign infrastructure supporting modern Digital Experience Platforms (DXPs).

The company is adding senior staff in London while expanding support for customers and partners working with Sitecore, Contentstack and Optimizely. The move comes as enterprises increasingly adopt composable and headless DXP architectures, alongside AI capabilities that can increase the complexity and volume of digital traffic.

Rather than positioning itself as another DXP vendor, Dataweavers operates at the infrastructure and platform-operations layer. Its proposition is to manage the underlying cloud environment while allowing enterprises to retain control of their Azure infrastructure and data.

That distinction is becoming more important as digital platforms move from conventional web publishing toward API-driven architectures and AI-enabled experiences.

Dataweavers Builds Its EMEA Team

To support its regional expansion, Dataweavers has appointed Nigel McHugh as Senior Account Executive and Kingsley Hibbert as Senior Solutions Architect.

McHugh brings 15 years of experience in enterprise DXP architecture, consulting and sales, while Hibbert will work with technology leaders and digital teams to align business objectives with technical strategy.

The appointments suggest the company is targeting complex enterprise transformation projects rather than smaller web-hosting deployments.

For organizations running global websites and digital commerce operations, DXP modernization increasingly involves coordinating content management, APIs, cloud infrastructure, identity, security, analytics and front-end applications. The infrastructure layer can become a significant operational burden when internal teams are simultaneously migrating from monolithic platforms to composable architectures.

Arc Becomes Available Through Sitecore

Dataweavers is also making Arc by Dataweavers commercially available directly through Sitecore.

The arrangement gives Sitecore customers a procurement path to Dataweavers' platform operations alongside their existing Sitecore and Scrunch investments, reducing the need for a separate vendor evaluation.

The timing is relevant as Sitecore continues developing its AI-oriented product strategy, including SitecoreAI.

For enterprises moving toward AI-enabled Sitecore environments, having the cloud infrastructure layer established before migration could reduce one element of deployment complexity.

The model also illustrates how DXP ecosystems are becoming increasingly interconnected. Content management platforms are no longer operating as isolated systems; they rely on cloud infrastructure, APIs, digital asset management, personalization, analytics and increasingly AI services.

Data Sovereignty Becomes a Competitive Factor

One of Dataweavers' central selling points in EMEA is data sovereignty.

The company says every deployment operates entirely within the customer's own Azure tenant rather than shared infrastructure. Enterprises retain control of their data while Dataweavers manages platform operations around that environment.

For multinational businesses, that can be an important distinction.

GDPR has made data governance a central concern across Europe, while organizations operating in Gulf markets face a growing patchwork of national data-protection and data-localization requirements. Requirements vary by jurisdiction and industry, but the common issue is that enterprises increasingly need greater visibility into where data is stored, processed and accessed.

A customer-controlled Azure environment does not automatically solve every regulatory requirement. Data residency, cross-border transfers, access controls, encryption, subcontractors and processing arrangements still need to be assessed against the applicable laws.

But keeping infrastructure inside an enterprise's own cloud tenant can provide a stronger foundation for governance than relying on a shared hosting environment.

AI Is Changing the Infrastructure Equation

The company's EMEA expansion also reflects a less obvious shift: AI agents could fundamentally change how enterprise digital platforms experience traffic.

Traditional websites primarily serve human users navigating pages through browsers. AI agents increasingly interact with content through APIs, structured data and machine-readable services.

That can produce very different traffic patterns.

An AI agent can make repeated API requests, retrieve large amounts of information quickly and generate unpredictable spikes in demand. For enterprises operating high-volume content or commerce platforms, infrastructure must therefore accommodate not only conventional web traffic but also machine-driven consumption.

This is one reason the infrastructure beneath a DXP is becoming strategically important.

Dataweavers says its platform is designed to handle high-frequency, unpredictable demand while maintaining security and governance requirements.

The challenge for enterprise technology teams will be balancing that flexibility with cost control. AI-driven traffic can create additional infrastructure consumption, and organizations will need monitoring and optimization mechanisms to understand which machine-driven requests generate meaningful business value.

Competing in the Modern DXP Infrastructure Layer

Dataweavers operates in a market that includes hyperscale cloud providers such as Microsoft Azure, Amazon Web Services and Google Cloud, alongside managed-service providers and specialist DXP infrastructure companies.

Its differentiation is the combination of Azure-native infrastructure, DXP specialization and managed platform operations.

That positioning may appeal to enterprises that already have strategic commitments to Microsoft Azure but do not want internal teams to manage every operational aspect of a complex Sitecore, Contentstack or Optimizely environment.

The company is also betting that data sovereignty will become increasingly important as organizations expand internationally.

What the Expansion Means for Enterprise Marketing Teams

For marketing and digital teams, infrastructure decisions can feel several layers removed from campaign execution. In practice, they influence page performance, API availability, content delivery, personalization and the reliability of customer-facing experiences.

That becomes even more important when AI is integrated into the DXP.

Marketing teams increasingly expect content platforms to support real-time personalization, automated content operations and AI-powered customer experiences. Those capabilities require infrastructure that can handle variable workloads while maintaining data controls.

Dataweavers' EMEA expansion reflects that convergence.

The company's challenge will be proving that its managed platform approach can deliver operational efficiency and resilience at enterprise scale while giving customers enough transparency and control over their environments.

If demand for composable, AI-enabled DXPs continues to accelerate, the infrastructure layer beneath those platforms could become an increasingly important part of the enterprise MarTech stack.

Market Landscape

The DXP market is moving away from traditional monolithic content management toward composable, headless and API-driven architectures. Sitecore, Contentstack and Optimizely are competing to provide the application and experience layers, while Microsoft Azure, AWS and Google Cloud provide the underlying infrastructure.

This creates a growing operational gap for enterprises that want the flexibility of composable platforms without taking on all the complexity of cloud architecture, security, scaling and observability themselves.

Data sovereignty adds another layer. European privacy requirements and evolving data regulations across Middle Eastern markets are pushing multinational organizations to scrutinize cloud architecture, processing locations and access controls more closely.

Dataweavers is positioning its Azure-native managed-services model directly within that intersection of DXP modernization, cloud operations, AI traffic and data governance.

Strategic Outlook

The next generation of enterprise DXPs will likely be defined as much by infrastructure as by content-management functionality.

AI agents, headless applications and API-driven customer experiences can generate new traffic patterns and operational requirements. At the same time, enterprises need stronger controls over data, identity, security and cloud environments.

That creates an opportunity for managed infrastructure providers that understand both the technology stack and the operational requirements of enterprise DXPs.

Dataweavers' EMEA expansion is an early bet on that market. Its success will depend on whether enterprises view customer-controlled cloud infrastructure and specialized DXP operations as a strategic advantage rather than simply another managed hosting option.

Top Insights

• Dataweavers is expanding across EMEA to support enterprises modernizing Sitecore, Contentstack and Optimizely environments with Azure-native managed platform operations.
• The company's customer-tenant deployment model targets growing enterprise concerns around data sovereignty, governance and infrastructure control across Europe and Middle Eastern markets.
• Arc by Dataweavers' availability through Sitecore simplifies procurement for customers adopting SitecoreAI while adding managed infrastructure to the broader DXP modernization journey.
• AI agents could generate higher-frequency API traffic, increasing demand for scalable infrastructure capable of supporting machine-driven digital experiences alongside conventional web users.
• Dataweavers is competing between hyperscale cloud providers and specialist DXP services by combining Azure expertise with managed operations and sovereignty-focused architecture.

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