LiveRamp has integrated NVIDIA AI infrastructure into its clean room environment, allowing brands and AI partners to train and run advanced machine learning models significantly faster. The move signals a broader shift in adtech: clean rooms are evolving from privacy tools into full-scale AI execution environments.

For several years, data clean rooms have been marketed primarily as privacy-safe collaboration platforms where advertisers, publishers, and data partners can analyze shared datasets without exposing raw user information.

Now, that category is expanding.

LiveRamp announced native support for NVIDIA AI infrastructure, upgrading its clean room architecture with GPU-optimized computing designed for large-scale model training and inference. The company says brands and AI partners can now run compute-intensive workloads at up to 15x faster speeds than CPU-based environments, while maintaining data controls and protecting proprietary models.

The announcement is significant because it moves clean rooms from passive measurement environments into active AI production systems.

Why GPUs Matter for Marketing AI

Modern AI models—especially those used for prediction, optimization, recommendation, and generative workloads—perform far better on graphics processing units (GPUs) than traditional CPUs.

GPUs are designed to handle massively parallel computations, making them ideal for workloads such as:

• Audience scoring models
• Campaign performance prediction
• Bid optimization systems
• Conversion propensity models
• Identity graph matching
• Generative content systems
• Multi-touch measurement models

Until now, many marketing clean rooms were built on CPU-centric infrastructure, which often slowed training times and limited more advanced model architectures.

By integrating NVIDIA accelerated computing, LiveRamp is attempting to remove that bottleneck.

What the Upgrade Changes

According to LiveRamp, AI partners can now bring existing models into its clean rooms without rewriting code for CPU environments.

That matters because model reengineering can be expensive, time-consuming, and technically risky.

The updated infrastructure allows brands and partners to:

• Train models on first-party data without exposing raw records
• Run inference at greater scale and speed
• Keep proprietary code and model weights private
• Collaborate with external model providers securely
• Activate AI insights through LiveRamp Marketplace integrations

For marketers, the practical benefit is faster experimentation and shorter optimization cycles.

Instead of waiting days for training runs or data preparation, teams may be able to iterate in hours.

Why This Matters for Enterprise Marketers

The pressure on CMOs and performance teams has changed. They are expected to use AI for measurable growth, not just experimentation.

That means marketing organizations increasingly need infrastructure that combines:

• Trusted first-party data
• Identity resolution
• Cross-channel activation
• Secure collaboration
• High-performance computing

Gartner and Forrester have both noted that first-party data strategies and AI readiness are becoming central to digital marketing competitiveness.

Without usable data foundations, many enterprise AI initiatives stall.

LiveRamp’s pitch is that brands already using its identity and collaboration network can now extend those assets directly into AI workflows.

Clean Rooms Become AI Platforms

The broader industry implication may be even larger.

Data clean rooms were initially adopted to replace third-party cookie-era targeting and enable privacy-compliant analytics with publishers such as retail media networks, commerce platforms, and walled gardens.

Now they are becoming environments where models can be trained directly against governed datasets.

That changes the value proposition from compliance to performance.

Competing or adjacent vendors in this space include:

• Google Ads Data Hub
• Amazon Web Services clean room ecosystems
• Snowflake collaboration environments
• Databricks lakehouse AI stacks
• Infosum

LiveRamp appears to be differentiating through a combination of identity graph scale, marketing network relationships, and now GPU compute access.

Why NVIDIA Benefits

For NVIDIA, the deal underscores how its AI infrastructure is spreading beyond traditional enterprise IT and into vertical SaaS and marketing technology.

Advertising systems increasingly rely on machine learning for audience modeling, dynamic creative optimization, pricing, fraud detection, and measurement.

That makes martech and adtech a growing downstream demand source for GPU compute.

Marketplace Expansion Signals Ecosystem Strategy

LiveRamp also noted recent expansion of its Marketplace to include data, models, AI applications, and agents.

This suggests a platform strategy where brands may eventually shop for AI models the same way they once licensed audience segments or data feeds.

If that model develops, clean rooms could become marketplaces for governed intelligence rather than just secure query environments.

Market Outlook

The next phase of marketing AI may depend less on chatbot interfaces and more on infrastructure.

Brands need places where sensitive customer data, identity signals, and advanced models can work together safely.

LiveRamp’s NVIDIA integration suggests the future of data collaboration platforms will be judged not only by privacy controls, but by how fast and effectively they help enterprises operationalize AI.

Market Landscape

Marketing infrastructure is converging across clean rooms, identity resolution, cloud data platforms, and AI compute. Vendors that combine trusted data access with scalable model execution may define the next generation of adtech and martech stacks.

Top Insights

•  LiveRamp integrated NVIDIA GPU infrastructure into its clean rooms for faster AI model training and inference.
• The company says workloads can run up to 15x faster than CPU-based environments.
• Brands can train models on first-party data without exposing raw data or proprietary model weights.
• Clean rooms are evolving from privacy tools into AI execution environments.
• Marketing teams increasingly need secure compute infrastructure, not just data access.

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Integral Ad Science has launched IAS Total TV, a new Connected TV (CTV) measurement suite designed to give advertisers what many have long wanted from streaming media: visibility similar to traditional linear television buying. The product aims to help marketers understand where ads run, what content surrounds them, and whether premium CTV spend is delivering measurable results.

Connected TV advertising has become one of the fastest-growing segments in media buying, but it still carries a persistent problem for enterprise marketers: transparency.

Advertisers have followed audiences from cable and broadcast into streaming platforms, shifting billions of dollars into ad-supported video environments. Yet many media buyers still struggle to answer basic questions about where their ads actually appeared, which programs delivered performance, and whether inventory quality matched premium pricing.

Integral Ad Science is attempting to solve that with the launch of IAS Total TV, a new suite of tools that combines content-level insights, verification signals, supply path intelligence, and outcomes measurement inside a single interface.

The company says the platform can provide aggregate show, genre, rating, language, and program-level data from major publishers including Disney, NBCUniversal, Paramount, and Prime Video, along with opted-in publishers using Publica.

Why Transparency Is a Growing CTV Issue

CTV has attracted premium brand budgets because it combines television-scale storytelling with digital targeting capabilities.

But unlike traditional linear TV, where buyers knew exactly which channels and programs they purchased against, streaming inventory has often been fragmented across apps, devices, exchanges, and programmatic pathways.

That fragmentation creates several recurring concerns:

• Limited visibility into content adjacency
• Difficulty validating premium inventory quality
• Complex cross-platform reporting
• Exposure to invalid traffic or misclassified devices
• Unclear attribution between impressions and outcomes

When CPMs are high, those blind spots become expensive.

According to Nielsen, as of Q4 2025, 74.2% of all U.S. TV viewing was ad-supported, while streaming accounted for 45.6% of that viewing mix, making it the largest share ahead of traditional TV. That shift explains why brands want TV-grade accountability in digital environments.

What IAS Total TV Does

IAS says the new platform gives marketers a unified view of campaign performance across streaming inventory.

Core capabilities include:

• Real-time aggregate show and program-level transparency
• Genre, rating, language, app, and platform insights
• Cross-channel suitability controls
• Third-party verification for quality and fraud prevention
• Supply path intelligence for spend efficiency
• CTV outcome measurement including reach and conversions

For marketers, that means campaigns can be optimized not only by audience targeting, but by content environment and inventory quality.

A brand may choose to appear in family-friendly programming, premium sports content, or specific language environments while avoiding unsuitable placements.

Why This Matters for Media Buyers

Large advertisers increasingly want CTV to behave like both television and digital media at the same time.

They expect:

• The contextual certainty of linear TV
• The measurability of digital advertising
• The accountability of performance media
• The scale of streaming ecosystems

That combination has been difficult to achieve because publisher data often sits in silos.

IAS is positioning Total TV as a neutral measurement layer between buyers and sellers, giving agencies and brands an independent source of truth.

That aligns with a wider market trend where advertisers demand third-party verification rather than relying solely on platform-reported metrics.

Competitive Landscape

The CTV measurement and verification space has intensified as ad budgets shift into streaming.

IAS competes or overlaps with players such as:

• DoubleVerify
• Nielsen
• Comscore
• The Trade Desk data ecosystem
• Amazon Ads streaming ad tools

IAS’s differentiator appears to be combining suitability, content transparency, verification, and outcomes measurement inside one workflow rather than treating them as separate tools.

Why Publishers Also Benefit

Streaming publishers face growing pressure to prove inventory quality while protecting viewer privacy.

IAS said the system is privacy-safe and compliant with the Video Privacy Protection Act (VPPA), an important consideration as advertisers seek deeper content data without exposing sensitive user-level information.

For publishers, verified transparency can justify premium pricing and attract larger brand budgets.

Market Outlook

CTV is no longer an experimental channel. It is becoming core media infrastructure for global advertisers.

As budgets rise, the market is moving into a more mature phase where transparency, fraud controls, suitability, and incrementality matter as much as reach.

That means the next winners in CTV may not simply be those with inventory, but those that can prove value clearly.

IAS Total TV enters the market at a moment when advertisers increasingly want streaming to deliver the trust they once associated with linear television—and the precision they expect from digital media.

Market Landscape

Connected TV is converging with programmatic advertising, measurement science, and premium brand media. Buyers now expect unified reporting across streaming platforms, while publishers need tools that protect pricing power and validate inventory quality.

Top Insights

•  IAS launched Total TV to provide linear-style transparency for connected TV campaigns.
• The platform includes content-level insights from Disney, NBCUniversal, Paramount, and Prime Video.
• Marketers can optimize by show, genre, language, rating, and platform context.
• CTV spend is rising, making third-party verification and premium inventory validation more important.
• Streaming media is entering a mature buying phase focused on accountability and outcomes.

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SignNow has introduced a new Docgen API designed to automate business document creation directly from live enterprise data sources such as CRM and ERP systems. The launch aims to remove one of the most persistent workflow bottlenecks in digital agreements: the manual work required between operational data and a signed document.

The e-signature market solved a major business problem over the past decade: replacing paper-based approvals with digital signatures. But for many enterprises, the bigger friction point now happens earlier in the process.

Before a contract is signed, it still has to be created.

That often means sales teams copying CRM fields into templates, operations staff verifying pricing tables, legal teams checking clauses, and finance manually routing approvals. While the signature itself may be digital, the preparation process remains highly manual.

SignNow is targeting that gap with the launch of its Docgen API, a developer-focused product that automatically generates contracts, quotes, forms, and agreements using live business data, then routes those documents directly into an eSignature workflow.

The product extends SignNow’s position beyond signatures and into the broader agreement automation market.

Why Document Creation Remains a Bottleneck

Many organizations already store the data needed to create contracts and proposals inside systems such as:

• Salesforce
• HubSpot
• Microsoft Dynamics 365
• SAP
• Oracle ERP environments

Yet many revenue and operations teams still move that information into documents manually.

That creates several recurring business problems:

• Slower sales cycles
• Data entry mistakes
• Inconsistent legal language
• Approval delays
• Higher operational costs
• Poor scalability as volume grows

For fast-growing companies, more contracts often means more headcount instead of more automation.

What the Docgen API Does

SignNow says the Docgen API allows developers and ISVs to generate documents dynamically using templates populated with live data from connected systems.

The platform also supports:

• Conditional logic for clauses or fields
• Dynamic product line items
• Automated approval routing
• Jurisdiction-specific agreement terms
• Direct handoff into eSignature workflows

That means a contract can be created automatically when a CRM opportunity closes, populated with the correct customer data, routed for internal approval if thresholds are exceeded, and then sent for signature without human intervention.

In practical terms, this turns multiple manual steps into a single automated workflow trigger.

Why Developers and ISVs Matter

The launch is notable because it targets developers and independent software vendors, not just business end users.

That strategy reflects a broader SaaS trend: infrastructure products that become embedded inside other software platforms can scale faster than standalone point solutions.

For example, vertical SaaS companies serving real estate, insurance, healthcare, logistics, or HR may want native contract generation and signature capabilities inside their own products rather than sending customers to separate apps.

The Docgen API gives those vendors a faster path to offer embedded agreement workflows.

That model has parallels with API-first platforms such as Stripe in payments or Twilio in communications.

Competitive Landscape

The agreement automation market has become increasingly crowded.

Major competitors and adjacent vendors include:

• DocuSign
• Adobe Acrobat Sign
• PandaDoc
• Dropbox Sign
• airSlate broader automation stack

Where SignNow may differentiate is by positioning itself as agreement execution infrastructure, combining generation plus signing in one programmable workflow.

That could appeal to enterprises seeking fewer disconnected tools.

Why This Matters for Revenue Teams

For sales organizations, document delays often translate directly into lost revenue momentum.

A contract that takes two days to prepare instead of two minutes can slow deal velocity, reduce win rates, and frustrate buyers.

Forrester and Gartner have both emphasized that buyer experience and sales process efficiency are increasingly tied to revenue performance.

The same logic applies to procurement, vendor onboarding, partner agreements, and HR forms.

If companies can automate data-driven document creation at scale, the ROI may come from faster cycle times as much as labor savings.

Market Outlook

The bigger shift is that e-signature is no longer enough.

Enterprises increasingly want end-to-end agreement operations: create, approve, sign, store, analyze, and renew.

That opens opportunities for vendors that can own the full lifecycle rather than just the signature moment.

SignNow’s Docgen API suggests the next phase of agreement software may be less about signing faster and more about eliminating every step before the signature appears.

Market Landscape

Digital agreement platforms are converging with workflow automation, CRM systems, CPQ tools, and developer APIs. Buyers now want full agreement lifecycle automation instead of isolated e-signature functionality.

Top Insights

•  SignNow launched Docgen API to automate contract and quote creation from live CRM and ERP data.
• The platform routes generated documents directly into eSignature workflows with no manual handoff.
• Developers and ISVs can embed document automation inside their own software products.
• Agreement automation is shifting beyond signatures into full pre-signature workflow orchestration.
• Faster document creation can improve sales velocity, compliance consistency, and operational scale.

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pharosIQ has introduced atlasIQ Intelligence at the Forrester B2B Summit North America 2026, positioning buyer intelligence as the next foundation for AI-driven go-to-market execution. The announcement reflects growing pressure on B2B revenue teams to replace broad intent signals with more precise, contact-level buying insights

For years, B2B marketers have relied on intent data to prioritize accounts, route leads, and guide demand generation campaigns. But as buying committees become larger, decision cycles more complex, and AI automation enters revenue operations, many organizations are discovering a core limitation: intent data often reveals interest, not actual buyers.

That challenge is creating demand for a new category of go-to-market intelligence.

pharosIQ announced atlasIQ Intelligence, a data intelligence offering designed to help enterprises identify real buying groups, understand decision formation at the contact level, and activate signals across sales and marketing systems in real time.

The company unveiled the platform during Forrester’s B2B Summit North America 2026 in Phoenix, one of the largest annual gatherings for B2B marketing, revenue, and product leaders.

Why Intent Data Is Losing Ground

Traditional intent data has become a staple of account-based marketing and pipeline acceleration strategies. Vendors typically infer demand using content consumption, keyword activity, ad engagement, or third-party browsing behavior.

That model helped fuel account-based marketing growth over the past decade.

But modern GTM teams increasingly need deeper answers:

• Who is actually involved in the purchase?
• Which contacts are influencing decisions?
• How advanced is the buying process?
• Which signals are meaningful versus noisy?
• When should sales engage?

Intent platforms can surface “in-market” accounts, but often struggle to identify real buying committees with enough precision for autonomous workflows or AI agents.

That gap is becoming more visible as revenue teams try to automate outreach, prioritization, and pipeline forecasting.

What atlasIQ Intelligence Does

pharosIQ says atlasIQ Intelligence is built to move from account-level intent toward decision-ready buyer intelligence.

According to the company, the platform combines:

• First-party engagement data
• Contact-level buyer activity signals
• AI-driven contextual modeling
• Continuous signal validation
• Real-time activation across GTM systems

The stated goal is to help companies identify active buying groups rather than anonymous account interest.

That distinction matters because B2B purchases often involve multiple stakeholders across finance, procurement, IT, security, and business units. Knowing an account is researching a category is useful. Knowing which five people are driving the decision is far more valuable.

Why It Matters in the AI Era

The timing of the launch reflects a broader market shift toward agentic GTM systems—AI-powered workflows that can score demand, personalize outreach, recommend next actions, and optimize pipeline programs with less manual intervention.

However, AI systems are only as strong as the data feeding them.

If models rely on weak or outdated intent signals, automation can amplify inefficiency rather than improve outcomes.

That is why many revenue leaders are refocusing on data quality, identity resolution, and first-party engagement sources.

Forrester and Gartner have both emphasized that B2B buying journeys are increasingly nonlinear, involving digital self-education, anonymous research, and consensus-driven decision making.

In that environment, static lead scoring and shallow account signals may no longer be enough.

Competitive Landscape

The buyer intelligence market overlaps with several established categories:

• Intent data platforms
• Account-based marketing software
• Revenue intelligence systems
• Sales engagement platforms
• Customer data platforms

Potential competitors or adjacent vendors include 6sense, Demandbase, ZoomInfo, Salesforce, and HubSpot.

pharosIQ appears to be differentiating by emphasizing first-party buyer intelligence and contact-level decision visibility rather than broad account scoring.

If successful, that could resonate with enterprise teams frustrated by inflated intent volumes that fail to convert into pipeline.

Growth Signals and Market Timing

The company also said it achieved double-digit organic year-over-year revenue growth in 2025, with continued momentum in 2026.

That claim is notable because many GTM technology vendors have relied on acquisitions for expansion. Organic growth may indicate stronger product-market fit if sustained.

The macro environment also favors measurable pipeline tools. CMOs and CROs are under pressure to prove ROI, shorten sales cycles, and align marketing spend directly with revenue outcomes.

Any platform that can improve buying group identification and reduce wasted targeting budgets could attract attention.

Market Outlook

The bigger story is not one product launch—it is the shift in how B2B demand generation is measured.

The next generation of GTM systems may care less about which company clicked and more about which humans are actually deciding.

That transition could redefine lead generation, ABM, sales prioritization, and pipeline forecasting over the next several years.

If that happens, buyer intelligence may become as important to revenue teams as CRM systems were in the previous era.

Market Landscape

B2B go-to-market technology is moving from account-level targeting toward identity-rich, AI-ready buyer intelligence. As automation expands, enterprises need data that explains not only where demand exists, but who is driving it and when to act.

Top Insights

•  pharosIQ launched atlasIQ Intelligence to help GTM teams identify real buyers rather than broad account intent signals.
• The platform uses first-party engagement data, AI modeling, and continuous validation for real-time execution.
• Intent data remains useful, but many teams now want contact-level buying committee visibility.
• Buyer intelligence is becoming critical for agentic sales and marketing automation systems.
• The shift could reshape ABM, lead generation, and pipeline forecasting categories.

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Hyland has appointed Tracy Roccasalva as Chief Marketing Officer, signaling a stronger go-to-market push as the enterprise content management (ECM) market shifts toward AI-driven content intelligence. The leadership move comes as software vendors race to reposition traditional content platforms for the next phase of enterprise automation.

The enterprise content management market is entering one of its most significant reset cycles in years.

Once centered on document storage, records compliance, and workflow digitization, ECM platforms are increasingly being recast as intelligence layers for AI systems. Enterprises now want platforms that can not only store content, but understand it, classify it, secure it, and activate it inside automated business processes.

Against that backdrop, Hyland has appointed Tracy Roccasalva as Chief Marketing Officer to lead its global marketing organization and sharpen its category positioning.

The company said Roccasalva will oversee worldwide go-to-market strategy as Hyland accelerates growth around its Content Innovation Cloud, a platform the company markets as AI-native.

Why the Appointment Matters

Executive marketing hires often reflect broader strategic priorities. In Hyland’s case, the move suggests the company sees market education and category creation as central to growth.

The ECM sector has become increasingly competitive as legacy content vendors face pressure from cloud-native challengers, workflow automation platforms, and hyperscale ecosystems from Microsoft, Google, and Adobe.

Meanwhile, enterprise buyers are reassessing how unstructured data—contracts, emails, PDFs, scanned forms, case files, invoices, media assets, and knowledge repositories—can fuel generative AI initiatives.

That creates an opportunity for vendors that can frame content not as archived data, but as strategic business infrastructure.

Hyland’s CEO Jitesh S. Ghai described the company’s ambition as leading the “content-powered Agentic Enterprise” category, language that reflects a broader industry trend toward AI agents executing workflows using enterprise data.

Roccasalva’s Enterprise Software Background

Roccasalva brings more than two decades of enterprise technology marketing experience across several major software and cybersecurity brands.

Her prior roles include leadership positions at:

• Informatica
• VMware
• RSA Security
• FireEye
• Cisco

Most recently, she served in senior marketing leadership at Ping Identity, where she helped guide the company through product transformation and pipeline growth.

That background is relevant because Hyland’s next growth phase likely depends on combining brand repositioning with measurable revenue execution. Modern B2B CMOs are expected to own both narrative and pipeline, especially in crowded enterprise categories.

ECM Is Becoming an AI Category

The bigger story may be what this appointment says about the ECM market itself.

Traditional enterprise content management focused on governance, retention, and process efficiency. The new AI era is changing buyer expectations.

Organizations increasingly want systems that can:

• Extract meaning from documents automatically
• Route content into workflows using AI agents
• Improve customer service with knowledge retrieval
• Automate compliance and classification
• Surface insights from decades of stored records
• Connect content to ERP, CRM, and HR systems

That is why many content vendors are repositioning around intelligent content services, automation, and AI orchestration.

Gartner has previously shifted market language from ECM toward content services platforms, reflecting how enterprise buyers prioritize modular, cloud-connected systems over monolithic archives.

Now, generative AI may accelerate another renaming cycle.

Why Marketing Will Be Critical

Hyland’s challenge is not only product innovation—it is category clarity.

Many enterprise buyers still associate ECM with back-office document management. But AI-era budgets may come from CIO modernization programs, customer experience teams, operations leaders, or line-of-business owners seeking automation gains.

That means marketing must translate technical capability into business outcomes such as faster onboarding, lower service costs, reduced compliance risk, and smarter decision-making.

Roccasalva highlighted that shift directly, saying enterprises are moving from AI experimentation to real execution.

That framing is important. Many software buyers in 2026 are no longer asking whether to use AI. They are asking where ROI can be proven first.

Competitive Landscape

Hyland competes in a broad field that includes legacy ECM vendors, intelligent automation providers, document cloud platforms, and adjacent enterprise suites.

Potential competitive pressure comes from:

• OpenText
• Microsoft SharePoint and Copilot ecosystem
• Box
• Adobe document workflows
• Salesforce workflow integrations

In this environment, differentiated messaging can be as important as feature parity.

Market Outlook

Hyland’s CMO appointment signals the company believes the next battle in enterprise content software will be won through AI positioning, ecosystem relevance, and demand generation discipline.

If enterprises increasingly treat content as fuel for AI agents and workflow automation, vendors with deep repositories of governed enterprise data may hold an advantage.

The challenge will be proving that old content systems can become modern AI infrastructure.

Market Landscape

The ECM market is converging with automation, search, knowledge management, and generative AI. Buyers want platforms that transform content from static storage into active intelligence embedded across enterprise workflows.

Top Insights

•  Hyland appointed Tracy Roccasalva as CMO during a major AI-driven transformation in the ECM market.
• The company is positioning content as the orchestration layer for AI-powered enterprise workflows.
• Roccasalva brings experience from Ping Identity, Informatica, VMware, and Cisco.
• Enterprise content platforms are evolving from archives into intelligence systems for automation.
• Marketing leadership will be key as vendors compete to define the AI-era ECM category.

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OneLayer is expanding its go-to-market strategy with the launch of the Sentry Partner Program, a new channel initiative designed to help systems integrators and service providers deliver Zero Trust security for private LTE and 5G networks. The move comes as enterprises accelerate adoption of private wireless infrastructure but face growing visibility, onboarding, and security gaps that traditional IT tools often fail to address.

Private cellular networks are moving from niche industrial pilots to mainstream enterprise infrastructure. Manufacturers, utilities, logistics operators, campuses, and public sector organizations are increasingly deploying private LTE and 5G networks to support connected devices, operational technology, robotics, and mission-critical communications.

But while connectivity adoption is accelerating, security models have lagged behind.

That gap is where OneLayer is placing its next growth bet.

The company announced the launch of its Sentry Partner Program, a formal channel ecosystem aimed at certifying integrators and managed service providers to deploy Zero Trust network access controls, automated device onboarding, and centralized visibility across private wireless environments.

Founding partners include Burns & McDonnell, Logicalis, World Wide Technology, MCA, STEP CG, and several specialist wireless infrastructure firms.

The announcement reflects a wider reality in enterprise networking: private 5G deployment is becoming easier, but securing thousands of devices across fragmented carrier and on-premise environments remains difficult.

Why Private Cellular Security Has Become a Priority

Unlike traditional corporate networks, private LTE and 5G environments often support mixed fleets of sensors, tablets, cameras, vehicles, handheld devices, and industrial equipment.

Many of those endpoints lack modern security controls or centralized identity management.

Enterprises also increasingly operate across multi-carrier APN environments, where devices connect through multiple public carriers alongside private wireless systems. That can create operational blind spots, inconsistent policy enforcement, and weak asset visibility.

OneLayer’s platform is designed to solve those challenges through:

• Zero Trust network access controls
• Automated SIM provisioning and onboarding
• Device discovery and inventory management
• Unified visibility across carriers and private networks
• Policy enforcement from a centralized console

For enterprises, the appeal is straightforward: secure every connected device, regardless of network origin.

Why the Channel Strategy Matters

Launching a partner program is a strategic move because private wireless buying cycles are heavily influenced by integrators, telecom consultants, managed service providers, and infrastructure specialists.

Unlike mainstream SaaS products, private 5G projects often involve hardware procurement, RF planning, carrier coordination, cybersecurity design, and long deployment timelines.

That means channel partners frequently control customer trust and implementation success.

By formalizing the Sentry Program, OneLayer is attempting to become embedded in that ecosystem rather than selling direct-only.

Partners receive benefits including:

• Sales and technical certifications
• Deal registration incentives
• Market development funds
• Performance bonuses (SPIFs)
• Access to OneLayer Scout for pre-deployment surveys and device inventories

That commercial structure mirrors mature channel programs from enterprise vendors such as Cisco, Palo Alto Networks, and Fortinet, suggesting OneLayer wants to scale through partners rather than build a large direct sales force.

Ecosystem Positioning in a Crowded Market

OneLayer also emphasized interoperability with a broad ecosystem including Ericsson, Nokia, Cisco, HPE Athonet, Druid, Celona, Digi, ServiceNow, and others.

That matters because the private cellular market remains fragmented. Enterprises often combine radio vendors, core software, security stacks, and device management platforms from multiple providers.

Vendors that integrate broadly rather than force rip-and-replace deployments may have an advantage.

Why Zero Trust Is Expanding Beyond IT Networks

Zero Trust security has largely been associated with corporate identity systems, cloud access, and remote workforce protection. But the next phase is expanding into operational environments where connected assets can create physical and financial risk.

Gartner and IDC have both identified industrial IoT and edge security as major enterprise priorities. Utilities, transportation firms, factories, and smart campuses increasingly need policy-based security for machine-connected networks.

That makes private cellular a natural next frontier.

If a compromised tablet, gateway, or field sensor can access critical systems, network segmentation alone may no longer be enough.

Market Outlook

OneLayer’s Sentry Program suggests the company sees partner-led expansion as the fastest route to market share.

As enterprises move from pilot deployments to large-scale production networks, demand will likely shift from connectivity-first buying toward security-first operating models.

That creates room for vendors that can answer three enterprise questions:

1. What devices are on the network?
2. Who should trust them?
3. How can policies be enforced at scale?

OneLayer is betting those questions will define the next stage of private LTE and 5G adoption.

Market Landscape

Private wireless infrastructure is converging with cybersecurity, asset management, and managed services. Network vendors provide connectivity, but enterprise buyers increasingly want full-stack solutions that combine coverage, onboarding, visibility, and Zero Trust controls.

Top Insights

•  OneLayer launched the Sentry Partner Program to scale Zero Trust security for private LTE and 5G enterprise deployments.
• Channel partners will help deliver onboarding, policy enforcement, and visibility across multi-carrier APN environments.
• Founding partners include WWT, Logicalis, Burns & McDonnell, and other enterprise infrastructure specialists.
• Private wireless adoption is shifting from connectivity-led pilots to security-driven production deployments.
• Zero Trust principles are expanding from IT networks into industrial and operational cellular environments.

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TeamViewer is expanding its enterprise IT automation strategy with new AI-driven scripting capabilities for Tia. Announced at the Gartner Digital Workplace Summit 2026 in London, the update allows Tia to turn previously resolved support incidents into reusable automations, a move that pushes TeamViewer closer to its vision of Autonomous Endpoint Management (AEM).

Enterprise IT teams have long struggled with a recurring problem: the same device issues appear repeatedly, technicians resolve them manually, and valuable remediation knowledge disappears once the ticket closes.

TeamViewer is attempting to break that cycle.

The company announced new AI scripting features for Tia, its TeamViewer Intelligent Agent, designed to learn from historical support sessions and convert successful fixes into ready-to-review automation scripts. The result is a system that can help IT departments standardize proven solutions, reduce repetitive help desk workloads, and respond faster to common endpoint problems.

The launch is significant because it moves TeamViewer beyond remote support software and deeper into autonomous IT operations—a fast-growing category that blends endpoint management, automation, observability, and AI assistance.

How Tia’s New AI Automation Works

According to TeamViewer, the new capability operates in two connected phases.

First, Tia analyzes historical support interactions and AI-generated session summaries to identify remediation steps that previously solved similar issues. Instead of relying solely on generic AI troubleshooting logic, the agent uses an organization’s own support history and environment context.

Second, once a support case is resolved, IT teams can instruct Tia to generate a script based on those remediation steps. Administrators can then review, refine, and deploy the automation across selected devices or groups.

That workflow turns tribal support knowledge into operational assets.

In practical terms, if a company repeatedly fixes printer driver conflicts, VPN failures, software crashes, or misconfigured system settings, Tia can help transform those repetitive manual fixes into repeatable automations.

Why It Matters for Enterprise IT Teams

For CIOs and IT operations leaders, repetitive endpoint support remains expensive and difficult to scale.

Gartner has consistently highlighted automation, employee digital experience, and endpoint resilience as top priorities for digital workplace leaders. Meanwhile, IDC has noted that support teams face growing device complexity as hybrid work expands across laptops, mobile devices, virtual desktops, and distributed endpoints.

That creates pressure to resolve more tickets without proportionally increasing headcount.

TeamViewer’s message is that every resolved ticket should improve future operations.

Instead of treating incidents as isolated events, Tia turns them into reusable playbooks that can reduce recurrence and shorten resolution times.

For enterprise IT teams, this could mean:

• Lower volume of repeat tickets
• Faster mean time to resolution (MTTR)
• More standardized remediation processes
• Reduced dependence on individual technician memory
• Greater scalability across global device fleets

TeamViewer ONE and the AEM Strategy

The announcement also reveals how TeamViewer is repositioning its broader platform.

Historically known for remote desktop connectivity, TeamViewer has increasingly expanded into digital workplace management through TeamViewer ONE, its unified platform combining remote support, endpoint visibility, AI guidance, and device management.

The company describes its roadmap as progressing through several stages:

1. Secure remote support
2. Real-time endpoint observability
3. AI expert assistance during support sessions
4. Knowledge capture from resolved incidents
5. AI-generated automations
6. Autonomous Endpoint Management

That mirrors a wider market shift where vendors are racing to unify previously separate categories such as Remote Monitoring and Management (RMM), Digital Employee Experience (DEX), endpoint management, and AI copilots.

Competitors in adjacent spaces include Microsoft with Intune and Copilot integrations, NinjaOne, ServiceNow, and VMware offerings.

TeamViewer’s differentiation appears to be grounding automation in real customer support history rather than purely synthetic AI recommendations.

Why Customer Data Matters in AI Support

Many enterprise buyers remain cautious about AI tools that produce generic or unverified actions. In IT environments, incorrect scripts can create outages, security gaps, or configuration drift.

By using previously successful internal fixes as source material, TeamViewer is positioning Tia as safer and more context-aware.

That could be important in regulated sectors such as finance, healthcare, manufacturing, and government where change control matters as much as speed.

It also aligns with a broader AI enterprise trend: domain-specific copilots trained on internal operational data often deliver stronger ROI than general-purpose assistants.

Market Outlook

Autonomous Endpoint Management is likely to become a major battleground in enterprise IT over the next several years.

As device fleets grow and skilled IT labor remains constrained, enterprises want platforms that can detect issues, recommend fixes, deploy remediations, and prevent repeat incidents automatically.

If TeamViewer can convert its remote support footprint into a broader automation platform, it could expand from a mature connectivity brand into a higher-value enterprise operations vendor.

The challenge will be execution. Buyers increasingly want open integrations, measurable ROI, and trustworthy AI controls—not just chatbot features.

Still, this release signals that TeamViewer sees the future of IT support less as screen sharing and more as self-healing digital workplaces.

Market Landscape

Endpoint management is converging with AI operations, observability, and employee experience software. Traditional RMM tools handled monitoring, while DEX platforms measured user friction. The next generation of platforms aims to unify detection, remediation, and automation into autonomous workflows.

Top Insights

•  TeamViewer added AI scripting to Tia, enabling support teams to convert successful fixes into reusable endpoint automations.
• The update advances TeamViewer’s Autonomous Endpoint Management roadmap beyond traditional remote support tools.
• Tia uses customer support history rather than generic AI advice, improving context and operational relevance.
• Enterprises could reduce repeat tickets, improve MTTR, and standardize IT remediation processes at scale.
• Autonomous endpoint operations may become a major growth category across hybrid workplace IT infrastructure.

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Assemble is expanding its executive communities, summit events, AI-enabled content offerings, and leadership team as demand rises for peer-driven decision support. The company says senior leaders are facing a new challenge: not a shortage of data, but too much information and too little actionable insight

Executives today have access to more dashboards, market reports, analyst notes, software alerts, and AI-generated recommendations than ever before. Yet many leadership teams still struggle to make timely, confident decisions.

That tension is creating a new category of enterprise demand: decision intelligence grounded not only in analytics, but in trusted peer experience.

Assemble, a company focused on what it calls peer intelligence, is positioning itself to capture that opportunity. The business announced a broad expansion across member communities, executive events, content products, and senior leadership as it scales operations in 2026.

The company also reported 38% year-over-year revenue growth in Q1 2026, suggesting growing enterprise appetite for curated leadership networks and practical market insight.

The timing reflects a wider shift in B2B markets. Traditional research sources remain valuable, but many executives increasingly want faster answers tied to real-world implementation. In volatile markets shaped by AI disruption, economic pressure, and changing buyer behavior, decision-makers often seek to understand what peers are doing now—not what worked last year.

That trend has helped fuel demand for communities, advisory networks, private executive forums, and benchmarking platforms.

From Information Overload to Decision Support

Assemble’s thesis is straightforward: leaders do not primarily need more information. They need higher-confidence decisions.

That message lands at a moment when generative AI tools from Microsoft, Google, Salesforce, and Adobe are flooding enterprise workflows with summaries, forecasts, recommendations, and content outputs.

While those tools can accelerate productivity, they also create a new management challenge: separating signal from noise.

Assemble’s answer is peer intelligence—structured access to senior operators sharing market-tested lessons, buying insights, and execution strategies.

This model differs from traditional analyst research firms or software dashboards. Instead of top-down reports, peer intelligence relies on practitioner knowledge from executives actively managing similar challenges.

Community Expansion Targets New Leadership Functions

Assemble said it launched three new boards over the past year:

• AEO Board
• Manufacturing Board
• Learning & Development Board

The move suggests the company is broadening beyond classic HR and leadership circles into emerging growth categories.

The AEO Board is particularly notable. Answer Engine Optimization (AEO) has become an important topic as brands adapt content strategies for AI systems such as ChatGPT, Google Gemini, and Perplexity. Executive communities focused on AEO indicate how quickly AI-driven search behavior is entering boardroom planning.

Manufacturing and L&D communities point to another shift: digital transformation is no longer confined to marketing or IT teams. Operations leaders, workforce leaders, and supply chain executives increasingly need strategic peer networks as automation changes core business functions.

Executive Events Become a Growth Channel

Assemble is also expanding its summit portfolio with new events including:

• North American Procurement Executive Summit
• North American Finance Executive Summit
• North American Learning Executive Summit
• North American Marketing Leadership Summit
• Additional fall edition of the North American HR Executive Summit

That strategy mirrors a wider B2B media trend. In-person executive gatherings have become one of the fastest-growing monetization channels for enterprise communities and information businesses.

Gartner and Forrester have long used events as premium engagement channels. Newer platforms are now combining memberships, events, and digital communities into recurring revenue ecosystems.

For Assemble, summits likely serve three purposes: lead generation, member retention, and premium sponsorship revenue.

AI-Enabled Content Moves Beyond Reports

The company said it is investing in AI-enabled benchmarking, best practices, and buying guidance.

That matters because enterprise content is changing rapidly. Static whitepapers and annual trend reports are giving way to dynamic, continuously updated intelligence products. Buyers increasingly expect real-time peer benchmarks, vendor comparisons, and implementation guidance.

IDC has repeatedly noted that enterprise buyers want faster access to decision-ready intelligence rather than large research libraries. Assemble appears to be aligning with that demand.

Leadership Hires Signal Scale Ambitions

Assemble also announced senior leadership expansion across content, finance, and product strategy.

Pete Buer joins with deep experience from CEB, now part of Gartner. He will oversee content strategy and event production.

Joyce Liu brings experience from CEB and Politico, including acquisition and growth-stage finance expertise.

Katrina Tofflemire was promoted to lead platform strategy, operations, and member experience.

Together, the hires suggest Assemble is evolving from a niche executive network into a scaled information platform.

Why It Matters for Enterprise Leaders

For enterprise decision-makers, Assemble’s growth highlights a broader market reality: trusted peer context is becoming a competitive asset.

Analytics tools can show what happened. AI tools can predict what might happen. Peer intelligence can explain what is working in practice.

As companies navigate AI adoption, budget scrutiny, hiring changes, and vendor sprawl, that combination may become increasingly valuable.

Market Landscape

Peer intelligence sits at the intersection of executive communities, research subscriptions, B2B events, and AI-powered advisory tools. Competitors include analyst firms, membership networks, private communities, and enterprise media brands. The next wave may favor platforms that combine human expertise with AI searchability and real-time benchmarking.

Top Insights

•  Assemble reported 38% Q1 revenue growth, signaling strong enterprise demand for decision support built around executive peer networks.
• New boards in AEO, manufacturing, and learning show peer intelligence expanding into high-priority business functions.
• Executive summits create recurring revenue through memberships, sponsorships, and premium relationship-driven experiences.
• AI-enabled benchmarking suggests static research is being replaced by real-time decision intelligence products.
• Leadership hires from Gartner-era CEB talent indicate ambitions to scale into a larger enterprise insights platform.

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