RingCentral is doubling down on its AI-first vision for contact centers—and analysts are taking notice. The cloud communications giant has been named a Leader in the IDC MarketScape: Worldwide AI-Enabled Contact Center Workforce Engagement Management 2025–2026, a recognition that underscores how seriously the market is taking AI’s role beyond chatbots and agent assist tools.

At the center of this recognition is RingWEM, RingCentral’s workforce engagement management suite, which is built natively into its AI-powered contact center platform, RingCX. Unlike many workforce tools that still rely on bolt-on integrations, RingWEM is designed as a core layer of the platform, handling forecasting, scheduling, quality management, and performance analytics in one cloud-native system.

Why IDC’s Call Matters

IDC’s MarketScape doesn’t just look at feature checklists. Its evaluations focus on how well vendors help organizations solve real operational problems—especially at scale. According to IDC Research Director Lou Reinemann, RingCentral stands out for enterprises with global footprints, hybrid workforces, and geographically distributed teams.

That’s a key point. Workforce engagement management has become significantly more complex as contact centers move away from centralized, on-premise models. Predicting demand across regions, managing agent performance remotely, and maintaining service levels while controlling labor costs are now table stakes. IDC’s assessment suggests RingCentral is meeting those demands with an architecture designed for modern, distributed operations—not legacy call center assumptions.

AI Moves From the Agent Desktop to the Ops Room

Much of the recent hype around AI in contact centers has focused on agent-facing tools: real-time prompts, automated summaries, and sentiment analysis. RingCentral’s strategy goes further upstream.

RingWEM applies AI to operational planning itself, using continuous forecasting models that adapt to changing demand patterns without heavy manual intervention. Instead of supervisors relying on static schedules and historical averages, the system dynamically adjusts staffing needs based on real-time and predictive inputs.

This shift has practical implications:

• Forecasting becomes adaptive, not periodic

• Scheduling reflects actual demand, not best guesses

• Quality management scales automatically, using AI-driven monitoring rather than random sampling

• Supervisors focus more on coaching, less on spreadsheets

In short, AI becomes part of how contact centers are run, not just how agents are assisted.

Native Integration as a Competitive Advantage

One of RingCentral’s most notable differentiators is that RingWEM is natively embedded within RingCX, rather than stitched together from multiple third-party tools. This matters more than it sounds.

Many enterprises today operate with fragmented contact center stacks—one vendor for routing, another for workforce management, another for quality assurance, and yet another for analytics. Each handoff introduces latency, data gaps, and administrative overhead.

By unifying forecasting, scheduling, screen recording, performance analytics, and quality management in a single platform, RingCentral is positioning RingCX as a system of record for both agent experience and customer experience. For large organizations, that can translate into faster decision-making, lower operational complexity, and more consistent service delivery across regions.

Labor Costs, Meet Automation

Labor remains the largest cost center for most contact operations. RingCentral’s pitch—and IDC’s validation—hinges on using AI to make workforce decisions more precise and less reactive.

AI-driven automation helps organizations:

• Reduce overstaffing during low-demand periods

• Prevent understaffing that hurts customer satisfaction

• Maintain consistent service levels without inflating headcount

• Align agent experience with performance outcomes

This approach reflects a broader industry trend: AI is increasingly being used to optimize economics, not just interactions. Vendors that can tie AI investments directly to cost control and productivity gains are gaining favor with enterprise buyers.

Executive Perspective: Practical AI, Not Flashy AI

RingCentral executives are framing this recognition as validation of a pragmatic approach to AI. According to Jim Dvorkin, SVP of Customer Experience Products, the company’s goal isn’t to showcase AI for its own sake, but to deliver “practical intelligence” that improves conversations and operational resilience.

That emphasis aligns with where many enterprise buyers are today. After years of experimentation, organizations are now asking harder questions: Does this AI reduce effort? Does it improve outcomes? Does it scale without adding complexity?

IDC’s Leader designation suggests RingCentral is answering “yes” often enough to stand out in a crowded market.

Market Context: Workforce Engagement Is Having a Moment

The timing of this recognition is telling. Workforce engagement management is emerging as one of the fastest-evolving segments of the contact center market, fueled by:

• Hybrid and remote work becoming permanent

• Rising customer expectations for speed and consistency

• Pressure to do more with leaner teams

• Maturation of AI models capable of real-time and predictive analysis

Vendors that treat WEM as a secondary add-on are increasingly at risk of falling behind. RingCentral’s decision to make RingWEM a core pillar of RingCX positions it well against rivals that still rely on fragmented ecosystems.

The Bigger Picture

Being named a Leader in the IDC MarketScape doesn’t guarantee market dominance—but it does signal credibility. For enterprises evaluating long-term contact center platforms, especially those with global operations, RingCentral’s AI-driven, integrated approach to workforce engagement is likely to resonate.

More broadly, this recognition highlights a shift in how the industry defines “AI-powered contact centers.” It’s no longer just about smarter agents. It’s about smarter planning, smarter staffing, and smarter use of human talent at scale.

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As U.S. privacy enforcement sharpens, consent banners alone are no longer enough—and regulators are making that clear. Ketch, the AI-powered privacy management company, has announced the general availability of Ketch Opt-Out Sync, a new capability designed to finally close one of the most persistent gaps in enterprise privacy operations: fragmented opt-out enforcement.

Opt-Out Sync extends Ketch’s Identity Sync framework, giving organizations a way to honor “Do Not Sell or Share” requests consistently across browsers, devices, and backend systems—without forcing consumers through repetitive forms or identity verification hoops.

Regulators Are Raising the Bar

Recent settlements involving companies such as Honda, Healthline Media, and Jam City have sent a strong signal from U.S. regulators: opt-out experiences must be frictionless, transparent, and comprehensive. Partial compliance—where some systems honor opt-outs while others quietly continue data sharing—is increasingly viewed as unacceptable.

Yet that partial enforcement remains common. Many organizations still rely on disconnected mechanisms that don’t talk to each other. A user may opt out via a consent banner, stopping browser-based tracking, while their data continues to circulate in CRM or email systems. In other cases, a webform opt-out flags backend records tied to an email address, but cookies, pixels, and ad trackers keep firing.

The result is growing compliance risk—and a failure to fully respect consumer intent.

From Front-End Signals to Back-End Enforcement

Ketch Opt-Out Sync is designed to eliminate these blind spots by connecting consent banners, DSR webforms, and backend systems into a single automated workflow. Built on Identity Sync, the system recognizes users across logged-in and logged-out states and applies opt-out choices everywhere data flows.

Crucially, this happens without requiring consumers to repeatedly identify themselves or submit multiple requests. One action is meant to govern all downstream use of their data.

What’s New Under the Hood

Ketch positions Opt-Out Sync as a shift from surface-level compliance to operational enforcement. Key capabilities include:

• Single-action opt-out enforcement, honoring “Do Not Sell or Share” requests without identity verification or repeat submissions

• Adaptive opt-out experiences, where forms dynamically adjust based on what the system already knows, minimizing unnecessary data collection

• Unified workflows for known and unknown users, eliminating parallel opt-out processes

• Automatically linked consent signals, ensuring banner choices and webform submissions stay in sync

• End-to-end, audit-ready enforcement, propagating opt-outs across CRM, CDPs, ad platforms, DSPs, and partners—with identity-based logs showing exactly how and where enforcement occurred

This approach reflects a broader industry realization: compliance isn’t just about collecting consent signals—it’s about enforcing them everywhere, consistently.

Why This Matters Now

As state-level privacy laws proliferate across the U.S., enforcement is becoming more aggressive and more technical. Regulators are no longer satisfied with symbolic compliance measures. They’re looking for proof that opt-outs actually stop data sharing across the full marketing and advertising ecosystem.

For brands, that means privacy tooling must integrate deeply with identity resolution, advertising infrastructure, and customer data platforms. Manual processes and siloed tools don’t scale—and they don’t stand up well in audits.

A Shift in Privacy Infrastructure

According to Max Anderson, Co-Founder of Ketch, the industry has focused too long on adding more banners and forms while leaving enforcement fragmented behind the scenes. Opt-Out Sync, he argues, flips that model by tying identity, consent, and enforcement together so a single opt-out actually governs how data is used everywhere it exists.

That framing aligns with a larger MarTech trend: privacy is becoming infrastructure, not just interface. Tools that can automate enforcement across increasingly complex data flows are quickly becoming essential—not optional.

For organizations under pressure to demonstrate real compliance, Ketch’s Opt-Out Sync positions itself as a practical answer to a problem regulators are no longer willing to overlook.

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As publishers grapple with shrinking referral traffic, zero-click search, and the growing influence of large language models, OpenX is reshaping its leadership bench to double down on publisher monetization and strategy. The omnichannel supply-side platform has announced two key moves within its Publisher Development organization, signaling where it sees the next phase of growth coming from.

Akhil Savani has joined OpenX as Vice President of Publisher Development, while Rebecca Bonell, a longtime OpenX executive, has been promoted to Regional Vice President of Publisher Development, The Americas. Together, they will oversee how OpenX works with publishers to expand supply, unlock new revenue models, and navigate a rapidly shifting ad and data landscape.

A Strategic Bet on Publisher-Led Growth

The appointments come at a moment when publishers are under pressure from multiple directions. Search traffic is increasingly intercepted by AI-generated answers, audience data strategies are being reshaped by privacy regulation, and monetization is fragmenting across formats like CTV, audio, and digital out-of-home.

OpenX is positioning its publisher development team not just as a sales function, but as a strategic partner helping publishers articulate and defend the value of their inventory and data. The company says its continued investment in leadership talent and global infrastructure is designed to help publishers adapt to emerging technologies while accessing incremental monetization opportunities.

Savani Brings Buy-Side Perspective to the SSP

In his new role, Savani will oversee publisher business development and account management across the Americas. Working closely with Bonell, he will focus on expanding high-quality, direct publisher supply across CTV, video, display, native, DOOH, and audio—with particular emphasis on strengthening OpenX’s connected TV offering.

Savani joins OpenX after nearly a decade at The Trade Desk, where he led global inventory partnerships and operational teams. His experience on both sides of the auction is notable at a time when publishers are increasingly questioning how their inventory is valued in automated marketplaces.

That dual perspective is expected to play a key role as OpenX works with publishers, OEMs, content owners, and virtual MVPDs to drive differentiated demand in CTV—an area where transparency, data access, and yield optimization are top priorities.

Bonell Expands Her Mandate Across the Americas

Bonell’s promotion formalizes a role she has effectively been playing for years. With close to a decade at OpenX, she has been instrumental in building long-term relationships with many of the company’s most strategic publisher partners.

As Regional VP, she will lead publisher business development across the Americas, focusing on onboarding new supply while deepening collaboration with existing partners. Her remit includes helping publishers clearly communicate the value of their inventory and audiences in an increasingly complex, insight-driven marketplace.

Bonell has consistently emphasized quality and trust over sheer scale—a stance that aligns with OpenX’s broader positioning as an SSP focused on fair value exchange rather than volume-first monetization.

Why This Matters for the Market

These leadership changes reflect a broader shift in the ad tech ecosystem. As AI reshapes discovery and consumption, publishers are being forced to rethink how they monetize attention and data. SSPs that simply facilitate auctions are no longer enough; publishers are looking for partners that can help them navigate privacy constraints, optimize yield across formats, and maintain control over their businesses.

By strengthening its publisher development leadership, OpenX is signaling that it sees publisher success as central to its own growth—particularly in high-growth environments like CTV and mobile, where competition among platforms is intensifying.

The move also underscores how talent with deep cross-market experience is becoming increasingly valuable. Savani’s buy-side background and Bonell’s publisher-first approach suggest OpenX is aiming to bridge longstanding gaps between demand and supply, especially as AI-driven decisioning becomes more prevalent.

Looking Ahead

With Savani and Bonell working in tandem, OpenX appears to be betting on a more consultative, partnership-driven model for publisher engagement. As zero-click search and LLM-powered interfaces continue to erode traditional traffic models, that approach may prove critical for publishers looking to protect and grow revenue in the next phase of digital media.

For OpenX, the message is clear: publisher development isn’t just about selling inventory—it’s about helping publishers survive and compete in an AI-shaped internet.

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Vonage is tightening the link between enterprise messaging and marketing automation. The Ericsson-owned communications platform has launched Vonage Conversations for Agentforce Marketing (formerly Salesforce Marketing Cloud), a new solution that embeds two-way messaging channels—including SMS, WhatsApp, and Rich Communication Services (RCS)—directly into Salesforce’s marketing environment.

The move reflects a broader shift in enterprise marketing: conversations, not campaigns, are becoming the primary unit of customer engagement. By integrating programmable communications APIs straight into Salesforce, Vonage is aiming to help brands meet customers where they already are—on messaging apps—without forcing marketers to jump between disconnected tools.

Messaging Becomes Native to the Salesforce Workflow

At its core, Vonage Conversations for Agentforce Marketing allows teams to manage personalized, two-way customer conversations from a single Salesforce interface. Marketers can send and respond to messages, automate interactions, and orchestrate omnichannel journeys using the customer data already stored in Salesforce.

The integration supports a blend of live agents and agentic AI, enabling always-on conversations that scale beyond human availability. Vonage positions this as hyper-personalization at scale: AI-driven interactions that adapt in real time while still allowing human intervention when needed.

Instead of treating messaging as a bolt-on channel, the solution makes it a native part of campaign execution, journey orchestration, and customer lifecycle management.

Why This Matters Now

Enterprise messaging is no longer just about alerts or one-way notifications. Consumers increasingly expect conversational, interactive experiences—and they expect them on the platforms they use daily.

Analysts see this embedded approach as critical. According to Pamela Clark-Dickson, Principal Analyst at the Mobile Ecosystem Forum, the real value of programmable communications emerges when messaging is built directly into everyday business platforms. Embedding AI-powered, omnichannel conversations into Agentforce Marketing allows enterprises to move faster, personalize more deeply, and drive more meaningful engagement without reworking existing workflows.

In practical terms, this means fewer handoffs between systems, faster execution of campaigns, and a clearer path from message delivery to measurable outcomes like clicks and conversions.

AI at the Center of the Conversation

Vonage’s communications APIs don’t just deliver messages—they automate decision-making around them. The platform analyzes customer data in real time to trigger relevant interactions, handle routine tasks, and maintain consistent messaging across channels.

With a single composer inside Salesforce, marketers can design and deploy conversations that adapt dynamically based on customer behavior. Vonage says this approach improves engagement rates while still respecting regulatory and compliance requirements across regions and channels—a growing concern as messaging becomes more interactive and data-driven.

RCS and WhatsApp Are Reshaping Business Messaging

The timing of the launch aligns with major shifts in global messaging behavior. RCS, often positioned as the successor to SMS, is becoming more visual and interactive, making it attractive for branded business communication. Global RCS traffic is projected to exceed 200 billion messages by 2029, signaling growing enterprise adoption.

At the same time, WhatsApp has become a cornerstone of business communication worldwide. Globally, 57 percent of consumers use WhatsApp to engage with businesses or service providers. In EMEA, WhatsApp adoption for business messaging now outpaces SMS, underscoring how quickly consumer preferences are evolving.

By supporting RCS, WhatsApp, and SMS within a single Salesforce-native experience, Vonage is betting that enterprises want flexibility without fragmentation.

Built for Enterprise Scale

Vonage emphasizes that its APIs are already trusted across industries such as retail, finance, and healthcare—sectors where reliability, performance, and compliance are non-negotiable. According to Christophe Van de Weyer, President and Head of Business Unit API at Vonage, the goal isn’t just to send more messages, but to help brands create meaningful, branded conversations across every customer touchpoint.

The integration is designed to support high-volume use cases while maintaining consistent performance, a requirement for global enterprises running complex, always-on customer engagement programs.

Part of a Broader AI Strategy

The launch also fits into Vonage’s larger AI Hub strategy—a portfolio of low-code and no-code components designed to accelerate digital transformation and enable personalized conversations across channels.

It builds on the existing Vonage Conversations for Salesforce offering, extending unified engagement across Agentforce Marketing, Sales, and Service through cross-cloud integration. It also complements Vonage Contact Center, positioning Vonage as an end-to-end player spanning marketing, service, and contact center interactions.

The Bigger Picture

As marketing platforms evolve, messaging is moving from the edge to the center of customer engagement strategies. Vonage’s integration with Salesforce Agentforce Marketing reflects that shift, blending AI, real-time data, and omnichannel messaging into a single operational layer.

For enterprises already invested in Salesforce, the appeal is straightforward: richer conversations, less complexity, and a clearer path to scalable personalization. For Vonage, it’s another step toward making programmable communications an invisible—but essential—part of how modern marketing gets done.

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Pavonis Group is betting that the next competitive edge in commercial real estate won’t come from more data—but from smarter ways to use it. The CRE technology integration firm has acquired CRE Marketing Hub, an AI-powered platform purpose-built for commercial real estate professionals, signaling a deeper push into industry-specific artificial intelligence.

The acquisition brings CRE Marketing Hub’s suite of AI agents into the Pavonis ecosystem, where it will operate alongside RealNex, Bid4Real, and Pix-Virtual. Together, these platforms are intended to cover the full CRE lifecycle, from research and prospecting to marketing, underwriting preparation, and transaction execution.

Why This Deal Matters

Commercial real estate has no shortage of tools, but many remain fragmented—CRM in one place, marketing automation in another, underwriting models somewhere else. Pavonis Group’s strategy is to reduce that fragmentation by embedding AI directly into operational workflows rather than treating it as a standalone add-on.

CRE Marketing Hub fits squarely into that vision. Founded by Ross and Tracee Jones, the platform focuses on AI agents trained specifically for CRE use cases, not generic marketing or productivity tasks. The Jones team will continue to collaborate with Pavonis Group, expanding the platform while supporting client adoption.

AI Agents Built for CRE, Not Generic Use

Unlike broad AI assistants, CRE Marketing Hub is designed around the day-to-day realities of brokers, investors, and advisors. Its tools span multiple stages of the deal and business development process, including:

• Client Insights, which uses AI-driven persona modeling to analyze prospects and client profiles

• Deal Coach, offering strategic guidance and preparation support for transactions

• Marketing Center, automating content creation, campaigns, and marketing collateral

Beyond these core modules, the platform includes a wide range of specialized utilities tailored to CRE workflows—translation tools, calculators, prompt libraries, social media content generation, scripts, property profiles, image libraries, and automated offering memorandum creation.

The emphasis is on reducing manual effort while preserving domain-specific accuracy, a key concern in an industry where nuance and local market knowledge matter.

“Real Intelligence,” Not Just AI

Pavonis Group Managing Partner Mark Kingston framed the acquisition as part of a broader philosophy he calls “Real Intelligence”—combining large language models with deep industry expertise.

In his view, AI tools are already reshaping how business gets done, but the real advantage goes to firms that not only adopt AI, but become experts in applying it within their domain. Pavonis believes that pairing CRE-specific AI agents with established platforms like RealNex can help clients deliver better service and compete more effectively.

That message reflects a growing trend across B2B software: buyers are becoming more skeptical of generic AI claims and more interested in solutions that demonstrate clear, industry-aligned outcomes.

Tight Integration With RealNex

A key part of the deal is the planned integration of CRE Marketing Hub into the RealNex operating system. RealNex already functions as a central platform for CRM, investment and lease analysis, presentations, and marketing within CRE organizations.

By embedding CRE Marketing Hub into RealNex, Pavonis aims to streamline how users ingest data, enrich existing records, conduct research, and execute business development strategies—all within a single environment. The goal is faster workflows, fewer handoffs, and improved transactional efficiency.

From prospecting to deal execution, AI agents are positioned to assist at each step, pulling insights from structured and unstructured data while maintaining continuity across systems.

A Broader Platform Play

The acquisition also strengthens the connective tissue between Pavonis Group’s portfolio companies. Bid4Real and Pix-Virtual address transaction and visualization needs, while RealNex serves as the operational backbone. CRE Marketing Hub adds an AI-driven intelligence layer on top, focused on decision support and automation.

This platform-first approach mirrors what’s happening in other vertical software markets, where vendors are consolidating capabilities to reduce tool sprawl and increase stickiness.

The Bigger Picture for CRE Tech

As CRE firms face tighter margins, slower deal cycles, and more demanding clients, automation and intelligence are becoming strategic necessities rather than nice-to-haves. AI that understands CRE-specific language, calculations, and workflows has the potential to meaningfully change productivity—not just incrementally improve it.

With this acquisition, Pavonis Group is positioning itself as a vendor that doesn’t just plug AI into CRE, but weaves it directly into how CRE professionals research, market, and transact.

For an industry often criticized for slow technology adoption, the deal underscores a clear message: specialized, workflow-aware AI is moving from experimentation to core infrastructure.

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Diligent is making a decisive move into one of the fastest-growing pressure points in governance, risk, and compliance. The GRC SaaS provider has acquired 3rdRisk, an AI-native third-party risk management platform based in the Netherlands, strengthening its position as a dominant player in enterprise risk technology.

The deal expands Diligent’s ability to help organizations manage increasingly complex vendor ecosystems—an area that has quickly escalated from an operational concern to a board-level priority. It also reinforces Diligent’s rare status as the only GRC vendor recognized as a “Leader” by all five major analyst firms: Gartner, IDC, Forrester, Chartis, and Verdantix.

Why Third-Party Risk Is Now a Boardroom Issue

Third-party risk management, particularly IT vendor risk, has become one of the fastest-growing segments within GRC. Enterprises today rely on sprawling networks of software providers, cloud platforms, data processors, and outsourcing partners—each introducing new regulatory, cyber, and operational risks.

Rising regulatory scrutiny, escalating cyber threats, and deeper digital dependencies have exposed the limits of manual vendor assessments and spreadsheet-driven oversight. Many organizations struggle to maintain real-time visibility into how external partners affect their overall risk posture.

That’s the gap Diligent is aiming to close with this acquisition.

What 3rdRisk Brings to the Table

Founded with an AI-first approach, 3rdRisk focuses on automating the most time-consuming aspects of third-party risk management. Its platform uses AI to handle vendor profiling, assessment workflows, and document analysis across contracts, certifications, and compliance artifacts.

Instead of quarterly review cycles, 3rdRisk is designed to give organizations a near real-time view of vendor performance and risk exposure. Diligent says this can help teams achieve audit readiness in weeks rather than quarters—a meaningful advantage as regulatory expectations continue to rise.

By integrating 3rdRisk into its broader platform, Diligent is extending risk visibility beyond internal controls to the full external ecosystem of suppliers and partners.

From the Boardroom to the Vendor Network

Diligent’s leadership frames the acquisition as a way to unify governance and execution. According to Scott Bridgen, General Manager of Risk and Audit at Diligent, combining Diligent’s AI platform with 3rdRisk’s capabilities creates a more holistic view of risk—one that spans from board oversight down to vendor-level dependencies.

That end-to-end perspective is increasingly important as boards demand clearer answers to questions about supply chain resilience, cyber exposure, and regulatory compliance tied to third parties.

Rather than treating vendor risk as a siloed function, Diligent is positioning it as an integral part of enterprise governance.

AI as an Enabler, Not a Replacement

3rdRisk’s leadership emphasizes that AI is meant to augment—not replace—human judgment. The platform is designed to eliminate manual processes that consume risk teams’ time, allowing professionals to focus on evaluating what actually matters.

According to Bram Ketting, co-founder and CEO of 3rdRisk, joining Diligent accelerates that vision by bringing global scale while preserving the domain expertise that customers expect. It also signals a broader industry trend: AI in GRC is moving beyond experimentation into core infrastructure.

Building on a Year of AI Expansion

The acquisition follows a year of rapid AI-driven product expansion at Diligent. Recent launches include:

• GovernAI, aimed at streamlining governance workflows

• AI Risk Essentials, focused on strengthening enterprise risk management

• AI-enhanced Diligent Entities, modernizing entity and subsidiary management

• ACL AI Studio, delivering faster, data-driven insights for audit and compliance teams

Adding third-party risk management to this lineup extends Diligent’s AI strategy into one of the most operationally complex areas of GRC.

Competitive Implications

The GRC market has become increasingly crowded, with vendors racing to bolt AI onto legacy platforms. Diligent’s approach—acquiring an AI-native specialist rather than retrofitting existing tools—suggests a push toward deeper, purpose-built capabilities.

As regulators and boards demand more continuous, defensible risk oversight, platforms that can unify internal and external risk data are likely to gain an edge. The Diligent–3rdRisk combination puts pressure on rivals that still treat third-party risk as an add-on module rather than a core capability.

The Bigger Picture

For enterprises, the message is clear: third-party risk is no longer peripheral. It’s central to governance, compliance, and resilience. By bringing 3rdRisk into its ecosystem, Diligent is betting that AI-driven, real-time visibility into vendor risk will become a standard expectation—not a premium feature.

As digital dependencies continue to grow, so will scrutiny. This acquisition positions Diligent to meet that moment with a broader, more integrated view of risk—one that extends well beyond the organization’s own walls.

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FiscalNote is drawing a clear line under its legacy era. The AI-driven policy and regulatory intelligence provider announced it has successfully migrated customers from its older FiscalNote platform to PolicyNote, marking a pivotal step in its effort to unify products under a single, AI-first experience.

For a company operating at the intersection of public policy, data, and artificial intelligence, the move is less about housekeeping and more about long-term positioning. PolicyNote is designed to be the centerpiece of FiscalNote’s product-led growth strategy, replacing fragmented tools with a cohesive platform that blends monitoring, analysis, forecasting, and drafting into one workflow.

In an industry where policy professionals are often juggling multiple dashboards, alerts, and reports, consolidation matters. FiscalNote is betting that fewer tools—when powered by deeper AI—can deliver more value.

Why the Migration Matters

Platform migrations are notoriously risky. Customers resist change, workflows break, and churn can spike. FiscalNote says it avoided most of those pitfalls.

According to the company, the transition was completed with minimal migration-related churn and has already resulted in stronger engagement across key features. That outcome is significant, especially as enterprise and government-facing software buyers tend to be conservative about tooling changes.

PolicyNote replaces the legacy FiscalNote platform with a unified environment where legislative tracking, regulatory intelligence, impact analysis, and forecasting live side by side. The goal is to reduce friction in daily research while giving users more actionable insights, faster.

From a market perspective, the move aligns with a broader shift in B2B intelligence platforms: customers increasingly expect AI to surface relevance, not just data. Raw information is abundant. Context is the differentiator.

AI Features Driving Engagement

FiscalNote shared several usage metrics that suggest PolicyNote’s AI-centric approach is resonating.

One standout is Impact Summaries, which automatically translate policy developments into organization-specific implications. Users are now generating a custom Impact Summary in 34% of search sessions—a signal that AI-generated synthesis is becoming a default behavior, not an optional add-on.

Advanced analytical tools are also seeing heavier use. Features such as Bill Comparison and Similar and Related Bills—designed to help users understand how legislation evolves and connects across jurisdictions—have nearly doubled in usage since launch. That suggests policy teams are leaning into deeper analysis rather than surface-level tracking.

Perhaps most telling is the reported 252% increase in weekly active users on PolicyNote. FiscalNote attributes that growth to tighter integration of AI-generated summaries, forecasting, impact analysis, and drafting tools directly into everyday monitoring workflows.

In practical terms, the platform isn’t just helping users read policy—it’s helping them decide what to do next.

From Monitoring to Anticipation

Customer feedback underscores the strategic shift. Clarence Mingo, Vice President of Corporate Affairs and Government Relations at The Marzetti Company, describes PolicyNote as a move from reactive monitoring to proactive intelligence.

According to Mingo, customized dashboards and real-time insights now centralize critical information in one place, allowing teams to stay ahead of emerging issues rather than scrambling after legislation advances.

That framing reflects a broader trend across regulatory and compliance technology. As policy environments grow more complex and faster-moving, organizations want tools that anticipate change, not just report it. AI-powered forecasting and impact analysis are increasingly table stakes, especially for global enterprises navigating multiple jurisdictions.

Product-Led Growth, Applied to Policy Tech

FiscalNote CEO and President Josh Resnik positions PolicyNote as more than a product refresh. He calls it the cornerstone of the company’s product-led growth strategy—a notable phrase in a sector traditionally driven by enterprise sales and long contracts.

By focusing on usability, integration, and AI-driven insights, FiscalNote appears to be borrowing from SaaS playbooks more common in MarTech and analytics platforms. The logic is straightforward: if users engage more deeply and more often, retention improves and expansion becomes easier.

Completing the migration also creates operational leverage. Supporting one primary platform instead of multiple legacy systems allows FiscalNote to ship features faster, iterate on AI models more efficiently, and maintain a more consistent customer experience.

Resnik frames the milestone as both validation and a launchpad—proof that the strategy is working and a foundation for faster innovation ahead.

Competitive Context: AI as the Differentiator

FiscalNote operates in a crowded market that includes traditional policy tracking services, boutique research firms, and emerging AI-native platforms. What sets PolicyNote apart, at least on paper, is the depth of AI integration across the workflow.

While many competitors layer AI summaries or alerts on top of existing databases, FiscalNote is positioning AI as the connective tissue of the platform—linking monitoring, analysis, forecasting, and drafting in a single environment.

That approach mirrors trends in adjacent markets like MarTech and RevOps, where point solutions are increasingly giving way to unified platforms promising fewer handoffs and smarter automation.

The risk, of course, is execution. AI-driven insights only build trust if they are accurate, transparent, and consistently useful. Early engagement metrics are encouraging, but sustained adoption will depend on how well PolicyNote performs as regulatory complexity—and scrutiny—continues to rise.

What This Signals for the Industry

FiscalNote’s completed migration highlights a broader inflection point for policy and regulatory intelligence. The market is shifting from information delivery to decision enablement.

Policy professionals no longer just want to know what happened. They want to understand why it matters, how it compares to related developments, and what actions to take next—all without stitching together half a dozen tools.

By consolidating its offerings into PolicyNote, FiscalNote is signaling confidence that AI-driven synthesis and forecasting will define the next generation of policy intelligence platforms.

Whether that bet pays off long term will depend on how effectively the company continues to evolve the platform. But for now, the migration milestone marks a clear strategic reset—and a strong statement of intent in an increasingly competitive space.

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Netradyne is pushing fleet intelligence closer to the road—literally. The AI-powered fleet safety and performance company has launched Video LiveSearch, an industry-first capability that lets fleet managers search live video across every vehicle in real time using natural language prompts.

Unlike traditional fleet video workflows that depend on cloud uploads and manual digging, Video LiveSearch runs directly on the vehicle using edge AI. The result: near-instant access to the most relevant video moments, without waiting hours—or days—for footage to process.

For fleets that live and die by response time, that’s a meaningful shift.

From Reactive Investigations to Real-Time Awareness

Until now, most fleet video reviews followed a familiar pattern: an incident is reported, someone figures out which vehicle might be involved, cloud footage is requested, and teams sift through long timelines hoping they’ve guessed the right window.

That reactive model slows investigations and limits how proactively fleets can manage safety, compliance, and performance.

Video LiveSearch is designed to flip that dynamic. Fleet managers can type a simple, free-text query—such as a safety event, roadside condition, or operational scenario—and instantly see the most relevant before-during-after clips across a single vehicle or the entire fleet.

Instead of hunting for video, teams get immediate line-of-sight into where to look and what to pull.

Natural Language Search, Powered at the Edge

What makes Video LiveSearch different is where the intelligence lives. Netradyne processes and indexes nearly 100% of road-facing drive time directly on the vehicle, using its edge AI hardware.

Because the video is already searchable at the source, LiveSearch doesn’t have to wait for cloud processing to begin returning results. Searches complete in seconds, even across large fleets.

Every query surfaces the top matching clips, allowing teams to download only the footage that matters. The days of guessing vehicle IDs, timestamps, or trip details—and hoping for the best—are largely removed from the workflow.

According to Netradyne CEO and co-founder Avneesh Agrawal, this gives fleets “faster situational awareness to proactively understand what’s happening across their operations,” enabling quicker action and safer, more efficient outcomes.

Context-Aware AI That Understands the Road

Under the hood, Video LiveSearch relies on Netradyne’s context-aware edge intelligence, trained on real-world road scenes and driving behavior. The system doesn’t just match keywords—it interprets scenarios.

That allows a simple prompt to surface relevant video tied to operational and safety use cases such as school bus stop-arm compliance, proof of service, cracked windshield detection, or claims support. Crucially, it also delivers surrounding context, showing what happened before, during, and after the moment of interest.

This kind of semantic understanding is becoming increasingly important as fleets generate massive volumes of video data. Without smarter filtering, more cameras simply mean more noise.

Hardware as a Physical AI Sensor

Video LiveSearch is enabled by Netradyne’s D-810 device, which CTO and co-founder David Julian describes as turning each vehicle into an intelligent, multimodal sensor.

By combining video, AI reasoning, and on-device processing, the system can interpret what’s happening around drivers, vehicles, and passengers in real time. LiveSearch then makes that intelligence immediately accessible through search, rather than buried inside alerts or reports.

Netradyne frames this as a foundational step toward its broader Physical AI platform—technology that continuously interprets the physical world to support both rapid discovery and precision operations.

Two-Speed AI: Discover Fast, Act Precisely

Video LiveSearch also plays a central role in Netradyne’s “Two-Speed AI” strategy.

On one level, broad semantic search allows teams to quickly explore what’s happening across operations without waiting for new product features or rule-based models. On another, high-precision, domain-specific AI continues to power real-time coaching, safety alerts, and compliance workflows.

In practice, LiveSearch helps fleets identify where risks or inefficiencies exist, while more specialized AI systems handle enforcement and intervention. Discovery informs where deeper automation delivers the most value.

Responsible AI Built In, Not Bolted On

As AI-powered video search becomes more powerful, governance becomes harder to ignore. Netradyne says Video LiveSearch embeds responsible AI controls directly into its architecture.

An AI screening layer evaluates natural language prompts before they reach the edge reasoning engine, automatically blocking searches outside approved operational intent—such as identifying individuals or tracking license plates.

This design-first approach aims to preserve driver trust and prevent misuse by default, rather than relying on policy alone.

What It Means for Fleet Tech

Video LiveSearch reflects a broader trend in fleet and mobility technology: moving intelligence from the cloud to the edge to reduce latency, cost, and complexity.

As fleets scale and regulatory scrutiny increases, the ability to instantly surface the right evidence—without over-collecting or over-processing data—becomes a competitive advantage. Netradyne is betting that real-time, on-device search will be a key differentiator as fleet operators demand faster insights and tighter operational control.

For now, Video LiveSearch positions Netradyne at the forefront of what it calls Physical AI for fleets—where understanding the road, the vehicle, and the driver happens continuously, and insight is only a search away.

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