Programmatic advertising has spent the past decade scaling reach, channels, and data. In 2026, that era of expansion is giving way to something more decisive: consolidation.

That’s the central takeaway from StackAdapt’s newly released State of Programmatic Advertising 2026 report, which argues that programmatic is entering a defining transition. Marketers who continue to rely on fragmented tools and siloed execution are falling behind, while those unifying workflows, consolidating technology, and applying AI with intent are pulling sharply ahead.

Based on insights from 484 senior marketers across the U.S., Canada, and the UK, combined with platform data from more than 6,000 global advertisers, the report paints a picture of a maturing channel—and a widening performance gap between leaders and laggards.

The message is blunt: programmatic complexity is no longer a badge of sophistication. It’s a competitive disadvantage.

A market that looks healthy—until you look closer

On the surface, the programmatic market appears to be thriving. According to the report, 75% of marketers expect their budgets to grow, and 84% report stronger year-over-year performance. Optimism is high, spend is increasing, and confidence in programmatic remains strong.

But dig deeper, and the data reveals a growing maturity gap.

Only a subset of marketers—what StackAdapt defines as top performers—are converting that momentum into sustained gains across performance, efficiency, and growth. These marketers report significantly stronger year-over-year results than their peers, not because they’re experimenting more, but because they’re operating differently.

The difference isn’t ambition. It’s execution.

What separates top performers from the rest

StackAdapt’s report identifies three behaviors that consistently distinguish top-performing marketers:

1. Unified channel strategy

2. Consolidated technology stacks

3. Pragmatic, embedded use of AI

Rather than layering new tools on top of old ones, leaders are simplifying. They are bringing creative, data, media, and measurement closer together—often within a single platform—so insights can move faster and decisions can scale.

This shift reflects a broader realization across digital marketing: more tools do not automatically mean better outcomes. In fact, excess tooling often slows teams down, creates data silos, and makes optimization harder, not easier.

As Yang Han, Co-Founder and CTO of StackAdapt, puts it:
“The marketers seeing the strongest gains aren’t adding more tools—they’re consolidating around platforms that can connect channels, data, and AI in one system.”

The hidden cost of fragmented execution

One of the most striking findings in the report is the disconnect between how marketers describe their strategies and how they actually operate.

While 75% of marketers say they run omnichannel campaigns, most lack the infrastructure and workflows needed to consistently act on cross-channel insights. Data may be visible, but it’s not actionable. Learnings remain trapped in dashboards, teams, or channel-specific tools.

The result is a familiar set of problems:

• Fragmented activation across channels

• Inconsistent messaging and creative sequencing

• Delayed optimization decisions

• Wasted spend due to duplicated or misaligned efforts

In other words, omnichannel in name, but not in practice.

StackAdapt’s data suggests that top performers have moved beyond this stage. They aren’t just measuring across channels—they’re executing across them, using shared data and AI-driven optimization to inform decisions throughout the funnel.

AI: from novelty to operational advantage

AI is no longer the differentiator it was just a few years ago. Nearly every marketer now claims to be “using AI” in some form. What’s changed is how AI is being applied—and how uneven that application has become.

According to the report, top performers are not treating AI as a bolt-on feature or experimental add-on. Instead, they’re embedding it directly into execution, where it accelerates decision-making and removes friction from everyday workflows.

That includes:

• Automating optimization across channels

• Identifying performance patterns faster

• Connecting creative signals to media outcomes

• Scaling learnings without manual intervention

Less mature organizations, by contrast, often deploy AI in isolated ways—testing tools without integrating them into core operations. The result is limited impact and growing skepticism about AI’s value.

“Measurement has finally caught up, but execution hasn’t,” Han noted. “In 2026, the advantage will belong to marketers who turn visibility into action.”

Why consolidation is becoming inevitable

The report’s emphasis on consolidation reflects a broader shift across the ad tech landscape.

For years, marketers assembled best-of-breed stacks to handle planning, buying, measurement, and creative across channels. While that approach offered flexibility, it also created operational drag. Each additional platform introduced new workflows, integrations, and learning curves.

As programmatic matured, the cost of that fragmentation became harder to ignore.

StackAdapt’s findings suggest that leading marketers are now prioritizing platforms that can orchestrate multiple channels and functions in one place. Not because specialization has lost value, but because speed, cohesion, and scalability matter more at this stage of growth.

In a market where marginal performance gains can translate into significant revenue impact, reducing latency between insight and action is critical.

The performance gap is widening

Perhaps the most consequential insight in the report is that the gap between top performers and everyone else is growing—not shrinking.

As programmatic tools become more powerful, the upside of using them well increases. At the same time, the penalty for inefficient operations becomes more severe. Marketers who fail to unify workflows and consolidate technology aren’t just leaving gains on the table; they’re actively falling behind competitors who move faster and learn quicker.

This dynamic mirrors what happened in search, social, and ecommerce advertising as those channels matured. Early on, experimentation drove advantage. Later, operational excellence took over.

Programmatic appears to be entering that same phase.

Implications for marketers in 2026

For marketing leaders, the report carries several clear implications:

First, tool proliferation is no longer a growth strategy. If your stack is slowing down decision-making or creating data silos, it’s likely hurting performance.

Second, omnichannel success requires operational change, not just planning alignment. Shared insights only matter if teams can act on them consistently and at speed.

Third, AI value comes from integration, not experimentation. The biggest gains come when AI is embedded into daily workflows and tied directly to outcomes.

Finally, organizational maturity now matters as much as media strategy. Teams, processes, and platforms must evolve together.

What this means for the ad tech market

From an industry perspective, StackAdapt’s report reinforces a trend already underway: platforms are moving from point solutions toward orchestration.

As advertisers demand simplicity without sacrificing sophistication, vendors that can unify channels, data, and AI stand to gain. Those that remain siloed risk being relegated to niche roles—or phased out altogether.

It also raises the bar for differentiation. In a world where “AI-powered” is table stakes, the real question becomes whether technology actually changes how marketers work.

The bottom line

The State of Programmatic Advertising 2026 report doesn’t argue that programmatic is broken. On the contrary, it suggests the channel is stronger than ever—but less forgiving.

Growth is still available. Budgets are still expanding. Performance is still improving. But the rules have changed.

In 2026, programmatic winners will be defined less by how many tools they use and more by how effectively they connect them. The marketers pulling ahead are doing fewer things better—unifying channels, simplifying stacks, and using AI to turn insight into action.

For everyone else, the warning is clear: complexity is no longer neutral. It’s a liability.

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First Brands Group, a major global supplier of aftermarket automotive parts, is officially testing the market.

The company announced it has launched a formal sale process to market and sell its business—either as a whole or in parts—as it works toward emerging from Chapter 11 and transitioning to new ownership. The move is designed to maximize stakeholder value while preserving continuity for customers, vendors, and employees during the restructuring.

The process could reshape ownership of some of the most recognizable names in the automotive aftermarket, including FRAM, Raybestos, Trico, Autolite, and Reese.

A sale process tied to Chapter 11 exit

First Brands’ decision comes as part of its ongoing Chapter 11 proceedings, where the company is seeking a faster and more certain path to stability under new ownership.

According to the company, the sale process is intended to:

• Accelerate emergence from bankruptcy

• Support a transition to long-term ownership

• Position its core brands for their next growth phase

The company said it is exploring a range of outcomes, from selling the business in its entirety to divesting specific segments.

Charles Moore, Interim CEO of First Brands Group, framed the move as a strategic inflection point rather than a liquidation.

“Launching the marketing process represents a decisive step toward positioning our brands for long-term stability under new ownership,” Moore said, pointing to what he described as “significant value across the First Brands portfolio” and strong growth potential in the aftermarket sector.

DIP financing and a potential stalking horse bidder

To support operations during the sale, First Brands is also in discussions with an ad hoc group of lenders about securing additional debtor-in-possession (DIP) financing. That same lender group is expected to serve as a stalking horse bidder for certain business segments once agreements are finalized and approved by the bankruptcy court.

If approved, the financing would allow the company to:

• Maintain supply continuity

• Continue servicing customers across core brands

• Avoid operational disruption during the sale process

Stalking horse bids are often used in Chapter 11 cases to set a baseline valuation and encourage competitive bidding—suggesting lenders see underlying value in specific First Brands assets.

Why First Brands remains attractive to buyers

Despite its restructuring, First Brands controls a broad and strategically valuable portfolio in the automotive aftermarket—a sector that continues to benefit from aging vehicle fleets, higher repair costs, and longer vehicle ownership cycles.

The company’s assets include:

• A global lineup of mission-critical auto parts across brakes, filters, spark plugs, wipers, pumps, lighting, towing, and accessories

• Category-leading brands such as FRAM, Raybestos, Trico, Autolite, and Reese, each with deep relationships across retail and commercial channels

• A global manufacturing and distribution footprint supporting both aftermarket and OEM products

• Strong brand recognition within the estimated $410 billion North American automotive aftermarket

Management also highlighted operational and financial improvements implemented during the Chapter 11 process, positioning the business as more efficient and disciplined than before.

For potential buyers—whether strategic acquirers or private equity firms—the combination of durable demand, brand equity, and operational reset could prove compelling.

Industry context: consolidation and selective bets

The automotive aftermarket has been steadily consolidating, as suppliers seek scale, margin resilience, and portfolio breadth. At the same time, investors have become more selective, favoring assets with strong brand loyalty and defensible market positions.

First Brands’ willingness to sell assets individually reflects that reality. Some buyers may be interested in marquee brands like FRAM or Raybestos, while others may target specific product categories or regional operations.

That flexibility could broaden the bidder pool and help drive value—especially if multiple strategic buyers see synergies within different parts of the portfolio.

What happens next

First Brands expects to file a motion with the bankruptcy court seeking authorization to formally conduct the sale and marketing process under Section 363 of the U.S. Bankruptcy Code. That process is designed to deliver the highest or best bid for the company’s assets.

The company said it plans to move quickly, targeting completion of the sale process in the first quarter of 2026, subject to court approval.

Until then, operations are expected to continue as normal, supported by DIP financing and lender backing.

The bigger takeaway

First Brands’ announcement underscores a familiar theme in industrial and manufacturing restructurings: even amid financial distress, strong brands and essential products retain significant value.

For the aftermarket auto industry, the sale could trigger a reshuffling of ownership among some of its best-known names. For First Brands’ stakeholders, the coming months will determine whether that value is best realized under one roof—or several.

Either way, the process marks a pivotal moment for a company whose brands are fixtures in garages, repair shops, and retail shelves across North America.

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As enterprises race to operationalize AI, one question is becoming impossible to ignore: Who’s actually governing these systems once they’re live? Prodapt wants to make its answer unmistakably clear.

The technology services firm has announced it has been awarded ISO 42001, the world’s first—and currently only—global standard for AI Management Systems (AIMS). The certification positions Prodapt among a small group of providers able to demonstrate formal, auditable governance for AI across strategy, technology, and operations.

In a market crowded with AI claims and pilot projects, ISO 42001 represents something more concrete: proof that AI can be scaled responsibly, not just rapidly.

Why ISO 42001 matters right now

ISO 42001 arrives at a critical moment for enterprise AI. As organizations move from experimentation to AI-driven decision-making, concerns around risk, accountability, transparency, and compliance are intensifying—especially in regulated and high-stakes industries.

Unlike technical model benchmarks, ISO 42001 focuses on how AI is governed, not just how it performs. The standard establishes requirements for managing AI across its full lifecycle, from design and deployment to monitoring, enhancement, and eventual decommissioning.

For enterprises under pressure from regulators, boards, and customers, that governance layer is quickly becoming non-negotiable.

By achieving ISO 42001, Prodapt is signaling that its AI offerings are not only advanced, but operationally disciplined and enterprise-ready.

What the certification validates

The certification, awarded by an independent accredited body, validates Prodapt’s enterprise-grade AI management framework, with an emphasis on accountability and control.

Key areas highlighted in the evaluation include:

• Executive-led AI oversight, ensuring governance is owned at the highest levels

• Risk management and ethical AI practices, embedded into day-to-day operations

• Human-in-the-loop controls, built systematically into AI workflows

• Clear ownership and escalation models, with traceable decision-making

• Transparency and auditability, supported by comprehensive documentation

In short, the standard confirms that AI systems at Prodapt are designed to be responsibly governed throughout their lifecycle—not treated as black boxes once deployed.

Governance beyond the model

One of the most notable aspects of ISO 42001 is its scope. The standard extends well beyond algorithms and models, covering organizational processes, controls, and accountability structures.

Prodapt’s certification recognizes governance across:

• Design and build of AI systems

• Deployment and enhancement as models evolve

• Monitoring and risk mitigation in live environments

• Deprecation and retirement, often overlooked in AI programs

That end-to-end focus reflects a growing industry realization: unmanaged AI technical debt can become just as risky as unmanaged software debt—if not more so.

From theory to large-scale practice

ISO certifications often draw skepticism if they appear disconnected from real-world execution. Prodapt counters that by grounding its governance framework in multiple large-scale enterprise AI implementations already in production.

According to the company, these deployments have helped shape practical controls around accountability, escalation, and continuous monitoring—allowing innovation to scale without undermining trust or compliance.

That experience matters. Many enterprises are discovering that scaling AI introduces new failure modes, from biased outcomes to opaque decisions that are hard to explain internally, let alone to regulators.

Prodapt’s approach suggests governance is being treated not as a compliance afterthought, but as an enabler of scale.

Executive signal: trust as a growth lever

Manish Vyas, CEO and Managing Director of Prodapt, framed the certification as a strategic commitment rather than a symbolic milestone.

“As enterprises transition to AI-driven decision-making, trust and governance become non-negotiable,” Vyas said, describing ISO 42001 as a global benchmark for operationalizing AI responsibly.

The subtext is clear: in the next phase of enterprise AI adoption, trust will differentiate vendors as much as capability. Buyers increasingly want proof that partners can manage AI risk at scale—not just build impressive demos.

How this compares to the broader market

While many technology and services providers talk about responsible AI, relatively few can point to a formal, independently audited management system aligned to a global standard.

ISO 42001 is still new, and adoption remains limited—giving early achievers like Prodapt a potential credibility advantage, especially with global enterprises navigating overlapping regulations such as the EU AI Act, data protection laws, and industry-specific compliance requirements.

As AI governance standards mature, certifications like ISO 42001 may become table stakes. For now, they serve as a strong signal of readiness.

The bigger takeaway for enterprise AI

Prodapt’s announcement reflects a broader shift underway in enterprise AI: success is no longer defined solely by model performance or speed to deployment.

Instead, organizations are asking tougher questions:

• Who owns AI decisions?

• How are risks identified and mitigated?

• Can outcomes be explained, audited, and defended?

• What happens when models change—or fail?

ISO 42001 is designed to answer those questions systematically.

For enterprises looking to scale AI without inviting regulatory or reputational risk, governance frameworks like this are becoming foundational infrastructure—not optional safeguards.

Bottom line

By earning ISO 42001, Prodapt is staking a clear position in the AI services market: scalable AI must be governed as rigorously as it is engineered.

As AI moves deeper into core business decisions, that stance may prove just as valuable as any technical breakthrough.

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UserTesting, a leading enterprise customer insights platform, has acquired User Interviews, a prominent participant recruitment marketplace for user research, market research, and AI training. The move brings together two complementary strengths—insights technology and high-quality participant access—aimed at creating one of the most comprehensive and scalable customer insights solutions for global enterprises.

The acquisition reflects growing enterprise demand for faster, more reliable customer understanding as organizations redesign products, services, and experiences for an AI-driven economy. By unifying research execution and participant recruitment under one platform, UserTesting aims to reduce friction in the insights process and help teams make high-stakes decisions with greater confidence.

A Strategic Bet on Trust and Speed in the AI Era

“As companies reimagine their products and experiences to win in the AI era, the need for trusted customer insights has never been greater,” said Eric Johnson, CEO of UserTesting. “By bringing UserTesting and User Interviews together, we’re creating the fastest and most reliable way for teams to understand their customers and make better, smarter decisions with confidence.”

User Interviews, founded to help organizations hear directly from the audiences that matter most to their business outcomes, has built a strong reputation for its participant panel quality and recruitment precision. Its platform is widely used by research, product, and data teams seeking access to hard-to-reach or highly specialized participants.

Basel Fakhoury, CEO of User Interviews, said the combination significantly expands enterprise capabilities. “Combining User Interviews’ panel capabilities with UserTesting’s platform gives enterprises a more scalable, trusted way to access the right audiences and turn insights into action.”

Bringing Together Platform Intelligence and Premium Panels

At the core of the acquisition is a tighter integration between UserTesting’s category-leading insights platform and User Interviews’ large-scale, premium participant marketplace. UserTesting contributes its global general population network, real-time feedback tools, and AI-powered analysis. User Interviews adds depth through its vetted participant panels, advanced recruitment workflows, and support for complex research criteria.

Together, the companies aim to make participant recruitment faster, easier, and more cost-effective—whether teams are running live moderated sessions, unmoderated studies, or large-scale quantitative research. Enterprises can recruit across geographies, industries, and audience types without juggling multiple vendors or tools.

The combined reach spans everyday consumers, B2B professionals, niche experts, and hard-to-reach roles, enabling organizations to ground decisions in authentic customer voices rather than assumptions or proxy data.

Advancing Participant Management at Enterprise Scale

One of the most significant outcomes of the acquisition is the expansion of panel breadth, depth, and speed. Enterprises gain access to millions of participants with precise targeting across demographic, behavioral, attitudinal, and industry-specific attributes.

Key capabilities of the combined offering include:

• Broad reach: Consumers, niche audiences, specialized experts, B2B professionals, and difficult-to-source roles

• Precise targeting and matching: Rich segmentation across demographics, behaviors, attitudes, and industries

• Proprietary fraud prevention: Controls designed to protect data quality and participant trust

• Rapid scale: Fast access to participants for live, unmoderated, and large-scale quantitative research

• Enterprise-grade trust: Built-in security, privacy, and data governance designed for global organizations

These capabilities are particularly relevant as enterprises increasingly rely on customer data to inform AI deployments, personalization strategies, and automated decision-making systems.

Grounding AI and Product Decisions in Real Customer Voices

The acquisition positions UserTesting to play a larger role in how enterprises validate AI-driven initiatives. From training models with representative data to testing AI-powered experiences before launch, access to the right participants is becoming a critical input to responsible innovation.

By embedding participant recruitment more deeply into the insights workflow, the combined platform allows teams to validate assumptions earlier, iterate faster, and reduce the risk of deploying experiences that miss the mark. Every AI deployment, product enhancement, marketing program, or customer experience can be grounded in direct customer feedback rather than inferred behavior alone.

This approach aligns with broader enterprise trends toward explainability, accountability, and trust in AI systems—areas where qualitative and quantitative customer insights are increasingly seen as strategic assets.

Empowering Cross-Functional Teams to Move Faster

Designers, researchers, product managers, and marketers stand to benefit from the expanded capabilities. The acquisition enables teams to identify the exact audiences they need, engage them quickly, and extract insights at speed using AI-powered analysis tools.

By reducing the operational overhead of recruitment and panel management, teams can spend more time interpreting insights and translating them into action. The result is a shorter path from customer understanding to confident decision-making—an advantage in markets where speed and relevance are critical.

Strengthening UserTesting’s Enterprise Position

For UserTesting, the acquisition reinforces its ambition to be the system of record for customer understanding in large organizations. By owning both the insights platform and the participant pipeline, the company can deliver higher data fidelity while maintaining control over quality, privacy, and governance.

Being able to offer end-to-end insights—from participant sourcing to analysis—also strengthens UserTesting’s competitive position as enterprises consolidate vendors and seek platforms that can scale globally without compromising trust.

As customer expectations evolve and AI reshapes digital experiences, the combined UserTesting and User Interviews platform aims to help enterprises stay grounded in what matters most: real customer perspectives, delivered at the speed and scale modern business demands.

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Bloomreach, the AI company behind personalized digital experiences, has announced that its AI-powered marketing and search solutions are now available on Amazon Web Services (AWS) Marketplace. The move makes it easier for enterprises running on AWS to discover, procure, and deploy Bloomreach’s personalization technology, expanding global access to its proprietary Loomi AI platform.

By joining AWS Marketplace, Bloomreach is aligning its offerings more closely with how modern enterprises buy and deploy software—through cloud-native ecosystems that support speed, scalability, and operational simplicity. The listing allows marketing, commerce, and digital teams to integrate Bloomreach solutions directly into existing AWS-based technology stacks with minimal friction.

Expanding Access to Loomi AI Through AWS

At the core of Bloomreach’s platform is Loomi AI, the intelligence layer that powers all of the company’s products. Loomi AI captures and unifies rich first-party customer and product data, applies contextual understanding and AI-driven decisioning, and activates that intelligence across digital touchpoints.

These capabilities span email, SMS, web, mobile apps, on-site search, and commerce experiences, enabling brands to tailor interactions in real time based on customer behavior, preferences, and intent. With availability on AWS Marketplace, organizations can now deploy these tools more quickly while maintaining alignment with their existing cloud infrastructure and governance models.

For enterprises already standardized on AWS, the integration removes common barriers related to procurement, security reviews, and deployment complexity—accelerating time to value for personalization initiatives.

Personalization at Scale for Marketing and Commerce Teams

The AWS Marketplace listing allows businesses to easily discover Bloomreach’s AI-powered solutions and activate personalization across the customer journey. As marketing and commerce teams increasingly compete on relevance rather than reach, the ability to deliver timely, contextual experiences has become a critical differentiator.

Bloomreach’s platform is designed to support real-time personalization across channels, helping brands respond dynamically to customer signals rather than relying on static segments or rules-based campaigns. By leveraging first-party data and AI decisioning, Loomi AI enables enterprises to move beyond one-size-fits-all messaging toward individualized experiences at scale.

This approach is particularly important as privacy regulations and platform changes reduce reliance on third-party data, pushing brands to maximize the value of their own customer insights.

Aligning With How Enterprises Buy Software Today

Availability on AWS Marketplace reflects a broader shift in enterprise software adoption. Cloud marketplaces have become a preferred channel for discovering and purchasing technology, offering streamlined billing, simplified contracts, and tighter integration with cloud environments.

For Bloomreach, the move expands reach among organizations that rely heavily on AWS for infrastructure, data, and analytics. Customers can now deploy Bloomreach solutions directly within their AWS environments, benefiting from native compatibility and enterprise-grade cloud security.

Rachel Fefer, VP of Global Strategic ISVs and AMER Partnerships at Bloomreach, said the listing underscores the company’s focus on accessibility and ecosystem alignment. “This milestone expands our commitment to meeting customers where they are and making our solutions more accessible to the vast number of businesses leveraging AWS infrastructure.”

Supporting Real-Time, Data-Driven Experiences

Bloomreach’s platform is built to help brands turn complex data into actionable insights across the customer lifecycle. Loomi AI continuously learns from customer interactions, refining recommendations, search results, and messaging to improve relevance over time.

By making these capabilities available through AWS Marketplace, Bloomreach enables more organizations to operationalize real-time personalization without extensive custom integration work. This is especially valuable for global enterprises managing multiple brands, regions, and digital properties on shared cloud infrastructure.

The integration also supports scalability, allowing businesses to handle spikes in traffic, seasonal demand, and rapid growth while maintaining consistent customer experiences.

Strengthening Bloomreach’s Position in the Cloud Ecosystem

The AWS Marketplace launch reinforces Bloomreach’s position as an enterprise-ready AI platform for personalization and search. As competition intensifies in digital experience and commerce technology, ease of deployment and ecosystem compatibility are becoming as important as feature depth.

By aligning with AWS, Bloomreach signals its intent to serve large, cloud-native organizations that prioritize flexibility, security, and performance. The listing also opens new opportunities for joint customers to combine Bloomreach with other AWS services, such as analytics, data lakes, and AI tools, to build more advanced personalization architectures.

Enabling Better Experiences for End Customers

Ultimately, Bloomreach’s expansion on AWS Marketplace is aimed at improving the experiences consumers receive from the brands they engage with. As more marketing and commerce teams activate Loomi AI-powered personalization, customers can expect interactions that are more relevant, timely, and aligned with their needs.

From personalized product discovery and search results to tailored messaging across channels, Bloomreach’s technology is designed to help brands build stronger relationships through meaningful, data-driven engagement.

As enterprises continue to invest in AI-powered customer experience platforms, Bloomreach’s presence on AWS Marketplace positions the company to play a larger role in how personalization is deployed and scaled in the cloud era.

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NinjaOne is making a strong claim on the future of endpoint management. The company announced it has been named a Leader in the 2026 Gartner Magic Quadrant for Endpoint Management Tools, a recognition that underscores its momentum as enterprises move away from fragmented, legacy IT tools toward unified, automated platforms.

While Gartner reports are not endorsements, Leader placement typically signals a combination of execution strength and clarity of vision. For NinjaOne, the evaluation reflects its focus on simplifying IT operations by consolidating endpoint management, patching, backup, and software deployment into a single, cloud-native platform.

At a time when IT teams are under pressure to do more with fewer resources, that positioning resonates.

Endpoint Management at an Inflection Point

Endpoint management is no longer just about keeping devices online. With distributed workforces, rising cyber risk, and expanding compliance mandates, endpoints have become a frontline for resilience, security, and cost control.

Many organizations still rely on a patchwork of tools built for a different era—on-premise environments, slower release cycles, and siloed workflows. NinjaOne’s pitch is straightforward: replace that sprawl with one modern system that provides real-time visibility and automated control across the entire IT estate.

Built on a cloud-native, multi-tenant architecture, NinjaOne aims to help IT teams move faster without compromising reliability or security. Its platform is designed to automate routine operational tasks while maintaining consistent performance at scale, from small businesses to global enterprises and managed service providers (MSPs).

Unifying IT to Reduce Friction and Cost

According to NinjaOne, unification is the core differentiator. By centralizing endpoint management functions that are often spread across multiple vendors, the platform reduces operational overhead and technical debt.

Automation plays a central role. Tasks like patching, software deployment, and endpoint monitoring are increasingly handled through automated workflows, reducing manual effort and minimizing human error. The result, the company argues, is lower cost, faster response times, and greater operational resilience.

Rahul Hirani, Chief Product Officer at NinjaOne, framed the challenge bluntly: IT teams don’t have the time or resources to manage fragmented tools. “Customers consistently tell us they want a simpler, more unified way to manage and secure their environments,” he said, pointing to real-time insights and automated workflows as essential capabilities rather than nice-to-haves.

AI-Driven Patch Management Gains Attention

One of the most notable innovations highlighted in the Gartner evaluation period is Autonomous Patch Management, launched by NinjaOne in 2025. Traditional patching remains one of IT’s most persistent pain points—too slow, too risky, or too disruptive.

NinjaOne’s approach uses AI-driven insights and vulnerability data to prioritize patches based on risk and impact, then automates remediation workflows. The goal is to close security gaps faster without introducing instability, a balance many organizations struggle to achieve.

As ransomware and zero-day vulnerabilities continue to dominate headlines, smarter patching has become a board-level concern. Platforms that can reduce exposure while minimizing downtime are increasingly attractive to both IT and security leaders.

Compliance and Public Sector Expansion

Another factor strengthening NinjaOne’s position is its expansion into regulated environments. Over the past year, the company achieved FedRAMP, GovRAMP, and Texas-RAMP authorizations, significantly expanding its appeal to government agencies and highly regulated industries.

These certifications are not trivial. They signal that the platform meets stringent requirements for security, data handling, and operational controls—often a barrier for cloud-native vendors attempting to serve public-sector customers.

Alongside compliance milestones, NinjaOne has broadened operating system and application support, helping organizations modernize endpoint management without leaving legacy environments behind.

Rapid Growth Signals Market Validation

NinjaOne’s customer growth adds further context to its Leader placement. The company reports more than 35,000 customers across 140+ countries, spanning healthcare, financial services, government, education, retail, and manufacturing.

That breadth matters. Endpoint management tools must operate reliably across diverse environments, device types, and regulatory frameworks. Broad adoption suggests the platform is flexible enough to handle real-world complexity, not just idealized use cases.

In mid-2025, NinjaOne was also named a Strong Performer in the Gartner Peer Insights Voice of the Customer for Endpoint Management Tools, with 96% of customers indicating they would recommend the platform—the highest percentage in that report at the time.

Customer sentiment does not always align with analyst evaluations, but when it does, it strengthens the credibility of a vendor’s market position.

Why This Matters for IT Leaders

The endpoint management market is crowded, with long-established incumbents and newer cloud-native challengers competing for relevance. Gartner’s 2026 Magic Quadrant suggests a continued shift toward platforms that emphasize automation, unification, and cloud-first design.

For IT leaders, NinjaOne’s recognition reinforces a broader trend: operational simplicity is becoming a strategic advantage. Reducing tool sprawl, automating repetitive tasks, and gaining real-time visibility are no longer optional—they are prerequisites for resilience and scale.

The emphasis on AI-driven workflows also points to where endpoint management is headed. Rather than reacting to issues after they occur, platforms are increasingly expected to predict risk, prioritize action, and execute remediation with minimal human intervention.

Competitive Implications

NinjaOne’s Leader status puts pressure on legacy vendors still dependent on complex, modular product portfolios. As budgets tighten and IT teams prioritize efficiency, platforms that require extensive integration and maintenance may struggle to justify their cost.

At the same time, newer entrants will need to demonstrate enterprise-grade reliability, compliance, and global scale—areas where NinjaOne is clearly investing.

While the Magic Quadrant snapshot reflects a specific moment in time, it signals that the competitive bar in endpoint management is rising. Unified platforms that combine automation, security, and usability are increasingly setting expectations for the category.

Looking Ahead

NinjaOne’s recognition in the 2026 Gartner Magic Quadrant highlights how quickly endpoint management is evolving from a back-office function into a strategic layer of modern IT operations.

As organizations continue to modernize infrastructure, support hybrid work, and defend against escalating cyber threats, endpoint management platforms that simplify complexity without sacrificing control will play a central role.

For now, NinjaOne’s Leader placement reinforces its ambition to be that platform—one that helps IT teams reclaim time, reduce cost, and focus on higher-value work rather than tool maintenance.

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Brand discovery no longer starts—or ends—on Google. Ahrefs is betting on that reality with a major expansion of Brand Radar, its fastest-growing product, now extending brand visibility tracking to YouTube, TikTok, and Reddit. The update reflects a broader shift in how consumers find, evaluate, and trust brands—and how marketers need to measure that influence.

With this launch, Brand Radar moves beyond traditional web mentions and SEO metrics to track where attention is actually being formed: video platforms and online communities. For marketing teams navigating fragmented discovery surfaces, Ahrefs is positioning Brand Radar as a single source of truth for understanding brand presence across AI, search, video, and community-driven platforms.

Why Video and Reddit Matter Now

Search is no longer the first stop for many buying journeys. Product research increasingly starts with YouTube explainers, TikTok recommendations, or Reddit threads surfaced directly in Google search results. These platforms shape opinions long before a user ever lands on a brand website.

Yet most brand monitoring tools still treat YouTube, TikTok, and Reddit as disconnected channels—tracked separately, often manually, and rarely in relation to search or AI visibility. Ahrefs’ expansion aims to collapse those silos.

Brand Radar now brings these high-attention surfaces into a unified dashboard, allowing teams to see:

• Where their brand is mentioned

• Which competitors dominate attention

• Which platforms are driving real discovery, not just impressions

The shift aligns with Ahrefs’ broader strategy: helping brands stay discoverable everywhere attention flows, not just where rankings are easiest to measure.

How Brand Radar Tracks YouTube and TikTok Mentions

In beta, Brand Radar now scans YouTube and TikTok video titles, descriptions, and transcripts to identify brand mentions. Rather than dumping full transcripts into reports, the platform surfaces only relevant snippets where a brand is actually mentioned—saving teams from wading through hours of video content.

This approach prioritizes speed and context. Marketers can quickly identify:

• Which creators are referencing their brand

• The tone and placement of mentions

• How frequently competitors appear in similar content

As video continues to dominate product discovery—especially among younger audiences—this kind of visibility is becoming table stakes. Influencer mentions, organic reviews, and comparison videos often influence purchasing decisions more than paid ads or landing pages.

Reddit Visibility Inside Google Search

Reddit’s growing prominence in Google Search has quietly reshaped SERPs. Product queries increasingly surface Reddit threads, especially for reviews, comparisons, and troubleshooting. Ahrefs’ Reddit tracking doesn’t just monitor mentions within Reddit itself—it tracks where Reddit results appear inside Google Search.

Brand Radar now identifies:

• Reddit titles and descriptions shown in SERPs

• Subreddit snippets associated with brand mentions

• How frequently brands appear in Reddit-powered search results

This matters because Reddit often carries perceived authenticity. A single highly visible thread can shape brand sentiment more than dozens of polished marketing pages. For SEO and brand teams, understanding that exposure has become critical.

Powered by AI Visibility Infrastructure at Scale

Behind the scenes, Brand Radar runs on the same infrastructure Ahrefs uses for AI visibility tracking, one of the company’s most aggressive recent investments.

That system processes more than 239 million prompts every month, indexing how brands appear across AI-generated and attention-driven surfaces. Current coverage includes:

• 162M+ AI Overviews

• 29M+ AI Mode queries

• 100M+ queries across ChatGPT, Copilot, Gemini, and Perplexity

This scale allows Brand Radar to connect dots most tools can’t. Teams can now analyze how brand mentions on YouTube, TikTok, and Reddit correlate with downstream visibility in AI-generated answers and search experiences.

In other words, it’s not just about where brands appear—it’s about what creates demand before AI and search respond to it.

From Mentions to Market Signals

According to Ahrefs CMO Tim Soulo, AI platforms are becoming an important discovery layer—but they’re not where demand originates.

“Demand is created on platforms that capture massive attention — YouTube, TikTok, and Reddit — where people talk, share, and influence each other,” Soulo said. “By monitoring brand mentions across these platforms, Brand Radar helps teams understand what’s driving their visibility in AI, not just where they end up being mentioned.”

That distinction matters. As generative AI reshapes search, brands risk focusing too narrowly on optimizing outputs rather than influencing inputs. Brand Radar’s expansion reflects a growing belief across the industry: visibility is upstream, not just algorithmic.

Competitive Context: Monitoring Tools Are Playing Catch-Up

The brand monitoring and SEO intelligence space is crowded, but most platforms still center on web pages, backlinks, and keyword rankings. Social listening tools, meanwhile, often lack strong ties to search or AI visibility.

Ahrefs is attempting to bridge that gap—positioning Brand Radar as a cross-surface intelligence layer rather than a traditional monitoring tool. By integrating video, community platforms, search, and AI into a single system, the company is responding to a market where discovery is fragmented and nonlinear.

Competitors are experimenting with AI SERP tracking and influencer analytics, but few have attempted to unify all of these surfaces at scale. If Brand Radar’s beta features mature into reliable benchmarks, it could pressure both SEO tools and social analytics platforms to rethink their scope.

What This Means for Marketers

For brand, SEO, and content teams, the implications are clear:

• Discovery is happening earlier, faster, and outside owned channels

• Video and community mentions influence AI and search visibility

• Monitoring isolated channels no longer reflects real brand exposure

Brand Radar’s expansion doesn’t eliminate the need for social listening or influencer tools—but it reframes brand visibility as an ecosystem, not a channel-by-channel checklist.

As attention continues to fragment and AI reshapes how information is summarized and surfaced, tools that connect conversation, content, and computation may define the next phase of marketing intelligence.

Ahrefs’ move signals where that future is heading—and how urgently brands need to adapt.

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As media channels splinter, buying cycles compress, and AI becomes central to marketing operations, one long-standing problem continues to slow the industry down: advertising intelligence lives in too many places, in too many formats, and rarely where decisions actually happen.

MediaRadar thinks it has a fix.

The marketing intelligence firm today introduced MediaRadar Data Cloud, a new platform designed to make advertising data immediately usable across analytics tools, activation platforms, and AI workflows. Rather than forcing teams to extract insights from dashboards and manually push them downstream, the Data Cloud embeds MediaRadar’s intelligence directly into the systems where marketers, publishers, and adtech teams already work.

It’s a clear response to a market reality: insight delayed is insight wasted.

Why MediaRadar’s Data Cloud Matters Now

Marketing intelligence hasn’t kept pace with how modern teams operate. While AI adoption accelerates and media fragmentation deepens, advertising data often remains siloed—locked inside reporting tools, disconnected from planning systems, or unusable by AI models that need clean, structured context.

MediaRadar’s Data Cloud is positioned as a shift from “intelligence as a destination” to “intelligence as infrastructure.” Instead of asking users to come to the data, the platform pushes intelligence into analytics environments, planning tools, and AI systems in real time.

That’s increasingly table stakes.

CMOs and revenue leaders now expect competitive insights to inform everything from media mix decisions and budget allocation to automated recommendations generated by large language models. Data that can’t be activated quickly—or safely—loses value fast.

From Insight to Action, Without the Friction

At its core, the Data Cloud makes MediaRadar’s advertising intelligence interoperable across the modern data stack.

Clients can work with mission-critical datasets—such as competitive ad spend, creative trends, and media mix analysis—inside their own environments rather than toggling between platforms. That means teams can analyze markets, shape strategy, and activate insights across planning, measurement, and optimization workflows without breaking momentum.

More notably, MediaRadar is positioning the Data Cloud as AI-native, not AI-adjacent.

The platform is designed to connect trusted advertising data to AI models and agents running on platforms like ChatGPT, Anthropic, and Gemini. That opens the door for AI-driven workflows that don’t just summarize data, but reason over it—spotting shifts in spend, identifying emerging competitors, or flagging whitespace opportunities before rivals react.

Future support for the Model Context Protocol (MCP) suggests MediaRadar is thinking ahead to a world where advertising intelligence must move seamlessly across multiple AI agents, tools, and teams without losing consistency or governance.

A Data Foundation Measured in Billions, Not Dashboards

MediaRadar’s confidence in launching a data-first platform comes from the scale of its underlying intelligence.

The company’s data foundation spans:

• $280 billion in tracked media spend

• 35 million-plus creative assets

• 30+ media channels, including social, digital video, programmatic, CTV, AVOD, linear TV, and retail media

That breadth matters. As advertisers diversify spend across channels and formats, intelligence that only covers part of the ecosystem becomes less reliable. MediaRadar’s pitch is that comprehensive coverage enables cleaner benchmarking, stronger competitive analysis, and more trustworthy AI outputs.

In practical terms, brands and agencies using the Data Cloud can:

• Detect shifts in competitor spend and creative strategy in near real time

• Adjust media plans faster as market conditions change

• Benchmark share of voice across channels rather than in silos

• Identify underutilized channels or formats before they become crowded

This is especially relevant as retail media and CTV continue to blur traditional planning boundaries.

Built for Advertisers—and the Companies Selling to Them

While advertisers and agencies are an obvious audience, MediaRadar is also aiming squarely at publishers and adtech platforms.

Selling ads has become harder, not easier. Buyers are more selective, sales cycles are longer, and proof of value matters earlier in the conversation. The Data Cloud is positioned as a commercial intelligence engine that helps publishers and platforms compete more effectively for advertiser budgets.

With access to brand- and product-level advertising activity, sales teams can:

• Identify advertisers most likely to spend, not just those already in-market

• Tailor pitches based on actual media behavior, not assumptions

• Align outreach with emerging trends before budgets are fully allocated

In theory, this shortens sales cycles and increases win rates—two metrics publishers care about deeply as competition intensifies.

What’s Under the Hood: Key Capabilities Explained

Rather than positioning the Data Cloud as a single feature, MediaRadar is rolling it out as an integrated ecosystem built around interoperability, consistency, and AI-readiness.

AI-Enabled Brand Identity System
At the foundation is a parent-child taxonomy that acts as a single source of truth for brands, sub-brands, products, and co-ops. This structure ties together media spend, creative assets, and campaigns across channels. The payoff is cleaner analysis, better benchmarking, and fewer errors when AI systems interpret brand relationships.

Accessible Wherever Teams Work
The Data Cloud is fully cloud-native, delivering creative, competitive, commercial, and market intelligence directly into analytics platforms, planning tools, and AI environments. The goal is to eliminate lag between insight and action—especially in revenue and optimization decisions.

Context-Rich Semantics
Standardized metadata for brands, creatives, and campaigns ensures consistent meaning across datasets. That consistency is critical for AI systems, which are only as reliable as the context they’re given.

AI-Ready by Design
By harmonizing spend, creative, and campaign data into a single interoperable framework, MediaRadar aims to provide AI systems with the structured inputs they need to generate accurate insights, recommendations, and forecasts at scale.

Why AI-Ready Advertising Data Is the Real Differentiator

AI adoption in marketing has moved past experimentation. Teams now expect models to support planning, forecasting, competitive analysis, and even sales enablement. But many AI initiatives stall because the data feeding them is fragmented or poorly structured.

MediaRadar is betting that data readiness—not algorithms—will be the limiting factor for most organizations.

With clean, consistently classified datasets, clients can:

• Train AI models on higher-quality advertising intelligence

• Improve prediction accuracy for spend shifts and competitive moves

• Accelerate go-to-market and product decisions

• Identify whitespace opportunities before competitors see them

In other words, the Data Cloud isn’t just about better reporting—it’s about making AI outputs more reliable and more defensible.

A Signal of Where Marketing Intelligence Is Headed

MediaRadar’s launch reflects a broader shift across martech and adtech: intelligence platforms are becoming infrastructure layers rather than standalone tools.

As AI agents take on more decision support, data providers must ensure their insights are portable, interoperable, and governed. Platforms that remain closed or dashboard-centric risk becoming irrelevant—no matter how good their data is.

By focusing on interoperability, AI integration, and real-time activation, MediaRadar is aligning itself with how modern marketing teams actually operate. The challenge, as always, will be execution—particularly as more vendors make similar claims.

Still, the direction is clear. In an AI-driven advertising market, the winners won’t just have the most data. They’ll have the data that moves fastest, travels farthest, and makes the most sense to both humans and machines.

Get in touch with our MarTech Experts.