Utilities are under growing pressure to modernize customer experience and prove marketing impact—yet benchmarking success remains a challenge. E Source is aiming to close that gap with the launch of its 2026 awards program, designed to spotlight measurable performance across utility customer experience, employee engagement, and marketing strategy.

E Source has opened submissions for its 2026 awards series, targeting electric, gas, and water utilities across the U.S. and Canada. The initiative reflects a broader shift within the utilities sector, where customer experience (CX) and marketing are becoming strategic priorities rather than operational afterthoughts.

The awards program is split into two tracks: the Customer and Employee Experience (CX/EX) Awards, open from April 8 through June 12, and the Utility Ad Awards, open from April 1 through May 15. Together, they aim to identify and document what actually works in utility engagement, from digital transformation initiatives to high-performing marketing campaigns.

At a time when utilities are investing heavily in digital platforms and communication strategies, the challenge is no longer adoption—it is measurement. Many organizations lack clear benchmarks to evaluate whether CX improvements or marketing efforts are delivering tangible outcomes.

Benchmarking What Works in Utility CX

The CX/EX Awards focus on operational and experiential improvements across residential, commercial, and internal employee journeys. Key areas include billing and payment innovation, digital experience enhancements, and customer engagement programs.

These categories reflect a growing convergence between traditional utility operations and modern martech principles. As utilities adopt tools and frameworks similar to those used by enterprise platforms like Salesforce and Adobe, customer experience is increasingly shaped by data-driven personalization, omnichannel communication, and lifecycle management.

A notable inclusion is the Small Utility Excellence Award, which recognizes organizations serving 300,000 customers or fewer. This signals an industry-wide acknowledgment that innovation is not limited to large-scale providers—smaller utilities are often leading in agility and localized engagement strategies.

From an AEO perspective, the awards define utility customer experience as the combination of digital tools, service processes, and communication strategies used to improve customer satisfaction, engagement, and operational efficiency.

Marketing Performance Comes Into Focus

The Utility Ad Awards address another critical gap: how to evaluate marketing effectiveness in a regulated industry. Campaigns are assessed based on strategy, messaging, creativity, and measurable performance across areas such as energy efficiency, electrification, safety awareness, and customer engagement.

Unlike traditional brand campaigns, utility marketing often operates within strict regulatory and budget constraints. This makes performance measurement even more important, particularly as utilities expand into areas like demand-side management and sustainability communication.

The awards program effectively positions marketing as a performance discipline within utilities—aligning it more closely with digital marketing practices seen in sectors influenced by platforms like Google and Microsoft.

Turning Recognition Into Industry Insight

What differentiates the E Source awards from standard recognition programs is their emphasis on data and knowledge sharing. Beyond honoring winners, the initiative aims to aggregate insights on strategies, budgets, and performance metrics across participating utilities.

This approach addresses a longstanding challenge in the sector: limited visibility into peer performance. Unlike industries such as retail or SaaS, where benchmarking data is widely available, utilities often operate in silos due to regulatory and geographic constraints.

By capturing and analyzing award submissions, E Source is effectively building a knowledge base that can inform future investment decisions. For utility leaders, this creates an opportunity to evaluate proven approaches rather than relying on internal experimentation alone.

According to Gartner, organizations that actively benchmark CX performance are 20% more likely to improve customer satisfaction metrics year over year. Similarly, McKinsey & Company has found that companies prioritizing customer experience can achieve revenue growth rates up to twice that of their peers.

Why It Matters Now

The timing of the awards launch is significant. Utilities are navigating a complex landscape shaped by electrification, decarbonization, and rising customer expectations. At the same time, digital transformation initiatives are accelerating, bringing new tools—and new challenges—into the ecosystem.

Customer expectations are increasingly influenced by experiences in other industries, from e-commerce to banking. This puts pressure on utilities to deliver seamless, personalized, and transparent interactions, even within regulated frameworks.

For enterprise marketing and CX leaders in utilities, the message is clear: success will depend on the ability to measure impact, optimize strategies, and learn from industry peers. Programs like the E Source awards provide a structured way to surface those insights.

Market Landscape

The utility sector is undergoing a gradual but meaningful transformation toward customer-centric operations. Investments in digital infrastructure, customer data platforms, and marketing technologies are rising as utilities seek to improve engagement and operational efficiency.

This shift mirrors trends in broader enterprise technology, where CX and marketing are tightly integrated into business strategy. However, utilities face unique constraints, including regulatory oversight and legacy systems, which make benchmarking and knowledge sharing particularly valuable.

As a result, initiatives that provide visibility into performance—such as the E Source awards—are becoming critical tools for industry advancement.

Top Insights

• E Source’s 2026 awards program highlights measurable success in utility customer experience and marketing, helping organizations benchmark performance and identify proven strategies across CX, EX, and campaign execution.
• The CX/EX Awards emphasize digital transformation, billing innovation, and engagement programs, reflecting the growing role of data-driven customer experience in utility operations and service delivery.
• Utility Ad Awards position marketing as a performance discipline, evaluating campaigns on measurable impact across energy efficiency, safety, and electrification initiatives within regulated environments.
• Benchmarking remains a major gap in the utility sector, with the awards program aggregating insights to help organizations compare strategies, budgets, and outcomes more effectively.
• Increasing investments in CX and marketing align utilities with broader martech trends, as organizations adopt enterprise tools and frameworks to improve engagement and operational efficiency.

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Influencer marketing is undergoing a structural shift—from brand awareness play to measurable performance channel. New data from Later suggests enterprise brands are accelerating that transition, consolidating creator programs around platforms that can deliver ROI visibility, predictive analytics, and scalable execution.

Later, a platform historically associated with social media scheduling, is repositioning itself as an enterprise-grade influencer marketing and social commerce solution. The company reported more than 100% year-over-year growth in enterprise business in Q1 2026, signaling a broader industry move toward performance-driven creator marketing.

At the center of this shift is measurement. Later says it now powers $2.9 billion in verified influencer-driven purchases and has facilitated over $250 million in creator payouts. These figures point to a growing expectation among enterprise marketers: influencer campaigns must now demonstrate the same level of accountability as paid media across platforms like Google and Amazon.

From Brand Play to Performance Channel

The rise of influencer marketing as a performance channel reflects a maturing digital ecosystem. Enterprise brands including Nike, Southwest Airlines, Wayfair, and Unilever are expanding their investments with Later, using the platform to centralize creator discovery, campaign execution, and performance measurement.

What’s changing is not just scale, but mindset. Influencer marketing is increasingly evaluated through the lens of return on ad spend (ROAS), conversion rates, and attributable revenue—metrics traditionally associated with programmatic advertising and search marketing.

Later’s CEO has framed this shift as a move away from intuition-based campaigns toward data-driven systems. The platform’s ability to track verified purchases tied to creator activity positions it closer to performance marketing infrastructure than traditional social media tools.

This evolution aligns with broader trends across martech stacks, where platforms from Salesforce and Adobe are integrating influencer data into customer journey analytics and attribution models.

AI Becomes the Core Layer

A key driver behind Later’s growth is its investment in AI, particularly through its proprietary engine, Later EdgeAI. The platform uses machine learning to automate creator discovery, forecast campaign performance, and optimize engagement outcomes.

According to the company, AI-enabled workflows have allowed marketers to:

• Manage over 70% more creators per campaign
• Achieve more than 40% higher engagement rates
• Reduce creator costs by over 30%

These gains highlight how AI is transforming influencer marketing from a manual, relationship-driven process into a scalable, data-centric discipline. Instead of manually vetting creators, marketers can now rely on predictive models to identify high-performing influencers and simulate campaign outcomes before launch.

This mirrors developments in adjacent sectors like adtech and customer data platforms, where AI-driven decisioning is becoming foundational. As with programmatic advertising, automation is reducing operational friction while increasing precision.

Leadership and Platform Evolution

To accelerate its AI roadmap, Later appointed Mohsin Hussain as Chief Technology Officer. Hussain brings experience from LiveRamp, where he led engineering efforts across a global customer base.

His background in machine learning, data infrastructure, and large-scale systems signals Later’s ambition to compete not just as a marketing tool, but as a data platform. The company is positioning its creator dataset as a strategic asset—one that can inform everything from audience targeting to revenue forecasting.

The timing is notable. As privacy regulations reshape digital advertising and limit third-party data access, first-party and creator-driven data sources are becoming increasingly valuable. Influencer marketing, in this context, offers both reach and deterministic signals tied to real consumer behavior.

Rebranding for the Enterprise Era

Later’s Q1 momentum coincides with a broader brand transformation. The company unveiled a rebrand at SXSW 2026, introducing a new identity that reflects its evolution from a scheduling tool to a unified creator intelligence platform.

The launch of its “Made You Look” campaign underscores this repositioning. Rather than focusing on social media management, Later is emphasizing its role in driving measurable business outcomes through creator-led strategies.

Recognition from G2—where Later was named a Leader in influencer marketing platforms for the fifth consecutive year—adds further validation to its enterprise push.

Why It Matters for Enterprise Marketing Teams

For enterprise marketers, the implications are clear: influencer marketing is no longer optional or experimental. It is becoming a core component of performance marketing strategies.

Platforms like Later are enabling organizations to:

• Treat creators as scalable acquisition channels
• Integrate influencer data into broader marketing analytics
• Align creator campaigns with revenue and ROI targets

This shift is particularly relevant as brands look to diversify beyond traditional paid channels. Rising acquisition costs and signal loss in programmatic ecosystems are pushing marketers toward alternative growth levers—many of which rely on authentic, creator-led engagement.

According to Forrester, influencer marketing budgets are expected to grow at double-digit rates through 2027, driven by demand for measurable outcomes. Meanwhile, Statista estimates the global influencer marketing market will surpass $30 billion within the next two years.

Later’s growth suggests that enterprise adoption is accelerating faster than those projections, particularly as AI closes the gap between creativity and performance measurement.

Market Landscape

The influencer marketing platform space is becoming increasingly competitive, with vendors racing to integrate AI, attribution, and commerce capabilities. Platforms are evolving from campaign management tools into full-stack creator ecosystems.

Enterprise solutions are converging with broader martech infrastructure, integrating with CRM systems, analytics platforms, and commerce stacks. This convergence is blurring the lines between influencer marketing, affiliate marketing, and performance advertising.

As a result, differentiation is shifting toward data ownership, AI sophistication, and the ability to prove ROI—areas where platforms like Later are investing heavily.

Top Insights

• Later reported over 100% enterprise growth in Q1 2026, signaling rapid adoption of influencer marketing platforms as performance-driven infrastructure among Fortune 500 brands and large-scale marketing teams.
• The platform now tracks $2.9 billion in influencer-driven purchases, reflecting growing demand for measurable ROI and attribution in creator-led campaigns across enterprise marketing ecosystems.
• AI-powered tools like Later EdgeAI are transforming influencer marketing by automating creator discovery, improving engagement rates, and reducing campaign costs at scale.
• Enterprise brands are consolidating creator programs into unified platforms, aligning influencer marketing with broader martech stacks and performance measurement frameworks.
• The appointment of a new CTO and a major rebrand highlight Later’s strategic shift toward becoming a data-driven, AI-first creator intelligence platform.

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AI is no longer an experimental layer in email marketing—it is becoming the operating system behind high-performing campaigns. New research from Validity suggests that organizations embedding AI deeply into their workflows are significantly outperforming peers on ROI, compliance, and campaign efficiency.

The latest State of Email 2026 report from Validity’s Litmus platform offers a data-backed look at how AI maturity is reshaping email marketing performance. Based on responses from more than 500 marketers across the U.S., U.K., Australia, and New Zealand, the study draws a clear line between AI adoption depth and measurable business outcomes.

At its core, the report answers a question many enterprise marketing teams have been asking: does AI meaningfully improve email marketing ROI? According to Validity’s data, the answer is yes—but only when AI is fully integrated. Advanced adopters—defined as teams embedding AI into campaign workflows, analytics, and decision-making—are 75% more likely to achieve returns exceeding 45:1.

That level of ROI places email among the highest-performing digital marketing channels, even as platforms like Google and Meta continue to dominate paid media ecosystems. What’s changing is how those returns are achieved. AI is shifting email marketing from a manual, campaign-based function into a continuously optimized system.

AI as a Performance Multiplier

AI’s impact goes beyond automation. The report highlights how advanced adopters are improving campaign quality and compliance simultaneously—two areas traditionally seen as trade-offs.

Teams with mature AI integration are:

• 54% more likely to adhere to Web Content Accessibility Guidelines
• 52% more likely to comply with the European Accessibility Act

This suggests that AI is increasingly being used not just for personalization, but for governance. Instead of relying on manual checks, AI systems can enforce compliance at scale, reducing the risk of regulatory penalties and reputational damage.

In practical terms, AI enables marketing teams to generate campaigns faster, analyze performance in real time, and optimize targeting with greater precision. These capabilities are particularly relevant as enterprise stacks grow more complex, often spanning tools from Salesforce, Adobe, and Microsoft.

The AI Maturity Gap

Despite the clear performance benefits, most organizations are still early in their AI journey. Only 12% of respondents describe their AI maturity as “integrated,” while 17% report pausing or avoiding AI initiatives altogether.

The gap is not due to lack of interest. Instead, it reflects structural challenges:

• Integration with existing systems remains the top barrier (34%)
• Skills gaps within teams limit execution (27%)
• Poor data quality undermines AI effectiveness (25%)
• Difficulty measuring ROI slows adoption (23%)

These findings align with broader industry trends. According to Gartner, more than 60% of AI projects fail to move beyond pilot stages due to data and operational constraints. Similarly, McKinsey & Company has reported that companies capturing value from AI are those that integrate it into core workflows rather than treating it as a standalone tool.

Rethinking Email Strategy in 2026

Beyond AI, the report surfaces a shift in what defines high-performing email programs. The highest ROI teams—roughly the top 8%—are not simply sending more emails. They are sending smarter ones.

Relational content, including newsletters and onboarding sequences, is emerging as a key driver of engagement. These formats prioritize long-term subscriber relationships over short-term conversions, aligning with broader trends in customer lifecycle marketing.

At the same time, list strategy is evolving. While overall sending volume declined in 2025, top-performing teams are focusing on smaller, highly engaged audiences. Those achieving click-through rates above 5% are 30% more likely to send emails daily, indicating a shift toward frequency with precision rather than scale.

Privacy and consent are also becoming performance levers. Marketers in Australia and New Zealand—regions with stricter data protection frameworks—are 63% more likely to achieve ROI above 45:1 compared to their U.S. and U.K. counterparts. This suggests that stronger data governance can directly translate into higher engagement and trust.

Why It Matters for Enterprise Teams

For enterprise marketing leaders, the implications are clear. AI in email marketing is no longer about incremental gains—it is about redefining operational efficiency and competitive advantage.

Teams that succeed are those that:

• Build centralized, high-quality data infrastructure
• Integrate AI across campaign creation, targeting, and analytics
• Align compliance, personalization, and performance strategies

This shift mirrors broader changes across martech and adtech ecosystems, where AI-driven decisioning is becoming foundational. As platforms evolve, email remains a critical owned channel—but one that increasingly depends on intelligent automation to stay competitive.

Validity’s findings reinforce a larger industry reality: the future of email marketing belongs to organizations that treat AI not as a feature, but as infrastructure.

Market Landscape

The email marketing ecosystem is undergoing a structural transformation driven by AI, privacy regulation, and platform consolidation. Vendors across the martech stack—from customer data platforms to marketing automation suites—are embedding AI to enhance personalization and performance.

Major ecosystems like Salesforce Marketing Cloud and Adobe Experience Cloud are increasingly integrating AI copilots and predictive analytics. Meanwhile, standalone platforms like Validity’s Litmus are focusing on execution quality, deliverability, and compliance.

As competition intensifies, differentiation is shifting from feature sets to data quality, AI maturity, and integration depth—factors that directly influence ROI.

Top Insights

• Advanced AI adopters in email marketing are 75% more likely to achieve ROI above 45:1, highlighting how deeply embedded AI drives measurable business outcomes across campaign execution and analytics.
• Only 12% of organizations have fully integrated AI into marketing workflows, revealing a significant maturity gap despite strong industry-wide investment in AI-driven marketing technologies.
• AI is improving both performance and compliance, with advanced users significantly more likely to meet accessibility and regulatory standards, reducing risk while enhancing campaign effectiveness.
• High-performing teams are shifting toward smaller, highly engaged audiences and relational content strategies, prioritizing long-term subscriber value over bulk email volume.
• Regional privacy regulations in markets like Australia and New Zealand are contributing to higher ROI, ցույցing how strong data governance directly impacts customer trust and engagement.

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As competition intensifies in the enterprise data and analytics market, leadership strategy is becoming as critical as technology innovation. YipitData has appointed Henry Schuck, founder and CEO of ZoomInfo, to its Board of Directors—signaling a push toward its next phase of growth in data-driven intelligence.

The move brings one of the most recognized operators in the B2B data ecosystem into YipitData’s leadership structure, at a time when demand for proprietary, real-time insights is accelerating across enterprise and investment markets.

What the Appointment Means

Henry Schuck is widely known for building ZoomInfo into a category-defining revenue intelligence platform, reshaping how sales and marketing teams leverage data for go-to-market execution. His experience scaling a data platform from startup to publicly traded company positions him as a strategic addition for YipitData.

For YipitData, the appointment is less about governance and more about growth execution. The company has built its reputation on alternative data—non-traditional datasets used to generate insights into company performance, consumer behavior, and market trends.

By bringing Schuck onto the board, YipitData is tapping into expertise in scaling proprietary data assets, expanding enterprise adoption, and building repeatable revenue models.

The Rise of Alternative Data in Enterprise Strategy

YipitData operates in a segment that has gained significant traction over the past decade: alternative data. Unlike traditional datasets, which rely on financial filings or structured reporting, alternative data draws from sources such as transaction data, web activity, and operational signals.

These datasets provide more granular and timely insights, making them particularly valuable for institutional investors and enterprise decision-makers.

According to McKinsey & Company, organizations that effectively leverage advanced analytics and alternative data can outperform peers in decision-making speed and accuracy. Meanwhile, IDC reports that enterprises are increasing investments in external data sources to enhance predictive capabilities.

YipitData’s model—analyzing billions of data points daily—positions it within this trend, offering insights that go beyond traditional reporting cycles.

Why Leadership Matters in Data Platforms

Scaling a data platform presents unique challenges. Unlike software products, data businesses must continuously acquire, validate, and enrich datasets while maintaining trust and compliance.

Schuck’s experience at ZoomInfo is directly relevant here. Under his leadership, ZoomInfo built a robust data acquisition and validation engine, enabling it to deliver high-quality insights at scale.

This operational expertise is likely to influence YipitData’s strategy as it expands its product portfolio and customer base.

The appointment also reflects a broader trend in enterprise technology: companies are prioritizing leadership with experience in scaling data-driven businesses, not just building them.

Enterprise Demand for Decision Intelligence

YipitData serves a diverse customer base, including institutional investors, retailers, and Fortune 100 companies. These organizations increasingly rely on data-driven intelligence to inform strategic decisions.

This shift is part of a larger movement toward decision intelligence—where analytics, AI, and data platforms converge to support real-time business decisions.

Major technology ecosystems, including Google, Microsoft, and Amazon, are investing heavily in data infrastructure and AI capabilities to support this trend.

YipitData’s focus on proprietary datasets differentiates it from these platforms, positioning it as a complementary layer that provides unique insights rather than general-purpose analytics.

Strategic Signal: Expansion and Integration

The timing of the appointment is notable. It comes as YipitData continues to expand its offerings, including SpendHound, a platform focused on software spend management.

The adoption of SpendHound by ZoomInfo following Schuck’s appointment highlights the potential for deeper integration between data platforms and enterprise applications.

This reflects a broader industry pattern where data providers are extending into adjacent categories—such as SaaS optimization, financial analytics, and operational intelligence—to capture more value.

Competitive Landscape

The market for data-driven intelligence is becoming increasingly competitive. Alongside established players like ZoomInfo, newer entrants are leveraging AI and machine learning to enhance data collection and analysis.

At the same time, enterprises are building internal data capabilities, reducing reliance on external providers in some areas while increasing demand for specialized datasets in others.

YipitData’s strategy appears to focus on differentiation through data quality, validation, and proprietary sources—areas where competition is less commoditized.

Schuck’s experience navigating competitive dynamics in the data industry could prove valuable as YipitData scales.

What It Means for Martech and Enterprise Teams

For marketing and go-to-market teams, the appointment underscores the growing importance of high-quality data in driving performance.

Platforms like ZoomInfo have already demonstrated how data can transform sales and marketing workflows. YipitData’s approach extends this concept into broader enterprise decision-making, including investment strategy and operational planning.

As martech stacks become more data-centric, the ability to integrate external intelligence with internal systems will become a key differentiator.

Looking Ahead

YipitData’s addition of Henry Schuck signals a strategic focus on scaling its platform and expanding its market presence. The company is positioning itself to capitalize on growing demand for high-quality, alternative data in enterprise decision-making.

The broader implication is clear: in the next phase of the data economy, success will depend not just on access to data, but on the ability to operationalize it at scale.

With experienced leadership guiding its growth, YipitData is aiming to strengthen its role in that evolving landscape.

Market Landscape

The enterprise data market is evolving toward alternative data and decision intelligence platforms. As organizations seek faster, more granular insights, proprietary datasets and advanced analytics are becoming critical components of competitive strategy.

Top Insights

• YipitData appoints Henry Schuck to its board, bringing proven expertise in scaling data-driven platforms and expanding enterprise adoption in competitive markets.
• The move highlights growing demand for alternative data, which provides more timely and granular insights than traditional datasets for decision-making.
• Leadership experience in building revenue intelligence platforms positions YipitData to expand its product offerings and enterprise footprint.
• Integration opportunities, including SpendHound adoption by ZoomInfo, signal deeper connections between data platforms and enterprise applications.
• The appointment reflects broader industry trends toward decision intelligence, where data, analytics, and AI converge to drive business outcomes.

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As enterprises embed AI deeper into operational systems, a recurring limitation is becoming clear: workflows and AI agents often act on incomplete context. A new partnership between Qlik and ServiceNow aims to address that gap by connecting governed enterprise data directly to workflow execution.

Announced at Qlik Connect 2026, the collaboration focuses on integrating Qlik’s analytics and AI capabilities with ServiceNow’s Workflow Data Fabric, enabling organizations to move from insight to action with greater accuracy and confidence.

From Data Insight to Workflow Execution

At a high level, the partnership is designed to solve a critical enterprise challenge: bridging the divide between data analysis and operational decision-making.

ServiceNow’s Workflow Data Fabric already aggregates operational data across systems. Qlik extends this by introducing broader enterprise context—pulling in signals from ERP, CRM, billing, supply chain, and support systems. The combined dataset is then analyzed through Qlik’s Analytics Engine and AI to surface patterns, relationships, and recommendations.

Those insights are not confined to dashboards. Instead, they are fed directly into workflows and AI agents within ServiceNow, enabling real-time, context-aware decisions.

This shift reflects a broader evolution in enterprise AI. Rather than operating as standalone analytics tools, AI systems are increasingly embedded into workflows where business actions occur.

What the Partnership Delivers

The collaboration introduces two key capabilities.

First, Qlik metadata collectors for the ServiceNow Data Catalog enhance data discovery and governance. These collectors provide visibility into data lineage, structure, and movement across environments—an essential requirement for enterprises managing complex, distributed data ecosystems.

Second, Qlik’s analytics and AI capabilities are integrated into ServiceNow workflows, enabling cross-system insights to influence operational decisions. This allows workflows to adapt based on real-time conditions rather than static rules.

Together, these capabilities create a more cohesive system where data, analysis, and execution are tightly linked.

Why Context Is Critical for AI Workflows

AI agents are increasingly being tasked with more than simple automation. They are expected to interpret business conditions, recommend actions, and in some cases execute decisions autonomously.

However, their effectiveness depends heavily on the quality and completeness of the data they access. Without broader context, even advanced models can produce suboptimal or misleading outcomes.

The Qlik-ServiceNow partnership addresses this by enriching workflow data with governed enterprise context. This ensures that decisions are informed by a more comprehensive view of the business.

This approach aligns with industry trends. Platforms from Microsoft, Google, and Amazon are increasingly focused on integrating AI with enterprise data ecosystems to improve decision quality.

Governance and Trust at the Core

A key aspect of the partnership is its emphasis on governance. As AI systems take on more responsibility, ensuring data integrity and transparency becomes critical.

The integration with ServiceNow’s Data Catalog, enhanced by Qlik metadata collectors, provides enterprises with better visibility into how data is sourced, transformed, and used. This supports compliance, reduces risk, and improves trust in AI-driven decisions.

According to Gartner, organizations that prioritize AI trust, risk, and security management (TRiSM) frameworks are better positioned to scale AI adoption. Meanwhile, IDC reports that data governance is a top priority for enterprises operationalizing AI.

Competitive Landscape: Convergence of Data and Workflows

The partnership highlights a broader convergence in enterprise software. Historically, analytics platforms and workflow systems operated independently. Today, vendors are working to unify these layers.

Qlik brings expertise in data integration, analytics, and AI, while ServiceNow provides a widely adopted workflow platform. Together, they are positioning themselves against a growing field of competitors that are integrating similar capabilities.

For example, Salesforce is embedding AI into CRM workflows, while Microsoft is integrating analytics and AI into its Power Platform and Dynamics ecosystem. Adobe, meanwhile, is focusing on data-driven customer experience workflows.

The differentiator for Qlik and ServiceNow lies in their focus on connecting governed data with operational workflows—creating a direct path from enterprise intelligence to enterprise action.

Implications for Enterprise Teams

For enterprise marketing, operations, and IT teams, the partnership offers a more streamlined approach to decision-making.

In marketing technology stacks, for instance, insights from customer data platforms and analytics tools can be directly applied to campaign workflows. This enables more responsive and personalized engagement strategies.

Operations teams can use the platform to optimize processes based on real-time conditions, while IT teams benefit from improved data governance and visibility.

The result is a more integrated enterprise environment where data-driven insights are immediately actionable.

Market Direction: Toward Context-Aware Enterprise AI

The announcement reflects a broader shift toward context-aware AI systems. As organizations move beyond experimentation, the focus is shifting to practical deployment—ensuring AI delivers measurable business outcomes.

According to IDC, by 2027, a majority of enterprise workflows will incorporate AI-driven decision-making. Gartner similarly emphasizes the importance of integrating AI into existing systems rather than deploying standalone solutions.

The Qlik-ServiceNow partnership aligns with this direction, embedding AI into workflows while enhancing the context those systems rely on.

What Comes Next

The challenge for enterprises will be operationalizing these capabilities at scale. Integrating data across systems, maintaining governance, and ensuring performance will require coordinated effort across teams.

Still, the trajectory is clear. As AI becomes embedded in the fabric of enterprise operations, the ability to connect data, insight, and action will define competitive advantage.

For Qlik and ServiceNow, the partnership represents a step toward that future—where workflows are not just automated, but intelligent, context-aware, and continuously optimized.

Market Landscape

Enterprise software is converging around unified platforms that integrate data, analytics, and workflows. As AI adoption accelerates, context-aware systems that connect insights to execution are becoming critical for driving operational efficiency and business outcomes.

Top Insights

• Qlik and ServiceNow partner to integrate analytics and AI directly into workflows, enabling context-aware decision-making across enterprise systems and operational processes.
• New metadata collectors enhance governance within ServiceNow Data Catalog, improving data lineage visibility, discovery, and compliance across distributed environments.
• The integration bridges the gap between insight and execution, allowing AI agents and workflows to act on real-time, cross-system intelligence.
• Growing demand for context-rich AI aligns with enterprise trends toward embedding intelligence into operational systems rather than relying on standalone analytics tools.
• The partnership reflects broader industry convergence as vendors unify data platforms, AI, and workflow automation to deliver measurable business outcomes.

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As physical security systems evolve into data-driven intelligence platforms, video management software is increasingly being evaluated not just on monitoring capabilities, but on how effectively it integrates with broader enterprise infrastructure. Salient Systems has been recognized within that shift, earning “Video Surveillance Platform of the Year” for 2026 from Enterprise Security Magazine.

The award highlights the company’s CompleteView VMS platform, which is designed to unify video surveillance with access control and operational data—reflecting a growing demand for context-aware security systems.

What CompleteView VMS Does

CompleteView VMS is an enterprise video management platform that aggregates video feeds, access credentials, and system data into a single operational interface. The goal is to provide situational awareness rather than isolated alerts.

In practice, this means security teams can correlate events across systems—linking camera footage with badge access logs, user activity, and historical data. This integrated view enables faster and more informed decision-making, particularly in complex environments such as campuses, transportation hubs, and industrial facilities.

The platform supports more than 25,000 conformant cameras and devices, reflecting its focus on large-scale deployments. Its hybrid-cloud architecture allows organizations to operate across on-premises, cloud, and mixed environments without requiring a full infrastructure overhaul.

Why Context Matters in Modern Surveillance

Traditional video surveillance systems have often been criticized for generating excessive alerts without actionable context. This “alert fatigue” can reduce effectiveness, as operators struggle to distinguish between routine activity and genuine threats.

Salient’s approach emphasizes context-driven intelligence—combining multiple data sources to provide a clearer understanding of events. Instead of reacting to isolated alerts, operators can assess situations based on a broader dataset.

This shift mirrors trends in enterprise software, where platforms are moving toward unified data environments. Companies like Microsoft and Amazon have been advancing similar concepts in cloud and analytics platforms, emphasizing integration and real-time insights.

Hybrid Infrastructure and Enterprise Adoption

One of the key factors behind the platform’s recognition is its hybrid-cloud design. Many organizations are in the process of modernizing legacy security systems while maintaining existing investments.

CompleteView VMS allows enterprises to extend their infrastructure rather than replace it. This approach aligns with broader IT strategies, where hybrid environments remain the norm as companies balance flexibility, cost, and compliance requirements.

The platform’s open architecture further supports integration with a wide range of devices and systems, making it adaptable to diverse operational environments.

Governance and Access Control

As video surveillance becomes more integrated with enterprise systems, governance and access control are becoming critical considerations. CompleteView incorporates role-based access controls using Active Directory, ensuring that users only access data relevant to their responsibilities.

This is particularly important in regulated industries, where data privacy and compliance requirements are stringent. By embedding governance into the platform, Salient is addressing a growing concern among enterprises deploying large-scale surveillance systems.

Commercial Model Reflects IT Convergence

Another notable aspect of the platform is its flexible pricing model. Salient offers per-camera pricing, along with options for perpetual licensing and subscription-based models.

This reflects a broader convergence between traditional security procurement and modern IT purchasing strategies. As IT departments take greater ownership of physical security infrastructure, demand is increasing for solutions that align with both capital expenditure (CAPEX) and operational expenditure (OPEX) models.

The shift also underscores how physical security is becoming part of the broader enterprise technology stack, rather than a standalone function.

Market Trends: Surveillance Meets Data Intelligence

The global video surveillance market is undergoing rapid transformation as AI, cloud computing, and analytics reshape the category. According to IDC, organizations are increasingly integrating video data with other enterprise systems to improve operational efficiency and risk management.

Gartner has similarly highlighted the rise of “smart” surveillance systems that combine video with analytics and contextual data to deliver actionable insights.

Salient’s recognition reflects these trends, particularly the move toward platforms that provide not just visibility, but intelligence.

Beyond Security: Operational Use Cases

While security remains the primary use case, video management platforms are increasingly being used for operational insights. Retailers, for example, use video data to analyze customer behavior, while manufacturers monitor workflows and safety compliance.

This expansion into operational intelligence positions VMS platforms as part of the broader data ecosystem—intersecting with analytics, IoT, and AI-driven decision-making.

For enterprise teams, this creates new opportunities to leverage existing infrastructure for business insights, not just risk mitigation.

What It Means Going Forward

The recognition of CompleteView VMS signals a shift in how video surveillance platforms are evaluated. Performance is no longer measured solely by recording and monitoring capabilities, but by the ability to integrate, contextualize, and operationalize data.

As enterprises continue to modernize their infrastructure, platforms that can bridge physical and digital systems will play an increasingly important role.

For Salient Systems, the award reinforces its positioning in a competitive market. For the industry, it highlights a broader transformation—where video surveillance becomes a key component of enterprise data and decision-making frameworks.

Market Landscape

The video surveillance market is evolving into a data-driven ecosystem, where platforms integrate video, access control, and analytics to deliver real-time operational intelligence. Hybrid-cloud architectures and AI-driven insights are becoming standard in enterprise deployments.

Top Insights

• Salient Systems earns recognition for its CompleteView VMS platform, which integrates video surveillance with access control and system data for context-driven decision-making.
• The platform’s hybrid-cloud architecture enables enterprises to modernize security infrastructure without replacing legacy systems, supporting flexible deployment models.
• Growing demand for situational awareness is driving adoption of unified platforms that reduce alert fatigue and improve operational response accuracy.
• Integration with enterprise IT systems reflects the convergence of physical security and digital infrastructure, aligning with broader cloud and analytics trends.
• Expanding use cases beyond security, including operational analytics and workflow monitoring, position VMS platforms as part of the enterprise data ecosystem.

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As enterprises push artificial intelligence deeper into operational workflows, a critical weakness is becoming harder to ignore: AI systems often act on data that hasn’t been validated in the moment it matters. Qualytics is attempting to address that gap with a new architecture it calls the Data Control Layer.

The launch reflects a broader shift in enterprise AI—from systems that analyze data to those that execute decisions. In that transition, data quality is no longer a reporting issue; it becomes a real-time control problem.

From Data Validation to Decision-Time Governance

At the center of Qualytics’ announcement is a concept it calls “validate-at-use.” Instead of relying on traditional data quality checks embedded in pipelines, the platform evaluates data at the exact moment it is used by AI systems.

This approach challenges the prevailing model of data governance. Historically, organizations have focused on validating data upstream—ensuring accuracy before it enters analytics systems. But AI agents and copilots increasingly retrieve and combine data dynamically, often bypassing static validation layers.

The implication is significant: by the time traditional checks are applied, AI systems may have already acted.

Qualytics’ Data Control Layer is designed to insert governance directly into AI reasoning processes. It provides real-time signals about data quality, allowing systems to determine whether the context they rely on is trustworthy before taking action.

What the Data Control Layer Does

The platform combines multiple inputs—including AI-inferred rules, human-defined policies, anomaly detection, and historical data signals—into a unified governance layer.

These signals are then made accessible across different interaction models. Human users can interact through dashboards and workflows, while AI copilots and agents can access the same governed context through APIs and Model Context Protocol (MCP) integrations.

External systems such as ChatGPT and Microsoft Copilot can tap into these signals to improve decision-making accuracy. Autonomous systems, meanwhile, can enforce thresholds in real time—effectively turning data quality into an operational control mechanism.

Qualytics says customers are already running tens of thousands of rules in production, with the majority inferred by AI. This reflects a hybrid model where automation handles scale while human teams guide governance policies.

Why AI Changes the Stakes for Data Quality

The rise of agentic AI is fundamentally altering the role of data. In traditional analytics environments, poor data might lead to inaccurate dashboards or flawed reports. In AI-driven systems, it can trigger automated actions—financial transactions, workflow changes, or customer interactions—at machine speed.

This shift is widening the gap between validated data and acted-upon data. As AI systems operate across distributed environments, ensuring consistency and trust becomes increasingly complex.

Qualytics’ approach reframes data quality as a continuous, real-time process rather than a one-time validation step. This aligns with the needs of modern AI systems, which require dynamic context to function effectively.

Positioning in a Crowded Data Stack

The data quality and observability market is already competitive, with vendors offering tools to monitor pipelines, detect anomalies, and enforce governance policies. However, most of these solutions are designed for batch processing and static workflows.

Qualytics is positioning the Data Control Layer as a departure from traditional observability. Instead of focusing on what happened, the platform aims to influence what happens next—embedding quality signals directly into decision-making processes.

This places the company at the intersection of data governance, AI infrastructure, and real-time analytics.

The concept also aligns with broader industry trends. Major platforms from Google, Microsoft, and Amazon are investing in AI governance frameworks, recognizing that trust and control are becoming critical to enterprise adoption.

Enterprise Impact: From Data Teams to Marketing Ops

For enterprise organizations, the implications extend beyond IT and data engineering teams. Marketing, finance, and operations functions increasingly rely on AI-driven systems to automate decisions and personalize experiences.

In marketing technology stacks, for example, AI models drive campaign optimization, audience segmentation, and real-time personalization. If the underlying data is flawed, the impact can cascade across customer journeys.

By introducing real-time validation, the Data Control Layer could help ensure that AI-driven decisions are based on reliable inputs—improving both performance and compliance.

This is particularly relevant as organizations integrate AI copilots into everyday workflows. Ensuring that these systems operate on governed, high-quality data is becoming a prerequisite for scaling AI initiatives.

Market Direction: Toward Real-Time AI Governance

The launch comes amid growing emphasis on AI governance frameworks. According to Gartner, organizations are increasingly investing in trust, risk, and security management (TRiSM) to ensure responsible AI deployment.

Meanwhile, IDC reports that data quality and governance are among the top priorities for enterprises operationalizing AI at scale.

Qualytics’ validate-at-use model reflects this shift, emphasizing the need for continuous validation in dynamic environments.

What Comes Next

As AI systems become more autonomous, the demand for real-time control mechanisms is likely to grow. Enterprises will need to ensure not only that their data is accurate, but that it remains trustworthy at the moment of use.

The Data Control Layer represents one approach to solving this problem—embedding governance directly into AI workflows rather than treating it as a separate function.

Whether this model becomes a standard will depend on adoption and integration with broader enterprise ecosystems. But the direction is clear: in the AI era, data quality is no longer just about accuracy—it’s about control.

Market Landscape

The rise of agentic AI is driving demand for real-time data governance solutions. Traditional data quality and observability tools are evolving toward dynamic, context-aware models that can support autonomous decision-making across enterprise systems.

Top Insights

• Qualytics introduces the Data Control Layer, shifting data quality from static validation to real-time governance at the moment AI systems make decisions.
• The “validate-at-use” model addresses risks in agentic AI, where automated systems act on dynamic data without traditional pipeline checks.
• Integration with AI copilots and APIs enables governed data context across human, AI-assisted, and autonomous workflows in enterprise environments.
• Growing adoption of AI-driven automation increases the need for real-time controls to prevent errors in financial, operational, and customer-facing systems.
• The launch reflects broader industry trends toward AI governance frameworks as enterprises prioritize trust, compliance, and decision accuracy.

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As artificial intelligence matures from experimentation to enterprise deployment, a new constraint is emerging—one that has less to do with algorithms and more to do with the data powering them. Prosper Insights & Analytics is spotlighting what it يرى as the next competitive frontier: access to high-quality, forward-looking data.

The argument, outlined in a recent Forbes article by co-founder and CEO Gary Drenik, reframes a widely held assumption in the AI industry. While much of the focus has been on model innovation and scale, Prosper contends that the real bottleneck now lies in the scarcity of reliable, predictive data inputs.

From Algorithms to Data Scarcity

The central thesis is straightforward: AI systems are only as good as the data they are trained on. And increasingly, that data is falling short.

Drenik compares high-quality datasets to “rare earth elements”—critical but difficult-to-source inputs that underpin modern technologies. The analogy reflects a growing realization across the industry: while compute power and model architectures have advanced rapidly, the availability of clean, structured, and forward-looking data has not kept pace.

Most AI systems today are trained on historical data—clickstreams, transaction logs, and scraped web content. While useful for pattern recognition, these datasets often lack the depth and context needed to explain why behaviors occur or to reliably predict future outcomes.

This limitation becomes more pronounced as enterprises push AI into mission-critical use cases such as forecasting, planning, and strategic decision-making.

Why Forward-Looking Data Matters

Forward-looking data refers to datasets that capture intent, sentiment, and behavioral shifts before they are reflected in traditional metrics. These signals—often derived from longitudinal studies, proprietary panels, or structured surveys—offer a more predictive view of consumer and market dynamics.

According to Phil Rist, the competitive advantage in AI is shifting toward the quality of these signals. When AI systems are trained on data that reflects evolving human behavior in real time, they move beyond automation into the realm of strategic intelligence.

This distinction is critical for enterprise applications. In marketing, for example, predictive insights can inform campaign strategies before trends become visible in analytics dashboards. In finance, forward-looking data can improve risk modeling and macroeconomic forecasting.

Enterprise Implications: From Data Exhaust to Data Strategy

The shift has significant implications for how organizations approach data strategy. Historically, many companies have relied on “digital exhaust”—the byproducts of online activity—as the primary fuel for AI systems.

But as AI adoption scales, this approach is proving insufficient. Enterprises are increasingly recognizing the need for curated, auditable, and representative datasets that can support explainability and compliance requirements.

This aligns with broader trends identified by Gartner, which has emphasized the importance of AI trust, risk, and security management (TRiSM) frameworks. Similarly, IDC reports that organizations are prioritizing data governance and quality as key enablers of AI success.

For marketing technology teams, this evolution is particularly relevant. Customer data platforms (CDPs), analytics tools, and AI-driven personalization engines all depend on high-quality inputs. Without reliable data, even the most advanced models can produce misleading or biased outputs.

Competitive Landscape: Data as a Strategic Asset

The growing importance of data quality is reshaping the competitive landscape across the AI ecosystem. Major technology providers—including Google, Microsoft, and Amazon—are investing heavily in data infrastructure, governance tools, and proprietary datasets.

At the same time, enterprises are exploring ways to build or acquire their own data assets. This includes investing in first-party data collection, forming data partnerships, and developing internal data products.

Prosper’s perspective suggests that the next phase of AI competition will be defined less by who has the largest models and more by who has access to the most valuable data.

This shift also has implications for regulation and ethics. As organizations rely more heavily on proprietary datasets, questions around transparency, representativeness, and bias will become increasingly important.

Beyond Open-Web Data

Another key theme is the declining marginal value of open-web data for advanced AI use cases. While large-scale web scraping has been instrumental in training foundational models, it may not be sufficient for enterprise-grade applications that require precision and accountability.

Prosper argues that proprietary, signal-rich datasets—particularly those with longitudinal depth—are becoming more valuable. These datasets enable organizations to track changes over time, providing a more nuanced understanding of behavior and trends.

This perspective is echoed in industry discussions around synthetic data, data augmentation, and domain-specific training sets, all of which aim to address gaps in traditional datasets.

What It Means for the Future of AI

The implications of this shift extend beyond technology. Businesses, investors, and policymakers will need to rethink how data is sourced, governed, and valued.

For enterprises, the message is clear: building effective AI systems requires more than investing in models and infrastructure. It requires a deliberate focus on data quality, governance, and strategic alignment.

For the AI industry as a whole, the challenge is to develop new approaches to data acquisition and management that can support the next generation of applications.

As AI continues to evolve, the “rare earths of data” may prove to be the defining factor in determining which organizations succeed.

Market Landscape

The AI market is entering a new phase where data quality, governance, and proprietary datasets are becoming key differentiators. As enterprises scale AI adoption, the focus is shifting from model performance to data integrity, explainability, and predictive accuracy.

Top Insights

• Prosper Insights highlights a critical shift in AI: high-quality, forward-looking data is becoming the primary constraint, replacing algorithms as the main competitive bottleneck in enterprise AI.
• Enterprises relying on historical “digital exhaust” face limitations in prediction and explainability, driving demand for longitudinal and intent-based datasets.
• Proprietary, signal-rich data is emerging as a strategic asset, enabling more accurate forecasting, decision-making, and AI-driven business intelligence.
• Growing emphasis on data governance aligns with trends from Gartner and IDC, as organizations prioritize trust, compliance, and transparency in AI systems.
• The competitive landscape is shifting toward organizations that control high-integrity datasets, rather than those with the largest or fastest AI models.

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