In the race to operationalize AI inside financial services, point tools are starting to look dated. Today, Provenir is betting that consolidation—not more fragmentation—is what banks and lenders need.

The company has launched a revamped Decision Intelligence platform that brings together data ingestion, machine learning models, decision orchestration, optimization, and now agentic AI capabilities into a single, continuous system. The goal: help financial institutions turn raw customer data into real-time, explainable decisions without bouncing between disconnected systems.

It’s a bold pitch in a market where AI decisioning has moved from “nice-to-have differentiator” to operational necessity.

What’s Actually New

At the heart of the announcement is a more tightly integrated platform architecture—and the addition of agentic AI features designed to actively assist users rather than simply surface analytics.

1. Unified Decision Intelligence Core

Provenir’s platform combines:

• Data ingestion and enrichment

• Machine learning model management

• Real-time and batch decisioning

• Continuous optimization and feedback loops

Instead of separating analytics, decision engines, and monitoring tools, Provenir claims its system executes decisions, measures outcomes, learns from results, and recommends improvements—all within one environment.

That closed-loop design is increasingly important in regulated sectors like lending, where speed must coexist with auditability.

2. Agentic AI Assistant

A newly embedded AI assistant introduces natural language access to platform capabilities. Users can:

• Query datasets conversationally

• Understand decision logic and outputs

• Automate tasks such as document review

• Interact with workflows without deep technical expertise

This mirrors a broader shift across enterprise software, where AI copilots are becoming standard in everything from CRM to cloud management platforms. The difference here is domain specificity: Provenir is embedding agentic AI directly into risk and credit decisioning workflows.

3. Advanced Model Management and Simulation

Provenir is also emphasizing improved model governance and testing capabilities. Users can:

• Monitor and compare machine learning model performance

• Run simulations to test strategy shifts

• Reduce testing cycles from months to weeks—or even days

In volatile economic conditions, that kind of agility matters. Institutions can quickly test policy changes against shifting credit risk environments or regulatory updates before deploying them live.

AI With Guardrails: Human-in-the-Loop Design

Financial services is not Silicon Valley’s playground; it’s heavily regulated terrain. Provenir is positioning its “human-in-the-loop” framework as a key differentiator.

The platform offers:

• Transparency into how AI models generate decisions

• Explainability tools for audit and compliance

• Governance controls aligned with regulatory standards

With growing scrutiny around AI accountability in lending and underwriting, explainability isn’t optional—it’s existential.

LLM Integration: OpenAI, Anthropic, and Private AI via AWS

Provenir is also expanding its Global Data Marketplace into what it describes as a unified hub for both data and AI.

The company now integrates leading public and private large language models, including:

• OpenAI

• Anthropic

Customers can access these models through pre-integrated APIs or deploy private instances hosted via Amazon Web Services Bedrock for sensitive workloads.

This hybrid AI strategy reflects a growing enterprise trend: organizations want cutting-edge LLM capabilities but without sacrificing data residency, compliance, or control.

By embedding LLM access directly into decision workflows, Provenir is positioning itself as a governed gateway rather than a generic AI layer.

Why This Matters Now

The timing isn’t accidental.

Financial institutions are facing:

• Rising customer expectations for personalization

• Increased M&A activity

• Economic uncertainty affecting credit risk

• Regulatory tightening around AI transparency

• Pressure to modernize legacy risk systems

Traditional decisioning stacks often involve siloed data lakes, separate model environments, disconnected rule engines, and patchwork compliance tools. That fragmentation slows innovation and complicates governance.

Provenir’s platform approach aims to collapse those silos into a single operational layer for decision intelligence.

If it works as advertised, the benefits could include:

• Faster deployment of new lending products

• Improved risk/reward optimization

• Reduced operational overhead

• More consistent decision logic across channels

• Better alignment between business goals and AI outcomes

Competing in a Crowded AI Decisioning Market

Provenir operates in a competitive landscape that includes credit bureau decisioning platforms, fintech orchestration engines, and enterprise AI vendors pushing into financial services.

What differentiates Provenir’s announcement is its emphasis on:

• End-to-end orchestration

• Continuous learning loops

• Embedded LLM integration

• Human oversight built into the system

Rather than positioning AI as an overlay, Provenir is pitching AI as infrastructure.

That distinction could resonate with mid-sized lenders and large financial institutions looking to modernize without assembling multi-vendor AI stacks.

Use Cases Across Risk and Credit

The platform supports:

• Real-time underwriting

• Fraud detection

• Customer onboarding

• Credit line management

• Portfolio monitoring

• Regulatory reporting

It scales from smaller lenders to large multinational banks, handling both real-time and batch processing environments.

For institutions operating across jurisdictions, the ability to localize decision logic while maintaining centralized governance may prove particularly valuable.

The Bigger Picture: Decision Intelligence as Strategy

“Decision intelligence” is increasingly becoming its own category, sitting at the intersection of AI, analytics, and business strategy.

Instead of focusing solely on predictive models, organizations are now asking:

• How do we connect decisions to measurable outcomes?

• How do we adapt policies in near real time?

• How do we ensure AI decisions are compliant and explainable?

Provenir’s unified platform strategy speaks directly to those concerns.

If AI adoption in financial services is moving from experimentation to operationalization, then infrastructure-level solutions—rather than isolated AI features—are likely to define the next phase.

What to Watch

The key questions for Provenir going forward:

• How seamlessly can institutions migrate from legacy systems?

• Will customers adopt public LLM integrations or default to private AI deployments?

• Can Provenir maintain performance and compliance as regulatory frameworks evolve?

The agentic AI layer adds appeal, but execution will determine whether this is incremental innovation or meaningful transformation.

What’s clear is that AI-powered decisioning is no longer optional. Institutions that can’t adapt risk being outpaced by competitors who can move faster, personalize smarter, and manage risk more precisely.

Provenir is betting its unified Decision Intelligence platform is the engine that makes that shift possible.

Get in touch with our MarTech Experts.

The medical AI market has matured. It’s no longer enough to boast benchmark-beating algorithms. Hospitals and imaging centers now want something more practical: AI that works inside existing systems without slowing clinicians down.

That’s the premise behind a new partnership between Coreline Soft and INFINITT North America, which has delivered a fully automated, “zero-click” AI reading solution now live in U.S. radiology practices.

Instead of asking radiologists to jump between dashboards or sign into yet another platform, the companies have embedded Coreline’s AVIEW AI directly into the INFINITT PACS environment. The result: AI-powered insights appear natively within the radiologist’s existing workflow—no extra logins, no manual triggers, no workflow detours.

In a field where seconds matter and cognitive load is high, that detail is more than cosmetic. It’s strategic.

From Algorithm Accuracy to Operational Infrastructure

The global medical AI conversation has shifted over the past five years. Early entrants focused heavily on detection rates and sensitivity metrics. But clinical adoption has lagged when tools disrupted established reading patterns.

Radiologists don’t want another screen. They want smarter readings inside the one they already use.

By deeply embedding Coreline’s AVIEW platform into INFINITT PACS, the integration transforms AI from an optional add-on into background infrastructure. The system automatically:

• Performs coronary artery calcium (CAC) scoring

• Enhances lung nodule detection

• Reduces overall case reading times

For high-volume radiology groups processing more than 6,000 cases per quarter, these efficiencies compound quickly.

A “Zero-Click” Model in Practice

The first U.S. site to adopt the solution, ImageCare Radiology, reported a rapid rollout across its network—completed within one month. According to its leadership, the impact was immediate in both speed and diagnostic accuracy.

The phrase “zero-click” isn’t marketing fluff here. It signals something critical: the AI activates without user intervention.

That matters because adoption in radiology often fails at the friction point. Even small workflow disruptions can stall usage. By eliminating manual triggers and secondary interfaces, the system keeps the radiologist’s attention on interpretation rather than navigation.

This approach aligns with broader industry trends. Enterprise AI vendors in healthcare are increasingly pursuing deep PACS and EHR integrations rather than standalone AI dashboards. The goal is invisible intelligence, not visible complexity.

Revenue Impact: From Detection to Follow-Up

Beyond clinical metrics, the integration carries financial implications.

Improved detection sensitivity and automated CAC scoring can increase downstream follow-up exams. In value-based care environments and screening programs, that translates directly into measurable ROI.

For imaging networks handling thousands of cases quarterly, even modest increases in follow-up rates can create meaningful revenue uplift. AI that identifies more actionable findings doesn’t just improve care—it expands billable opportunities.

That dual impact—clinical performance plus financial return—is becoming a decisive factor in AI procurement decisions.

The INFINITT Integration Suite: Built for U.S. Reimbursement

To accelerate adoption in the U.S., Coreline Soft has launched the INFINITT Integration Suite, a dedicated package tailored specifically for INFINITT users.

The suite enables:

• Rapid deployment of AI modules

• Support for CPT-based reimbursement

• Fully automated, hands-free workflows

Reimbursement alignment is particularly important in the U.S. healthcare system, where financial viability often dictates technology adoption. By incorporating CPT code support, the companies are addressing a common bottleneck in AI commercialization.

Instead of forcing radiology groups to navigate reimbursement uncertainty, the integration is designed to plug directly into established billing structures.

A Competitive Thoracic AI Portfolio

Coreline Soft isn’t stopping at workflow integration. The company is positioning its broader thoracic AI portfolio for visibility at the 2026 annual meeting of the Society of Thoracic Radiology.

The lineup includes:

• AVIEW LCS, positioned as a “First Reader” solution for lung cancer screening

• AVIEW Lung Texture for interstitial lung disease (ILD)

• AVIEW COPD for chronic obstructive pulmonary disease assessment

The portfolio also emphasizes Opportunistic Screening—extracting multiple clinical insights from a single low-dose CT scan. That means simultaneously evaluating:

• Lung cancer risk

• COPD indicators

• Cardiovascular risk markers

This multi-condition analysis from one imaging study reflects a growing industry push toward comprehensive, AI-enhanced diagnostic value from existing scans.

Why This Matters in 2026

Radiology is under mounting pressure. Workforce shortages persist, imaging volumes continue to rise, and demand for screening programs—particularly lung cancer screening—is expanding.

At the same time, AI vendors are proliferating.

What differentiates this deployment is not simply detection capability but operational embedment. In an increasingly crowded AI imaging market, seamless integration may matter more than marginal gains in sensitivity.

Healthcare IT buyers are asking new questions:

• Does the AI fit into existing PACS systems?

• Can it scale across multi-site networks quickly?

• Does it support reimbursement models?

• Will clinicians actually use it?

By eliminating friction, Coreline Soft and INFINITT are attempting to answer all four at once.

The Bigger Trend: Invisible AI Wins

The most successful AI in healthcare may be the least noticeable.

As the market evolves from pilot programs to production environments, solutions that operate quietly inside established systems—rather than demanding attention—are likely to win.

The “zero-click” approach signals a shift in medical AI maturity. Instead of asking clinicians to adapt to software, vendors are adapting software to clinicians.

If this model scales beyond early adopters, it could set a new baseline for how imaging AI is delivered in U.S. radiology practices.

And in a field where workflow efficiency and diagnostic precision are both mission-critical, invisible intelligence may be the smartest innovation of all.

Get in touch with our MarTech Experts.

BMC is doubling down on cloud—and on Amazon. The enterprise software veteran has signed a five-year strategic collaboration agreement (SCA) with Amazon Web Services to expand how enterprises orchestrate application workflows and data pipelines at scale.

At the center of the deal is BMC’s Control-M SaaS platform, which will now run on AWS as its preferred cloud provider. The move formalizes a long-standing relationship and positions Control-M as a cloud-native orchestration layer for hybrid, data, and AI-driven enterprises.

For CIOs and data leaders grappling with sprawling multi-cloud environments and AI initiatives, the message is clear: BMC wants to be the automation backbone that connects it all—now with deeper AWS integration and generative AI baked in.

What’s New: GenAI and Agentic AI Meet Enterprise Orchestration

The headline innovation isn’t just infrastructure. It’s intelligence.

Through the partnership, BMC is embedding its generative AI capabilities—most notably Jett, its AI-powered advisor—into Control-M running on AWS. Jett delivers intelligent guidance, automated insights, and context-aware recommendations designed to help teams modernize faster across AWS environments.

BMC is also pushing into agentic AI, positioning Control-M as more than a job scheduler. The company aims to deliver native AWS-based automation that can autonomously manage data pipelines and workflows across hybrid and cloud systems.

For organizations building machine learning pipelines in services like Amazon SageMaker, the integration promises out-of-the-box orchestration that ties together data prep, model training, deployment, and monitoring. That’s a practical win for enterprises trying to move AI projects from pilot to production without creating new silos.

Why It Matters: Orchestration Is the New Control Plane

As enterprises scale AI and data initiatives, orchestration has quietly become mission-critical. Modern architectures often span on-prem infrastructure, multiple clouds, containers, and managed AI services. Without a unified layer to coordinate workflows, complexity spirals fast.

That’s where Control-M fits in.

Already available in AWS Marketplace, the platform enables end-to-end orchestration of data pipelines across hybrid environments. With this expanded collaboration, BMC is betting that AWS customers want a single control plane to manage everything from legacy workloads to containerized microservices to AI jobs.

The timing aligns with broader industry shifts. Enterprises are accelerating cloud modernization while contending with governance, data residency, and compliance demands. BMC recently expanded Control-M SaaS availability to the AWS Sydney Region, alongside deployments in Ireland, Canada, and the U.S.—a nod to rising demand for localized data processing and resiliency.

Deep AWS Integration Across Services

The collaboration extends beyond hosting. BMC continues to roll out monthly integrations with AWS services, including:

• Amazon Athena

• Amazon Bedrock

• Amazon Elastic Container Service (ECS)

• AWS CloudFormation

• AWS Mainframe Modernization

• Amazon SageMaker

This expanding ecosystem suggests a deliberate strategy: make Control-M the connective tissue between AWS-native services and legacy enterprise systems.

For customers like Air Europa, the appeal is immediate integration with tools such as Amazon SageMaker to support data, machine learning, and AI roadmaps. The orchestration layer becomes the operational glue that ties data strategy to execution.

Competitive Context: The Automation Arms Race

BMC’s move comes amid intensifying competition in automation and workload orchestration. Vendors across the IT operations, DevOps, and data engineering spectrum are racing to embed AI into workflow management. Meanwhile, hyperscalers like AWS are expanding their own native orchestration capabilities.

By aligning closely with AWS rather than competing head-on, BMC is taking a pragmatic route. It strengthens its relevance inside the AWS ecosystem while differentiating through enterprise-grade automation depth and hybrid support—areas where many cloud-native tools still lag.

The five-year term also signals commitment. Strategic collaboration agreements with AWS typically involve joint go-to-market efforts, co-innovation, and tighter technical alignment. For BMC, that means greater visibility inside one of the world’s largest cloud marketplaces.

The Bigger Picture: Modernization Without Disruption

For enterprises, modernization isn’t just about moving to the cloud. It’s about orchestrating legacy systems, data platforms, AI workloads, and emerging services without breaking what already works.

By making AWS its preferred cloud for Control-M SaaS, BMC is betting that customers want flexibility without fragmentation. The promise: unified orchestration across hybrid, cloud, data, and AI workloads—paired with AI-driven insights to reduce operational drag.

If BMC delivers, Control-M could evolve from a trusted scheduling platform into a strategic AI-era control layer for enterprise operations.

In a market obsessed with generative AI headlines, this deal is less about flashy demos and more about plumbing—the kind that determines whether digital transformation efforts actually scale.

And in enterprise IT, the plumbing is where the real battles are won.

Get in touch with our MarTech Experts.

Application security is colliding with a new reality: thousands of repositories, globally distributed teams, and a surge of AI-generated code. Today, Black Duck is responding with a major update to its Polaris platform, rolling out enhanced, native integrations across all major source code management (SCM) systems.

The upgraded Black Duck Polaris Platform now delivers built-in integrations with GitHub, GitLab, Azure DevOps, and Bitbucket—not as bolted-on scripts, but as natively engineered connections designed for enterprise scale.

In an era when code is written by both humans and machines, Black Duck is making a clear bet: security has to move at the speed of development, or it becomes irrelevant.

What’s New: Native, Automated, and AI-Aware AppSec

Polaris has long combined static application security testing (SAST), software composition analysis (SCA), and dynamic application security testing (DAST) in a SaaS model. What’s new here is the depth of automation and orchestration across SCM environments.

The enhanced integrations introduce:

• Instant onboarding for thousands of repositories without manual setup

• Continuous synchronization as repos are renamed, branched, or created

• Automated scan triggers on pull request creation, updates, and pre-merge events

• Single-click policy enforcement across large repo estates

• Automatic user and role synchronization

For organizations managing hundreds—or thousands—of repositories, that shift matters. Manual onboarding and piecemeal security tools often lead to blind spots. Polaris now aims to eliminate those gaps by auto-detecting changes across SCM systems and maintaining continuous coverage.

In practical terms, security scans can now trigger automatically during the pull request process, embedding vulnerability detection directly into code review workflows. Developers see findings inside the pull request itself, reducing the need to switch tools or escalate late-stage issues.

That’s DevSecOps without the “Sec” slowing things down.

AI-Powered Security for AI-Generated Code

The rise of generative coding tools has fundamentally changed the attack surface. Enterprises are now grappling with code that may be syntactically correct but security-naïve—or worse, subtly flawed at scale.

Black Duck is leaning into AI to counter AI.

Through Black Duck Signal, organizations can run AI-powered scans directly in the IDE or through CI/CD pipelines, all centrally managed in Polaris. Signal is designed to surface meaningful security insights in both human- and AI-generated code, before it ever makes it into production.

Meanwhile, Code Sight extends that coverage directly into the developer’s desktop environment. It triggers Polaris scans in real time while coding, and when combined with Black Duck Assist’s AI-driven remediation guidance, offers contextual fixes instead of abstract vulnerability reports.

The goal: catch vulnerabilities before commit, not after deployment.

In a market crowded with AI security claims, the differentiator here is workflow placement. Black Duck isn’t just adding AI to dashboards—it’s embedding intelligence at the precise points where code changes happen.

Customizable Scanning for Modern Development Cadence

Another key addition is flexible scanning depth. Teams can opt for:

• Full, deep analysis for comprehensive security checks

• Rapid analysis for ultra-fast feedback in high-velocity workflows

This dual-mode capability reflects a broader industry trend: security must adapt to different pipeline contexts. A hotfix merge doesn’t require the same scanning depth as a major release candidate. Polaris now allows enterprises to tailor scanning to the moment, balancing speed with rigor.

Why It Matters: Scaling DevSecOps Without Adding Friction

Enterprise software development has become massively distributed. Teams are global. Repositories multiply quickly. AI accelerates output. But security headcount doesn’t scale linearly.

That imbalance creates risk.

Black Duck’s enhanced SCM integrations aim to solve the operational bottleneck: instead of manually onboarding projects and enforcing policies repo by repo, organizations can automate coverage across their entire SCM footprint.

The company claims no other solution combines this breadth of SCM support with universal event- and policy-based automation, alongside AI-powered depth of analysis.

While competitors in the AppSec space are increasingly emphasizing platform consolidation and AI assistance, Polaris positions itself as both comprehensive and workflow-native. The strategy reflects a growing realization in the industry: fragmented security tools don’t just slow teams—they create coverage gaps attackers exploit.

The Bigger Picture: AppSec in the Age of Code Explosion

Software supply chains are expanding rapidly. Microservices, third-party libraries, and AI-generated snippets have made applications more modular—and more vulnerable.

At the same time, enterprises are racing to operationalize AI, often across sprawling codebases managed in mixed SCM environments. Security leaders are under pressure to ensure policy consistency across GitHub, GitLab, Azure DevOps, and Bitbucket simultaneously.

By offering unified, automated coverage across all four major SCM platforms, Black Duck is targeting that exact pain point.

The result isn’t just tighter integration. It’s an attempt to make security ambient—always present, always synchronized, and invisible until needed.

If Polaris delivers on its promise, enterprises may finally be able to scale DevSecOps without scaling friction alongside it.

Get in touch with our MarTech Experts.

Financial crime compliance is getting more complex—and more expensive. In response, Feedzai and Neterium have announced a strategic partnership aimed at consolidating watchlist and transaction screening into a single, AI-powered platform.

The deal embeds Neterium’s cloud-native screening technology directly into Feedzai’s RiskOps platform, expanding its Watchlist Screening solution with newly launched Transaction Screening capabilities. The result: a unified AML and sanctions screening engine built for real-time payments and modern compliance demands.

For financial institutions juggling fragmented compliance stacks and mounting regulatory scrutiny, the message is simple—fewer integrations, faster deployment, and smarter detection.

What’s New: Real-Time Transaction Screening Meets Watchlist Intelligence

Feedzai’s Watchlist Screening solution already delivers API-driven, ultra-low-latency compliance checks. With Neterium’s advanced algorithmic matching now integrated, the platform extends beyond static name checks to dynamic transaction screening in real time.

That matters in the instant payments era. As funds move in seconds, compliance checks must keep pace without slowing down the customer experience.

The upgraded platform promises:

• Frictionless real-time processing that scales to peak transaction volumes

• AI-driven holistic matching to reduce false positives

• Automated global sanctions updates to maintain regulatory accuracy

• Explainable decisioning and audit-ready reporting

• Integrated fraud and AML insights across Feedzai’s broader suite

By embedding Neterium’s infrastructure directly into its financial crime prevention stack, Feedzai is positioning itself as a single control layer for sanctions screening, transaction monitoring, and fraud prevention.

Why It Matters: False Positives Are Draining Compliance Teams

For banks and fintechs, false positives aren’t just an annoyance—they’re a cost center. Analysts spend hours clearing alerts that pose no real threat, while true risks can slip through fragmented systems.

Neterium’s algorithmic matching is designed to cut through that noise. Smarter entity resolution and contextual screening aim to reduce unnecessary alerts while improving detection precision.

Equally important is transparency. Regulators increasingly demand explainable AI models and detailed audit trails. Feedzai says the unified platform delivers end-to-end visibility and compliance-ready reporting, addressing both operational efficiency and regulatory defensibility.

At a time when global sanctions lists evolve rapidly and regulatory bodies tighten expectations, automated, real-time data updates eliminate manual list management—a persistent pain point for compliance teams.

A Strategic Bet on Platform Consolidation

The partnership reflects a broader market shift toward consolidation in RegTech and financial crime prevention. Institutions are under pressure to simplify their tech stacks while maintaining comprehensive coverage across fraud, AML, sanctions, and transaction screening.

Rather than building from scratch, Feedzai is extending its capabilities through embedded infrastructure—folding Neterium’s cloud-native screening engine into its RiskOps architecture.

For Neterium, the deal expands reach into Feedzai’s global banking customer base. For Feedzai, it strengthens its claim as an AI-native, end-to-end financial crime platform.

The timing is notable. Instant payments, cross-border transfers, and digital banking growth have expanded both transaction volumes and exposure to sanctions risk. Regulators expect faster detection with fewer errors—an increasingly difficult balance to strike.

The Bigger Picture: Compliance at Machine Speed

Financial crime prevention is moving toward continuous, real-time decisioning powered by AI. Static batch screening models no longer suffice in an ecosystem defined by instant payments and embedded finance.

By integrating transaction screening directly into its platform, Feedzai is aiming to align compliance with transaction velocity. The promise isn’t just faster checks—it’s smarter, explainable risk decisions that reduce friction for legitimate customers.

If successful, the collaboration could signal a new baseline for compliance platforms: unified screening, integrated fraud insights, and AI-driven matching—all delivered through a single API-powered ecosystem.

For compliance leaders navigating escalating risk and shrinking operational tolerance for inefficiency, that consolidation may be more than convenient. It may be necessary.

Get in touch with our MarTech Experts.

Validity Inc. has launched Validity Engage, a next-generation AI platform built to help marketing teams identify risk before campaigns go live, optimize performance, and execute faster with fewer surprises.

Unveiled at Litmus Live 2026, Engage marks a significant shift for Validity—from monitoring and diagnostics to predictive, agentic AI embedded directly into the campaign lifecycle.

For marketers drowning in performance data yet still reacting to problems after send, Engage promises something different: foresight.

Four AI Agents, One Campaign Workflow

At the core of Engage are four specialized AI agents designed to operate across every email send:

• Ignite Agent flags and fixes rendering, code, and compliance risks before deployment.

• Guardian Agent monitors subscriber experience and deliverability signals to catch issues early.

• Expression Agent generates on-brand copy and subject line variants to maintain consistency and lift engagement.

• Insight Agent benchmarks performance against competitors and surfaces missed revenue opportunities.

Together, they aim to shift email marketing from reactive troubleshooting to proactive optimization.

The structure reflects a broader industry trend toward agentic AI—systems that don’t just analyze but act. Instead of siloed tools for testing, deliverability, and copywriting, Engage embeds automated decision support across the full send process.

Powered by 2.5 Billion Daily Data Points

What differentiates Engage, according to Validity, is its data backbone.

Unlike point solutions trained only on internal client datasets, Engage draws from Validity’s global email intelligence network, which processes more than 2.5 billion data points daily—representing a substantial share of commercial email traffic worldwide.

That scale matters. Deliverability and engagement patterns vary widely by region, ISP, and industry. By training AI models on a broader data stream, Validity claims it can anticipate inbox placement outcomes and campaign risk with greater accuracy.

In practical terms, that means identifying potential rendering issues, compliance red flags, or inbox placement risks before they dent open rates—or revenue.

Litmus Gets Deeper Deliverability Intelligence

Engage isn’t the only product update.

Validity also expanded deliverability visibility within Litmus, bringing aggregated inbox, spam, and tab placement data directly into the platform marketers already use to build and test campaigns.

For the first time, Litmus users can see where recent campaigns landed—primary inbox, promotions tab, or spam—without relying solely on ESP-level reporting.

That’s a notable development. Many email service providers provide high-level engagement metrics but limited transparency into inbox placement across ISPs. By embedding placement insights directly into Litmus, Validity is positioning itself as an end-to-end email intelligence ecosystem.

Unlimited Pricing: A Strategic Shift

Alongside the AI rollout, Validity introduced an unlimited pricing model across its solutions, eliminating seat caps and usage-based restrictions.

It’s a quiet but strategic move. In large enterprise environments, seat-based pricing often limits adoption across creative, operations, and compliance teams. Removing those barriers could increase platform penetration and encourage broader data sharing—fuel for the AI engine itself.

Why It Matters: Email’s AI Inflection Point

Email remains one of the highest-ROI marketing channels, yet it’s also one of the most operationally complex. Deliverability shifts constantly. Compliance regulations tighten. Content expectations rise. And now, generative AI is accelerating production speed—sometimes faster than quality control can keep up.

Engage enters a crowded martech landscape where AI copy tools, deliverability monitors, and testing platforms already exist. The difference Validity is betting on: consolidation plus predictive intelligence.

Rather than stitching together tools for content generation, inbox monitoring, and benchmarking, enterprise teams can operate inside a unified AI environment that flags risks, suggests optimizations, and surfaces revenue gaps automatically.

If the promise holds, Engage could transform email from a performance channel marketers react to into one they actively steer with AI guidance.

For an industry where a single inbox placement shift can mean millions in revenue impact, that predictive edge may be more than a convenience—it may become table stakes.

Get in touch with our MarTech Experts.

Managed security providers are under pressure to do more than monitor alerts—they’re expected to secure hybrid environments, tame cloud sprawl, and protect identity systems without slowing innovation. This week, Vandis, Inc. earned recognition for that effort, landing on the 2026 MSP 500 list in the Security 100 category from CRN, a property of The Channel Company.

The annual MSP 500 list spotlights North America’s leading managed service providers, with the Security 100 category specifically honoring companies delivering advanced cybersecurity services.

For Vandis, the recognition underscores its growing footprint in managed security services—particularly across complex networking, cloud, and identity ecosystems.

What the Recognition Signals

CRN’s MSP 500 is widely viewed in the channel as a barometer of innovation and operational excellence among MSPs. The Security 100 subset focuses on providers that are helping customers navigate escalating cyber threats, regulatory scrutiny, and digital transformation demands.

Vandis’ managed services portfolio centers on proactive security models tailored to each client’s infrastructure. The company emphasizes scalable protection for hybrid IT environments, freeing internal teams to focus on strategic initiatives rather than day-to-day threat management.

In an era where ransomware, identity-based attacks, and cloud misconfigurations dominate headlines, MSPs increasingly function as outsourced security operations centers for midmarket and enterprise customers alike.

Security Services in a Shifting Threat Landscape

The recognition arrives as the managed security services market continues to expand. Organizations are grappling with:

• Rapid cloud adoption

• Increasingly distributed workforces

• Growing identity and access management complexity

• More sophisticated threat actors

MSPs that can integrate networking, cloud security, and identity governance into a unified service model are seeing heightened demand.

Vandis positions its managed offerings as both defensive and optimization-focused—protecting environments while improving operational efficiency. That balance is increasingly critical, as security budgets face scrutiny even while threat volumes climb.

Channel Impact and Market Context

For solution providers, placement on the MSP 500 list is more than symbolic. It enhances visibility among channel partners and enterprise buyers seeking vetted providers.

CRN’s editorial leadership frames the 2026 list as recognizing MSPs that help organizations maximize IT investments while maintaining agility. That’s a subtle but important shift: security services are no longer viewed solely as risk mitigation but as business enablers.

As digital transformation accelerates, MSPs capable of delivering scalable, proactive security are becoming foundational to enterprise growth strategies.

For Vandis, inclusion in the Security 100 category reinforces its standing in a competitive managed security market—one where differentiation increasingly depends on depth of expertise across networking, cloud, and identity domains.

Get in touch with our MarTech Experts.

Direct mail is no longer the nostalgic sidekick to digital marketing. It’s commanding serious budget—and serious scrutiny.

In its fourth annual State of Direct Mail: Business Insights 2026 report, Lob reveals that direct mail now accounts for 25% of marketing budgets, with nine in ten teams increasing investment year over year. That’s not incremental growth—that’s a strategic shift.

But here’s the twist: while spend is rising, operational maturity isn’t keeping pace. And that disconnect is costing marketers money.

Direct Mail Is Gaining Budget Share—Fast

According to Lob’s findings, direct mail is earning a larger slice of the marketing mix as brands chase trust, attention, and measurable performance in a noisy digital landscape.

That 25% budget allocation signals something significant. In a world dominated by paid social, search, and programmatic, marketers are rediscovering the power of tangible, high-impact channels—particularly as third-party cookies fade and digital CPMs fluctuate.

The channel’s resurgence isn’t just about novelty. It’s about performance. Direct mail consistently delivers high engagement when done right. The problem? “Done right” now requires the same rigor applied to digital channels.

As Lob CEO Ryan Ferrier notes, teams seeing the strongest returns are those treating logistics, data, and delivery with the same discipline as performance marketing dashboards.

Logistics Blind Spots Are Undermining Returns

Here’s where things get messy.

Despite increased investment, 87% of marketing leaders say printing, shipping, and delivery remain blind spots. Even more telling: 82% report unexpected costs or missed delivery windows.

Only 39% claim full, real-time visibility into mail delivery status.

For a channel consuming a quarter of marketing budgets, that lack of transparency is more than inconvenient—it’s risky.

Without clear operational ownership, teams struggle to tie spend directly to outcomes. That disconnect can erode executive confidence, particularly as CMOs face mounting pressure to prove ROI across every channel.

In digital, marketers obsess over attribution models and performance dashboards. In direct mail, many are still operating with fragmented logistics oversight and delayed delivery data. The result is a channel with strong potential but inconsistent execution.

USPS Changes Add Another Layer of Complexity

Operational friction doesn’t stop at internal processes.

The report highlights that 84% of leaders struggle to track updates or anticipate changes related to United States Postal Service operations. More than half—51%—say USPS changes significantly disrupt campaign planning and forecasting.

That uncertainty forces teams into reactive mode. Instead of optimizing creative and segmentation strategies, they’re scrambling to adjust timelines and budgets.

In performance marketing, predictability equals control. When delivery windows shift unpredictably, campaign timing—and revenue impact—becomes harder to forecast.

AI Adoption Is Widespread—But Not Equal

Automation may be table stakes, but the report suggests the real differentiator lies in how AI is deployed.

Among high-ROI teams:

• 74% use AI for personalized messaging

• Only 23% of lower-ROI teams do the same

The gap is striking.

Top-performing organizations are using AI not just to automate workflows but to personalize messaging based on customer behavior, optimize delivery timing, and strengthen attribution. Lower-performing teams, by contrast, appear to treat AI as a surface-level enhancement rather than a core operational engine.

That difference shows up in measurable results.

Personalization: From Buzzword to Bottom-Line Driver

Nearly all leaders—96%—agree personalization improves outcomes. But the report emphasizes that relevance, not novelty, drives impact.

The most effective programs rely on real customer signals: behavioral data, preferences, account milestones, and life events. Timely, context-aware mail outperforms generic personalization tokens.

This aligns with Lob’s earlier consumer research, which found that engagement spikes when mail feels purposeful rather than promotional.

In other words, personalization works—but only when it reflects actual customer intelligence.

Operational Maturity Is the New Performance Metric

The report makes one point clear: in 2026, direct mail performance hinges less on creative strategy and more on operational execution.

High-performing teams are more likely to:

• Assign clear ownership of logistics

• Build delivery intelligence into planning processes

• Proactively monitor USPS updates

• Integrate AI for delivery optimization and attribution

These organizations report fewer surprises and greater confidence as budgets grow.

That’s a notable shift. For years, direct mail was often siloed—managed separately from digital channels, with limited cross-channel data integration. Now, top teams are treating it as a fully connected, data-driven component of the marketing stack.

The Bigger Trend: Physical Meets Digital Precision

As digital advertising grows more crowded and privacy regulations tighten, marketers are rediscovering channels that offer tangible engagement. Direct mail’s tactile advantage gives it staying power.

But the report suggests nostalgia alone won’t sustain growth.

The future of direct mail lies in merging physical execution with digital precision—real-time visibility, AI-driven personalization, predictive logistics modeling, and integrated attribution.

That convergence could redefine how brands think about omnichannel marketing. Instead of direct mail as a standalone tactic, it becomes an orchestrated touchpoint informed by the same data pipelines powering email, paid media, and CRM.

For marketers willing to modernize their operational backbone, the opportunity is clear. For those who don’t, rising budgets may simply magnify inefficiencies.

In 2026, direct mail isn’t just back. It’s becoming performance-critical. The question is whether teams are ready to operate it like a digital channel—or continue treating it like a legacy one.

Get in touch with our MarTech Experts.