Fingerpaint Group has become the first independent agency network to win Network of the Year at the 2026 Manny Awards, marking a notable shift in the competitive healthcare marketing landscape. The company also secured Best Consumer Digital Campaign, reinforcing its growing influence across life sciences commercialization, healthcare advertising, and integrated agency services.

Fingerpaint Group’s win at the 36th annual Manny Awards in New York City signals more than a trophy moment for the healthcare agency sector. It highlights how integrated commercialization models are reshaping the relationship between pharmaceutical companies and their external partners.

Traditionally, large holding-company networks have dominated top honors in healthcare marketing. Fingerpaint Group’s recognition as Network of the Year breaks that pattern and suggests that independent agency structures can now compete at scale with larger multinational groups.

The Manny Awards, one of the most recognized honors in healthcare advertising, celebrate performance across creative execution, medical communications, market access, and commercial innovation. For Fingerpaint Group, the award reflects a strategy centered on combining disciplines that have historically operated in silos.

The company says its operating model connects branding, marketing, medical affairs, medical communications, and market access under one platform. That matters because pharmaceutical launches have become significantly more complex. Drugmakers now need coordinated support spanning physician education, patient engagement, reimbursement strategy, omnichannel media, and regulatory communication.

Fingerpaint’s broader 2026 nominations demonstrate the range of that platform. The group received recognition across multiple divisions, including Fingerpaint Medical, Fingerpaint Market Access, and Fingerpaint Marketing. That cross-functional visibility is increasingly valuable as life sciences companies look to reduce vendor fragmentation.

According to the company, it now works with 95% of the top 20 global pharmaceutical companies. It also reports that two-thirds of clients engage multiple service lines. That suggests a rising demand for fewer, more strategic partners capable of handling multiple commercialization functions.

This trend aligns with wider market research. McKinsey & Company has noted that commercial excellence in pharma increasingly depends on cross-functional orchestration rather than isolated marketing execution. Meanwhile, Gartner has repeatedly emphasized integrated customer experience models as a growth lever for enterprise organizations managing complex buyer journeys.

For healthcare marketers, that shift is practical. A product launch today may require AI-powered audience segmentation, payer communications, patient adherence programs, CRM automation, medical content review workflows, and omnichannel media activation. Agencies that can coordinate these pieces inside one ecosystem can potentially move faster and reduce operational friction.

Fingerpaint’s latest win also reflects how digital capability has become central to healthcare communications. Its Best Consumer Digital Campaign award underscores growing investment in direct-to-consumer engagement, personalized media experiences, and measurable performance campaigns. As privacy regulations tighten and media channels fragment, healthcare brands increasingly need first-party data strategies and advanced analytics similar to those used in mainstream martech environments.

The company’s rise comes during broader consolidation in healthcare marketing. Many agencies have pursued mergers or specialized acquisitions to build end-to-end capability. Fingerpaint appears to be positioning itself differently: not as a loose federation of acquired brands, but as a connected operating system for commercialization.

That distinction may matter. Enterprise buyers often complain that agency networks promise integration but deliver disconnected teams, separate profit centers, and inconsistent execution. A unified structure can be a competitive advantage if it improves accountability and speed.

CEO Bill McEllen framed the award as validation of a people-led strategy supported by technology and collaboration. The company also says it has promoted leadership internally, an approach that can help preserve culture during rapid expansion.

For competitors, Fingerpaint’s win is a signal that independent firms with strong specialization and integrated delivery models can now challenge established networks. For pharmaceutical companies, it expands the shortlist of partners capable of supporting global launches and ongoing brand growth.

The bigger takeaway is that healthcare marketing is no longer just about campaigns. It is about commercialization infrastructure — the combination of data, messaging, market access expertise, medical credibility, and digital execution required to bring therapies to market efficiently.

Fingerpaint Group’s Manny Awards performance suggests that agencies built around that model may define the next phase of healthcare communications.

Market Landscape

Healthcare marketing is undergoing structural change as pharmaceutical companies seek fewer vendors with broader capabilities. Rising launch costs, tighter regulatory scrutiny, AI-driven personalization, and omnichannel engagement are pushing demand for integrated commercialization partners. Agencies competing in this space increasingly overlap with enterprise martech providers, CRM ecosystems such as Salesforce, analytics platforms, and content supply chains powered by Adobe and Microsoft technologies.

Top Insights

• Fingerpaint Group became the first independent agency to win Manny Awards Network of the Year, challenging legacy healthcare holding-company dominance.
• The company also won Best Consumer Digital Campaign, highlighting the growing importance of digital healthcare engagement strategies.
• Fingerpaint says it works with 95% of the top 20 pharma companies, indicating strong enterprise market penetration.
• Two-thirds of clients use multiple services, showing demand for integrated marketing, medical, and market access solutions.
• The win reflects a broader industry move toward commercialization platforms instead of siloed agency relationships.

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TestMu AI, the company formerly known as LambdaTest, has introduced Kane CLI, a browser automation tool designed for both developers and AI coding agents. The launch targets a growing software delivery problem: AI can generate code quickly, but teams still need reliable ways to verify whether that code actually works inside a browser.

TestMu AI has launched Kane CLI, a new command-line browser automation platform built to validate software created by both humans and AI coding systems. The product arrives as enterprises increasingly adopt tools such as GitHub Copilot, Claude Code, Codex CLI, Cursor, and Gemini CLI to accelerate development workflows.

The broader issue Kane CLI attempts to solve is becoming more visible across engineering teams. AI assistants can now generate production-ready code, fix bugs, and build new features in minutes. Yet browser verification — confirming whether a user journey actually functions in Chrome or another browser — often still depends on manual testing or brittle automation frameworks.

That creates a trust gap. Code can be produced faster than many QA teams can test it.

Kane CLI is positioned as a verification layer for modern development teams. Instead of relying heavily on selectors or hand-coded scripts, the platform uses intent-based browser control. Users describe a flow in plain language, and the system executes it while returning a pass-or-fail result, screenshots, and a step-by-step trace.

This approach reflects a broader shift in software testing. Traditional browser automation tools such as Selenium and Playwright remain widely used, but they often require technical maintenance when UI elements change. TestMu AI is betting that natural-language workflows and adaptive execution will appeal to teams managing faster release cycles.

One of Kane CLI’s most notable features is its support for AI agent workflows. In Agent Mode, the tool outputs structured NDJSON responses that coding agents can parse programmatically. That means an AI assistant generating code could potentially run browser tests automatically, inspect the result, and decide whether further fixes are needed.

For enterprises investing in autonomous development pipelines, that capability could become increasingly relevant. According to Gartner, AI-assisted software engineering is expected to become mainstream across enterprise development organizations this decade. As code generation scales, software validation becomes a larger operational challenge.

Kane CLI also includes support for exporting tests into Playwright code, allowing teams to convert plain-English flows into maintainable scripts. It can also migrate existing Selenium or Playwright tests into Kane CLI workflows, lowering switching costs for teams with existing automation estates.

Another technical differentiator is vision-based dynamic waiting. Rather than relying purely on network events or DOM states, the system detects loaders, animations, and rendered screen states before taking the next action. That could help in modern web applications built with canvas rendering, Shadow DOM architectures, or JavaScript-heavy interfaces that can frustrate conventional automation tools.

The platform also addresses real-world interruptions such as OTP prompts and CAPTCHA checkpoints. Instead of failing silently, Kane CLI pauses and asks for human input, then resumes the run. For organizations building AI-led QA systems, this introduces a human-in-the-loop control model rather than forcing full automation where it may not be practical.

TestMu AI says Kane CLI is ready for CI/CD environments including GitHub Actions, GitLab CI, Jenkins, and Bitbucket Pipelines. Standard exit codes allow engineering teams to plug the tool into existing deployment workflows without custom integration work.

The timing is strategic. DevOps pipelines are increasingly optimized for continuous delivery, while AI-generated code is accelerating release velocity. That combination raises the cost of broken user journeys reaching production. Browser automation tools that combine speed, flexibility, and AI compatibility could see rising demand.

TestMu AI, which built its reputation in cloud testing infrastructure as LambdaTest, appears to be broadening into what it calls Agentic Quality Engineering. The rebrand signals a move beyond browser compatibility testing toward automated trust systems for AI-era software development.

For developers, Kane CLI offers a faster route to browser validation from the terminal. For QA leaders, it may reduce reliance on fragile manual regression cycles. For enterprises experimenting with AI coding agents, it provides a mechanism to confirm whether generated software actually works before code reaches production.

The larger market takeaway is clear: as software creation becomes automated, quality assurance is becoming the next strategic battleground.

Market Landscape

The software testing market is shifting from manual QA and scripted automation toward AI-native verification systems. Vendors such as Microsoft GitHub, BrowserStack, Sauce Labs, and open-source ecosystems like Playwright are expanding automation capabilities. Meanwhile, enterprises want tools that fit CI/CD pipelines, support autonomous coding agents, and validate real user experiences. Kane CLI enters this market as demand rises for browser testing built specifically for AI-generated software.

Top Insights

• TestMu AI launched Kane CLI to help developers and AI agents verify browser-based software workflows directly from the terminal.
• The tool uses intent-based automation, reducing dependence on fragile selectors and traditional script-heavy testing methods.
• Agent Mode enables AI coding systems to run tests, read results, and take next actions programmatically.
• Kane CLI supports Playwright and Selenium migration, lowering friction for teams with existing automation frameworks.
• The launch reflects growing enterprise demand for trust and QA controls in AI-generated software pipelines.

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At SAS Innovate, SAS unveiled a major refresh of its cloud-native data management portfolio, aiming to solve one of enterprise AI’s biggest bottlenecks: poor data readiness. Built on the SAS Viya platform, the update adds governance, lineage, automation, and analytics acceleration features designed to help organizations deploy AI agents and copilots with greater trust and operational control.

As enterprise AI adoption accelerates, many organizations are discovering that model innovation is no longer the main barrier to progress. Data quality, governance, fragmented infrastructure, and slow engineering workflows are increasingly what stand between AI pilots and real production value.

That is the problem SAS is targeting with the latest refresh of SAS Data Management, announced at the company’s annual SAS Innovate conference. The update expands the company’s cloud-native portfolio with new capabilities focused on AI-ready data environments, embedded governance, agentic AI support, and faster analytics execution across distributed data estates.

The message from SAS is straightforward: companies cannot scale AI reliably if the underlying data foundation remains disorganized.

This challenge is backed by market research. SAS cited joint research with IDC showing that 49% of organizations view poorly optimized cloud data environments as the leading obstacle to AI progress, while 44% cite weak governance processes. Separately, Gartner has projected that 60% of AI initiatives could fail because organizations lack AI-ready data.

That puts data management back at the center of the enterprise AI conversation.

Governance Built Into the Workflow

Traditional enterprise data stacks often treat governance as a compliance layer added after systems are built. SAS is taking a different approach by embedding lineage, transparency, and control directly into workflows for accessing, preparing, and activating data.

For regulated sectors such as banking, healthcare, insurance, and public services, this matters. AI systems increasingly need explainability, access controls, and auditable decision paths. Without these controls, deploying generative AI assistants or autonomous agents becomes significantly riskier.

SAS appears to be positioning Viya as a platform where governance is native rather than bolted on later — a distinction increasingly relevant as organizations navigate regulations around AI accountability and privacy.

Bringing Analytics to the Data

Another core theme of the launch is reducing data movement.

Many enterprises still copy data between warehouses, lakes, analytics tools, and AI platforms. That process creates latency, cost, duplication risk, and governance challenges. SAS says its strategy is to bring analytics directly to where the data already resides.

The company highlighted SAS SpeedyStore, a cloud-native analytical data platform integrated with Viya. It is designed to run analytics and AI workloads close to distributed data sources, reducing transfers while maintaining lineage and auditability.

SAS is also extending analytics into third-party ecosystems through SAS Data Accelerator, allowing workloads to run inside existing cloud data warehouses and lakehouse environments. That puts SAS into direct competition with vendors trying to make AI tools interoperable with platforms from Microsoft Azure, Amazon Web Services, Google Cloud, and Snowflake-style architectures.

For customers, the value proposition is clear: use existing infrastructure without rebuilding everything around a single vendor stack.

AI Agents Need Better Data Foundations

Perhaps the most forward-looking part of the announcement is SAS’s focus on agentic AI.

As enterprises experiment with AI agents that automate tasks and decisions with limited human supervision, poor-quality data becomes a larger operational risk. Agents can only be as reliable as the data pipelines feeding them.

SAS says its new copilots and agents are designed to improve data preparation and governance earlier in the lifecycle, before AI systems consume that information.

One example is SAS Viya Copilot for Data Discovery, which uses natural language to help users locate trusted data assets faster. Instead of manually searching across fragmented systems, business teams can ask what data exists, how it can be used, and whether it is reliable.

Another is SAS Viya Copilot for Code Assistance, which helps developers generate and refine SAS or Python code inside SAS Studio. That places AI coding support inside a governed enterprise environment rather than external developer tools.

The company also introduced SAS Data Maker, a synthetic data tool designed to create privacy-safe datasets that preserve statistical and temporal relationships found in real data. Synthetic data is becoming increasingly valuable for model training, software testing, and cross-team collaboration when access to real customer data is restricted.

Why This Matters Now

The broader enterprise market is shifting from experimental AI deployments to production systems tied to revenue, risk, and operations. That shift favors vendors with strong governance, analytics heritage, and hybrid-cloud compatibility.

SAS has long been associated with advanced analytics and regulated industries. This latest move suggests it wants to remain relevant in the generative AI era by repositioning data management as the control layer for autonomous enterprise AI.

For CIOs, data leaders, and enterprise marketing teams using predictive analytics, the takeaway is simple: AI strategy now depends less on model selection and more on whether enterprise data can be trusted, accessed, and operationalized at scale.

SAS is betting that the next AI winners will be decided in the data layer.

Market Landscape

Enterprise data platforms are becoming strategic AI infrastructure. Vendors including Microsoft, Google Cloud, AWS, Snowflake, Databricks, Oracle, and Salesforce are racing to connect data management with AI services. Meanwhile, demand is rising for governance-first platforms that support copilots, synthetic data, and analytics inside existing cloud estates. SAS enters this phase leveraging decades of analytics credibility, especially in highly regulated industries.

Top Insights

• SAS refreshed its Data Management portfolio to help enterprises build AI-ready data environments with governance embedded into workflows.
• The company is targeting common AI blockers such as fragmented cloud data systems and weak governance controls.
• New copilots for discovery and coding aim to speed enterprise AI adoption while preserving oversight.
• SAS Data Maker uses synthetic data to enable testing and model development without exposing sensitive information.
• The strategy positions SAS as a governance-first AI infrastructure vendor for regulated enterprises.

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Creo, the influencer marketing division of Omnicom Media, has introduced a new AI-powered content editing solution developed with Google Cloud. The platform is designed to help brands review, modify, and approve creator content in real time, reducing production delays, cutting revision costs, and improving brand compliance across influencer campaigns.

Influencer marketing has matured into a core media channel for global brands, but operational inefficiencies remain one of its biggest challenges. Content reviews, compliance checks, legal approvals, and reshoots often slow campaigns that are meant to move at internet speed.

Creo, the influencer marketing arm of Omnicom Media, is attempting to solve that friction with a new AI-powered creator content editing system built in partnership with Google Cloud.

The announcement introduces advanced post-production editing tools to Creo’s existing AI platform, allowing brands to automatically detect and correct issues in creator content before campaigns go live. Powered by Google Cloud’s Gemini Enterprise Agent Platform, the upgraded system uses multimodal AI models including Gemini and Veo to analyze images and video, flag policy concerns, and make light-touch edits instantly.

That marks a shift from content diagnostics to automated remediation.

Previously, many influencer review systems could identify problems — such as a competitor logo in frame, a prohibited product in the background, or missing legal disclosures — but still required manual back-and-forth with creators. Creo now says those issues can be corrected in minutes instead of days.

Examples include removing restricted objects, blurring logos, adjusting wardrobe colors, or ensuring mandatory disclaimers remain visible in regulated sectors such as alcohol and gambling.

For marketers, the commercial logic is clear. Influencer campaigns often depend on timing tied to trends, launches, or cultural moments. A three-day approval delay can materially reduce campaign relevance and performance.

Creo claims the new system can shorten review and approval timelines by two to three days, with some content now approved the same day. It also says brands can avoid revision fees, reshoots, and additional editing costs.

The timing is significant. According to Statista, the global influencer marketing market has grown into a multi-billion-dollar category, while enterprise advertisers increasingly demand the same governance and measurement standards expected in paid media, CRM, and programmatic advertising.

That creates tension between creator authenticity and enterprise control.

Brands want creator-led storytelling, but they also need legal compliance, brand safety, visual consistency, and campaign scalability. Manual workflows struggle to balance both, particularly when brands manage hundreds or thousands of creator assets across markets.

Creo’s answer is automation embedded into the creator lifecycle.

Alongside the enhanced Content Vetting Agent, the company says its AI suite includes a Creator Discovery Agent, which identifies creators aligned to campaign goals, and a Creator Briefing Agent, which evaluates submissions against custom brand guidelines.

Taken together, the stack resembles a martech workflow tailored specifically for influencer operations: discovery, qualification, briefing, compliance review, editing, and activation.

This matters because influencer marketing is increasingly moving out of experimental budgets and into core performance and brand media plans. As that shift continues, brands are demanding systems that integrate creators into enterprise marketing operations rather than treating influencer activity as a standalone channel.

The Google Cloud partnership also reflects a larger industry trend. Major cloud vendors including Google, Microsoft, and Amazon Web Services are racing to embed generative AI into advertising, commerce, and content production workflows. For agencies, partnering with hyperscalers provides access to enterprise-grade AI models without building foundational infrastructure independently.

For Omnicom Media, Creo’s move could strengthen its pitch to multinational clients seeking scalable influencer execution with measurable controls.

Still, adoption will depend on how creators respond.

Automated edits raise questions around creative ownership, authenticity, and transparency. Creo says edited content is returned to creators for final approval, preserving the original storytelling while reducing operational friction. That approval step may be crucial in maintaining trust between brands and talent.

The broader takeaway is that influencer marketing technology is evolving beyond creator marketplaces and campaign dashboards. It is becoming a sophisticated content operations layer powered by AI.

For enterprise marketing teams, the opportunity is speed. For legal and brand teams, it is control. For agencies, it is margin efficiency.

And for creators, it may signal a future where campaign content is optimized collaboratively by humans and machines before it ever reaches an audience.

Creo says the capability is currently available in the United States, with global expansion planned later this year.

Market Landscape

Influencer marketing platforms are entering a new enterprise phase shaped by AI automation, compliance controls, and scalable content production. Vendors such as CreatorIQ, Sprinklr, Impact, and agency-owned platforms are competing to become the operating system for creator commerce. Meanwhile, cloud ecosystems from Google, Microsoft, and AWS are supplying the generative AI infrastructure powering next-generation content workflows.

Top Insights

• Creo launched an AI content editing tool with Google Cloud to automate creator compliance reviews and campaign approvals.
• The platform uses Gemini and Veo models to detect issues and apply edits in real time.
• Brands can reduce review cycles by two to three days, improving campaign speed and relevance.
• Regulated industries gain automated support for disclosures, logo controls, and policy compliance.
• The move signals influencer marketing’s shift toward enterprise-grade martech infrastructure.

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XTM International has appointed a new Chief Marketing Officer and Vice President of Engineering as the localization software provider looks to accelerate growth in the AI era. The company named Niki Sotiropoulou as CMO and Sean Mooney as VP of Engineering, signaling renewed investment in go-to-market expansion and product execution.

XTM International, a provider of AI-driven localization technology for global enterprises, has expanded its executive leadership team with two strategic hires aimed at supporting the company’s next growth phase.

The company announced that Niki Sotiropoulou will join as Chief Marketing Officer, while Sean Mooney takes the role of Vice President of Engineering. Together, the appointments point to a dual-track strategy focused on market expansion and faster product delivery as demand rises for AI-enabled translation and localization platforms.

Localization technology has become increasingly important for multinational organizations managing digital customer experiences across languages, markets, and channels. As enterprises scale content production through AI, the ability to translate, adapt, and govern multilingual assets efficiently is becoming a competitive necessity rather than a back-office function.

That places vendors such as XTM in a favorable position.

Why the Leadership Changes Matter

Executive hires often reflect where a software company sees its next bottlenecks. In XTM’s case, strengthening marketing leadership suggests a push to sharpen positioning in a crowded language technology market, while adding engineering leadership indicates pressure to ship innovation faster.

CEO Lorcan Malone described the hires as a signal of the company’s ambitions, highlighting marketing scale and accelerated product execution as core priorities.

For SaaS companies competing in enterprise categories, those two areas are closely linked. Strong product capabilities are not enough if customers do not understand differentiated value. Likewise, aggressive demand generation cannot compensate for slow product cycles.

Marketing in the AI Localization Era

As CMO, Sotiropoulou will oversee XTM’s global marketing organization. Her remit includes brand strategy, demand generation, and customer communications, with a stated focus on embedding AI and data into marketing operations.

That reflects a broader shift across B2B technology firms. CMOs are increasingly expected to run revenue engines powered by automation, predictive analytics, and customer intent data rather than traditional brand-only programs.

For XTM, this could be particularly relevant. Localization buyers now include marketing teams, ecommerce leaders, product managers, and customer experience executives — not just procurement or operations departments. Winning those audiences requires clearer messaging around speed, automation, ROI, and integration with enterprise martech stacks.

Modern localization platforms increasingly connect with systems such as Adobe Experience Cloud, Salesforce, Microsoft Dynamics, Shopify, and content management tools. Marketing leadership capable of translating technical capabilities into business outcomes can help expand wallet share across these buying centers.

Engineering Speed as a Growth Lever

Sean Mooney joins with responsibility across engineering execution, including development, quality assurance, support, and architecture teams.

That role becomes more strategic as AI-driven software markets evolve quickly. Customers expect continuous feature releases, stronger integrations, enterprise-grade security, and reliable performance across cloud environments.

In localization, product innovation is moving beyond translation memory and workflow automation. Vendors are now competing on AI-assisted quality estimation, neural machine translation orchestration, content intelligence, terminology governance, and automation embedded directly into digital experience workflows.

Engineering execution can determine whether vendors capitalize on those trends or fall behind.

Mooney’s background in cloud systems, technical architecture, and modernization suggests XTM may be prioritizing platform scalability and faster delivery cycles. For enterprise customers, that often translates into shorter implementation timelines, stronger uptime, and quicker access to new capabilities.

The Bigger Market Context

The localization technology market is being reshaped by generative AI. Large language models can accelerate translation and content adaptation, but enterprise customers still need workflow controls, brand consistency, regulatory oversight, and human review layers.

That creates an opportunity for established platforms that combine AI automation with governance.

According to Statista, global digital transformation spending continues to rise, while multilingual content demand grows alongside ecommerce and cross-border customer acquisition. Meanwhile, Gartner has emphasized that AI projects succeed when paired with operational processes and trusted enterprise systems.

For XTM, the challenge is turning technical relevance into category leadership.

The company’s latest appointments suggest it wants to compete not only as a product vendor, but as a strategic platform for global content operations. Marketing will need to tell that story clearly. Engineering will need to deliver it consistently.

What It Means for Enterprise Teams

For enterprise marketing organizations, better localization technology can shorten campaign launch timelines, improve global brand consistency, and reduce manual content operations. For product and customer experience teams, it can streamline multilingual onboarding, support, and digital engagement.

XTM’s new leadership structure appears designed to address both sides of that equation: demand creation and product execution.

As AI reshapes enterprise software categories, vendors that combine technical innovation with disciplined go-to-market strategy may gain the strongest advantage.

Market Landscape

The localization software market is becoming a strategic layer of enterprise digital infrastructure. Vendors such as Smartling, Phrase, TransPerfect, RWS, and XTM are competing to power multilingual customer experiences across ecommerce, SaaS, and global marketing operations. AI translation is lowering barriers, but governance, integrations, and workflow automation remain key enterprise differentiators.

Top Insights

• XTM International appointed a new CMO and VP of Engineering to support its next stage of AI-driven growth.
• The hires indicate parallel priorities: stronger market positioning and faster product execution.
• Localization platforms are gaining relevance as enterprises scale multilingual content using AI.
• Marketing leadership can help XTM reach broader buyers across martech, ecommerce, and CX teams.
• Engineering speed is increasingly critical as AI software markets evolve rapidly.

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Catalyst IQ has appointed automotive marketing executive Michael Lein as Senior Vice President of Tier 3 Sales, adding experienced leadership as the newly launched company expands its automotive retail marketing platform. Lein will oversee business development and dealer success teams focused on helping retailers turn live market data into targeted advertising and sales growth strategies.

Catalyst IQ has named Michael Lein as Senior Vice President of Tier 3 Sales, strengthening its leadership bench as the automotive marketing technology company builds momentum following its recent brand launch.

Lein will lead Catalyst IQ’s Business Development and Dealer Success organizations, with responsibility for helping automotive retailers use real-time market intelligence to improve campaign targeting, customer acquisition, and dealership performance.

The appointment comes at a notable time for the automotive retail sector. Dealers are navigating higher consumer acquisition costs, inventory fluctuations, changing EV demand, and increasingly digital purchase journeys. That combination has elevated demand for data-driven advertising platforms capable of improving efficiency across local dealership marketing.

Catalyst IQ appears to be positioning itself directly in that space.

Why Tier 3 Marketing Matters

In automotive advertising, Tier 3 marketing typically refers to dealer-level or local retail campaigns. Unlike national brand advertising run by OEMs or regional co-op programs, Tier 3 focuses on individual dealership performance — driving leads, showroom visits, service appointments, and vehicle sales in specific markets.

That makes data quality and execution speed especially important.

Dealers need to understand local pricing pressure, competitor inventory, consumer search demand, media performance, and shifting buyer behavior in near real time. Traditional monthly reporting cycles are often too slow for today’s market conditions.

Catalyst IQ says its proprietary MarketAI® platform helps retailers make “smarter decisions at market speed,” suggesting a model built around live market intelligence combined with campaign activation tools.

Why Michael Lein’s Appointment Matters

Leadership hires in specialized martech categories often indicate growth priorities. In this case, Catalyst IQ’s decision to bring in Lein suggests a focus on scaling revenue operations and dealer relationships.

Lein brings automotive retail marketing experience from previous leadership roles at C-4 Analytics, Affinitiv, and Higher Gear CRM — companies known for dealership digital marketing, customer retention, CRM, and performance media solutions.

That background is relevant because automotive retail remains one of the most performance-driven marketing sectors. Vendors are often measured directly on lead quality, cost per sale, fixed operations growth, and return on ad spend.

Executives who understand consultative selling in this environment can help bridge the gap between technology promises and measurable dealer outcomes.

Catalyst IQ CEO Andy Lobred emphasized Lein’s ability to build high-performing sales teams and improve client marketing results. That suggests the company may be focused not only on customer acquisition, but also on scaling internal go-to-market execution.

A Consolidation Play in Auto Martech

Catalyst IQ itself is a newly formed brand created when parent company Advance Automotive combined four existing businesses: Adpearance, Fox Dealer, Search Optics, and ZeroSum.

That consolidation reflects a broader industry trend. Automotive retailers increasingly prefer fewer vendors offering integrated solutions across websites, search marketing, inventory intelligence, paid media, SEO, and analytics rather than managing fragmented point solutions.

By combining multiple specialist brands, Catalyst IQ appears to be creating a unified platform model for dealer marketing operations.

This mirrors trends seen across broader martech, where software and services vendors are bundling capabilities into larger ecosystems that promise better attribution, faster optimization, and lower operational complexity.

The Competitive Landscape

The automotive retail marketing market includes established players across CRM, inventory marketplaces, digital advertising, and dealership software. Competitors range from agency groups to platform providers tied to retail systems and OEM programs.

To stand out, vendors increasingly need proprietary data, automation, and measurable ROI.

That is where AI and analytics are becoming central differentiators. Predictive demand signals, audience targeting, pricing intelligence, and automated media optimization can materially impact dealer performance, especially in volatile markets.

According to Statista, digital advertising spend in the automotive sector continues shifting toward measurable online channels, while dealership groups are investing more heavily in first-party data and customer lifecycle marketing.

What It Means for Dealers

For automotive retailers, Catalyst IQ’s leadership move could mean stronger consultative support and more aggressive investment in growth programs. Dealer groups are increasingly looking for partners that can unify advertising, analytics, and conversion strategy across rooftops and markets.

Lein’s experience in sales execution and client enablement may help Catalyst IQ scale those relationships faster.

For the broader market, the appointment underscores how automotive retail advertising is becoming a sophisticated data infrastructure business — not just a media buying category.

As dealers face margin pressure and more digitally empowered consumers, platforms that turn market signals into immediate action may have the strongest edge.

Catalyst IQ’s latest hire suggests it wants to compete aggressively in that next phase.

Market Landscape

Automotive retail marketing is rapidly evolving toward AI-powered local advertising, first-party data activation, and omnichannel lead management. Vendors such as Cars Commerce, Cox Automotive, DealerOn, Affinitiv, and specialist agencies compete to help dealerships acquire customers more efficiently. Consolidation is increasing as dealer groups seek unified martech stacks instead of disconnected vendors.

Top Insights

• Catalyst IQ appointed Michael Lein as Senior VP of Tier 3 Sales to strengthen dealer-focused growth efforts.
• Lein brings experience from C-4 Analytics, Affinitiv, and Higher Gear CRM.
• Tier 3 automotive marketing is increasingly data-driven, requiring fast optimization and local targeting.
• Catalyst IQ was formed by combining four automotive marketing businesses under one platform brand.
• The move reflects rising demand for integrated martech solutions in dealership retailing.

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Rakuten International and impact.com have announced a strategic alliance aimed at reshaping the affiliate and performance marketing landscape. The deal combines Rakuten Advertising’s global publisher network and managed services, Rakuten Rewards’ consumer shopping data, and impact.com’s partnership automation platform to build a larger, more integrated ecosystem for advertisers, publishers, and creators.

Rakuten International and impact.com are joining forces in a move that reflects the rapid evolution of partnership marketing from a niche acquisition channel into mainstream growth infrastructure.

The two companies said their alliance will combine Rakuten Advertising’s global partner relationships, performance intelligence, and managed services with impact.com’s technology platform for contracting, tracking, and partner payments. Rakuten Rewards, the company’s cashback shopping platform, will also play a central role by contributing consumer intent and purchase signals.

The result is an ambitious attempt to create one of the industry’s most comprehensive partnership marketing ecosystems.

Why This Matters Now

Performance marketing is in transition. Rising customer acquisition costs, tightening privacy rules, and growing skepticism around traditional attribution models are pushing brands to diversify beyond paid search and social media.

Affiliate, creator, referral, and commerce partnerships have benefited from that shift. Instead of paying upfront for impressions or clicks, advertisers increasingly prefer channels tied to measurable outcomes such as sales, leads, or subscriptions.

That has helped elevate partnership platforms like impact.com and global networks like Rakuten Advertising.

Yet the sector remains fragmented. Many advertisers still rely on separate systems for tracking, publisher recruitment, payouts, incentives, and analytics. Managing multiple tools can slow growth and reduce visibility into true return on investment.

The Rakuten-impact.com alliance appears designed to solve that fragmentation problem.

Combining Reach, Data, and Infrastructure

Under the agreement, Rakuten Advertising will continue to provide strategic services, program management, and execution support, while impact.com supplies the platform infrastructure that powers partner lifecycle management.

That matters for enterprise brands managing global partnership programs. Many large advertisers need local publishers, region-specific payments, tax and contract controls, and consistent measurement across markets. Integrating services with software could make scaling easier.

Rakuten Rewards adds another differentiator.

Cashback platforms capture strong commercial intent because users are actively shopping when they engage. Those signals can help advertisers understand incrementality — whether a partner actually drove a sale that would not have happened otherwise.

Incrementality has become one of the most important topics in modern performance marketing. Brands are scrutinizing whether affiliate and coupon partners create net-new demand or simply intercept conversions already in progress.

By combining rewards data with tracking infrastructure, Rakuten and impact.com are positioning themselves as a stronger attribution alternative.

Pressure on Traditional Channels

The alliance also highlights pressure on conventional digital media models.

Paid search, display, and paid social remain major acquisition channels, but costs have risen significantly in many categories. Meanwhile, browser changes and privacy regulations have reduced the precision of legacy tracking methods.

Partnership marketing offers a different proposition: pay for outcomes rather than media exposure.

According to Forrester, partnership ecosystems are increasingly viewed as efficient growth channels because they align incentives between advertisers and partners. Meanwhile, Statista data shows continued growth in affiliate marketing spending globally.

Rakuten and impact.com are clearly betting that more budget will shift in that direction.

Benefits for Publishers and Creators

The companies said publishers and creators will gain access to a unified platform and one of the industry’s largest pools of advertisers, offers, and monetization options.

That is notable because creator commerce is blurring lines between affiliate marketing, influencer campaigns, and direct sales. Many creators now expect tracking links, branded storefronts, coupon integrations, and performance payouts rather than one-off sponsorship deals.

Platforms that unify those monetization models may become increasingly attractive.

AI and Automation Next

Rakuten said it will continue investing in AI, analytics, automation, and monitoring capabilities, while both companies plan to collaborate on real-time tracking, attribution, and personalized incentives during the shopping journey.

That suggests the next stage of competition will center on automation and intelligence rather than simple network scale.

Vendors are racing to help marketers predict partner performance, detect fraud, personalize offers, and optimize payouts dynamically. AI could make partnership channels more efficient and measurable — key priorities for CFOs and CMOs alike.

What It Means for Marketers

For advertisers, the alliance could simplify how partnership programs are launched and scaled globally. For publishers and creators, it could open more monetization opportunities with larger brands.

The broader takeaway is that partnership marketing is no longer an adjacent channel. It is becoming core growth infrastructure.

As acquisition economics become tougher across paid media, ecosystems that combine software, services, and verified outcomes may capture a larger share of enterprise budgets.

Rakuten and impact.com are positioning themselves for that future.

Market Landscape

The partnership economy is expanding as brands seek lower-risk, performance-based customer acquisition channels. Competitors include Awin, CJ, Partnerize, Everflow, and creator commerce platforms blending affiliate and influencer models. Meanwhile, ecosystems from Google, Meta, Amazon, and TikTok continue competing for conversion budgets, increasing demand for diversified growth channels with clearer ROI.

Top Insights

• Rakuten and impact.com formed an alliance to unify partnership marketing software, services, and consumer data.
• The deal combines Rakuten Advertising, Rakuten Rewards, and impact.com infrastructure into one ecosystem.
• Brands gain stronger attribution, incrementality measurement, and campaign scaling tools.
• Publishers and creators get access to more advertisers and monetization models.
• The move reflects growing pressure on paid media and rising demand for outcome-based marketing.

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A new global study from the CMO Council and WongDoody argues that artificial intelligence alone is not enough to transform marketing performance. Instead, organizations that combine AI with human creativity, judgment, and emotional intelligence are significantly more likely to generate ROI, build stronger customer relationships, and outperform slower-moving peers.

As generative AI adoption spreads across marketing departments, a new report suggests the real winners may not be the companies deploying the most AI tools — but those redesigning marketing operations around human-machine collaboration.

The CMO Council, in partnership with WongDoody, has released new research titled Marketing’s Power Partners: AI and the Human Essence, based on a survey of 371 senior marketing leaders worldwide. Its central conclusion is direct: AI creates the most business value when paired with human marketers rather than used as a standalone replacement strategy.

The report labels top-performing organizations as Power Partners — companies that intentionally combine machine intelligence with human decision-making, creativity, and emotional understanding.

A Clear Performance Gap

The data points to a widening divide between mature adopters and everyone else.

According to the study, 73% of Power Partners say they exceed ROI expectations or achieve measurable returns from AI investments, compared with only 22% of other organizations. Nearly 70% of Power Partners report consistently building strong emotional customer connections, versus 40% of peers.

The difference extends into campaign performance. 86% of Power Partners say AI has delivered moderate-to-major ROI impact, while only 43% of less advanced organizations report the same.

Those numbers reinforce a growing market reality: AI tools are widely available, but value creation is uneven.

That mirrors broader enterprise technology trends. According to Gartner, many AI initiatives fail not because of model quality, but because organizations lack operational readiness, trusted data, and clear ownership models.

Workflow Redesign Matters More Than Tool Adoption

One of the report’s strongest findings is that AI success depends less on software procurement and more on process redesign.

The study found 70% of Power Partners are prepared to redesign workflows for AI-human collaboration, compared with only 7% of peers. Meanwhile, 94% have clearly defined collaborative content processes, versus 42% of other respondents.

That suggests many marketing teams are still treating AI as a bolt-on productivity tool rather than a structural operating model change.

Examples of redesign may include using AI for research, audience clustering, media optimization, content drafts, and reporting, while human marketers focus on brand positioning, creative direction, emotional resonance, and strategic decisions.

In practice, that can turn AI from a tactical assistant into a multiplier.

Why Many Organizations Are Falling Behind

The report identifies several barriers slowing adoption:

• AI skills gaps and lack of training
• Low trust in AI-generated outputs
• Concerns around brand authenticity
• Weak or fragmented customer data
• Unclear division of labor between people and AI systems

These are less technical problems than organizational ones.

Many enterprises still operate with siloed teams, legacy approval processes, and disconnected martech stacks. Adding AI into those environments often produces isolated pilots rather than scaled transformation.

For CMOs, this is becoming a leadership challenge as much as a technology one.

Regional and Business Model Differences

The research also highlights geographic and sector-based divides.

In the United States, organizations appear further ahead in AI adoption and measurable returns, though maintaining emotional relevance at scale remains a challenge. Europe faces more structural constraints tied to fragmented data readiness. APAC shows strong investment momentum, but cultural resistance to change is slowing execution.

The gap is also visible across business models.

B2C and hybrid companies are more likely to achieve strong ROI and redesign workflows, likely because they have higher campaign velocity, larger customer datasets, and more pressure for personalization.

B2B organizations, by contrast, often use AI narrowly for productivity gains rather than end-to-end transformation.

That is notable because B2B marketing increasingly depends on account intelligence, predictive demand generation, sales alignment, and complex buying journeys — all areas where AI can create material advantage if integrated properly.

Marketing to Machines

One of the report’s more forward-looking ideas is that brands are increasingly marketing not only to people, but to machines that influence purchasing decisions.

AI assistants, recommendation engines, procurement algorithms, and autonomous buying systems are beginning to shape discovery and decision-making. That means future marketing strategies may need to optimize for both human emotion and machine evaluation.

This could reshape SEO, ecommerce merchandising, content strategy, and B2B buying experiences.

The Bigger Takeaway

The study’s core message is timely: AI does not eliminate the need for marketers. It changes where marketers create value.

If marketing teams are defined by repetitive tasks, automation may replace them. If they are defined by judgment, empathy, narrative building, and strategic interpretation, AI can amplify them.

For enterprise leaders, the implication is clear. Competitive advantage will not come from simply buying AI tools. It will come from redesigning teams, workflows, and decision systems around collaborative intelligence.

That divide is already forming — and according to the CMO Council, it is growing quickly.

Market Landscape

Marketing AI adoption is accelerating across platforms from Google, Microsoft, Adobe, Salesforce, HubSpot, and enterprise martech vendors. Yet many organizations remain stuck between experimentation and scaled ROI. The next phase of competition is shifting from tool access to workflow orchestration, trusted data, governance, and human-AI collaboration models.

Top Insights

• CMO Council found companies blending AI with human marketers are 3x more likely to achieve measurable ROI.
• Workflow redesign matters more than simply adding AI tools to legacy marketing processes.
• Power Partners outperform peers in emotional connection, speed, creativity, and adaptability.
• Skills gaps, trust issues, and weak data remain major blockers to AI success.
• B2B marketers risk falling behind if AI is used only for productivity instead of transformation.

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