A new study from Prohaska Consulting, commissioned by Rakuten Rewards, is challenging one of the marketing industry’s most trusted measurement frameworks. The report argues that traditional Marketing Mix Modeling (MMM) systematically undervalues affiliate marketing—potentially leading brands to misallocate budgets and lose competitive ground.

For decades, Marketing Mix Modeling has served as a cornerstone of enterprise marketing analytics, helping brands allocate budgets across channels like TV, search, and digital media. But according to new research titled “The Next Frontier of Measurement: Fair Evaluation of Affiliates in Marketing Mix Models,” the framework may be failing to keep pace with the evolving dynamics of performance marketing.

The report, based on interviews with more than two dozen senior marketing leaders and input from leading MMM providers, concludes that affiliate marketing—despite being widely adopted—does not fit cleanly into traditional measurement models. As a result, its true contribution to revenue is often undercounted.

Affiliate marketing, at its core, is a performance-driven channel where publishers earn commissions based on completed transactions. This structure makes it highly efficient. Data cited in the report shows that more than 80% of marketers use affiliate programs, with platforms like Rakuten Rewards delivering returns as high as 17 times the cost of commissions. Yet MMM frameworks frequently misinterpret this performance.

The issue stems from how MMM models attribute value. These models are designed to analyze correlations between marketing inputs—such as impressions, clicks, and spend—and business outcomes. Affiliate marketing, however, operates closer to the point of conversion. Because its activity aligns directly with sales, MMM often assumes affiliates are merely capturing existing demand rather than generating new demand.

This structural bias has real financial implications. In one case study highlighted in the report, two brands under the same parent company ran affiliate campaigns simultaneously. The actively managed program, which included dynamic cashback incentives and time-sensitive promotions, achieved up to 25 times higher return on ad spend compared to a passive approach. Yet without granular modeling, such performance differences can remain invisible.

Another example underscores the risk of misinterpretation. A major retailer paused its affiliate program after MMM analysis questioned its incremental value. The result was a sharp decline in customer volume—over 50%—as shoppers shifted to competitors. Even after reinstating the program, the retailer struggled to recover lost market share.

These findings point to two fundamental flaws in how MMM evaluates affiliate marketing.

First, there is a lack of standardized data. Affiliate marketing encompasses a wide range of sub-channels, including cashback platforms, coupon sites, influencer partnerships, and content publishers. However, MMM models often group all these under a single “affiliate” category. This aggregation obscures performance differences and prevents marketers from identifying high-performing segments.

Second, MMM frameworks rely heavily on upper-funnel metrics such as impressions and clicks—data points that are not always available or relevant in affiliate ecosystems. Since affiliates are typically compensated on conversions, they generate less of the upstream data MMM depends on, creating gaps in analysis.

The report argues that MMM alone is insufficient for evaluating affiliate marketing. Instead, it calls for a hybrid measurement approach that combines MMM with incrementality testing and granular data segmentation.

For enterprise marketing teams, the implications are significant. Misjudging affiliate performance can lead to underinvestment in a channel that reaches high-intent consumers at the moment of purchase—an increasingly valuable capability in a privacy-first digital landscape.

Industry leaders are beginning to recognize the need for change. Platforms across the martech ecosystem—from Google and Microsoft to Adobe and Salesforce—are investing in more advanced attribution and data modeling tools. These systems aim to integrate multiple data sources and provide a more nuanced view of customer journeys.

The Prohaska report outlines several practical steps to address the issue. Marketers are encouraged to break affiliate marketing into sub-categories within MMM datasets, track promotional variables such as cashback rates over time, and calibrate models using controlled experiments like geo-based holdouts.

Publishers, meanwhile, are advised to provide richer datasets—including impression and click data—and develop testing capabilities that align with MMM requirements. The report also calls for industry-wide standards to classify affiliate sub-channels, enabling more consistent and accurate measurement.

Ultimately, the research highlights a broader shift in marketing analytics. As performance channels become more complex and data ecosystems more fragmented, relying on a single measurement framework is no longer sufficient.

For CMOs and marketing analysts, the message is clear: understanding the limitations of MMM is just as important as leveraging its insights. In an environment where every budget decision carries strategic weight, measurement accuracy can determine whether brands capture growth—or cede it to competitors.

Market Landscape

The findings arrive at a time when marketing measurement is under intense scrutiny. According to Gartner, nearly 60% of CMOs report dissatisfaction with their current attribution models, citing challenges in cross-channel visibility and data fragmentation. Meanwhile, McKinsey & Company estimates that companies using advanced analytics and attribution frameworks can improve marketing ROI by 15–20%.

As privacy regulations limit access to user-level data, MMM has regained popularity due to its reliance on aggregated data. However, this resurgence also exposes its limitations—particularly in performance-driven channels like affiliate marketing. The Prohaska research suggests that the next evolution of MMM will depend on its ability to integrate granular, real-time data and adapt to modern digital ecosystems.

Top Insights

• The Prohaska study finds MMM systematically undervalues affiliate marketing by misattributing conversion-driven activity, leading enterprise brands to underinvest in a high-ROI, intent-driven channel.
• Affiliate marketing delivers up to 17x return on commissions, yet structural modeling limitations prevent accurate performance measurement across cashback, content, and influencer sub-channels.
• Case studies reveal misinformed MMM decisions can reduce customer volume by over 50%, demonstrating the financial risks of incomplete attribution frameworks.
• The research recommends hybrid measurement approaches combining MMM with incrementality testing, geo experiments, and granular affiliate segmentation for improved accuracy.
• Enterprise martech platforms from Google, Adobe, and Salesforce are evolving toward integrated attribution models, signaling a broader shift beyond traditional MMM frameworks.

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Two major independent brokerages—Brown Harris Stevens and FirstTeam—have announced a multi-year strategic marketing partnership aimed at expanding national reach and modernizing how real estate firms collaborate on growth. The move reflects a broader shift toward network-driven marketing strategies in an increasingly consolidated property market.

In an industry shaped by consolidation, franchise dominance, and rising digital competition, independent real estate brokerages are rethinking how they scale. The newly announced partnership between Brown Harris Stevens (BHS) and FirstTeam signals a strategic pivot toward collaborative marketing infrastructure—one that mirrors trends seen across enterprise MarTech ecosystems.

The agreement brings together two regionally dominant firms. BHS, with over 150 years of history, has established itself as a leading privately held brokerage on the U.S. East Coast, while FirstTeam has spent five decades building a stronghold across Southern California and Western markets. Together, the firms represent more than 5,000 agents across 70+ offices spanning key real estate hubs, including New York, Florida, California, and Arizona.

At its core, the partnership is about marketing scale. Both companies plan to integrate their capabilities across advertising, public relations, listing promotion, and agent networking. While the announcement does not center on a specific technology platform, the underlying strategy aligns closely with modern marketing principles: shared data, expanded audience reach, and coordinated campaigns across distributed teams.

In practical terms, the collaboration enables cross-market exposure for property listings—an increasingly valuable advantage in a digital-first home buying environment. High-net-worth buyers, relocation clients, and investors often search across regions, making national visibility a competitive differentiator. By pooling marketing resources, BHS and FirstTeam aim to extend the lifecycle and reach of listings beyond local MLS ecosystems.

The partnership also reflects a growing emphasis on “network effects” in real estate marketing. Similar to how enterprise platforms like Salesforce or Adobe enable cross-channel customer engagement, brokerage networks are evolving into interconnected ecosystems. These ecosystems combine agent expertise, localized insights, and shared promotional channels to deliver more consistent and scalable marketing outcomes.

For agents, the benefits are tangible. Access to a broader referral network and co-branded marketing initiatives can increase deal flow and improve conversion rates. For clients, the value lies in expanded exposure and potentially faster transaction cycles—especially in competitive or cross-regional markets.

The timing of the partnership is notable. The U.S. real estate sector is navigating a period of structural change, driven by digital platforms, shifting commission models, and evolving consumer expectations. Large national brokerages and proptech companies continue to consolidate market share, placing pressure on independent firms to differentiate.

Rather than pursuing mergers or acquisitions, BHS and FirstTeam are opting for a collaborative growth model. This approach allows both firms to retain operational independence while benefiting from shared scale—an increasingly common strategy across industries facing platform-driven disruption.

Industry executives frame the move as both defensive and opportunistic. By aligning marketing capabilities, the firms can compete more effectively with larger, vertically integrated players. At the same time, they can experiment with new campaign formats, co-branded initiatives, and data-sharing strategies without the complexity of full integration.

The partnership also hints at future opportunities for technology adoption. As marketing collaboration deepens, the need for unified data systems, customer relationship management tools, and performance analytics will likely grow. Platforms from companies like Microsoft and Google could play a role in enabling these capabilities, particularly in areas such as audience targeting, campaign measurement, and digital advertising optimization.

From a MarTech perspective, the announcement underscores an important trend: marketing is no longer confined to internal teams or single organizations. Instead, it is becoming a distributed function, spanning partnerships, ecosystems, and shared platforms.

For enterprise marketing leaders, the lesson extends beyond real estate. Collaborative marketing models—whether through partnerships, alliances, or data-sharing agreements—are emerging as a viable alternative to traditional growth strategies. They offer a way to scale reach, improve efficiency, and unlock new audience segments without the costs and risks associated with full consolidation.

As the BHS–FirstTeam partnership evolves, its success will likely depend on execution. Aligning brand messaging, integrating workflows, and maintaining consistent quality across markets are complex challenges. Yet if managed effectively, the collaboration could serve as a blueprint for how independent firms compete in an increasingly networked economy.

Market Landscape

The partnership reflects broader shifts in both real estate and marketing technology. According to Statista, digital channels now influence over 90% of home-buying journeys, highlighting the importance of online visibility and multi-channel marketing. Meanwhile, Gartner reports that marketing leaders are increasingly prioritizing ecosystem partnerships to extend reach and improve efficiency amid rising customer acquisition costs.

In real estate, this convergence of digital marketing and network strategy is accelerating. Brokerages are moving beyond traditional listing syndication toward integrated marketing ecosystems that combine branding, data, and cross-market collaboration.

Top Insights

• Brown Harris Stevens and FirstTeam are forming a multi-year marketing alliance to expand national reach, leveraging shared resources and cross-market exposure across more than 5,000 agents and 70 offices.
• The partnership reflects a shift toward network-driven marketing strategies, enabling independent brokerages to scale visibility and compete with consolidated real estate and proptech platforms.
• Cross-marketing initiatives, including advertising and PR collaboration, aim to improve listing performance and attract high-intent buyers across geographically diverse markets.
• The alliance highlights growing parallels between real estate and enterprise MarTech ecosystems, where shared data and coordinated campaigns drive efficiency and growth.
• For enterprise marketing leaders, the move signals the rising importance of collaborative marketing models as an alternative to mergers in achieving scale and competitive advantage.

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LinkSquares has introduced what it describes as the first fully agentic contract lifecycle management (CLM) platform, signaling a shift from passive contract repositories to AI-driven execution systems. The launch reflects a broader evolution in enterprise software, where automation is no longer limited to insights but increasingly drives real-time business operations.

The contract lifecycle management (CLM) market is entering a new phase—one defined not just by analytics, but by execution. With its latest release, LinkSquares is positioning itself at the forefront of this transition, unveiling an AI-native platform built to automate the entire contract process from drafting to renewal.

At the center of the platform is LinkSquares’ proprietary AI engine, LinkAI, which enables “agentic” workflows—software agents that can independently perform tasks such as drafting agreements, redlining documents, and managing approvals. Unlike earlier generations of AI tools that required manual prompting and follow-up, agentic systems are designed to plan, execute, and iterate within defined parameters.

In practical terms, this means legal teams can upload a contract, define review parameters, and receive a fully redlined version within minutes. Early users report significant time savings, with processes that once took hours now completed in near real time. The system also integrates clause libraries, playbooks, and governance frameworks to ensure outputs align with organizational standards.

The distinction between assistance and execution is critical. Traditional CLM platforms have largely functioned as systems of record—repositories for storing and retrieving contracts. More recent solutions have added AI features for search, summarization, and risk analysis. However, these tools often operate in isolation, requiring users to manually act on insights.

LinkSquares is attempting to close that gap. Its platform connects AI-generated outputs directly to workflows, enabling contract data to trigger approvals, track obligations, and initiate downstream processes automatically. The result is a system that not only understands contracts but actively moves them through the business lifecycle.

This approach aligns with a broader trend across enterprise software. Companies like Microsoft, Google, and Salesforce are investing heavily in AI agents capable of automating complex workflows across departments. In marketing, sales, and customer service, these systems are already reshaping how work gets done. Legal operations, historically slower to adopt automation, are now catching up.

The implications for enterprise teams extend beyond legal departments. Contracts sit at the intersection of revenue, compliance, and operations. Delays in contract execution can slow deal cycles, impact revenue recognition, and introduce risk. By automating routine tasks while maintaining human oversight, agentic CLM platforms aim to accelerate business processes without compromising control.

A key feature of the LinkSquares platform is its “Legal Front Door,” which standardizes intake, routing, and approvals. This allows non-legal teams—such as sales and procurement—to initiate contract workflows within predefined guardrails. The goal is to reduce bottlenecks while ensuring that legal teams retain visibility and governance over high-stakes decisions.

The platform also emphasizes transparency and trust. Outputs are supported by structured data and citation-backed insights, addressing a common concern with generative AI systems: reliability. For organizations handling sensitive agreements, this layer of validation is essential.

From a competitive standpoint, LinkSquares is entering a crowded CLM market that includes established players and emerging AI-first startups. Many vendors are retrofitting AI capabilities onto legacy architectures. LinkSquares’ strategy—rebuilding the platform around AI from the ground up—mirrors approaches seen in other SaaS categories, where native AI design is becoming a differentiator.

The concept of “agentic” software is still evolving, but its potential is significant. Instead of serving as tools that require constant user input, agentic systems function more like digital collaborators, capable of executing tasks autonomously within defined constraints. For legal teams, this could mean shifting focus from administrative work to strategic decision-making.

Adoption, however, will depend on more than technology. Enterprise buyers will evaluate factors such as integration with existing systems, data security, and change management. Legal workflows are deeply embedded in organizational processes, and any disruption must be carefully managed.

Still, early indicators suggest strong interest. The promise of reducing contract turnaround times from days to minutes is compelling, particularly in industries where speed and compliance are critical.

The launch also reflects a broader convergence between legal tech and enterprise MarTech stacks. As organizations seek to unify data and workflows across departments, platforms that can connect contract data with sales pipelines, marketing campaigns, and financial systems will become increasingly valuable.

For CMOs and revenue leaders, the implications are clear: faster contract execution can directly impact pipeline velocity and customer experience. In a competitive market, the ability to close deals quickly—without sacrificing accuracy—can be a decisive advantage.

Ultimately, LinkSquares’ new platform underscores a shift in how enterprise software is defined. The future is not just about systems that store information or generate insights, but systems that take action. In the context of contract management, that shift could redefine how organizations manage risk, drive revenue, and scale operations.

Market Landscape

The CLM market is expanding rapidly as organizations digitize legal operations. According to Gartner, by 2027, more than 50% of enterprise legal departments will adopt advanced AI tools to support contract management and compliance workflows. Meanwhile, IDC estimates that AI-driven automation can reduce operational costs in document-heavy processes by up to 30%.

As generative AI matures, the focus is shifting from augmentation to automation. Agentic platforms represent the next step, enabling end-to-end workflow execution rather than isolated task support. In this context, LinkSquares’ launch positions it within a growing category of AI-native enterprise platforms redefining how business processes are managed.

Top Insights

• LinkSquares has launched an AI-native, agentic CLM platform that automates drafting, redlining, and contract workflows, reducing manual legal tasks from hours to minutes while maintaining governance and oversight.
• The platform introduces execution-driven AI agents that move beyond analysis, enabling contracts to trigger workflows, approvals, and obligations tracking within a unified enterprise system.
• Early adoption signals strong ROI potential, with faster contract turnaround times improving deal velocity, compliance accuracy, and cross-functional collaboration between legal and business teams.
• The launch reflects a broader shift toward agentic enterprise software, where AI systems actively execute workflows rather than simply providing recommendations or insights.
• For enterprise organizations, the technology has implications for revenue operations, as faster contract cycles can directly impact sales efficiency and customer experience outcomes.

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Fullcast has rolled out a major upgrade to its Fullcast Pay platform, expanding its capabilities in commission automation and real-time sales compensation management. The release reflects a growing push among revenue operations teams to replace manual processes with integrated, AI-driven systems that connect planning directly to execution.

Sales compensation has long been one of the most operationally complex—and error-prone—functions within enterprise go-to-market (GTM) organizations. Despite advances in CRM and analytics platforms, many companies still rely on spreadsheets and disconnected tools to calculate commissions, often leading to disputes, delayed payouts, and limited transparency.

With its latest update, Fullcast is aiming to modernize that process. The upgraded Fullcast Pay platform introduces a more tightly integrated system that links territory planning, quota management, and compensation into a unified workflow. The goal is to transform commission management from a back-office function into a real-time operational lever.

At the core of the upgrade is automation. Changes made within Fullcast’s GTM planning environment—such as territory shifts or quota adjustments—now automatically flow into the commission engine. This eliminates the need for manual recalculations and reduces the lag between planning decisions and compensation outcomes.

For revenue leaders, this integration addresses a persistent challenge: aligning sales incentives with evolving business strategies. In traditional systems, compensation plans are often static, updated periodically rather than dynamically. Fullcast’s approach enables continuous alignment, allowing organizations to adjust incentives in response to market conditions, product launches, or organizational changes.

The platform also introduces support for complex sales structures through what it calls omni-role crediting. Modern sales organizations frequently involve multiple contributors to a single deal, including direct sales representatives, channel partners, and overlay teams. Managing credit allocation across these roles can be difficult, particularly when changes occur mid-cycle. Fullcast Pay automates this process, ensuring accurate attribution while maintaining a clear audit trail.

Another notable addition is automated roster and territory synchronization. When sales representatives change roles or territories are rebalanced, the system automatically reassigns quotas and pending commissions. This reduces administrative overhead and ensures that no revenue opportunity is left unaccounted for—a common issue in fast-moving sales environments.

Transparency is another focus area. The upgraded platform includes a rep-facing dashboard that provides real-time visibility into commissions, including backlog payouts tied to future milestones such as product shipments. By giving sales teams clearer insight into their earnings, the platform aims to reduce “shadow accounting,” where reps independently track commissions due to a lack of trust in official systems.

Integration plays a critical role in the platform’s design. Fullcast Pay now connects more deeply with enterprise systems like Salesforce, HubSpot, and Snowflake. These integrations allow revenue data to flow directly into the compensation engine, enabling near real-time synchronization and reducing the need for manual data transfers.

From a compliance standpoint, the platform includes enhanced auditability and adherence to SOC 2 standards. This is particularly important for finance and HR teams, which require detailed records to validate payouts and ensure regulatory compliance. As compensation structures become more complex, the ability to trace and verify every transaction is increasingly critical.

The broader significance of this release lies in its alignment with evolving revenue operations (RevOps) strategies. Organizations are moving toward unified systems that connect planning, execution, and measurement across sales, marketing, and customer success. Platforms that can bridge these functions are becoming central to enterprise GTM stacks.

This trend mirrors developments across the MarTech and SaaS landscape. Companies such as Microsoft and Google are investing in AI-driven workflow automation, while platforms like Adobe and Salesforce continue to expand their capabilities in customer data and revenue intelligence. Fullcast’s upgrade positions it within this broader shift toward integrated, AI-native enterprise systems.

For chief revenue officers (CROs) and chief financial officers (CFOs), the implications are practical. Real-time compensation visibility can improve sales performance by reinforcing desired behaviors, while automation reduces operational costs and minimizes errors. Faster, more accurate payouts can also enhance sales team satisfaction and retention—an important factor in competitive talent markets.

However, adoption will depend on how seamlessly the platform integrates into existing workflows. Many organizations have entrenched systems and processes, and transitioning to a fully automated model requires both technical and cultural change. Data accuracy, system interoperability, and user trust will be key factors in determining success.

Still, the direction is clear. As sales organizations become more complex and data-driven, the need for integrated compensation systems will continue to grow. Fullcast’s latest release highlights how automation and real-time data can reshape a traditionally manual function into a strategic component of revenue operations.

In that sense, Fullcast Pay is not just an incremental upgrade—it is part of a broader redefinition of how companies manage and optimize their go-to-market strategies.

Market Landscape

The demand for automated sales compensation solutions is rising as organizations scale their revenue operations. According to Gartner, more than 70% of enterprise sales organizations are investing in RevOps technologies to improve alignment and efficiency. Meanwhile, IDC estimates that automation in financial and operational workflows can reduce administrative costs by up to 30%.

As GTM strategies become more dynamic, the integration of planning and compensation systems is emerging as a critical capability. Platforms that can unify these functions are likely to play a central role in next-generation enterprise sales infrastructure.

Top Insights

• Fullcast has upgraded its Pay platform to automate commission workflows, linking territory planning and quota changes directly to real-time compensation outcomes for modern sales organizations.
• The platform introduces omni-role crediting, enabling accurate commission allocation across complex sales structures including direct, indirect, and hybrid revenue teams.
• Real-time dashboards improve transparency for sales representatives, reducing disputes and eliminating shadow accounting through clear visibility into commissions and payout timelines.
• Deep integrations with Salesforce, HubSpot, and Snowflake enable automated data synchronization, minimizing manual processes and improving operational efficiency.
• The upgrade reflects a broader shift toward unified RevOps platforms, where compensation becomes a strategic lever aligned with business performance and revenue growth goals.

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Thrive has introduced a major upgrade to its TransformIT platform, adding AI-powered automation workflows and streamlined deployment capabilities aimed at mid-market and small enterprises. Built on the ServiceNow ecosystem, the update reflects a broader push to bring enterprise-grade IT automation and service management tools to organizations traditionally constrained by cost and complexity.

As artificial intelligence moves from experimentation to execution, technology providers are racing to operationalize AI across enterprise workflows. The latest update to Thrive’s TransformIT platform underscores this shift, targeting a segment often overlooked in digital transformation conversations: mid-market and small businesses.

TransformIT, Thrive’s co-managed IT platform, is designed to unify IT service management (ITSM), automation, and advisory services into a single operating layer. With the new release, the company is embedding AI-driven workflows directly into the platform, enabling organizations to automate routine IT tasks, accelerate service resolution, and improve operational efficiency.

The platform is powered by ServiceNow, a widely adopted system for enterprise workflow automation. By leveraging ServiceNow’s architecture, Thrive is effectively packaging enterprise-grade capabilities—typically associated with large organizations—into a more accessible model for smaller businesses.

This approach addresses a persistent gap in the market. While large enterprises have invested heavily in AI, cloud, and automation platforms, mid-market organizations often lack the resources, expertise, or infrastructure to deploy similar systems. As a result, they face slower digital transformation cycles and higher operational inefficiencies.

Thrive’s strategy is to bridge that gap through a combination of technology and managed services. Rather than offering software alone, the company provides a co-managed model in which its engineering teams work alongside customers to implement and optimize workflows. This “high-touch” approach is intended to reduce the complexity of adoption while ensuring that AI capabilities deliver measurable outcomes.

The latest update introduces several key enhancements. Among them are ready-to-deploy AI automation workflows that streamline common IT processes, including onboarding and offboarding employees. These workflows are pre-configured based on industry best practices, reducing the need for extensive customization.

Another focus area is time-to-value—a critical metric for technology adoption. The updated TransformIT platform includes a more streamlined onboarding and deployment process, allowing organizations to implement the system more quickly. For businesses with limited IT resources, faster deployment can be a decisive factor in technology selection.

The platform also incorporates a self-service IT portal with AI-assisted issue entry, designed to improve the end-user experience. By simplifying how employees report and resolve issues, the system aims to reduce support workloads and improve satisfaction across the organization.

From a governance perspective, TransformIT includes built-in security and compliance tracking. This is increasingly important as organizations face growing regulatory requirements and cybersecurity risks. By embedding these capabilities into the platform, Thrive is positioning TransformIT as not just an operational tool, but a compliance and risk management solution.

The broader significance of the update lies in its alignment with evolving enterprise IT trends. Companies across industries are shifting toward workflow-centric architectures, where automation and orchestration replace manual processes. Platforms from Microsoft, Google, and Adobe are increasingly focused on integrating AI into everyday business operations, from customer engagement to internal workflows.

ServiceNow, in particular, has emerged as a central player in this transformation. Its platform enables organizations to automate workflows across IT, HR, and customer service functions. By building on this foundation, Thrive is able to extend these capabilities to a broader audience.

For enterprise marketing and operations leaders, the implications are indirect but meaningful. IT performance and workflow efficiency have a direct impact on customer experience, campaign execution, and overall business agility. Faster onboarding processes, improved system reliability, and streamlined service delivery can all contribute to more effective go-to-market strategies.

The emphasis on co-managed services also reflects a shift in how organizations approach technology adoption. Rather than building in-house expertise for every new platform, many are turning to partners that can provide both technology and operational support. This model reduces risk and accelerates implementation, particularly for organizations navigating complex digital transformations.

However, the success of such platforms depends on execution. Integrating AI into workflows requires clean data, well-defined processes, and user trust. Without these elements, even the most advanced systems can fall short of expectations.

Still, the direction is clear. As AI becomes embedded in core business systems, the distinction between technology and operations continues to blur. Platforms like TransformIT are not just tools—they are becoming the backbone of how organizations run their businesses.

With general availability set for June 2026, Thrive’s updated TransformIT platform represents a step toward democratizing enterprise IT capabilities. For mid-market organizations, the promise is compelling: access to advanced automation, faster deployments, and the expertise needed to turn technology investments into tangible business outcomes.

Market Landscape

The demand for AI-driven IT automation is accelerating across organizations of all sizes. According to Gartner, by 2026, over 70% of enterprises will adopt AI-powered workflow automation to improve operational efficiency. Meanwhile, IDC reports that organizations leveraging AI in IT service management can reduce incident resolution times by up to 40%.

For mid-market companies, these gains are particularly significant. As competition intensifies and digital expectations rise, the ability to deploy enterprise-grade tools without enterprise-level complexity is becoming a key differentiator.

Top Insights

• Thrive has enhanced its TransformIT platform with AI-powered automation workflows, enabling mid-market organizations to streamline IT operations and reduce manual processes through ServiceNow-based architecture.
• The platform combines enterprise-grade ITSM capabilities with co-managed services, allowing smaller businesses to access advanced technology without the complexity of full-scale implementation.
• New features focus on faster deployment and time-to-value, addressing a key barrier to adoption for organizations with limited IT resources and expertise.
• AI-driven workflows improve service resolution times, automate employee lifecycle processes, and enhance user experience through self-service IT portals.
• The update reflects a broader trend toward workflow automation and AI integration, positioning IT platforms as critical drivers of business performance and digital transformation.

Get in touch with our MarTech Experts

DdbuShen has introduced a strategy-driven, AI-powered automated trading platform designed to unify cryptocurrency and equity investing. The launch highlights a growing shift toward algorithmic, strategy-based execution models that aim to bring institutional-grade trading capabilities to retail investors.

As financial markets become faster, more volatile, and increasingly data-driven, the role of artificial intelligence in trading is expanding rapidly. With its latest platform launch, DdbuShen is positioning itself within a new generation of investment tools that move beyond trade execution to automate entire investment strategies.

The company’s system is built around a core premise: trading is no longer about individual decisions, but about structured, continuously optimized strategies. By combining AI-driven quantitative models with real-time execution and risk management, the platform enables users to deploy complex trading strategies across both cryptocurrency and equity markets through a single interface.

At a functional level, the platform allows users to select pre-built strategies—such as momentum trading, mean reversion, and volatility-based allocation—and activate them without writing code. Once deployed, the system autonomously processes market data, executes trades, and adjusts positions based on predefined risk parameters.

This approach reflects a broader industry shift from “tool-based” to “strategy-based” investing. Traditional retail trading platforms often provide charting tools and execution capabilities but leave decision-making entirely to the user. In contrast, AI-driven systems like DdbuShen’s aim to encode investment logic into repeatable, data-driven processes.

The timing aligns with accelerating adoption of algorithmic trading. Industry data cited in the announcement indicates that AI-driven and algorithmic trading volumes have increased by more than 40% year-over-year across major exchanges. This growth is being driven by the increasing difficulty for human traders to process real-time data at scale, particularly in 24/7 markets like cryptocurrencies.

The platform’s architecture integrates multiple data sources, including on-chain blockchain data, order book activity, and traditional market indicators. By combining these datasets, the system attempts to generate a more comprehensive view of market conditions—an approach commonly used by institutional trading firms.

Risk management is another central component. The platform incorporates automated controls such as stop-loss thresholds, take-profit triggers, and dynamic position sizing. These features are designed to reduce emotional decision-making, a well-documented challenge among retail investors.

DdbuShen is also emphasizing accessibility. The onboarding process is structured into three steps: account setup with KYC verification, strategy selection, and activation. The goal is to lower the barrier to entry for non-technical users, allowing individuals without programming expertise to access quantitative trading tools.

Interoperability is a key part of the platform’s appeal. It supports integration with major cryptocurrency exchanges such as Binance, Coinbase, and Kraken, as well as brokerage APIs including Interactive Brokers and Alpaca. This cross-market compatibility enables users to diversify portfolios and potentially explore arbitrage opportunities across asset classes.

The platform also includes backtesting capabilities, allowing users to simulate strategy performance using historical data before deploying capital. This feature, common in institutional trading environments, is increasingly being adopted in retail-facing platforms as competition intensifies.

From an industry perspective, DdbuShen’s launch reflects a convergence between fintech, AI, and SaaS delivery models. Similar to how enterprise platforms like Microsoft and Google are embedding AI into business workflows, financial platforms are integrating AI into investment processes, transforming how decisions are made and executed.

However, the rise of AI-driven trading also raises questions around regulation, transparency, and risk. While the platform includes configurable compliance features, users are responsible for adhering to local regulations. This highlights a broader challenge for global fintech platforms operating across jurisdictions with varying legal frameworks.

Early user feedback from markets such as the UK, Singapore, and Brazil suggests improved execution consistency and reduced manual monitoring. These outcomes align with the platform’s value proposition: automating not just trades, but the logic behind them.

Looking ahead, the competitive landscape is intensifying. Established fintech firms and emerging startups are investing heavily in AI-driven trading solutions. The differentiator will likely be the ability to combine usability, performance, and trust—particularly in a domain where financial risk is inherent.

For retail investors, the appeal is clear: access to tools that were once limited to hedge funds and institutional desks. For the broader market, the shift toward strategy automation signals a new phase in digital investing, where algorithms increasingly shape market behavior.

Market Landscape

The growth of AI-driven trading platforms is accelerating as investors seek more efficient ways to navigate complex markets. According to Juniper Research, AI-powered investment platforms are expected to manage over $3 trillion in assets by 2028. Meanwhile, Deloitte notes in its 2026 trading outlook that strategy automation is emerging as a key competitive differentiator in financial markets.

As algorithmic trading becomes more accessible, the line between retail and institutional capabilities continues to blur, reshaping how investment strategies are developed and executed.

Top Insights

• DdbuShen has launched an AI-powered trading platform that enables users to deploy strategy-driven quantitative models across cryptocurrency and equity markets without coding expertise.
• The platform reflects a shift toward strategy-centric investing, automating not just trade execution but the underlying decision-making logic and risk management processes.
• Integration with major exchanges and brokerages supports cross-market diversification, enabling users to manage portfolios and explore arbitrage opportunities within a unified system.
• Built-in risk controls and backtesting tools aim to improve consistency and reduce emotional decision-making, addressing common challenges faced by retail investors.
• The launch aligns with broader fintech trends, as AI-driven trading platforms scale rapidly and bring institutional-grade capabilities to a wider global audience.

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Pinterest reported strong first-quarter 2026 earnings, crossing the $1 billion revenue mark for the first time in Q1 while continuing double-digit user growth. The results highlight the platform’s accelerating shift toward AI-powered advertising and visual discovery as core drivers of monetization.

Pinterest has kicked off 2026 with a notable milestone: quarterly revenue exceeding $1 billion, underscoring the company’s progress in aligning user engagement with advertising performance. For the quarter ending March 31, the company reported $1.008 billion in revenue, representing an 18% year-over-year increase, alongside a growing global user base of 631 million monthly active users (MAUs).

The results mark Pinterest’s tenth consecutive quarter of double-digit user growth, reinforcing its position as a key player in the digital discovery and visual search ecosystem. While the company posted a GAAP net loss of $74 million, its adjusted EBITDA reached $207 million, indicating continued operational efficiency improvements.

At the center of Pinterest’s growth strategy is its evolving AI-powered advertising platform. CEO Bill Ready emphasized that the company is focusing on bridging the gap between inspiration and action—turning product discovery into measurable commercial outcomes. This approach positions Pinterest differently from traditional social platforms, leaning more heavily into intent-driven engagement.

Unlike purely social networks, Pinterest operates as a visual search engine where users actively seek ideas, products, and solutions. This high-intent behavior is increasingly attractive to advertisers, particularly in sectors like retail, home décor, fashion, and travel. As a result, the company is investing in AI to improve ad relevance, targeting, and performance measurement.

The broader advertising landscape provides important context. Platforms such as Google and Meta Platforms have long dominated digital advertising through search and social channels. Pinterest’s strategy blends elements of both—leveraging visual discovery to create a unique entry point into the consumer journey.

AI is playing a central role in this positioning. By enhancing visual search capabilities and recommendation algorithms, Pinterest aims to deliver more personalized content and ads. This not only improves user engagement but also increases the likelihood of conversion, a critical metric for advertisers.

The company’s financials reflect this momentum. Operating cash flow reached $328 million, with free cash flow at $312 million, providing Pinterest with flexibility to invest in product development and shareholder returns. Notably, the company repurchased approximately $2 billion in shares, signaling confidence in its long-term growth trajectory.

Geographically, Pinterest continues to expand its international footprint, though monetization remains uneven across regions. Average revenue per user (ARPU) is significantly higher in North America compared to other markets, a gap the company is actively working to close through improved ad products and localization strategies.

Looking ahead, Pinterest expects second-quarter revenue to range between $1.133 billion and $1.153 billion, representing 14% to 16% year-over-year growth. This guidance suggests continued momentum, albeit at a slightly moderated pace compared to Q1.

From a MarTech perspective, Pinterest’s performance highlights several key trends shaping the industry. First, the convergence of search, social, and commerce is accelerating, with platforms increasingly blurring traditional category boundaries. Second, AI is becoming a foundational layer for advertising platforms, driving both efficiency and effectiveness.

Competitively, Pinterest faces pressure from established giants and emerging players alike. Amazon continues to expand its advertising business, leveraging purchase data to offer highly targeted ads. Meanwhile, short-form video platforms are capturing user attention and ad budgets, intensifying competition for engagement.

However, Pinterest’s focus on intent-driven discovery provides a differentiated value proposition. Users come to the platform with a mindset oriented toward planning and purchasing, rather than passive consumption. This creates a more direct path from content to commerce, an advantage that advertisers are increasingly prioritizing.

For enterprise marketing teams, the implications are clear. Platforms that can demonstrate a measurable link between engagement and conversion are gaining traction in budget allocation decisions. Pinterest’s emphasis on performance-driven advertising aligns with this shift, making it a relevant channel for brands seeking ROI-focused campaigns.

At the same time, the company must navigate ongoing challenges. These include macroeconomic uncertainty, evolving privacy regulations, and the need to continuously innovate in a highly competitive market. Additionally, as AI becomes more deeply integrated into its platform, Pinterest will need to manage risks related to data usage, algorithmic bias, and content moderation.

Despite these challenges, the company’s Q1 results suggest a solid foundation for growth. By combining strong user engagement with improving monetization capabilities, Pinterest is positioning itself as a hybrid platform—part search engine, part social network, and increasingly, a commerce engine.

Market Landscape

The digital advertising market continues to expand, driven by AI and data-driven targeting. According to Statista, global digital ad spending is expected to surpass $800 billion by 2026. Meanwhile, Gartner reports that over 70% of marketers are increasing investment in AI-powered advertising tools to improve campaign performance and measurement.

Pinterest’s growth reflects these broader trends, particularly the shift toward performance marketing and intent-based advertising models.

Top Insights

• Pinterest surpassed $1 billion in Q1 2026 revenue, driven by 18% growth and continued expansion of its global user base to 631 million monthly active users.
• The company’s AI-powered advertising platform is central to its strategy, linking visual discovery with measurable conversion outcomes for advertisers.
• Strong cash flow and $2 billion in share repurchases signal financial stability and confidence in long-term growth despite a reported GAAP net loss.
• Pinterest’s intent-driven user behavior differentiates it from traditional social platforms, offering advertisers a more direct path from engagement to purchase.
• The company faces competition from Google, Amazon, and Meta, but its hybrid model positions it uniquely within the evolving digital advertising ecosystem.

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WRITER has unveiled the AI CMO Council, a new executive forum designed to help enterprise marketing leaders operationalize AI at scale. The initiative reflects a growing industry shift from experimentation toward “agentic” AI systems that actively execute workflows across marketing organizations.

Enterprise marketing is entering a new phase—one where artificial intelligence is no longer confined to pilots or productivity tools, but embedded into core operations. With the launch of its AI CMO Council, WRITER is positioning itself at the center of this transition, bringing together senior executives tasked with turning AI ambition into measurable business outcomes.

The Council, co-chaired by WRITER’s CMO Diego Lomanto, CXO network leader Elizabeth van den Berg, and former Vanguard Global CMO Colin Kelton, is structured as a peer learning community for senior marketing leaders. Its purpose is straightforward: provide a confidential environment where executives can exchange practical insights on deploying AI across large, complex organizations.

This is not a theoretical exercise. The founding cohort includes CMOs from major enterprises such as Barclays, The Clorox Company, and KPMG—organizations already navigating the operational realities of AI adoption.

The timing is significant. Marketing departments have emerged as early adopters of enterprise AI, largely because they sit at the intersection of data, customer experience, and revenue. From content generation to campaign optimization and personalization, marketing functions offer immediate use cases for AI deployment.

Yet the transition from experimentation to execution remains uneven. According to Deloitte, while 74% of organizations aim to use AI to drive revenue growth, only 21% are achieving that outcome. Even more telling, 84% have not redesigned workflows or job roles to fully integrate AI into their operations.

This gap between ambition and execution is where initiatives like the AI CMO Council aim to create value. By facilitating knowledge sharing among leaders actively implementing AI, the Council seeks to accelerate the development of practical frameworks for AI-native marketing.

The concept of “agentic marketing” is central to this effort. Unlike traditional AI tools that assist with specific tasks, agentic systems are designed to autonomously plan and execute workflows within defined parameters. In marketing, this could mean AI systems that not only generate content but also orchestrate campaigns, manage budgets, and optimize performance in real time.

Platforms from Salesforce, Adobe, and Google are increasingly incorporating these capabilities, signaling a broader industry shift toward automation at scale. WRITER’s positioning as an “enterprise AI agent platform” aligns with this trajectory, emphasizing execution over assistance.

For CMOs, the implications are both strategic and operational. On one hand, AI offers the potential to dramatically increase efficiency and campaign effectiveness. On the other, it introduces new challenges around governance, brand differentiation, and organizational design.

One of the more pressing concerns is maintaining brand integrity in an AI-driven environment. As generative AI tools become more widely adopted, there is a risk that brands could converge in tone and messaging. Ensuring distinctiveness requires not only technical controls but also clear governance frameworks—a topic the Council is expected to address.

Another challenge is workflow integration. Many organizations have adopted AI tools in isolation, leading to fragmented processes and limited impact. Moving to an AI-native model requires rethinking how teams operate, how data flows across systems, and how decisions are made.

The Council’s agenda reflects these priorities. Topics include AI-powered content creation, marketing technology orchestration, data strategy and privacy, hyper-personalization, and account-based marketing. These areas represent some of the most complex—and potentially transformative—applications of AI in marketing.

The format of the Council is designed to encourage ongoing collaboration. Members will participate in monthly virtual roundtables and quarterly in-person sessions in key global hubs such as New York, San Francisco, and London. This hybrid approach mirrors the distributed nature of modern enterprise teams.

From a broader MarTech perspective, the launch underscores the growing importance of executive-level collaboration in navigating technological change. As AI reshapes marketing, the role of the CMO is evolving from campaign oversight to system orchestration—managing a network of tools, data sources, and automated processes.

For enterprise organizations, the stakes are high. AI has the potential to redefine competitive dynamics, enabling faster execution, deeper personalization, and more efficient resource allocation. However, realizing these benefits requires more than technology—it demands new operating models, skill sets, and leadership approaches.

The AI CMO Council represents an attempt to address these challenges collectively. By pooling insights from leaders at the forefront of AI transformation, it aims to accelerate the development of best practices that can be scaled across industries.

In that sense, the initiative is as much about organizational change as it is about technology. As marketing becomes increasingly agent-driven, the ability to learn, adapt, and collaborate may prove to be one of the most valuable assets for enterprise leaders.

Market Landscape

The rise of AI-native marketing is reshaping enterprise strategies. According to Gartner, over 80% of marketing organizations are expected to adopt AI-driven automation in core workflows by 2027. Meanwhile, McKinsey & Company estimates that AI could generate up to $2.6 trillion in annual value across marketing and sales functions globally.

Despite this potential, execution challenges persist, particularly in integrating AI into existing workflows and aligning it with business objectives. Initiatives like the AI CMO Council highlight the growing need for structured collaboration and knowledge sharing at the executive level.

Top Insights

• WRITER has launched the AI CMO Council to help enterprise marketing leaders operationalize AI and transition from experimentation to scalable, AI-native marketing execution models.
• The initiative focuses on “agentic marketing,” where AI systems autonomously execute workflows, signaling a shift beyond productivity tools toward full operational automation.
• Founding members from Barclays, Clorox, and KPMG highlight strong enterprise interest in collaborative frameworks for AI adoption and governance.
• Deloitte data reveals a significant execution gap, with only 21% of organizations achieving AI-driven revenue growth despite widespread investment.
• The Council aims to address challenges such as brand governance, workflow integration, and martech orchestration through peer-driven insights and best practices.

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