Fintel Connect has released its 2026 Cost Per Acquisition Guide for Affiliate Customer Growth in Financial Services, offering benchmarks and strategic guidance for financial brands navigating changing acquisition economics. The report examines how AI-driven discovery, affiliate competition and evolving partnership strategies are influencing customer acquisition costs across the U.S. and Canada.

Customer acquisition has become a more complex equation for financial services brands. Rising competition for digital placements, fragmented customer journeys and the emergence of AI-powered discovery are changing how consumers find banking, lending, investing and insurance products.

Fintel Connect is attempting to provide marketers with a clearer benchmark through its newly released 2026 Cost Per Acquisition (CPA) Guide to Affiliate Customer Growth in Financial Services.

The report draws on proprietary benchmarks from thousands of financial affiliate campaigns across the United States and Canada. Rather than treating CPA as a standalone cost-control metric, Fintel Connect argues that financial brands should evaluate acquisition spending against the quality and long-term value of the customers generated through affiliate partnerships.

That distinction is increasingly important in financial services, where the cheapest acquired customer is not necessarily the most valuable one. A customer who opens a bank account, takes a loan or purchases an insurance product can generate materially different lifetime value depending on retention, product adoption, balances and cross-sell potential.

Fintel Connect CEO Nicky Senyard said leading financial brands increasingly need to treat CPA as a performance signal rather than simply a number to minimize. The company's approach emphasizes defining what a high-quality customer looks like and aligning that definition with affiliate partners.

The 2026 guide covers acquisition benchmarks across several financial services categories, including banking, investing, lending, business financial products and insurance. It is designed to help marketers compare their acquisition economics with broader market conditions while identifying the factors influencing performance.

One of the report's more timely areas of focus is the impact of artificial intelligence on affiliate discovery.

Consumers increasingly use AI-powered search and conversational assistants to research financial products. Instead of navigating multiple websites or comparing search results manually, prospective customers can ask AI systems to summarize options, compare providers or explain financial products.

That creates a new visibility challenge for affiliate publishers and financial brands. Traditional search engine optimization has historically focused on ranking pages for specific queries. Generative engine optimization and answer engine optimization introduce another layer in which AI systems synthesize information from multiple sources before presenting an answer.

For affiliate marketers, visibility in these environments could influence referral traffic, brand consideration and ultimately CPA. However, AI-generated discovery also raises questions about attribution. If a consumer encounters a financial product recommendation through an AI assistant before interacting with an affiliate publisher, determining which channel deserves credit can become more difficult.

This is particularly relevant as financial services companies invest more heavily in first-party data, customer analytics and marketing automation. Platforms from companies such as Salesforce, Adobe and Google are increasingly integrating AI into customer engagement and analytics workflows, while financial brands continue to diversify their acquisition channels.

Affiliate marketing offers a different model because brands generally pay based on defined outcomes rather than simply purchasing exposure. That performance orientation can make affiliate partnerships attractive when acquisition costs through traditional digital advertising become less predictable.

At the same time, affiliate programs can become difficult to manage as the number of publishers, placements and customer journeys increases. Financial brands need controls around partner quality, compliance, attribution, conversion definitions and customer value.

Fintel Connect's guide focuses on what it describes as the foundation for high-performing affiliate programs, alongside three principles it says leading brands use to sustain CPA performance and five common CPA mistakes that can restrict growth.

The emphasis on quality is particularly relevant to regulated financial services. Affiliate campaigns must operate within advertising, disclosure and consumer-protection requirements, meaning aggressive acquisition tactics can create compliance risks alongside higher operational costs.

The challenge for marketers is therefore not simply reducing CPA. It is determining the level of acquisition investment that produces customers who meet the company's broader business objectives.

This changes how affiliate programs should be evaluated. A financial institution might rationally accept a higher acquisition cost if the resulting customers demonstrate stronger retention or greater lifetime value. Conversely, an apparently inexpensive acquisition channel can become expensive if it produces low-quality customers or high levels of churn.

The Fintel Connect report arrives at a time when financial marketers are also adapting to a changing discovery ecosystem. Search remains important, but AI assistants, comparison platforms, publishers, influencers and specialized financial content sites are increasingly part of the research journey.

For affiliate managers, the strategic question is becoming broader: where does the customer discover the product, which partner influences the decision, and what happens after conversion?

Those questions move affiliate marketing closer to revenue operations and performance intelligence. Instead of managing partners primarily around commission rates and CPA targets, mature programs can evaluate the entire customer lifecycle.

Market Landscape

Financial services customer acquisition is increasingly shaped by competition across search, paid media, affiliate networks, comparison sites and emerging AI-powered discovery channels.

The affiliate model remains attractive because brands can connect marketing expenditure more directly to measurable outcomes. But rising competition among publishers and advertisers can increase placement costs and make benchmark data more valuable.

AI introduces another variable. Generative search can change which publishers receive visibility and how consumers compare financial products. Brands that previously relied heavily on conventional search rankings may need to evaluate how their products and affiliate partners are represented in AI-generated answers.

For financial services marketers, this means CPA should increasingly be analyzed alongside customer quality, lifetime value, conversion rates, retention and channel attribution.

Strategic Outlook

The next phase of affiliate marketing is likely to be less about achieving the lowest possible CPA and more about optimizing acquisition economics around customer value.

AI-driven discovery could accelerate this transition. As consumers use AI systems to research financial products, affiliate publishers will need stronger content authority and clearer product information, while financial brands will need more sophisticated attribution and partner measurement.

The strongest programs may ultimately be those that connect affiliate data with broader customer intelligence, allowing marketers to determine not just which partners generate conversions, but which partners generate valuable customers.

Top Insights

• Fintel Connect's 2026 CPA Guide provides financial affiliate benchmarks across U.S. and Canadian banking, investing, lending, business and insurance markets.

• The guide argues that financial brands should evaluate CPA against customer quality and business outcomes rather than treating acquisition cost as a metric to minimize.

• AI-driven discovery and GEO/AEO are emerging factors in affiliate visibility, potentially changing how financial products and publishers acquire customers online.

• Rising placement competition makes affiliate benchmarking increasingly important for acquisition teams managing partner economics, conversion performance and customer value.

• Mature affiliate programs can connect CPA with lifetime value, retention and customer quality to make more informed financial services acquisition decisions.

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As buyers increasingly use ChatGPT, Claude and Gemini to evaluate products before making purchasing decisions, marketers are experimenting with strategies designed for AI-assisted research. Good At Marketing, a Google Partner agency based in Boynton Beach, Florida, is calling its approach “Verdict Prompting,” a sales technique that gives prospects a structured prompt and lets their preferred AI assistant evaluate the underlying buying question.

The traditional sales pitch assumes the seller controls the conversation. A salesperson presents the problem, explains the solution and attempts to persuade the prospect to take the next step.

AI-assisted buying is changing that dynamic.

Instead of relying exclusively on a company's website, advertising or sales presentation, buyers can now ask an AI assistant to investigate a product category, compare alternatives, identify weaknesses and explain whether a proposed solution makes sense.

Good At Marketing believes that behavior creates a new opportunity for marketers. The agency has coined the term “Verdict Prompting” for a technique that effectively moves part of the sales conversation into the buyer's own AI environment.

The concept is relatively straightforward. Rather than telling prospects why they should purchase a product, a marketer provides a ready-made prompt that the prospect can paste into ChatGPT, Claude, Gemini or another AI assistant. The prompt asks the model to evaluate a specific technical or commercial question.

The resulting response comes from the AI rather than the seller.

That distinction is central to the agency's argument. Good At Marketing founder Donnie Strompf says buyers increasingly place significant trust in AI-generated recommendations, making it more effective to facilitate the research process than attempt to control it.

The approach also comes with an important limitation: the claims included in the prompt need to be accurate.

If the prompt contains misleading assumptions, an AI model may challenge them or produce an unfavorable conclusion. Good At Marketing therefore positions Verdict Prompting as a strategy that works best when the underlying product or service can withstand scrutiny.

That makes the technique different from conventional prompt engineering designed primarily to influence an AI response. The agency's stated approach is less about manipulating an AI model and more about framing a legitimate question that directs the buyer toward an evidence-based evaluation.

“Ask your AI about your own product first,” Strompf said, arguing that a negative AI verdict should be treated as a product problem rather than simply a marketing problem.

The concept arrives as generative AI increasingly becomes part of the buyer research journey. Google remains a major source of commercial discovery, but AI assistants are adding another layer between a prospect and the companies competing for their attention.

For marketers, this creates a new visibility challenge. Traditional search engine optimization focuses on helping webpages rank for queries. AI-driven discovery can involve a model synthesizing information from multiple sources before presenting an answer to a user.

That makes third-party coverage, authoritative content and consistent factual information increasingly relevant to how companies are represented in AI-generated answers.

Good At Marketing's approach combines those two elements. The agency says its proprietary software can generate earned media coverage for clients, while Verdict Prompting gives prospects a structured way to ask their AI assistants about the problem a product addresses.

The agency developed the approach partly through gocta.ai, an AI lead-intake software product founded by Strompf. One of its Verdict Prompts asks prospects to have their AI analyze why embedded iframe forms can disrupt paid advertising attribution and how native, single-line script implementations can preserve attribution.

The example illustrates how the strategy works. Instead of simply claiming that a particular implementation is better, the prompt asks the buyer's AI to investigate the technical problem and explain its implications.

There are clear parallels with answer engine optimization and generative engine optimization, although Verdict Prompting is more directly focused on the buyer's behavior than on optimizing content for AI crawlers.

The competitive landscape is also evolving. Google is integrating generative AI into search, Microsoft has embedded Copilot across its products, and OpenAI, Anthropic and Google are competing to become the interfaces through which consumers and business buyers conduct research.

That means marketers increasingly have two related visibility problems: appearing in traditional search results and becoming part of the information ecosystem AI systems use when generating recommendations.

Verdict Prompting does not solve the latter automatically. A prompt cannot guarantee a favorable AI response, and different models may reach different conclusions depending on their available information, system instructions and sources.

Its potential value lies elsewhere: it encourages marketers to design sales messaging around questions rather than claims.

That shift could prove important as buyers become more skeptical of traditional promotional content. A prospect who independently asks an AI to evaluate a technical problem may be more engaged than one who simply consumes a conventional advertisement.

The model also creates a useful feedback mechanism for marketers. If prospects repeatedly receive unfavorable answers about a product, the problem may reveal weaknesses in positioning, documentation, customer reviews or the product itself.

For enterprise marketing teams, that suggests a broader lesson. AI search optimization should not be treated solely as a content-generation exercise. Companies need accurate product information, credible third-party coverage and clear evidence that can survive independent evaluation by AI systems and human buyers.

Market Landscape

The emergence of AI-assisted purchasing is creating a new layer in the digital customer journey. Search engines remain important, but AI assistants can now summarize product categories, compare vendors and answer technical questions before a buyer visits a company's website.

Google, Microsoft, OpenAI, Anthropic and other technology companies are competing to shape that research experience.

This changes the role of marketing content. A webpage optimized for a keyword may attract a click, while information cited or synthesized by an AI assistant can influence a buyer before the company is directly contacted.

Verdict Prompting sits within this broader shift toward conversational buying. Its distinguishing feature is that the marketer provides the question while allowing the buyer's AI assistant to provide the explanation.

Strategic Outlook

The technique highlights a potentially important transition from persuasion-led marketing to evidence-led discovery.

As AI becomes a more prominent research intermediary, brands may increasingly need to optimize not only for rankings but also for factual consistency, authoritative third-party references and questions that AI systems can answer accurately.

The long-term advantage may belong to companies whose products, data and reputation remain credible when independently examined. In that environment, marketing can open the conversation, but the product itself has to survive the verdict.

Top Insights

• Good At Marketing's Verdict Prompting gives buyers structured AI questions, shifting part of the sales conversation from traditional advertising into ChatGPT, Claude and Gemini.

• The strategy depends on accurate claims, positioning AI scrutiny as a test of product quality rather than a mechanism for manufacturing favorable recommendations.

• AI-assisted buying adds another layer to search behavior, requiring marketers to consider conversational discovery alongside traditional SEO and paid advertising.

• Good At Marketing combines Verdict Prompting with earned media, aiming to influence both the questions buyers ask and information AI systems may encounter.

• The approach reflects a broader move toward evidence-led marketing as buyers increasingly use AI assistants to research products, services and technical decisions.

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Marketing Evolution is positioning marketing measurement as infrastructure rather than a reporting function with the broad availability of the Substrate, a platform designed to unify fragmented performance data and provide a persistent foundation for measurement, simulation, optimization and AI-driven decision-making.

Marketing organizations have accumulated more technology for managing campaigns, customers and channels than ever before. Yet the systems used to measure marketing performance often remain fragmented, leaving teams to reconcile different definitions, attribution methods and data sources before they can answer a basic question: what actually caused a business outcome?

Marketing Evolution is attempting to address that gap with the broad availability of the Substrate, a platform the company describes as a System of Record for Marketing Performance.

Rather than operating as another analytics dashboard, the Substrate is designed to create a persistent marketing intelligence layer by connecting paid and owned media, CRM systems, offline activity and third-party data. The platform then reconstructs portions of customer journeys that may be missing because of privacy restrictions, disconnected systems or limited channel-level visibility.

That approach is increasingly relevant as marketers navigate a more fragmented measurement environment. The decline of third-party cookies, privacy regulation, walled gardens and the proliferation of advertising platforms have made it harder to build complete customer journeys. At the same time, AI systems are creating new demand for reliable, contextualized data.

Marketing Evolution says the Substrate is designed to move organizations beyond reporting toward reasoning across measurement, simulation, optimization and AI-assisted decisions. Users can access the resulting intelligence through Darwin, the company's conversational interface, or connect it to their own AI models, agents and applications.

The underlying technology is based on Marketing Evolution's Journey Reconstruction and Enriched Data technology, which the company says has been developed over more than 25 years of measurement science. First deployed in 2020, the technology reconstructs customer journeys without depending on cookies or personally identifiable information.

One of the platform's notable claims is its shorter data requirement. Marketing Evolution says the Substrate can build models using as little as three months of historical data, compared with the years of historical information traditionally associated with marketing mix modeling, or MMM. The company also reports less than 1% parameter recovery error, although that figure is based on Marketing Evolution's own testing and should be evaluated against independent benchmarks.

The platform combines MMM, multi-touch attribution and incrementality within a single modeling environment. That is significant because these measurement approaches can produce different answers when operated independently.

MMM typically evaluates the relationship between marketing investment and aggregate business outcomes, while MTA focuses more heavily on customer-level touchpoints. Incrementality testing attempts to establish whether marketing activity caused an outcome that would not otherwise have occurred.

Bringing the approaches into a common intelligence layer could reduce the conflicting recommendations that often emerge when marketing teams use separate measurement systems. The larger objective is to give organizations a consistent foundation for budget allocation and optimization.

The Substrate also attempts to expand measurement beyond digital environments. Marketing Evolution says its platform can extend person-level measurement to television, radio and out-of-home advertising, where exposure data is generally less granular than digital advertising.

That capability places the product in competition with a broad range of marketing measurement and analytics platforms. Vendors across the MarTech ecosystem, including Google, Adobe, Salesforce and other enterprise technology providers, offer increasingly sophisticated analytics and customer intelligence capabilities. Specialized measurement companies compete on attribution, media mix modeling, experimentation and incrementality.

Marketing Evolution's differentiation is its attempt to combine these disciplines with a persistent data layer designed specifically for marketing performance.

The company says the Substrate has already operated in production since 2024 through custom enterprise deployments involving Fortune 500 insurance and financial services organizations and global agency holding companies. In one customer deployment, Marketing Evolution says the system identified data-quality problems affecting 29% of the dataset, saved 240 hours of analyst time and reduced reporting from several weeks to four days. The company says those improvements contributed to a $2.38 million annual run-rate margin benefit.

Those figures are customer and company reported rather than independently audited, but they illustrate the business case Marketing Evolution is targeting: improving measurement infrastructure can have a direct impact on analyst productivity, reporting speed and marketing economics.

The company's CEO Stephen Williams argues that this is where marketing measurement is changing. As AI moves into campaign planning, optimization and execution, performance data becomes more than an input for dashboards. It becomes the context AI systems require to make decisions.

That distinction may prove important. Generative AI can summarize campaign results, but without reliable definitions, historical context and causal relationships, it can simply automate the interpretation of flawed data. A persistent performance intelligence layer could instead provide AI systems with a consistent foundation for reasoning.

Marketing Evolution's relationship with Plus Company provides another example. Plus Company CEO Brett Marchand said the Substrate helped create a unified decisioning platform across its brand portfolio, with the company reporting improvements of up to 35% in return on ad spend and 23% in campaign efficiency.

The broader industry implication is that marketing data infrastructure may become as strategically important as the AI applications built on top of it. As organizations deploy more AI agents and automated decision-making systems, the reliability, lineage and context of the underlying performance data will increasingly determine whether those systems improve marketing or simply accelerate bad decisions.

Market Landscape

Marketing measurement is undergoing a structural shift as privacy restrictions, fragmented media environments and AI adoption challenge legacy attribution models.

Traditional marketing stacks often distribute measurement across separate platforms for analytics, attribution, CRM, media buying and customer data. This can create competing definitions of performance and make it difficult for executives to establish a single view of marketing's contribution to revenue.

The emergence of AI raises the stakes. Gartner has predicted that by 2026, more than 80% of enterprises will have used generative AI APIs or deployed generative AI-enabled applications, up from less than 5% in 2023. The rapid adoption of AI makes the quality of enterprise data increasingly important because automated systems depend on the information and context provided to them.

Marketing Evolution is competing in this environment by treating measurement intelligence as infrastructure. Its closest competitive categories include marketing mix modeling, attribution, incrementality testing, customer data platforms and marketing analytics.

The differentiator will ultimately be whether a unified approach can produce more consistent decisions than organizations achieve by stitching together multiple specialized systems.

Strategic Outlook

The Substrate reflects a broader evolution in MarTech from dashboards toward decision infrastructure.

As AI moves deeper into campaign planning and optimization, marketing organizations will need systems that can preserve historical context, explain where data originated, validate incoming information and maintain consistent definitions across models.

That could make the concept of a marketing performance “system of record” increasingly relevant. The winners in AI-driven marketing may not simply be companies with the most advanced models, but those with the strongest data foundations beneath them.

Top Insights

• Marketing Evolution's Substrate unifies fragmented marketing data, creating a persistent performance intelligence layer for measurement, planning, optimization and AI decision-making.

• The platform combines MMM, multi-touch attribution and incrementality, addressing conflicting measurement results that can complicate enterprise marketing budget decisions.

• Journey Reconstruction technology can rebuild incomplete customer journeys without cookies or personally identifiable information, expanding measurement across fragmented and privacy-constrained environments.

• Marketing Evolution says enterprise deployments have reduced reporting times and uncovered major data-quality problems, highlighting the operational value of stronger marketing intelligence infrastructure.

• As AI becomes embedded in marketing operations, reliable performance data and lineage could become more important competitive advantages than the AI applications themselves.

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Artmarket.com is accelerating a major shift in how Artprice uses its proprietary art-market data, moving from incremental AI deployment toward an AI-first architecture designed to turn decades of market records into decision intelligence. The strategy centers on its proprietary Intuitive Art Market and Blind Spot systems and could reshape how collectors, researchers and financial institutions access art-market intelligence.

Artmarket.com is preparing to reposition Artprice from a traditional art-market information database into an AI-driven intelligence platform, following a strategic review approved by its Board of Directors.

The company says the transition will focus on integrating its proprietary artificial intelligence architectures, including Intuitive Art Market® and Blind Spot®, throughout the Artprice database rather than adding AI capabilities as isolated features.

That distinction is important. Artprice has spent nearly three decades building specialized art-market information infrastructure. Its stated strategy is now to make that proprietary data the foundation of a vertical AI system capable of analyzing, contextualizing and eventually automating more complex market research and decision-making tasks.

The company says it will delay the broader subscriber rollout of the new architecture while it completes an internal transformation of its data and production workflows. Employees and production teams are being equipped with dedicated AI hardware and edge computing resources, while data collection, standardization and enrichment pipelines are being redesigned around deep-learning processes and proprietary algorithms.

The approach reflects a broader enterprise AI trend: organizations are discovering that adding a chatbot or generative AI interface to legacy software is often easier than rebuilding the underlying data architecture required for reliable AI.

Stanford University's 2026 AI Index, drawing on McKinsey research, found that 88% of surveyed organizations reported using AI in at least one business function in 2025, up from 78% in 2024. Yet enterprise-wide scaling remains considerably less mature, suggesting that infrastructure and workflow redesign remain significant barriers.

Artmarket.com is betting that its historical data advantage can help address that problem in the art market.

The company says Artprice's infrastructure contains nearly 180 interconnected proprietary databases, alongside a collection of manuscripts and auction catalogs spanning from 1700 to the present. Those assets provide a specialized data corpus that differs substantially from the broad, heterogeneous information used by general-purpose large language models.

This is where vertical AI becomes strategically relevant. A general-purpose model can answer questions using information drawn from enormous datasets, but its output can be difficult to audit and may contain errors when source material is incomplete or ambiguous. A specialized AI system built around controlled, domain-specific data can potentially provide more traceable results within a defined market.

Artmarket.com describes this transformation as an “ontological mutation,” arguing that Artprice is evolving from an information repository into a cognitive architecture. In practical terms, that means shifting the platform from helping users locate historical information toward helping them interpret market relationships, identify patterns and potentially model future scenarios.

The company's longer-term vision includes AI agents capable of performing more complex tasks for subscribers, including market analysis, scenario modeling, arbitrage research and risk assessment. It also points toward predictive APIs and inference systems that could eventually embed Artprice intelligence into institutional workflows rather than requiring users to interact with the platform directly.

That direction places Artprice within a broader enterprise software trend toward agentic AI. Salesforce, Microsoft, Google and other major technology companies are increasingly positioning AI agents as systems that can act on data and execute multistep workflows rather than simply respond to prompts.

Artmarket.com is applying a similar concept to a much narrower domain: the global art market.

The company also argues that proprietary data becomes more valuable as synthetic content proliferates online. While its release cites a Gartner and Europol Innovation Lab estimate that 70% of online data is uncontrollable synthetic data as of June 30, 2026, that specific figure could not be independently verified from publicly accessible Gartner material during this review. The broader issue, however, is well established: AI systems increasingly face challenges around data quality, provenance and reliability.

That makes Artprice's emphasis on proprietary and curated datasets commercially significant. The company is effectively arguing that its competitive moat is not simply the AI model but the combination of historical data, domain expertise, data-processing infrastructure and proprietary algorithms.

Artmarket.com says the AI-first transformation will ultimately create a closed feedback loop in which user interactions and analyses can contribute to metadata enrichment and model refinement. Such a system could become more valuable as usage increases, although its effectiveness will depend on governance, data quality, model evaluation and the ability to demonstrate that AI-generated insights are accurate.

The company also says its financial trajectory remains stable while the AI deployment schedule is adjusted. Its first-quarter 2026 revenue was €4.108 million, up 3.63% from €3.964 million in the same quarter of 2025, according to figures disclosed in its May financial communication.

Artmarket.com has previously reported that Artprice integrated proprietary AI tools into its internal database production during 2025 and said the technology increased its internal processing capacity while improving data quality.

The next phase is therefore less about whether Artprice will use AI and more about how deeply AI becomes embedded in the platform's architecture.

If the strategy succeeds, Artprice could evolve from a subscription database into a specialized decision-intelligence platform for an industry where provenance, historical context and transaction data are unusually important. That would put the company's AI strategy closer to the emerging model of vertical intelligence platforms than conventional generative AI software.

Market Landscape

The enterprise AI market is moving from experimentation toward deeper integration, but the transition remains uneven. McKinsey's 2025 State of AI survey found that 88% of respondents said their organizations regularly used AI in at least one business function, while most companies had not yet scaled AI across the enterprise.

For data-intensive industries, the challenge is particularly acute. AI systems require reliable, structured and sufficiently contextualized information to generate useful results. This is creating opportunities for companies that control specialized datasets rather than relying exclusively on publicly available information.

Artprice's competitive position is therefore different from that of general-purpose AI providers. Google, Microsoft, Amazon, Salesforce and Adobe compete on broad AI infrastructure and enterprise applications, while Artmarket.com is pursuing a vertical model focused specifically on art-market intelligence.

The company's advantage will ultimately depend on whether its proprietary data and domain-specific models produce insights that users cannot obtain as effectively through general-purpose AI tools.

Strategic Outlook

Artmarket.com's AI-first strategy illustrates a larger shift in enterprise software: the most valuable AI products may increasingly be built around proprietary data rather than AI models alone.

For Artprice, the opportunity is to turn decades of specialized records into an intelligent layer for collectors, galleries, auction houses, researchers, banks, insurers and investors.

The bigger test will be execution. Building an AI interface is relatively straightforward compared with creating a reliable, explainable and commercially useful decision system. Artprice's decision to prioritize internal workflow transformation before a full subscriber rollout suggests the company is treating data architecture as the foundation of its AI strategy.

If the resulting system can consistently produce traceable market intelligence, its proprietary database could become more than an information asset. It could become the core infrastructure for an AI-powered art-market intelligence business.

Top Insights

• Artmarket.com is shifting Artprice toward an AI-first architecture, embedding proprietary AI across decades of specialized art-market data and workflows.

• The strategy centers on Intuitive Art Market and Blind Spot, positioning vertical AI as a decision-intelligence layer rather than a simple database search tool.

• Artprice's proprietary databases could become a competitive moat as businesses increasingly prioritize data provenance, accuracy and domain-specific information for AI systems.

• Stanford's 2026 AI Index reports 88% organizational AI adoption, but enterprise scaling remains limited, highlighting the importance of infrastructure and workflow redesign.

• Artmarket.com plans to extend Artprice intelligence toward AI agents, predictive APIs and institutional workflows, potentially broadening its role beyond subscription-based market research.

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MoxiWorks is expanding the connectivity of its AI-powered real estate technology stack with a new integration between RISE and Cloze. The connection is designed to give real estate agents a synchronized view of contacts and activity across the two platforms, reducing duplicate data entry while extending access to campaigns, presentations and automation.

Real estate agents often work across a patchwork of customer relationship management, marketing and transaction platforms. While those systems can provide specialized capabilities, keeping customer records synchronized across them can create a persistent operational burden.

MoxiWorks is addressing part of that problem through a new integration between RISE, its native-AI relationship intelligence platform, and Cloze, an AI-powered real estate platform. Brokerages using both systems can now synchronize contacts and activity between RISE and Cloze.

The integration is designed to establish a more consistent source of customer information across the platforms. When contacts are synchronized, agents can use RISE campaigns, presentations and automations for those relationships without manually recreating records. Activity generated in RISE is also shared with the Cloze Intelligence Engine, allowing the platform to incorporate that information into its broader view of a client relationship.

For agents, the immediate benefit is straightforward: less duplicate data entry. Instead of maintaining contact information independently in two systems, the integration allows information to move between them automatically.

"If you're running Cloze and RISE side by side, you shouldn't have to think about which system has the latest version of a contact," said Krista Hannahs, Principal Product Manager, Integrations, at MoxiWorks.

The integration also illustrates a broader shift in real estate technology toward connected ecosystems rather than isolated applications. Agents increasingly rely on multiple platforms for marketing, customer relationship management, advertising, transaction management and business intelligence. Requiring users to choose one platform for every function can limit the usefulness of specialized tools.

MoxiWorks is positioning RISE around that open-ecosystem approach. The company says Cloze joins a growing collection of RISE integrations, including a two-way Canva integration, Promote, its digital advertising platform powered by Evocalize, and RateMyAgent.

The strategy is particularly relevant as AI becomes more deeply embedded in real estate software. AI-driven recommendations, relationship intelligence and automated follow-up depend heavily on the availability of accurate customer data. When information is fragmented across multiple applications, AI systems can have an incomplete picture of a client or prospect.

Cloze's own platform is built around extracting relationship intelligence from an agent's contacts and activity. Sharing RISE activity with the Cloze Intelligence Engine therefore gives Cloze more information to work with, while RISE can use synchronized contacts to activate its own marketing and engagement capabilities.

"We built Cloze to find the business hiding in an agent's contacts," said Alex Coté, Co-founder and CMO at Cloze. He said the integration is intended to provide agents with the benefits of both platforms while keeping their relationships and data connected across the tools they use.

The data-sharing model is important because CRM interoperability is becoming a competitive factor in SaaS. Historically, software companies often used proprietary data structures and integrations to keep customers within their ecosystems. More open approaches can make it easier for businesses to assemble technology stacks around their specific workflows.

That does not mean integrations eliminate every data-management challenge. Synchronization between systems still requires consistent data models, clear ownership rules and reliable handling of changes. For brokerages with large contact databases, the quality of the integration will ultimately depend on how accurately updates and activity are exchanged between platforms.

The MoxiWorks-Cloze integration is available now for agents, offices and brokerages using both products in markets served by MoxiWorks, including the United States, Canada, Australia, New Zealand and the United Kingdom.

MoxiWorks is also signaling that RISE's ecosystem will continue to expand. The company says transaction management integrations are planned as part of its broader 2026 integration strategy.

For real estate technology buyers, the direction is significant. The next generation of AI-powered CRM and relationship platforms will need to do more than generate insights inside their own applications. They will need to access relevant information wherever agents work and turn that data into actionable intelligence without forcing users to maintain multiple versions of the same customer relationship.

Market Landscape

The real estate technology market is becoming increasingly fragmented, with brokerages relying on specialized systems for CRM, marketing automation, advertising, transaction management and customer intelligence.

Platforms such as Salesforce, HubSpot and Microsoft Dynamics 365 have demonstrated the broader enterprise value of connected customer data, while vertical SaaS providers are adapting similar principles for specialized industries.

In real estate, interoperability can be particularly valuable because agents frequently operate independently while brokerages manage larger technology environments. A connected ecosystem can reduce repetitive administrative work and give AI systems access to richer relationship data.

The competitive question is shifting from which platform offers the most features to which platforms can work effectively together.

Strategic Outlook

MoxiWorks' RISE strategy points toward an AI-powered real estate technology ecosystem in which relationship intelligence is not isolated from marketing, advertising or transaction workflows.

The integration with Cloze provides a practical example of that model. Rather than replacing an existing platform, RISE connects with it and adds another layer of functionality.

As AI agents and automation become more common in real estate software, integrations could become increasingly important. The value of an AI system will depend not only on its underlying models but also on the breadth, accuracy and freshness of the data it can access.

Top Insights

• MoxiWorks and Cloze now synchronize contacts and activity, reducing duplicate data entry for agents using both AI-powered real estate platforms.

• RISE users gain access to campaigns, presentations and automations for synchronized Cloze contacts, extending marketing capabilities without manual record creation.

• Cloze receives RISE activity through the integration, giving its Intelligence Engine a broader relationship history for identifying referrals and opportunities.

• The integration reflects a wider SaaS shift toward open ecosystems, allowing real estate brokerages to connect specialized platforms instead of replacing existing technology.

• MoxiWorks plans additional RISE integrations, including transaction management, signaling a broader strategy to make AI relationship intelligence part of connected real estate workflows.

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Strativera is expanding its position in the CRM consulting market with a new Salesforce AppExchange listing, giving the digital marketing and revenue operations agency credentials across both Salesforce and HubSpot. The move reflects a broader shift among mid-market companies toward CRM-independent consulting partners that can align technology choices with sales and revenue processes.

Digital marketing and revenue operations agencies have increasingly become part of the technology decision-making process as CRM platforms evolve from sales databases into central systems for marketing, customer data and revenue management. Strativera is leaning into that role with a new listing for its Salesforce consulting practice on Salesforce AppExchange.

The listing, “Strativera Revenue Operations and Salesforce Consulting,” places the company in Salesforce's public Consultants directory. Its services include Salesforce org audits and cleanup, Sales Cloud implementation, lead lifecycle design, CRM migrations and executive dashboards designed to connect marketing activity with closed revenue.

The development gives Strativera publicly visible credentials across both major CRM ecosystems. The company already holds a HubSpot Solutions Partner listing, creating a dual-platform positioning that could become increasingly relevant for mid-market businesses evaluating whether their existing CRM still fits their operating model.

For businesses, that distinction matters. Salesforce and HubSpot compete across overlapping CRM, marketing automation and customer engagement categories, but their architectures, pricing models, integrations and implementation requirements can lead to very different operational decisions. A consulting firm that works across both ecosystems can potentially evaluate the choice based on a company's sales process rather than its own technology specialization.

“Most agencies pick a CRM camp because credentialing across ecosystems is expensive and slow,” said Janae Tanner, Co-Founder and Vice President of Growth and Client Success at Strativera. She said the company intentionally pursued credentials across both platforms so clients can evaluate CRM options according to their sales processes.

The Salesforce AppExchange listing also adds a layer of third-party verification to Strativera's positioning. AppExchange functions as Salesforce's marketplace and ecosystem directory for applications and consulting services, allowing businesses to evaluate partners within the Salesforce environment rather than relying solely on agency-controlled marketing claims.

That verification model is becoming more important as the B2B services market becomes increasingly crowded. Agencies now compete not only on expertise and case studies but also on certifications, marketplace profiles, customer reviews and independently maintained reputation signals.

Strativera says its HubSpot Solutions Partner profile carries a public 5.0 rating and that its client ratings are 5.0 across Google, Clutch and GoodFirms. The company reports 24 Clutch reviews and eight GoodFirms reviews. It has also joined the Clutch Guarantee program, which provides a 14-day money-back commitment for qualifying new engagements originating through Clutch.

The company's strategy reflects a wider evolution in revenue operations consulting. CRM implementation is no longer limited to configuring fields, pipelines and dashboards. Marketing and sales organizations increasingly expect CRM infrastructure to connect campaign activity, lead management, customer data and revenue attribution.

That trend is also being accelerated by artificial intelligence. Salesforce is integrating AI capabilities into its CRM ecosystem through Agentforce and Data Cloud, while HubSpot is expanding AI across its customer platform. As AI-driven workflows become more dependent on reliable customer data and clean CRM architectures, implementation partners have a larger role in preparing organizations for automation.

For mid-market companies and private-equity-backed businesses, the stakes can be particularly high. CRM migrations and revenue operations projects often occur alongside growth initiatives, acquisitions or efforts to standardize processes across business units. Poorly structured implementations can create fragmented data and reporting problems that undermine the technology investment.

Strativera, founded in June 2025, says it now operates four offices, with headquarters in Cherry Hill Township, New Jersey, and additional locations in Manahawkin, New Jersey, Tampa, Florida, and Las Vegas, Nevada. The company reports more than $104 million in client-attributed revenue growth and an average 28% reduction in customer acquisition costs across engagements spanning more than 17 industries.

Those figures are company-reported rather than independently audited, making the firm's third-party marketplace and review profiles useful supplementary signals for prospective buyers.

The larger significance of the Salesforce listing is therefore less about another directory entry and more about how CRM consulting is evolving. As businesses become less willing to separate marketing technology from revenue strategy, agencies that can operate across multiple CRM ecosystems may have an advantage in the next phase of MarTech consolidation.

Market Landscape

The CRM market is increasingly defined by ecosystem depth rather than software alone. Salesforce continues to build around Sales Cloud, Data Cloud and Agentforce, while HubSpot combines CRM, marketing, sales and customer service capabilities in a more unified platform.

For mid-market organizations, the decision between platforms is rarely just a feature comparison. Data architecture, integrations, sales processes, marketing automation, reporting requirements and implementation resources can determine whether a CRM delivers measurable value.

This creates an opening for technology-neutral revenue operations consultants. Their value lies in translating business processes into technology architecture rather than forcing organizations into a platform based on an agency's existing credential set.

The competitive environment also includes Microsoft Dynamics 365 and other enterprise CRM platforms, meaning businesses increasingly have multiple technology ecosystems to evaluate. As AI becomes embedded across these platforms, clean data and well-designed workflows are becoming prerequisites for effective automation.

Strategic Outlook

Strativera's cross-platform credentialing points toward a broader change in B2B technology services: buyers increasingly want independently verifiable evidence of expertise before committing to complex CRM projects.

For agencies, certifications and marketplace listings can establish baseline credibility, but they are unlikely to be sufficient on their own. Demonstrable revenue outcomes, implementation expertise, customer references and the ability to integrate CRM with broader MarTech infrastructure will increasingly determine competitive differentiation.

The rise of AI adds another layer. Salesforce, HubSpot and Microsoft are making AI a core part of their CRM strategies, increasing demand for consultants capable of connecting customer data, automation and business processes. Agencies that can bridge those areas may find themselves moving from implementation vendors toward strategic revenue infrastructure partners.

Top Insights

• Strativera's Salesforce AppExchange listing gives the agency publicly verifiable consulting credentials across Salesforce and HubSpot, expanding its CRM options for mid-market buyers.

• The firm's Salesforce services cover Sales Cloud implementation, CRM migration, lead lifecycle design and revenue dashboards, addressing core revenue operations requirements.

• Cross-platform CRM expertise could help private-equity-backed and mid-market companies choose technology based on sales processes rather than agency platform preferences.

• Third-party directories and review platforms are becoming important trust signals as B2B technology buyers demand independently verifiable evidence before selecting consulting partners.

• Salesforce and HubSpot's growing AI capabilities increase the importance of clean CRM data, workflow architecture and experienced implementation partners for enterprise automation.

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Artificial intelligence is moving through marketing at a pace that increasingly resembles the early days of search engine optimization. The difference is that AI is no longer simply another emerging channel: it is changing how marketing teams create content, analyze data, automate workflows and compete for visibility across search and digital platforms.

For marketers who remember the early 2000s, the current AI transition may look familiar. Search engine optimization was once a relatively new discipline, and companies that invested early in technical infrastructure, content strategy and search visibility gained advantages that became increasingly difficult for slower competitors to replicate.

The Digital WOW, a digital marketing and technology consultancy, argues that artificial intelligence is creating a comparable opening today. After several years of research into AI's impact on digital marketing, websites and cloud software, the company says businesses are moving from AI experimentation toward more operational adoption.

That shift is visible in broader business data. The U.S. Census Bureau's Business Trends and Outlook Survey found that AI use among U.S. businesses remained between 17% and 20% from December 2025 through May 2026, while another 20% to 23% expected to begin using AI within six months. Among companies with at least 250 employees, 37% reported using AI in business operations.

For marketing organizations, adoption is considerably further along. Jasper's 2026 State of AI in Marketing report, based on 1,400 marketers, found that 91% of marketing teams now use AI, compared with 63% a year earlier. Yet only 41% said they could confidently demonstrate AI's return on investment, highlighting a growing divide between adoption and maturity.

The distinction matters. Simply adding generative AI to an existing workflow does not necessarily create a competitive advantage. The bigger opportunity lies in redesigning the workflow itself.

The Digital WOW says its client engagements have exposed a gap between AI's accelerating capabilities and the pace at which some marketing programs are being modernized. Older content processes, disconnected software systems and manual campaign operations can leave organizations trying to apply AI to infrastructure that was never designed for automated decision-making.

That problem extends beyond marketing agencies. Enterprise teams increasingly need their customer data platforms, marketing automation systems, analytics environments and content operations to work together. AI agents can potentially automate portions of research, audience analysis, content production, campaign optimization and reporting, but their effectiveness depends heavily on the quality of the underlying data and processes.

This is where the current AI transition differs from simply buying another marketing tool. Platforms such as Google, Microsoft, Amazon, Salesforce and Adobe are incorporating AI capabilities into broader technology ecosystems, reducing the distinction between traditional software and AI-powered software. Google's AI investments increasingly affect search and advertising, while Meta is applying AI to advertising creation and optimization. For marketers, the platforms themselves are becoming part of the adoption pressure.

The Digital WOW CEO Paul Ramkissoon describes the company's approach as operating more like a smaller, faster-moving technology organization than a large agency. The argument is that smaller agencies can change processes, test new tools and implement new AI capabilities without the organizational layers that can slow large enterprise deployments.

That positioning is plausible, but it is not unique. Boutique agencies, specialist consultancies and technology-focused service providers are competing for the same opportunity. At the enterprise level, the more important differentiator will likely be whether an organization can move from isolated AI tools to repeatable, governed systems that produce measurable business outcomes.

The SEO comparison is therefore useful, but only to a point. Early SEO rewarded organizations that understood a new source of digital visibility before it became mainstream. AI search introduces a similar strategic question: how should brands structure content, data and digital authority so that their information can be discovered and accurately represented by AI-powered search and answer systems?

That emerging discipline is increasingly connected to generative engine optimization, answer engine optimization and AI visibility. Unlike traditional SEO, however, visibility is not determined solely by ranking a webpage for a keyword. AI systems can synthesize information from multiple sources, making brand authority, structured information, content quality and entity recognition increasingly important.

For enterprise marketing teams, that means AI strategy should extend beyond productivity. Organizations need to evaluate where AI can improve customer engagement, predictive analytics, campaign operations and decision-making while establishing governance around data, brand standards and measurement.

The opportunity may ultimately be larger than simply adopting AI faster than competitors. Companies that redesign their marketing infrastructure around AI could build operational advantages that are difficult to reproduce later.

Market Landscape

AI adoption is entering a more consequential phase. The U.S. Census Bureau data suggests that business adoption is still far from universal, while marketing-specific research shows that AI has already become mainstream among marketers.

That creates an unusual market dynamic: adoption of AI tools is becoming widespread, but organizational maturity is not keeping pace. Jasper's research found that 91% of marketers use AI while only 41% can demonstrate AI ROI.

The next competitive battleground is therefore likely to be implementation quality. Salesforce and Adobe are embedding AI deeper into enterprise marketing ecosystems, while Google and Microsoft are reshaping search, productivity and advertising around AI. Amazon is similarly integrating AI across commerce and advertising infrastructure.

For agencies and marketing departments, competing effectively will require more than access to the same models. Data quality, proprietary customer knowledge, workflow architecture, experimentation and governance can become the differentiators.

Strategic Outlook

The SEO analogy offers a useful lesson: early adoption matters most when it is paired with infrastructure and expertise. Marketers that merely experiment with AI-generated content may see short-term productivity gains, but those that connect AI to first-party data, customer journeys, analytics and automation have a stronger path toward durable value.

The next phase of AI in marketing will likely shift attention from tool adoption to system design. The winners may not be the organizations using the most AI tools, but those that build the most effective operating model around them.

Top Insights

• AI adoption is accelerating across U.S. businesses, creating a strategic window for marketing teams that modernize infrastructure before AI becomes fully mainstream.

• Jasper reports 91% of marketers now use AI, but only 41% can prove ROI, exposing a growing gap between adoption and measurable business value.

• Google, Microsoft, Amazon, Salesforce and Adobe are embedding AI into core platforms, making AI adoption increasingly difficult for enterprise marketing teams to avoid.

• AI search creates a new visibility opportunity similar to early SEO, shifting competitive advantage toward authoritative content, structured data and strong digital entities.

• Agencies with faster implementation cycles may gain an advantage as enterprises seek AI modernization without the organizational friction associated with large-scale transformation.

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Sustainability is increasingly moving from a corporate responsibility function into the core operating model of businesses across Asia-Pacific. That shift was on display in Colombo this week, where more than 200 business leaders, policymakers, sustainability practitioners, and United Nations representatives gathered for Forward Faster Now APAC 2026.

The two-day event, organized by the United Nations Global Compact APAC Hub in collaboration with 14 Global Compact Country Networks covering 16 countries, brought together executives and sustainability professionals to examine how companies can respond to climate risks, geopolitical uncertainty, technological disruption, and changing stakeholder expectations.

Hosted by Global Compact Network Sri Lanka, the forum focused on practical approaches to sustainable and inclusive economic growth rather than sustainability as a standalone corporate initiative.

The distinction is becoming increasingly important for enterprise leaders. Climate exposure, supply-chain disruption, resource scarcity, changing regulations, and investor expectations can directly affect operating costs and long-term business resilience. As a result, sustainability strategies are increasingly being evaluated alongside financial performance, technology investment, risk management, and corporate governance.

The discussions at Forward Faster Now APAC 2026 covered climate action, sustainable finance, water resilience, governance and accountability, gender equality, business and human rights, digital innovation, circular economy models, and cross-sector partnerships.

A recurring theme was collaboration. Companies, governments, investors, civil society organizations, and international institutions increasingly need to work together because many sustainability challenges extend beyond the control of a single organization.

For technology and marketing leaders, that collaborative approach also has implications for how enterprises manage data, digital infrastructure, supply chains, and stakeholder communications. Digital tools can help companies monitor environmental performance, improve supply-chain visibility, automate reporting, and communicate sustainability metrics, but technology alone does not create responsible business practices.

The event also included an in-person meeting of the APAC Chief Sustainability Officers Peer Learning Group. The group brings together 20 chief sustainability officers from companies across the region and is designed to encourage peer learning and collective action.

One focus of the meeting was breaking down organizational silos between sustainability teams and other corporate functions. That issue is particularly relevant as companies attempt to move sustainability into procurement, finance, operations, technology, human resources, and marketing rather than keeping it within a dedicated sustainability department.

The approach mirrors a wider transformation in enterprise management. Sustainability data increasingly intersects with enterprise resource planning, customer data, financial reporting, supply-chain systems, risk platforms, and analytics infrastructure.

This creates opportunities for MarTech and enterprise technology providers. Marketing teams, for example, increasingly need reliable sustainability data when communicating environmental and social initiatives to customers. At the same time, companies face growing scrutiny over the accuracy of sustainability claims, making data governance and traceability increasingly important.

The financial dimension is also significant. Sustainable finance has become an important area of discussion as investors and financial institutions assess climate exposure, corporate governance, transition strategies, and long-term risk.

For businesses operating across Asia-Pacific, the challenge is complicated by the region's economic diversity. The area includes highly developed technology markets alongside fast-growing emerging economies, each with different regulatory frameworks, infrastructure capabilities, resource constraints, and sustainability priorities.

That makes regional knowledge-sharing particularly valuable. Strategies developed for a large multinational may not translate directly to a smaller company or an emerging market, while local innovations can sometimes provide scalable approaches to resource efficiency, financial inclusion, and resilient infrastructure.

The United Nations Global Compact's Forward Faster initiative is aimed at helping businesses translate sustainability commitments into measurable progress toward the Sustainable Development Goals. The APAC gathering reinforced that objective by emphasizing practical tools, peer learning, and partnerships.

The broader business message is increasingly difficult to ignore: sustainability is becoming intertwined with competitiveness.

Companies that can integrate sustainability data into operational decision-making may be better positioned to identify risks earlier, improve resource efficiency, strengthen supply chains, and respond to changing stakeholder expectations. But achieving that requires organizational changes as much as technology investments.

For enterprise leaders, the next phase of sustainability is therefore likely to be less about publishing commitments and more about embedding measurable objectives into business systems and accountability structures.

Forward Faster Now APAC 2026 offered a regional forum for that transition. Its emphasis on collaboration, digital innovation, sustainable finance, governance, and cross-functional leadership reflects a broader movement toward treating sustainability as an enterprise capability rather than a separate corporate agenda.

Market Landscape

Asia-Pacific is facing a combination of rapid economic growth, climate exposure, supply-chain complexity, technological change, and shifting regulatory expectations. These pressures are increasing the importance of resilience alongside traditional growth metrics.

The UN Global Compact's regional gathering comes as businesses face greater expectations to demonstrate measurable sustainability progress. The challenge is moving from broad commitments to operational execution, including better data, governance, reporting, supply-chain management, and accountability.

Technology is becoming an important enabler of that transition. Enterprise analytics, AI, IoT, cloud platforms, and automation can help organizations monitor resource use, identify risks, and improve decision-making. At the same time, organizations must ensure that sustainability data is accurate, auditable, and connected to business outcomes.

For technology vendors, this creates a growing market for platforms that integrate sustainability intelligence into existing enterprise workflows rather than treating it as an isolated reporting function.

Strategic Outlook

The next stage of corporate sustainability in Asia-Pacific is likely to focus increasingly on integration. Chief sustainability officers will need closer relationships with finance, operations, procurement, technology, HR, and marketing leaders as sustainability becomes part of enterprise performance management.

The APAC Chief Sustainability Officers Peer Learning Group reflects that direction by creating a structured forum for executives to exchange practical approaches and reduce organizational silos.

For businesses, the competitive opportunity lies in turning sustainability into measurable operational improvements. Companies that combine reliable data, technology, governance, and cross-sector partnerships may be better positioned to build resilience while pursuing long-term growth.

Top Insights

• More than 200 Asia-Pacific leaders gathered in Colombo to address climate resilience, sustainable finance, digital innovation, and inclusive business growth across the region.

• Forward Faster Now APAC 2026 emphasized embedding sustainability into core operations, affecting technology, finance, procurement, supply chains, governance, and marketing teams.

• The APAC Chief Sustainability Officers Peer Learning Group brings 20 executives together to exchange practical strategies and reduce organizational silos around sustainability.

• Digital innovation is becoming an important sustainability enabler, helping enterprises improve data visibility, supply-chain resilience, resource management, and sustainability reporting.

• Cross-sector collaboration remains critical as businesses address interconnected climate, geopolitical, technological, and social challenges that individual organizations cannot solve independently.

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