Fruchtman Marketing, a specialist marketing agency serving the fine jewelry industry, has been acquired by Erin Moyer and Manuel Carballea in a leadership transition that keeps the agency's name, team and vertical focus intact while moving its principal operations from Ohio to Miami.

The transaction was brokered by Website Closers, with veteran broker Mike Adams managing the deal. Founders Ellen and Michael Fruchtman built the business over 45 years, developing an agency focused exclusively on the marketing and consulting needs of jewelry retailers, manufacturers, designers, wholesalers, vendors and trade organizations.

Moyer will become CEO, while Carballea will serve as COO. The incoming leadership plans to preserve the agency's specialized positioning while expanding its use of analytics, digital strategy, technology and artificial intelligence.

That combination reflects a broader pressure on marketing agencies: specialization remains a differentiator, but clients increasingly expect measurable performance, integrated digital capabilities and technology-enabled decision-making alongside traditional creative services.

Fruchtman Marketing was founded in 1981 after Ellen Fruchtman left television production and launched the agency with a $750 loan from Michael. Over time, the company developed a model that effectively functions as an outsourced marketing department for jewelry businesses.

Its services span brand strategy, creative production, media planning and buying, digital advertising, social media, paid search, SEO, video, website development and business consulting. The agency's sector specialization gives its team knowledge of jewelry-specific purchasing cycles, inventory considerations, consumer behavior and the operating realities of independent retailers.

The acquisition preserves that model rather than shifting Fruchtman into a broader generalist agency. The incoming owners instead plan to add technology and analytical capabilities to the existing specialist foundation.

Moyer brings more than two decades of experience spanning brand strategy, content, digital innovation, entertainment, media and global consumer brands. Carballea has a background covering communications, software, enterprise sales, customer service and emerging technologies.

Carballea specifically identified analytics, customer segmentation, performance platforms and responsible AI applications as areas where the agency could strengthen its infrastructure. Those investments could give Fruchtman greater ability to connect marketing activity with measurable commercial outcomes.

The timing is notable. Gartner reported in 2025 that digital channels accounted for 61.1% of total marketing spend among surveyed organizations, while its research also found that marketers were using AI to improve productivity as budgets remained constrained.

McKinsey's research similarly found that marketing and sales was among the business functions with substantial generative AI adoption, with 42% of surveyed organizations reporting regular GenAI use in marketing and sales in its 2024 global survey.

For a specialized agency such as Fruchtman, the implication is that industry knowledge and technology increasingly have to work together. AI tools can accelerate research, content development, segmentation and analysis, but the value depends on how those capabilities are incorporated into a coherent marketing operation.

The ownership transition also includes continuity measures. Michael Fruchtman will retire following the sale, while Ellen will remain involved through the end of 2026 and continue as an adviser into 2027. David Turgeon has been promoted to Chief Client Officer, while Shane O'Neill becomes Chief Strategy and Growth Officer.

Those appointments provide a bridge between the agency's historical knowledge and its new leadership structure.

The acquisition therefore represents more than a change in ownership. It creates a test of whether a highly specialized agency can preserve the relationship-driven expertise that built its business while modernizing its data, digital and AI capabilities for the next phase of marketing.

Market Landscape

The agency market is undergoing a structural shift as marketers balance specialized expertise with pressure to improve efficiency and demonstrate measurable outcomes.

Gartner's 2025 CMO Spend Survey found that 39% of surveyed CMOs planned to reduce agency budgets, while 22% said GenAI had enabled them to reduce reliance on external agencies for creativity and strategy.

That does not eliminate the role of agencies, but it changes what clients may expect from them. Specialist firms increasingly need to demonstrate expertise in a specific market while also providing analytics, technology integration, performance measurement and AI-enabled workflows.

Fruchtman's continued focus on the jewelry industry gives it a defined vertical position. The planned investment in analytics and technology could determine how effectively that specialization translates into measurable client value.

Strategic Outlook

The acquisition illustrates an increasingly common agency challenge: modernization without losing the institutional knowledge that differentiates a specialist business.

For Fruchtman, the immediate strategy appears to be evolutionary rather than disruptive. The company will retain its brand, people and industry focus while adding capabilities around analytics, segmentation, digital platforms and responsible AI.

That approach could allow the agency to compete on two fronts simultaneously: deep knowledge of jewelry marketing and the technology infrastructure expected from modern full-service marketing partners.

For enterprise and mid-market marketing teams, the broader lesson is that AI adoption does not necessarily mean replacing specialist agencies. It can instead change the capabilities agencies are expected to provide and the way those capabilities are measured.

Top Insights

• Fruchtman is retaining its jewelry specialization, preserving decades of sector knowledge while the new owners plan to expand digital, analytics and technology capabilities.
• The ownership transition emphasizes continuity, with the founders supporting the handover and longtime executives moving into expanded client, strategy and growth positions.
• AI is becoming an agency capability, as Fruchtman evaluates responsible AI alongside customer segmentation, performance platforms and marketing analytics.
• Specialist agencies face new efficiency pressures, as marketers increasingly expect technology-enabled services, measurable outcomes and integrated digital execution.
• The acquisition creates a modernization test, showing whether a relationship-driven specialist agency can evolve its technology stack without weakening its industry expertise.

Get in touch with our MarTech Experts.

PolicyX.com is reshaping its digital marketing and customer experience strategy around Gen Z, combining an AI-powered insurance chatbot, mobile-first content and a planned campus mentorship program as younger consumers become a larger part of India's online insurance market.

The Gurugram-based insurance aggregator says the initiative is designed to address a gap between how young consumers manage other financial products and how they approach insurance. The company is positioning insurance information around shorter, clearer explanations and digital self-service rather than traditional agent-led interactions.

At the center of the technology push is Pixie, an insurance-focused AI chatbot that allows users to ask questions and receive information without calling an agent or navigating a conventional support process. PolicyX says the chatbot is intended to make insurance research more conversational and accessible to digitally native customers.

That reflects a broader change in financial-services marketing. Consumers increasingly encounter financial products through search engines, social platforms, comparison sites and conversational interfaces rather than relying exclusively on branch visits or sales representatives. For insurers and aggregators, the challenge is therefore shifting from simply generating leads to helping customers understand complex products during digital research.

PolicyX is also changing how it presents insurance information. The company says its platform provides comparisons across more than 20 insurers and 1,000 plans, while its content strategy is being expanded around specific life situations. Articles such as those addressing term insurance for freelancers target younger workers whose employment patterns may not fit traditional assumptions about salaried policyholders.

The company's Instagram presence is also being developed around Gen Z-oriented content. The broader strategy connects social media, educational content, product comparison and AI assistance into a single customer-acquisition journey.

That combination is important because insurance remains a relatively complex purchase. Policy terms, exclusions, premiums, coverage limits and eligibility requirements can create significant friction for first-time buyers. A conversational interface can potentially reduce that friction by allowing users to ask follow-up questions in natural language, although the accuracy and completeness of AI-generated insurance information remain critical considerations.

PolicyX founder and CEO Naval Goel is also taking the campaign offline. The executive has begun visiting college campuses, including Galgotias University and Masters' Union, for discussions with students about money and insurance. The company is developing a mentorship program intended to extend those interactions as students enter employment and begin making independent financial decisions.

The mentorship initiative is still being finalized, meaning its eventual format and scope have yet to be established.

Founded in 2013, PolicyX has grown into a more than 300-person organization based in Gurugram, covering health, motor, life and term insurance. Its Gen Z strategy represents a shift from treating younger consumers primarily as a future customer segment toward building digital experiences specifically around their current research and purchasing behavior.

The move also highlights a larger opportunity for financial-services marketers. AI can make insurance information easier to navigate, but technology alone does not eliminate the need for transparency, accurate product information and human oversight.

For PolicyX and other insurance platforms, the competitive question will ultimately be whether AI-powered discovery and simplified digital journeys can turn younger consumers' online research habits into informed insurance decisions.

Market Landscape

Financial-services companies are increasingly experimenting with AI assistants, conversational interfaces and personalized digital journeys. The trend extends beyond insurance into banking, investing and payments, where customers expect digital platforms to explain increasingly complicated financial products.

For insurance aggregators, AI introduces another layer to an already competitive comparison market. Platforms must balance convenience with the accuracy of policy information, particularly when AI is involved in explaining exclusions, eligibility or coverage.

The Gen Z opportunity is also broader than social-media marketing. Younger consumers are entering the workforce with different expectations around mobile experiences, self-service and financial education. That creates an opening for insurers and aggregators to redesign acquisition journeys around education and comparison rather than relying predominantly on traditional sales channels.

Strategic Outlook

PolicyX's strategy combines several elements of modern MarTech: AI-assisted customer engagement, content marketing, social media, digital comparison tools and community-based brand building.

The more significant development is the attempt to connect those channels into a continuous customer journey. A prospective customer might discover insurance content through social media or search, compare policies digitally and then use an AI assistant to clarify questions before making a purchase decision.

That model could become increasingly relevant as AI interfaces become embedded into consumer discovery. For insurance companies, however, success will depend on maintaining clear distinctions between automated information and regulated financial guidance while keeping product information current.

Top Insights

• PolicyX is using AI to simplify insurance discovery, with Pixie designed to answer customer questions and reduce dependence on traditional agent-led research.
• The Gen Z strategy connects content and technology, combining social media, mobile-first explainers, policy comparisons and conversational AI into one digital customer journey.
• Campus engagement expands the strategy beyond MarTech, with Naval Goel meeting students while PolicyX develops a longer-term mentorship initiative.
• Insurance aggregators face an education challenge, as younger customers may research policies digitally but still encounter complex terms, exclusions and coverage decisions.
• AI could reshape insurance acquisition, but accurate information, transparent comparisons and appropriate human oversight remain essential to customer trust.

Get in touch with our MarTech Experts.

Andy Parton, an AI marketing executive with experience at Diageo and BAT, has launched Destreza, an independent London consultancy focused on helping consumer businesses integrate artificial intelligence into brand strategy, marketing operations and execution.

Founded in May 2026, Destreza is built around what Parton describes as an “AI-Native Marketing Consulting” model. The consultancy targets marketing leaders and founders making decisions about AI operating models, brand development, internal capabilities and agency relationships.

The launch comes as generative AI moves from experimentation into broader marketing workflows. Tools based on large language models can now accelerate research, content development, creative ideation and analysis, but organizations still face questions about governance, quality, strategic differentiation and where human judgment should remain central.

Parton's career in AI marketing predates the current generative-AI boom. At Diageo, he led the 2019 launch of What's Your Whisky, an AI-powered whisky recommendation experience designed to help consumers navigate products according to taste. The company says the experience has since reached more than one million consumers worldwide.

That timeline is relevant to Destreza's positioning. Rather than treating AI as primarily a content-production technology, the consultancy is focused on applying it across the wider brand-building lifecycle, including consumer insight, strategy, creative development, activation, measurement and evaluation.

Before founding Destreza, Parton served as Global Head of Content at BAT, where his responsibilities included global brand building, premiumisation and AI-enabled marketing across the customer journey for three global brands.

The new consultancy is organized around its Destreza Operating Model, which combines specialist AI agents with a senior practitioner who remains accountable for strategic decisions and final recommendations. An Agentic Council is intended to review and refine outputs generated during the process.

In practical terms, the model separates high-volume analytical and production tasks from strategic accountability. AI agents can conduct research, develop campaign and brand strategies, support creative work, evaluate outputs and adapt recommendations for individual markets. Parton retains responsibility for diagnosing the business problem and deciding which recommendations should move forward.

That distinction reflects a larger issue confronting enterprise marketing teams: increasing access to AI does not necessarily translate into better marketing outcomes. As AI tools become widely available, differentiation may increasingly depend on the quality of an organization's data, strategic frameworks, governance processes and human decision-making.

“I want consumer businesses to come through the AI revolution with stronger brands, not just cheaper marketing,” Parton said.

Destreza initially plans to work with marketing leaders on AI operating models, brand strategy, organizational capability and agency configuration. Its consulting engagements are structured as Survey, a fixed-fee diagnostic; Shift, which addresses a defined business change end to end; and Focus, a longer-term senior partnership.

The company is also preparing to launch The Playbook for AI Brand Building in late September 2026. The ten-stage methodology is designed for founders and smaller marketing teams seeking a structured approach to building brands with AI.

The launch illustrates a broader evolution in AI marketing. Early enterprise adoption often centered on isolated applications such as automated content creation, personalization or campaign optimization. The next phase is increasingly concerned with redesigning the operating model itself: determining which activities should be automated, which should be augmented and which decisions require experienced human oversight.

For marketing organizations, that shift could prove more consequential than simply adding another AI tool to an existing technology stack.

Market Landscape

AI is increasingly becoming an operating-layer technology for marketing rather than a standalone content-generation capability. Platforms from Google, Microsoft, Salesforce and Adobe are incorporating AI into analytics, customer engagement, creative workflows and marketing operations, raising the competitive bar for organizations still treating AI as a series of disconnected experiments.

Destreza's model represents one response to that change: using multiple specialized AI agents for research and execution while placing a senior human practitioner at the center of strategic accountability.

The approach also reflects growing interest in agentic AI, where software agents can perform multi-step tasks rather than simply respond to individual prompts. The practical challenge for marketing leaders is establishing appropriate controls around accuracy, brand consistency, data use, intellectual property and approval processes.

Strategic Outlook

The significance of Destreza's launch is less about another AI marketing consultancy entering the market and more about the operating-model question behind it.

As AI capabilities become increasingly accessible, organizations may gain less competitive advantage from simply having access to AI tools. Instead, differentiation can shift toward how effectively businesses combine AI capabilities with proprietary knowledge, brand strategy, customer insight and human judgment.

For enterprise marketing teams, that means AI adoption increasingly intersects with organizational design. Decisions about internal capabilities, agency roles, workflows and governance could become as important as selecting individual AI platforms.

Top Insights

• Destreza applies AI across brand building, moving beyond content generation toward research, strategy, creative development and marketing evaluation for consumer businesses.
• Andy Parton's AI marketing experience predates generative AI's boom, including Diageo's 2019 AI-powered whisky selector, launched years before ChatGPT.
• The Destreza Operating Model combines AI agents with human accountability, keeping strategic diagnosis and final recommendations with a senior marketing practitioner.
• AI adoption is becoming an operating-model issue, forcing marketing leaders to reconsider agency structures, internal capabilities, workflows and governance.
• Agentic AI could reshape marketing execution, allowing specialized AI systems to handle multi-step research and production while humans retain strategic oversight.

Get in touch with our MarTech Experts.

As Google and generative AI platforms increasingly answer questions directly, businesses are facing a search environment where visibility depends on more than traditional rankings. At ProfitCON 2026, AI search and SEO expert Sandy Wardenburg Waggett will outline how accounting and bookkeeping firms can improve their chances of being discovered, cited and recommended across conventional search and AI-generated answers.

Sandy Wardenburg Waggett, founder of MSW Interactive Designs, is scheduled to take the main stage at ProfitCON 2026 with a presentation focused on the changing mechanics of digital discovery. Her session, “Stop Just Trying to Rank. Start Trying to Be the Answer: How to Get Your Firm Found, Cited, and Recommended in the Age of AI,” will focus on how professional services firms can adapt SEO strategies for AI-driven search.

The shift is significant because search behavior is moving beyond the traditional model of entering keywords, scanning a results page and visiting multiple websites. Generative AI interfaces can instead synthesize information and return direct answers, recommendations and comparisons.

For accounting firms, that changes the definition of search visibility. A high Google ranking remains relevant, but businesses also need consistent digital signals that help search engines and AI systems understand their identity, expertise, services, audience and credibility.

Waggett's framework centers on 15 tactics spanning five areas: foundation, identity, proof, content and differentiation. The recommendations include improving Google Business Profiles, auditing business identity and NAP information, implementing schema markup, strengthening expert and author profiles, documenting credentials and awards, developing review libraries, publishing pricing information and building FAQ resources.

The approach reflects a broader movement toward AI search optimization, where structured information and corroborating evidence can become as important as keyword targeting. Schema markup, authoritative content, business listings, reviews and third-party references can provide machine-readable and contextual signals about an organization.

Content strategy is another major component. Rather than producing articles primarily around isolated keywords, Waggett advocates creating resources that address questions buyers actually ask, including pricing, comparisons, costs, common problems and service suitability.

That approach overlaps with the wider evolution of answer engine optimization (AEO) and generative engine optimization (GEO). While traditional SEO focuses heavily on ranking web pages, AEO and GEO place greater emphasis on making information clear, authoritative and easy for AI systems to interpret.

The distinction is increasingly relevant for enterprise marketing teams as platforms from Google, Microsoft and other technology companies incorporate generative AI into discovery experiences. Businesses are consequently having to manage not only website optimization but also entity information, structured data, reputation signals and content consistency across multiple digital surfaces.

Waggett's presentation also emphasizes a practical implementation model. Her four-week plan begins with foundational business information before expanding into service pages, authority signals and an answer library built around buyer questions.

For smaller professional services businesses, that model highlights an important operational challenge: AI search optimization is not a single technical implementation. It combines local SEO, content strategy, structured data, reputation management, digital PR and ongoing information maintenance.

The larger implication is that search visibility is becoming increasingly connected to entity recognition and evidence. Firms need to make it straightforward for both potential customers and search systems to determine who they are, what they offer, who they serve and why their claims are credible.

Market Landscape

The rise of AI-generated search experiences is changing how marketers think about organic discovery. Traditional SEO remains a core component of digital visibility, but answer-oriented interfaces are creating additional opportunities and challenges around citations, recommendations and brand/entity recognition.

This evolution also increases the importance of structured and authoritative first-party information. For enterprise marketing organizations using platforms from Google, Microsoft, Salesforce or Adobe, maintaining consistent service descriptions, expert profiles, customer evidence and organizational data can become part of a broader digital information strategy.

The competitive environment is therefore shifting from “ranking for a query” toward a more complex model involving rankings, direct answers, citations, recommendations and brand mentions. The precise mechanisms used by individual AI systems remain proprietary, so businesses should treat AI visibility as an evolving optimization discipline rather than a guaranteed formula.

Strategic Outlook

Waggett's ProfitCON framework illustrates how SEO is expanding beyond conventional keyword and technical optimization. As AI interfaces increasingly mediate discovery, businesses may need to treat websites, business profiles, reviews, structured data, expert content and third-party references as connected components of a single digital identity.

For accounting and bookkeeping firms, the immediate opportunity is practical: build authoritative answers around real customer questions while ensuring the underlying business information is accurate and consistent. The same principle can extend to other professional services and B2B sectors as AI-powered discovery becomes more widely adopted.

Top Insights

• AI search shifts SEO beyond rankings, requiring businesses to establish clear identities, authoritative evidence and useful content that AI systems can interpret.
• Structured data and business profiles matter more as search platforms increasingly combine websites, directories, reviews and entity information when generating answers.
• Buyer-focused content gains importance when users ask AI about pricing, comparisons, services and recommendations instead of searching individual keywords.
• Professional services firms face a broader visibility challenge, connecting local SEO, content, reputation, expertise and digital PR across multiple platforms.
• AI visibility remains an evolving discipline, making accurate first-party information and credible third-party evidence important foundations for long-term search strategy.

Get in touch with our MarTech Experts.

Datasea Intelligent Technology is moving its AI agent digital marketing offering from agreements and pilots toward active customer delivery, with five customers now receiving services under arrangements that could represent up to approximately US$89.4 million in annualized service volume if maximum usage levels were sustained.

Datasea's latest announcement highlights a developing commercial model for AI agents: instead of selling artificial intelligence solely as software licenses or one-time implementation projects, providers are increasingly packaging agents as continuously consumed services.

The Nasdaq-listed company said its Chinese operating entities recently entered AI agent digital marketing service agreements and pre-order arrangements with five customers, including Beijing Judongjiujiu Technology. All five customers have begun receiving services, with approximately US$1.10 million in aggregate service value delivered as of the announcement.

The headline figure is substantially larger, but it requires an important qualification. Datasea estimates that the five arrangements could generate monthly service fees ranging from approximately US$2.24 million to US$7.45 million in aggregate. At the maximum level sustained for 12 months, that would equal about US$89.4 million in annualized service volume. The company explicitly says the amount is not guaranteed revenue, recognized revenue, backlog or a minimum purchase commitment.

That distinction matters as AI companies increasingly experiment with consumption-based commercial models.

Datasea's service offering combines several functions that traditionally sit across separate marketing technology categories. Its AI agent services can support task orchestration, large-language-model invocation, information processing, third-party system integration, content generation, data analytics, advertising execution and technical support.

The broader pre-order scope extends into campaign planning, advertising strategy, creative production, video generation, online advertising support, quality control, optimization, software and mini-program development, and data analysis.

Rather than treating the AI agent as a standalone interface, Datasea is positioning it as an operational layer that can execute multiple marketing tasks within a continuing service relationship.

That approach reflects a wider change in enterprise AI economics. McKinsey reported in 2026 that consumption-based software pricing has expanded as AI introduces more variable costs, with the number of software companies using consumption-based pricing more than doubling between 2015 and 2024.

For Datasea, the “recharge, consumption and service” model means customers can consume AI service credits according to their marketing requirements. This potentially gives the company a recurring commercial mechanism tied to usage rather than simply the number of software seats.

The model also fits a broader shift toward agentic marketing. McKinsey's 2026 research describes marketing increasingly moving toward continuous workflows that connect insights, content, personalization, commerce and orchestration.

Datasea's deployment, however, remains at an early commercial stage. The US$1.10 million in services already provided is materially different from the maximum potential annualized volume. Actual revenue will depend on customer consumption, advertising execution, content review, platform billing, customer confirmation, settlement, payment and accounting treatment.

The company is therefore testing more than the technical capabilities of AI agents. It is also testing whether enterprises will repeatedly consume AI-powered marketing functions as an operating service.

If that model scales, AI agents could increasingly become embedded between marketing strategy and execution—generating creative assets, coordinating campaigns, analyzing performance and adjusting activities within a connected workflow.

For MarTech buyers, the important question will be whether usage-based AI services can deliver measurable improvements in campaign efficiency and performance while maintaining the governance, integration and human oversight required for enterprise deployment.

Market Landscape

AI agents are moving from experimentation toward production, although adoption remains uneven. McKinsey's 2026 global AI survey found that 40% of respondents at organizations with more than US$1 billion in annual revenue reported scaling AI agents, compared with 27% the previous year.

Marketing is also becoming a significant target for agentic workflows. McKinsey says nearly 60% of marketers surveyed use AI multiple times per week, but fewer than 10% reported capturing value across end-to-end workflows.

That gap creates an opening for service providers that can connect AI capabilities directly to operational marketing outcomes rather than simply providing generative tools.

Datasea's model is particularly notable because it combines content generation, advertising operations, analytics and optimization under a consumption-based structure. The approach places the company closer to an AI-enabled marketing services platform than a conventional point solution.

Strategic Outlook

The commercial test for AI marketing agents is shifting from what the technology can generate to how reliably it can operate inside a business workflow.

Datasea's approach illustrates this transition. Customers can consume AI capabilities for individual marketing activities while the provider manages orchestration, integrations and technical support.

For enterprise marketing teams, this could simplify access to agentic capabilities, but it also creates new requirements around data governance, advertising controls, human approvals, attribution and AI cost management.

As usage scales, buyers will need to measure AI services against business outcomes such as qualified demand, campaign efficiency, content production speed and advertising performance—not simply the volume of AI activity.

The more significant development may therefore be the emergence of AI agents as continuously billed marketing infrastructure, rather than another standalone generative AI product.

Top Insights

• Datasea has moved into active delivery: Five customers have begun consuming AI agent digital marketing services under new commercial arrangements.
• US$89.4 million is potential service volume: The figure represents maximum annualized usage based on disclosed estimates, not guaranteed revenue or contracted backlog.
• Usage-based AI pricing is gaining relevance: Datasea's recharge-and-consumption model links customer spending directly to AI service utilization.
• Marketing agents are becoming operational: The offering combines content, advertising, analytics, optimization and technical execution within connected workflows.
• Enterprise adoption remains an execution challenge: AI usage is increasing, but organizations continue to struggle to translate experimentation into measurable end-to-end business value.

Get in touch with our MarTech Experts.

CMB has launched Intelligent Signals, an AI-powered market intelligence solution designed to combine primary research with continuously monitored external signals. The platform brings together social activity, news, search, AI visibility, financial and competitive data to help marketing, brand, product and strategy teams identify emerging market shifts earlier.

Market intelligence has traditionally depended on research studies, customer surveys and periodic tracking programs. CMB is taking a different approach with Intelligent Signals, an AI-powered solution designed to continuously monitor changes around customers and markets while incorporating primary research into the same intelligence workflow.

The solution is intended to address a problem facing enterprise marketing and strategy teams: relevant market signals are increasingly distributed across disconnected sources.

Social media conversations can reveal changing sentiment. Search behavior can indicate emerging interests. News coverage can expose competitive developments. Financial and market data can provide broader commercial context, while visibility across AI platforms is becoming another signal of how brands and topics are being surfaced to users.

CMB's Intelligent Signals brings those sources together with primary research rather than treating them as separate information streams.

The distinction matters because external signals do not necessarily explain why customer attitudes are changing. Primary research can provide direct evidence of what people think, need or experience, while external behavioral and market signals can show what is happening around those audiences.

CMB's solution is designed to connect those two perspectives.

The company says Intelligent Signals monitors relevant external sources according to individual client requirements. The platform can incorporate social media, news, search, AI visibility, financial information and competitive data, with additional sources selected for particular business questions.

That customization is important because market intelligence is rarely identical across industries. A consumer brand may need social and search signals, while a B2B technology company could place greater emphasis on competitive developments, industry news and AI discovery.

Rather than simply aggregating information, the platform uses AI to synthesize signals and identify patterns, changes, opportunities and potential risks. CMB says its content curation process is intended to maintain signal integrity before the information reaches the AI analysis layer.

This positions Intelligent Signals closer to an intelligence system than a conventional social listening or monitoring dashboard.

The approach also reflects a broader change in how marketing organizations are thinking about AI visibility. Search remains an important source of market intelligence, but generative AI platforms are creating another layer in which consumers and business buyers discover information, compare companies and formulate questions.

Monitoring AI visibility alongside search, social and news activity could therefore give marketing teams another way to understand how brands and markets are changing.

CMB is also building Intelligent Signals around Model Context Protocol (MCP)-compatible connections. MCP is designed to allow AI applications and agents to connect with external data and tools through a standardized interface.

For marketing and insights teams, that architecture could make market intelligence more accessible inside existing AI workflows rather than requiring analysts to repeatedly move information between disconnected platforms.

The practical objective is not simply to produce more data. It is to make disparate signals usable within the systems that teams already employ for analysis and decision-making.

That distinction is increasingly important as organizations experiment with AI agents and conversational analytics. An intelligence platform that can provide context to those systems has the potential to become part of an organization's broader AI infrastructure.

CMB says Intelligent Signals is being developed as a client-specific solution, with applications spanning marketing, communications, product, insights and strategy.

The model also creates a different role for primary research. Instead of serving as an isolated periodic measurement exercise, research can become one evidence layer within a continuous intelligence environment.

For marketers, that could mean moving from asking what customers think at a particular point in time toward continuously examining the combination of customer attitudes, external events, competitive movements and digital behavior that surrounds those attitudes.

The effectiveness of that model will ultimately depend on signal quality, source selection, data governance and the ability of AI systems to distinguish meaningful changes from market noise. But the underlying direction is clear: market intelligence is increasingly becoming an always-on technology workflow rather than a collection of periodic reports.

Market Landscape

The market intelligence category is expanding as companies gain access to more real-time digital signals while simultaneously adopting AI tools to interpret them.

Traditional research remains valuable because it directly captures consumer attitudes, needs and experiences. External signals add another dimension by showing activity around those audiences.

CMB's approach combines the two rather than positioning them as competing sources.

The addition of AI visibility is particularly relevant to the emerging search environment. As consumers increasingly use AI systems to discover information, brands need ways to understand how they appear within those environments alongside conventional search results and social conversations.

MCP compatibility adds another technology layer by allowing intelligence to potentially become accessible through AI applications and agents already embedded in enterprise workflows.

Strategic Outlook

The significance of Intelligent Signals lies less in the number of sources it monitors than in the attempt to connect them into a continuous decision-support system.

For marketing organizations, the opportunity is to detect changes earlier and investigate them before they become obvious through conventional reporting cycles. For product and strategy teams, the same signals could provide context around emerging customer needs, competitor activity or market risks.

The major challenge will be separating meaningful signals from noise. AI can accelerate synthesis, but organizations still need transparent sourcing, appropriate curation and human interpretation when market decisions carry significant commercial consequences.

If those elements are managed effectively, always-on intelligence could become another layer of the modern enterprise MarTech stack—connecting research, external data and AI-assisted analysis.

Top Insights

• CMB is combining primary research with external market signals, creating a continuous intelligence layer for marketing, brand, product and strategy teams.
• AI visibility becomes part of market monitoring, reflecting the growing importance of understanding how brands and topics appear across AI-driven discovery environments.
• MCP compatibility connects intelligence with AI workflows, potentially allowing teams to access market context through existing AI tools and agents.
• Client-specific signal selection is central to the model, allowing organizations to monitor sources relevant to their particular markets, audiences and business questions.
• The platform shifts market intelligence toward continuous monitoring, reducing dependence on isolated research cycles while retaining primary research as a core evidence source.

Get in touch with our MarTech Experts.

Marchex said an existing advertising and media customer has commercially deployed its AI Voice Agent after a paid pilot in which the system qualified prospective customers when sales representatives were unavailable. The expanded relationship currently generates approximately $400,000 in annualized revenue, with Marchex expecting more than $200,000 in additional annualized revenue from the customer in 2027.

The announcement is significant less because another company has launched a voice agent than because Marchex is using an existing conversation-analytics relationship as an entry point for AI-powered execution.

During the pilot, Marchex's Voice Agent was designed to engage callers when human sales representatives were unavailable, qualify potential customers and preserve opportunities that might otherwise have gone unanswered. The commercial deployment extends that use case beyond analytics into direct customer interaction.

That distinction is increasingly important in marketing technology. Conversation analytics traditionally focuses on understanding calls after—or while—they happen. AI voice agents introduce an additional layer: acting on those interactions in real time.

Marchex describes itself as an AI-driven conversation analytics, customer acquisition and optimization company. Its latest deployment therefore connects two functions that have often existed separately: understanding customer conversations and responding to them.

The commercial expansion currently produces approximately $400,000 in annualized revenue for Marchex, according to the company. Marchex expects incremental revenue during 2026 and more than $200,000 in annualized incremental revenue in 2027. The company has also said it has signed additional AI Voice Agent customers and is discussing paid pilots with several large enterprises. Those potential deployments remain contingent on successful pilot conversion.

The model reflects a broader shift toward AI agents in customer operations. McKinsey has described contact centers as an early application area for generative AI, with use cases spanning customer interactions, agent assistance, analytics and workflow automation. Its research also highlights persistent challenges around implementation, governance, security and defining measurable business outcomes.

For marketers, the commercial opportunity is particularly relevant when missed calls represent missed leads. An AI voice agent can potentially provide coverage outside normal staffing patterns, gather qualifying information and route or escalate interactions according to predefined business rules.

That puts the technology closer to marketing automation and customer acquisition than a conventional chatbot. Instead of simply answering frequently asked questions, a voice agent can participate in a lead-management workflow.

The competitive landscape is also moving beyond traditional interactive voice response systems. Modern conversational AI platforms increasingly combine speech recognition, large language models, business knowledge and workflow integrations. Marchex's differentiation is its existing emphasis on conversation intelligence and vertical customer-acquisition data.

Still, commercial deployment introduces questions that a successful pilot does not automatically answer. Enterprises need to evaluate accuracy, escalation processes, customer consent, data handling, integration with CRM systems and the ability to measure whether AI-assisted conversations actually produce qualified opportunities and revenue.

Marchex's announcement illustrates the industry's broader transition from AI as an analytics layer toward AI as an operational participant. The more consequential question for marketing teams will be whether these systems can consistently connect conversational interactions with measurable business outcomes.

AI is moving deeper into contact-center and customer-acquisition workflows. McKinsey estimates that generative AI could potentially improve productivity in customer-care functions by an amount equivalent to 30% to 45% of current function costs, although the estimate represents potential value rather than a guaranteed outcome for individual deployments.

McKinsey's research also identifies sales conversion as a significant application for generative AI in contact centers, including analyzing customer conversations, identifying buying signals and improving agent performance.

The market is therefore evolving from scripted IVR and basic chatbots toward systems capable of understanding natural language, interpreting context and taking action. Human agents remain important for complex interactions, escalation and relationship-driven situations.

For MarTech teams, this creates an increasingly connected stack spanning CRM, call analytics, customer data, marketing automation and conversational AI.

Strategic Outlook

Marchex's customer expansion highlights a broader commercial model emerging around AI software: start with analytics, demonstrate measurable value and then add automated execution.

That approach can reduce the conceptual distance between marketing intelligence and action. Instead of merely identifying why leads are being lost, an AI system can potentially intervene when an opportunity occurs.

For enterprise marketing organizations, the key evaluation criteria will increasingly include conversion impact, escalation quality, integration depth, governance and the reliability of AI-generated interactions—not simply whether an agent can hold a natural conversation.

Top Insights

• Marchex is connecting analytics with execution: Its AI Voice Agent moves conversation intelligence toward real-time lead qualification and customer acquisition workflows.
• The commercial deployment validates a cross-sell model: Marchex is expanding an existing analytics relationship rather than relying exclusively on new-customer acquisition.
• Voice AI is moving beyond IVR: Natural-language agents can potentially combine customer conversations, qualification and workflow actions within one interaction.
• Pilot economics remain important: Marchex's additional enterprise opportunities are still in pilot discussions, making conversion and measurable outcomes key indicators for future expansion.
• Marketing and contact-center technology are converging: AI voice agents increasingly connect lead generation, customer engagement, analytics and CRM-driven workflows.

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Lithuanian grocery retailer IKI is deploying SymphonyAI's Promotional Evaluation and Promotional Planning solutions across its 250-store network, reporting an improvement of more than 10 percentage points in promotional forecast accuracy in initial categories. The implementation highlights how retailers are using vertical AI to predict promotional demand before campaigns launch, connecting marketing decisions more closely with inventory and merchandising operations.

IKI is expanding its use of artificial intelligence in promotional planning, deploying SymphonyAI's Promotional Evaluation and Promotional Planning solutions across its network of approximately 250 stores.

The Lithuanian grocery retailer said its initial work with SymphonyAI across selected categories improved promotional forecast accuracy by more than 10 percentage points and reduced stock-outs during promotional periods. The results were announced by SymphonyAI on September 17, 2026.

For grocery retailers, promotional forecasting is a particularly complex marketing problem. Discounts can change demand rapidly, while the impact of a promotion can vary by store, product, season, competing products and existing purchasing patterns. A forecast that misses demand can create either excess inventory or empty shelves.

SymphonyAI's approach uses AI models to estimate the expected impact of promotions before retailers commit to them. Its promotional optimization technology is designed to project incremental sales, margin impact and potential product cannibalization at product- and store-group levels.

That moves promotional analytics beyond simply measuring whether a campaign worked after it ended.

Instead, the technology is intended to help category managers model different promotional scenarios before launch. Retailers can potentially assess expected lift, margin consequences and interactions between products before deciding which promotion to execute.

For IKI, that capability is being incorporated into a much broader retail operation. The company is Lithuania's second-largest retail chain, according to REWE Group, with around 250 stores and nearly 5,500 employees. IKI has operated in Lithuania since 1992 and is part of Germany-based REWE Group.

The implementation is therefore relevant beyond a single promotional campaign. It gives a large retailer a way to connect marketing planning with category management and operational decision-making.

That integration is becoming increasingly important as retailers attempt to balance price-sensitive shoppers, promotional competition and margin pressure. A promotion is not simply an advertising decision in grocery retail. It can affect purchasing requirements, replenishment, store-level inventory and the availability of products customers expect to find.

AI forecasting can provide a common analytical layer across those decisions.

SymphonyAI says its demand forecasting technology incorporates sales history, promotions, weather, seasonality and local events when generating item-, store- and day-level forecasts. The company reports that other European grocery deployments have produced five- to 10-point forecast-accuracy gains, although those results come from separate customers and should not be treated as equivalent to IKI's reported outcome.

The distinction is important because IKI's more-than-10-point improvement is an early result from selected categories, rather than evidence that the same improvement has been achieved across its entire store network.

The technology also illustrates the convergence of marketing and supply-chain intelligence.

Traditional promotional planning can separate the question of what customers are likely to buy from the question of whether stores will have enough inventory to satisfy that demand. AI-driven planning attempts to connect those variables. Better demand estimates can influence purchasing and replenishment decisions before the promotion reaches shoppers.

The potential business effect extends to waste and stock availability as well. SymphonyAI says its broader retail forecasting deployments have produced reductions in shortages and waste, but these are vendor-reported results from other implementations rather than independently verified results for IKI.

IKI's deployment also provides a potential test case for REWE Group. The retailer's parent operates across 21 European countries, giving successful AI applications within one market a potential pathway to broader evaluation.

The larger MarTech implication is that promotional technology is moving closer to real-time commercial decision-making. Marketing teams no longer have to view promotions solely as campaigns that generate customer response. In modern retail environments, promotional decisions can simultaneously affect demand forecasts, margins, inventory and customer experience.

For IKI, the next stage will be determining whether the initial forecasting gains can be reproduced across additional categories and promotional conditions.

If that expansion produces consistent results, the deployment could become an example of how vertical AI connects promotional strategy with the operational systems required to deliver on that strategy.

Market Landscape

Retail promotional planning sits at the intersection of marketing, merchandising, pricing, inventory and supply-chain management.

AI is increasingly being applied to these functions because retailers have to account for multiple demand signals simultaneously. SymphonyAI's retail forecasting technology, for example, incorporates historical sales, promotions, weather, seasonality and local events into forecasting models.

The market is consequently moving away from promotional analysis that happens primarily after an event toward scenario modeling that can influence decisions before a promotion launches.

For retailers, the objective is not simply greater forecast accuracy. Better predictions can potentially support fewer stock-outs, more efficient inventory allocation and improved promotional economics.

Strategic Outlook

IKI's implementation demonstrates how AI can turn promotional planning into a more predictive discipline.

The immediate question is whether the reported 10-plus-point improvement in selected categories can scale across the retailer's wider assortment and different promotional scenarios. Grocery demand is highly variable, so performance during one category or period does not automatically establish a universal improvement.

Longer term, the more significant opportunity is integration. Promotional AI can become more valuable when its forecasts connect with replenishment, pricing, merchandising, customer analytics and broader retail planning systems.

That creates a model in which marketing decisions are informed by operational consequences before the promotion reaches the shopper.

Top Insights

• IKI reports more than a 10-point improvement in promotional forecast accuracy in selected categories after adopting SymphonyAI's AI-driven planning capabilities.
• Promotional AI shifts planning from hindsight to prediction, allowing retailers to model expected sales lift, margin impact and product cannibalization before campaigns launch.
• Marketing and supply-chain decisions are becoming connected, because promotional demand directly affects inventory requirements and product availability.
• IKI's 250-store network provides a substantial deployment environment, giving the retailer an opportunity to evaluate whether initial results can scale across additional categories.
• AI forecasting is becoming a broader retail infrastructure layer, with applications extending from promotional planning to replenishment, allocation and demand forecasting.

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