Financial services marketing is increasingly being shaped by the convergence of brand, customer experience, data and digital product design. Empower is responding by expanding its marketing organization with four senior appointments spanning customer experience, brand marketing, communications and user experience, bringing experience from Meta, Marriott International, IBM, Unilever and Zeno Group.

Empower is restructuring key parts of its marketing leadership as the retirement and wealth-management company places greater emphasis on consumer experience, brand relevance and digital engagement.

The company announced four senior appointments under Chief Marketing Officer Sangita Woerner: Prashant Gaba as Chief Customer Experience Officer, Kim Blanding as Head of Brand Marketing, Tracey Thiele as Head of Brand Communications and Patrick Strange as Head of User Experience.

The appointments bring expertise spanning customer experience, artificial intelligence, CRM, communications, product design and consumer marketing. Together, the roles indicate a marketing model that extends beyond campaign execution into the broader customer journey.

Gaba joins from Marriott International, where he led global customer growth initiatives and helped develop the company's customer experience and CRM strategy. His 25-year career also includes 15 years at IBM. At Empower, he will oversee customer experience strategy with an emphasis on data, insights and technology.

Blanding brings a different dimension to the organization. Most recently, she led global marketing for Meta's AI wearables portfolio, including Ray-Ban Meta and Quest. Her background also includes Unilever and consumer-product entrepreneurship. At Empower, she will lead brand marketing across campaigns, partnerships and consumer experiences.

Thiele joins after 16 years at Zeno Group, where she worked on brands including Netflix, Robinhood, Four Seasons Hotels and Resorts, Team USA and Google. Her responsibilities at Empower span earned media, social, creator, executive and employee communications. The role brings corporate communications closer to the company's broader brand and consumer strategy.

Strange brings a product-design perspective to the leadership group. He spent a decade at Meta in senior roles covering content design, product design, business and payments platforms, and most recently worked at Mexican consumer-lending and buy-now-pay-later company Kueski. He will lead user experience at Empower.

The appointments come about a year after Empower appointed Woerner as its CMO. When she joined in September 2025, the company said her mandate included strengthening brand visibility, multi-channel marketing and customer engagement.

The organizational shift mirrors a broader change in financial-services marketing. Customer acquisition and retention are increasingly influenced by digital experiences, personalization and the quality of interactions across channels. McKinsey's September 2026 analysis says banks are using AI-powered personalization to tailor communications, offers and customer journeys, while stronger digital experiences are becoming increasingly important as fintech competitors raise consumer expectations.

For Empower, the new structure connects several traditionally separate disciplines. Customer experience, brand storytelling, communications and product UX can influence the same customer journey, from initial awareness through digital interaction and ongoing engagement.

That convergence is particularly relevant for a company operating across retirement services, financial planning and investments. Empower says it administers more than $2.1 trillion in assets for more than 20 million individuals.

The appointments therefore represent more than an expansion of marketing headcount. They establish senior ownership across the major components of a modern financial-services customer experience, with data, technology, brand and digital product design operating closer together.

Market Landscape

Financial-services companies are increasingly competing on digital experience, personalization and brand engagement alongside traditional products and pricing.

McKinsey's September 2026 research notes that digital-first financial providers are raising expectations for personalized experiences and that AI can help institutions tailor communications, offers and customer journeys.

The same research highlights the importance of integrating marketing performance data across channels and CRM systems, making the connection between marketing technology and customer experience increasingly important.

Empower's leadership structure reflects that convergence. Customer experience, brand marketing, communications and UX are being assigned dedicated senior leadership while remaining part of the company's broader marketing organization.

Strategic Outlook

The appointments point toward a customer-centric MarTech model in financial services, where marketing is increasingly connected to product experience and customer data rather than operating solely as a communications function.

For enterprise financial brands, that creates a need to coordinate CRM, analytics, personalization, content, social media, UX and marketing automation. AI adds another layer by enabling more individualized interactions and faster analysis of customer behavior.

The practical challenge will be maintaining consistency across those systems while meeting the trust, privacy and regulatory expectations associated with financial products.

Top Insights

• Empower is broadening marketing leadership, adding dedicated executives for customer experience, brand, communications and UX under CMO Sangita Woerner.
• Meta experience enters financial-services marketing, with Kim Blanding bringing leadership experience from AI wearables, consumer technology and global brand marketing.
• Customer experience gains dedicated leadership, as former Marriott and IBM executive Prashant Gaba takes responsibility for data-driven CX and CRM strategy.
• UX joins the marketing transformation, with former Meta and Kueski design executive Patrick Strange leading Empower's digital experience strategy.
• Financial marketing is becoming more integrated, as brand, customer data, digital products and personalized engagement increasingly converge across the customer journey.

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Artificial intelligence is moving deeper into advertising and marketing operations, but new research from the Advertising Research Foundation (ARF) suggests that rising adoption is being accompanied by an equally important question: how should marketers validate and govern AI-generated outputs? ARF research shows AI use expanding across creative development, media buying, customer engagement, analytics, measurement and synthetic data, while organizations increase testing and formal training.

Artificial intelligence is becoming a broader operating layer for marketing teams rather than a technology confined to content generation. New research from the Advertising Research Foundation examines how advertisers and agencies are applying AI across the marketing lifecycle and finds adoption expanding alongside confidence, testing and organizational training.

The ARF's research draws on two survey waves conducted six months apart among U.S. advertisers and agencies. Across the measured marketing activities, average AI use increased from 58% in November 2025 to 76% in May 2026. Confidence among marketers using AI-generated outputs rose from 82% to 93% over the same period.

The shift is broader than generative copy or image production. ARF reports that AI is being used for customer engagement, media buying, optimization, analytics, measurement and attribution, alongside creative development. Text generation remained the most common application at 87%, followed by image generation at 71%.

That expansion is changing the role AI plays inside marketing organizations. Instead of functioning primarily as an execution tool, AI is increasingly being used to support decisions about audiences, campaign performance and media allocation.

Synthetic data and AI-generated personas represent another part of that transition. ARF found that each was used by 36% of respondents in its research. Among synthetic-data users, 70% reported using it for research simulation, 68% for forecasting or analytics and 66% for testing campaign or creative variations.

The findings also challenge the idea that marketers are converging on a single dominant AI platform. Adoption varies considerably by task. Platform-native AI tools have an important role in media buying and measurement, while other providers are used for creative, analytics and related marketing applications.

For enterprise MarTech teams, that creates a technology-stack question. Rather than selecting one AI system for every marketing function, organizations may increasingly operate multiple specialized models and platform-native tools. Google, Microsoft, Adobe and Salesforce, for example, each approach AI through different combinations of marketing, advertising, analytics, customer data and workflow infrastructure.

Governance is becoming a more important counterweight to that proliferation. ARF found that 64% of respondents reported extensive AI testing, while formal training programs increased from 50% to 70%. Yet only 52% reported having formal internal AI policies.

That gap is significant because confidence does not necessarily establish accuracy. ARF's broader 2026 research argues that AI adoption needs to be accompanied by validation, governance and evidence about business outcomes.

The research also identifies data privacy, inaccurate outputs and brand safety as continuing concerns. Those issues become more consequential as AI moves closer to customer targeting, measurement and consumer research, where flawed inputs or unsupported outputs can affect both campaign decisions and customer experiences.

For marketing leaders, the emerging model is therefore not simply "more AI." It is a combination of specialized AI systems, human oversight, testing frameworks and governance practices. The ARF findings suggest that the next stage of adoption will depend less on whether organizations use AI and more on how reliably they can evaluate what the technology produces.

Market Landscape

AI is becoming embedded across the marketing technology stack, spanning creative production, media optimization, customer engagement, analytics and measurement.

ARF's research shows average use across measured marketing activities rising from 58% to 76% between November 2025 and May 2026. At the same time, the organization found that extensive testing reached 64% and formal AI training increased to 70%.

The market is also becoming more fragmented by use case. ARF notes that different AI providers and platform-native tools lead in different marketing functions rather than one system dominating the entire workflow.

That fragmentation has implications for enterprise MarTech architecture. Marketing teams increasingly need to consider interoperability, data governance, measurement consistency and human review alongside model capabilities.

Strategic Outlook

The next phase of AI adoption in marketing is likely to focus increasingly on accountability rather than experimentation alone.

ARF's research shows a notable increase in both confidence and formal training, but the gap between widespread use and formal policy demonstrates that organizational governance is still developing.

The challenge for enterprise teams will be establishing where AI can operate independently, where outputs require human validation and how organizations measure whether AI-assisted decisions actually improve marketing outcomes.

That makes testing, data quality, model evaluation and governance increasingly important components of the modern MarTech stack.

Top Insights

• AI adoption is broadening across marketing, moving from creative production into media buying, analytics, measurement and customer engagement workflows.
• Confidence in AI outputs is increasing, with ARF reporting a rise from 82% to 93%, while testing and formal training also expand.
• Marketing AI remains fragmented by use case, with platform-native and specialist tools serving different creative, media, analytics and measurement requirements.
• Synthetic data is entering marketing research, supporting simulation, forecasting and campaign testing while bringing new questions around validity and interpretation.
• Governance is lagging adoption, as formal AI policies remain less widespread than testing and training programs across surveyed organizations.

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B2B demand generation is moving from a volume-focused discipline toward one increasingly centered on pipeline quality, buyer signals and AI-assisted decision-making. Activate Marketing Services' 2026 State of Demand Gen Report examines how marketers are adjusting budgets, measurement strategies, content programs and lead qualification as they plan for 2027.

B2B marketing teams are heading into 2027 with a more complicated demand-generation mandate: create pipeline, demonstrate revenue impact and adapt to buyers who increasingly use digital and AI tools throughout the research process.

That shift is the focus of Activate Marketing Services' 10th annual State of Demand Gen Report, based on responses from 212 demand-generation and technology marketers. The research examines budget expectations, attribution, AI adoption, content performance, lead qualification and the buyer signals sales teams increasingly need.

The report's findings point to a demand-generation function becoming more closely tied to revenue operations. Activate's research identifies pipeline sourcing as the leading measure of success while marketers contend with increasingly fragmented attribution environments.

AI is also moving beyond its early role as a content-production tool. According to Activate, marketers are applying AI to areas including audience segmentation, customer-journey mapping and high-confidence lead scoring. That expands the technology's role from helping marketers create assets to helping them interpret buying signals and prioritize potential opportunities.

This development matters because B2B buying groups are becoming harder to interpret through individual lead activity. A single form submission or content download may represent only one participant in a much larger purchasing process. Demand-generation teams therefore need stronger signals about account engagement, buying-stage progression and whether activity is translating into pipeline.

Channel strategy is changing alongside those measurement priorities. Activate says high-performing teams are balancing account-based marketing with scalable digital channels and content syndication rather than relying exclusively on individual tactical campaigns.

Lead follow-up is another area where the research highlights a hybrid approach. Activate's report points to dedicated human qualification working alongside AI screening agents to provide sales teams with more validated signals.

The broader market supports the shift toward pipeline accountability. A separate 2026 DemandWorks survey of more than 250 B2B marketing and go-to-market leaders found that more than 60% measure demand generation through pipeline contribution, while 75% identified pipeline as a top priority for the year.

Yet AI adoption does not eliminate the need for human involvement. Research summarized by Marketing Hub Media cites Gartner findings that 69% of B2B buyers turn to sales representatives to validate AI-generated insights. That suggests the buying journey can involve both independent AI-assisted research and human confirmation.

For marketing leaders, the implication is less about adding AI to an existing stack and more about reconsidering how demand is measured. Content, paid media, ABM, intent data, AI qualification and sales follow-up increasingly need to connect to the same pipeline narrative.

Activate's research provides a snapshot of where marketers are placing those bets for 2027. The larger industry transition is toward demand-generation programs that prioritize identifiable buyer signals and revenue contribution over activity alone.

Market Landscape

The B2B demand-generation market is increasingly organized around pipeline accountability, buyer intelligence and AI-assisted execution.

Activate's research identifies budget optimism, expanded AI use and greater attention to lead qualification as major themes heading into 2027.

Other 2026 research points in a similar direction. DemandWorks found that more than 60% of surveyed B2B marketing and GTM leaders measure success through pipeline contribution.

At the same time, the buyer journey is becoming less linear. AI can provide information earlier in the research process, while sales representatives can remain important for validation and more complex buying decisions.

For MarTech teams, this increases the importance of integrating CRM, marketing automation, intent data, analytics, content and AI tools rather than evaluating each channel independently.

Strategic Outlook

The 2027 demand-generation landscape is likely to place greater emphasis on signal quality rather than lead volume.

AI can automate parts of segmentation, scoring and qualification, but the value of those systems depends on the quality of the underlying data and the connection between marketing signals and revenue outcomes.

For enterprise marketing teams, that means evaluating AI investments alongside attribution, CRM data quality, sales alignment and pipeline measurement. The technology layer is expanding, but the central question remains whether those systems help marketing understand which activity represents meaningful buying intent.

Top Insights

• AI is moving deeper into demand generation, expanding from content creation toward segmentation, journey mapping and lead scoring across B2B marketing operations.
• Pipeline contribution is gaining measurement priority, putting pressure on marketers to connect campaigns, channels and buyer signals with downstream revenue outcomes.
• Buyer signals are becoming more complex, as larger buying groups make individual lead activity less representative of an entire purchasing decision.
• Human and AI qualification are converging, with AI screening potential opportunities while human teams validate signals and handle higher-context sales conversations.
• 2027 planning is becoming more signal-focused, requiring demand-generation teams to balance ABM, digital channels, content syndication and emerging AI capabilities.

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Automotive dealerships generate large volumes of customer calls, but recordings alone offer limited insight when managers cannot realistically review more than a fraction of them. Volie is addressing that gap with Pulse, an AI-powered call intelligence solution designed to analyze dealership conversations and surface customer concerns, missed opportunities, follow-up issues and coaching signals.

Volie is expanding its artificial intelligence capabilities for automotive retail with Pulse, an AI-powered call intelligence solution designed to turn dealership customer conversations into searchable business intelligence.

The product analyzes inbound and outbound conversations at scale, giving dealership business development center (BDC) managers a way to identify potentially important interactions without manually listening to thousands of recordings.

That distinction matters in an industry where phone conversations can contain information that is difficult to capture through conventional CRM fields or call dispositions. A customer may express frustration, indicate interest in a vehicle, raise a service concern or receive a promise for follow-up without that context being consistently reflected in dealership systems.

Pulse is designed to extract those signals from conversations. According to Volie, its capabilities include AI-generated summaries and transcriptions, sentiment analysis, identification of missed opportunities and escalated concerns, recognition of promises and commitments, potential Do Not Call issues, disposition accuracy and coaching opportunities.

The system also allows managers to investigate flagged conversations. A manager can review why a call was surfaced, examine its summary and transcript, listen to the original recording and determine the appropriate next action.

This changes the role of call monitoring from a sampling exercise into a broader operational intelligence function. Instead of randomly selecting conversations for quality assurance, managers can use AI to identify calls that warrant closer attention.

The approach is consistent with a wider movement toward conversational intelligence across sales and customer-service operations. Platforms such as Salesforce and Microsoft have increasingly incorporated AI into customer interaction workflows, while specialized vendors have developed tools for analyzing sales and service conversations. Volie's differentiation is its focus on automotive dealership communications and the BDC workflow.

Pulse can analyze BDC calls within Volie's platform, covering outbound follow-up and inbound conversations. Through Volie Call Tracking, the company says the intelligence can also extend to inbound sales, service and advertising calls.

That broader coverage is important because dealership customer journeys often cross departments. A customer who calls about an advertisement may eventually interact with sales, service or a BDC representative. Capturing conversation-level signals across those touchpoints can provide managers with a more complete view of customer intent and experience.

Volie also positions Pulse as part of Volie Intelligence rather than as a standalone AI application. The company's platform connects customer data, campaigns, calls, text messages, email, workflows and performance management. That integration could make conversation intelligence more operationally useful because insights can potentially sit alongside the communication activity that generated them.

There are also governance considerations. Automated sentiment analysis and AI-generated interpretations should be treated as decision-support signals rather than unquestionable assessments of a customer's intent or emotional state. Potential Do Not Call issues similarly require appropriate human review before action.

For dealership groups, the larger opportunity is moving from call storage to conversation intelligence. When AI can examine interactions at scale, managers can potentially spend less time searching recordings and more time addressing the specific customer and employee situations that require intervention.

Market Landscape

Conversational AI is increasingly moving beyond automated chatbots and voice assistants toward intelligence derived from existing customer interactions.

For automotive retailers, this creates a particularly large data opportunity. Phone calls remain an important communication channel for sales, service appointments, lead follow-up and customer support, yet traditional reporting often reduces those interactions to basic metadata such as call duration, source and disposition.

Volie's Pulse takes a different approach by analyzing the conversation itself. That places the product alongside the broader category of conversation intelligence, while its automotive-specific workflows distinguish it from general-purpose sales intelligence platforms.

The competitive environment includes CRM vendors, contact-center platforms and specialist conversation-analysis providers. The strategic differentiator for vertical platforms will increasingly be how effectively AI insights connect with the workflows where businesses already manage customer interactions.

Strategic Outlook

AI-powered call analysis could become an increasingly important layer of dealership performance management as retailers accumulate larger volumes of customer interaction data.

The immediate value is not necessarily replacing managers or BDC representatives. It is reducing the amount of manual review required to identify conversations that deserve attention.

For dealership groups, the next stage will likely involve connecting conversational signals with CRM records, lead attribution, follow-up workflows and revenue outcomes. That could turn call intelligence from a quality-assurance tool into a broader customer-experience and sales-operations capability.

Top Insights

• Volie Pulse analyzes dealership conversations at scale, helping BDC managers identify customer concerns, missed opportunities and follow-up issues without manually reviewing every call.
• Conversation intelligence extends beyond recordings, transforming transcripts, sentiment and commitments into operational signals that managers can investigate and act upon.
• Volie integrates Pulse with its communication platform, connecting AI insights with customer data, calls, messaging, email, campaigns and dealership workflows.
• Automotive retailers gain broader call visibility, with intelligence covering BDC interactions and, through Call Tracking, sales, service and advertising calls.
• Human oversight remains important, particularly when AI interprets sentiment, identifies compliance risks or recommends follow-up based on conversation context.

Get in touch with our MarTech Experts.

Local digital platforms are increasingly moving beyond business listings and community feeds into broader ecosystems for discovery, communication, commerce and customer engagement. MyCommunityToday (MYCT) is expanding in that direction with a connected platform that combines local search, messaging, AI interaction, digital commerce, webinars, referrals, events and business analytics.

MyCommunityToday is expanding its connected community platform with a wider set of communication, commerce, AI and engagement capabilities aimed at bringing residents, local businesses, community organizations and content providers into one digital environment.

The San Diego-based company describes MYCT as an ecosystem rather than a single-purpose community application. Its platform combines location-based discovery with business profiles, deals and coupons, communication tools, e-commerce, video, events and interactive experiences. The company has also been adding AI and monetization features, including paid webinars and referral rewards.

At the center of the experience is the MYCT Home Feed, which surfaces local news, events, announcements, business promotions, alerts and other content according to location and user interests. Smart Search extends that discovery model to businesses, services, products, events and coupons, using factors such as category, distance, popularity and ratings.

That local-intent layer is significant for small businesses competing against large marketplaces and general-purpose search engines. Instead of treating discovery, communication and transactions as separate activities, MYCT is attempting to connect them within a single user journey.

The platform's most unusual component is Walking Talking Deal Chief, an AI-powered 3D interface designed to communicate through text, voice and video. According to the company, Deal Chief can participate in conversations and help users explore products, services, community information, deals and other digital experiences. MYCT's broader ecosystem also identifies Deal Chief as an AI sales agent for engaging and qualifying customer leads.

For businesses, this creates an alternative to assembling separate tools for local discovery, advertising, customer messaging, online stores and engagement analytics. MYCT's Merchant Portal already combines capabilities including store management, marketing, advertising, deals and coupons, content management and analytics.

The commerce component is similarly broad. Businesses can establish digital storefronts, promote offers and support e-commerce or mobile commerce, while users can discover local deals and save offers to a digital wallet. Marketplace functionality can also connect local sellers with customers and support pickup or delivery.

The latest expansion adds another layer: monetized digital events. MYCT Webinars supports individual, recurring and multi-session programs, while planned paid-webinar functionality is designed to handle pricing, registration, payments, capacity, refunds and host payouts.

The accompanying Webinar Referral Program turns attendees into potential distribution channels. Registered participants can share referral links, with hosts able to establish percentage-based rewards for qualifying paid-ticket referrals or fixed rewards for qualifying free-webinar registrations. Referral attribution is connected to completed registration or payment rather than simply a link click.

This positions MYCT closer to an integrated local commerce and engagement platform than a conventional community app. Its competitive challenge, however, is managing the complexity that comes with combining functions typically provided by platforms such as Meta, Google, Shopify, Zoom and dedicated CRM or marketing systems.

For enterprise and multi-location marketers, the model is particularly relevant as local customer acquisition becomes increasingly fragmented across search, social, messaging, marketplaces and commerce platforms. MYCT's approach is to consolidate those interactions around community and geographic context.

Market Landscape

The local-business technology market is increasingly converging around discovery, engagement and transactions. Google remains central to local search, while platforms such as Meta provide community and social discovery, Shopify focuses heavily on commerce infrastructure, and communication platforms such as Zoom specialize in meetings and webinars.

MYCT is pursuing a different model by combining several of these functions in one local-community environment. Its existing Merchant Portal includes business marketing, advertising, content, analytics, store management and deals tools.

The AI component also reflects a broader movement toward conversational interfaces. Rather than requiring users to navigate menus or search through listings, AI agents can potentially become an interactive layer between customers and business information.

The key industry question is whether users and businesses will value that integration enough to shift activity away from established single-purpose platforms.

Strategic Outlook

MYCT's expansion illustrates how local digital platforms are evolving from directories into operating environments for local commerce.

The combination of AI assistance, location-based discovery, communication, e-commerce and referral-driven events could give businesses more ways to convert community attention into measurable interactions. At the same time, the breadth of the platform creates a significant product challenge: maintaining a consistent user experience while connecting search, social engagement, transactions, payments and AI.

For marketers, the development points toward a future in which local customer journeys may increasingly happen inside integrated ecosystems rather than across a collection of disconnected services.

Top Insights

• MYCT is moving beyond local listings, combining discovery, communication, commerce and AI to create a broader digital operating layer for community businesses.
• Deal Chief adds conversational AI, giving customers an interactive way to explore local businesses, products, services and opportunities beyond conventional search interfaces.
• Paid webinars introduce monetization, allowing hosts to combine event registration, payments, refunds, session management and earnings within the same platform.
• Referral rewards turn attendees into distributors, creating a performance-based mechanism for businesses and organizations seeking additional webinar registrations and community growth.
• Location remains central to MYCT's model, connecting neighborhood discovery, business promotions, commerce and community activity around geographic and user-interest signals.

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Mortgage borrowers are increasingly turning to AI tools alongside traditional search when researching complex financial decisions, creating a new visibility challenge for loan officers and mortgage companies. Vonk Digital is responding with Authority, a managed SEO and answer engine optimization (AEO) service designed specifically for the mortgage industry.

Vonk Digital has formally launched Authority, a mortgage-focused SEO and AI search visibility platform that combines content publishing, technical optimization, structured data and compliance-oriented review for loan officers, mortgage brokers, branches and independent mortgage companies.

The product has been operating with selected mortgage professionals since March 2026, according to Vonk Digital. The company says the commercial launch follows development of its publishing infrastructure, compliance controls and reporting capabilities.

The timing reflects a broader change in how consumers conduct financial research. PwC's 2026 Consumer Lending Radar found that 45% of surveyed consumers had used a generative AI tool for a financial question during the previous year, while 67% expected AI to inform their next borrowing decision.

Mortgage research is not necessarily moving from Google to AI in a simple replacement cycle. Gartner reported in January 2026 that only about one-third of surveyed U.S. consumers considered GenAI chatbots as effective as search engines for learning new information. Its research also found that 51% of consumers surveyed said GenAI had changed their research habits.

That creates a more complicated search environment for mortgage marketers. A potential borrower may use an AI assistant to understand loan options, search Google for a local lender and then visit a mortgage professional's website to verify expertise, credentials and contact information.

Authority is designed around that multi-stage journey. Vonk says the platform publishes original articles several times a week based on a client's selected markets, loan programs and borrower niches. It also creates loan-program pages, geographic market pages, internal links and structured data intended to clarify the identity and expertise of the mortgage professional.

The use of structured data is particularly relevant to local financial businesses. Google says structured data can help its systems understand organizational details and local business information, although markup itself does not guarantee prominent placement in search results.

For mortgage companies, the compliance layer may be as important as the publishing engine. Authority reviews content for potential mortgage advertising and fair-lending issues before publication, according to Vonk. The company also states that clients remain responsible for their advertising compliance.

Vonk positions Authority as an alternative to fragmented content and SEO arrangements. The company says some clients previously spent $2,500 to $4,000 per month on content services, while Authority starts at $497 per month and operates on Vonk's mortgage website platform.

The competitive distinction is less about SEO alone and more about combining website infrastructure, content production and emerging AEO practices within one vertical-specific platform. Gartner has identified answer-engine visibility as an emerging market and advises organizations to adapt content strategies as consumers increasingly use AI-powered search experiences.

Vonk also avoids presenting AEO as a guaranteed lead-generation mechanism. That distinction matters because AI-generated answers can change based on query wording, sources, location and model behavior. Visibility in ChatGPT, Gemini, Google's AI experiences or other answer engines cannot be treated as equivalent to a conventional search ranking.

For enterprise mortgage marketers, the larger implication is strategic. Search optimization is expanding from ranking individual pages to establishing a consistent, identifiable body of expertise across websites, locations, products and borrower questions. Authority is Vonk Digital's attempt to package that shift into a managed mortgage-specific service.

Market Landscape

The mortgage industry is entering a search environment where traditional SEO and AI discovery increasingly operate together. Gartner says marketers should not treat GenAI as a replacement for established search channels, while its 2026 research identifies dedicated answer-engine visibility tools as an emerging category.

For mortgage professionals, credibility also becomes particularly important because borrowing decisions involve regulated financial products and substantial long-term commitments. Google's guidance emphasizes clear organizational information and structured data as mechanisms that can help search systems understand entities and businesses.

Authority therefore sits at the intersection of mortgage website technology, content marketing, traditional SEO, local search and AEO rather than functioning as a standalone AI marketing tool.

Strategic Outlook

The more significant development is the convergence of SEO, AEO, local search and first-party content infrastructure. Mortgage marketers will increasingly need content that answers specific borrower questions while clearly establishing who produced the information and what expertise that person represents.

The challenge will be maintaining quality and compliance as publishing volume increases. AI visibility cannot be reduced to producing more pages; the underlying content must remain accurate, useful and sufficiently differentiated to earn attention from both users and search systems.

Top Insights

• Vonk Digital's Authority combines mortgage SEO, AEO, content publishing and website infrastructure, giving smaller lending teams one system for digital visibility.
• AI is becoming another layer in borrower research rather than simply replacing Google, requiring mortgage marketers to optimize across traditional and generative search.
• Structured data and identifiable expert profiles can help search systems understand mortgage businesses, although neither guarantees rankings or AI citations.
• Mortgage content automation creates efficiency but also increases compliance and quality-control requirements for lenders operating in a regulated advertising environment.
• The emerging AEO market shifts attention from keyword rankings alone toward whether credible mortgage information can be discovered, understood and referenced by AI systems.

Get in touch with our MarTech Experts.

Fashion brands have spent years digitizing individual parts of product development, from computer-aided design and trend research to e-commerce photography. Raspberry AI is now attempting to connect those fragmented workflows with an agentic AI platform that follows a product from initial concept through commerce.

Raspberry AI announced an expansion of its platform that brings design, merchandising, wholesale, marketing and e-commerce into a connected AI workflow. The company says teams can begin with a natural-language brief, develop collections, explore fabrics and colorways, generate technical designs and create downstream marketing and commerce assets from the same product concepts.

The move reflects a broader shift in enterprise generative AI: from tools that generate individual assets toward systems that coordinate multiple tasks and workflows.

From Fashion Sketch to Commerce Asset

Traditional fashion development involves numerous handoffs between designers, merchandisers, technical teams, photographers, marketers and commerce organizations. Raspberry AI's platform is designed to reduce those handoffs by allowing teams to carry digital product concepts through multiple stages without recreating the work at each step.

A team could start with a collection brief, incorporate consumer and trend intelligence, create moodboards, experiment with materials from its own libraries and iterate designs before generating production-oriented technical designs. Those designs can then be used for on-model visualization, lookbooks, wholesale presentations, campaign creative, product-detail pages and other commerce assets.

The distinction is important. Generative image tools have already made it easier to visualize clothing concepts, but an enterprise fashion workflow requires more than image generation. Product specifications, color standards, assortments, approvals and commercial assets all have to remain connected.

Raspberry AI describes its system as an agentic platform, meaning AI agents can coordinate multiple functions rather than simply producing a single requested image or text output. Its current platform materials describe parallel workflows and agents that can determine which modules and prompts to use for a particular creative task.

AI Targets Fashion's Inventory and Speed Problem

The potential business case extends beyond creative productivity. Fashion companies routinely make product decisions months before merchandise reaches consumers, creating a mismatch between early forecasts and rapidly changing demand.

McKinsey's research has identified overproduction and markdowns as persistent problems for fashion businesses. Its State of Fashion 2025 research estimated that 2.5 billion to 5 billion excess garments were produced in 2023, representing $70 billion to $140 billion in sales value.

Shortening the distance between consumer insight, product design and market launch could therefore have implications for assortment planning and inventory risk. McKinsey has previously identified shorter time to market as one way apparel companies can respond more closely to changing demand.

Raspberry AI says brands using its platform have reported 2–5X faster speed to market, 60% lower sample costs, 80% lower photoshoot costs and 75% lower production costs. Those are company-reported figures rather than independently verified industry benchmarks.

Pantone Partnership Adds Color Intelligence

The platform is also expanding into fashion color and visual direction. For the Spring/Summer 2027 New York Fashion Week season, Pantone is working with Raspberry AI on AI-generated materials and moodboards based on the Pantone Fashion Color Trend Report.

Raspberry AI has integrated Pantone's Fashion, Home + Interiors digital color library, which contains 2,625 colors, allowing designers and creative teams to move from standardized color references into visual product and campaign concepts. Pantone separately confirms the SS27 collaboration and its use of AI-generated materials and imagery.

The collaboration illustrates where specialized generative AI can differ from general-purpose creative software: fashion-specific systems can combine visual generation with industry terminology, product workflows and standardized color information.

Enterprise Fashion Moves Toward AI-Native Workflows

Raspberry AI's customer roster includes brands such as SKIMS, ALO, Savage X Fenty, Walmart, TJX, Beyond Yoga, Columbia, Steve Madden and Tapestry's Kate Spade, according to the company.

The company's participation in the inaugural CFDA x OpenAI Innovation Hub also puts it within a broader effort to develop practical AI applications for fashion businesses. Raspberry AI was selected as one of six developer finalists for the 2026 program, which pairs AI developers with CFDA member brands.

For enterprise fashion organizations, the larger question is not whether AI can generate a convincing garment image. It is whether AI can become part of the operating infrastructure connecting product intelligence, design decisions, merchandising and digital commerce.

Market Landscape

Fashion AI is developing across several layers: visual design, product development, demand forecasting, virtual photography, merchandising and customer-facing commerce. General-purpose platforms from companies such as Adobe, Google and Microsoft provide increasingly capable creative and productivity models, while specialist vendors are building systems around fashion-specific workflows.

Raspberry AI is positioning its platform closer to the latter category. Its differentiation is the attempt to connect multiple stages of the product lifecycle rather than treating generative AI as an isolated design application.

That distinction could become important as fashion companies evaluate AI investments. The enterprise value of a creative model depends not only on image quality but also on how effectively generated outputs move into technical development, merchandising, marketing and commerce systems.

The underlying market problem remains substantial. McKinsey estimates that fashion brands produced $70 billion to $140 billion of excess stock in 2023, demonstrating why faster and more informed product decisions remain commercially relevant.

Top Insights

• AI connects fashion workflows: Raspberry AI links design, merchandising, marketing and e-commerce, potentially reducing handoffs between traditionally separate product-development teams.
• Agentic AI moves beyond generation: The platform coordinates multiple creative workflows, shifting AI from an individual asset generator toward a broader fashion operating environment.
• Speed has inventory implications: Faster product development can help brands respond closer to demand, addressing an industry where excess inventory remains a major commercial challenge.
• Pantone adds standardized color data: Integrating 2,625 Fashion, Home + Interiors colors connects AI-generated concepts with an established professional color ecosystem.
• Enterprise adoption is expanding: Raspberry AI says brands including SKIMS, ALO, Walmart and Beyond Yoga are using the platform across different parts of their organizations.

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Customer experience providers are moving beyond isolated generative AI pilots as enterprises look for systems that can operate across customer service, workforce support, analytics and automation. TELUS Digital's latest recognition by Everest Group reflects that broader shift.

TELUS Digital has been named a Leader in the Everest Group Customer Experience Management (CXM) Services PEAK Matrix Assessment 2026 – Americas for the eighth consecutive year. The company was one of eight providers classified as Leaders among 48 CXM service providers evaluated for the Americas.

The assessment evaluates providers based on market impact and vision and capabilities, including areas such as service scope, innovation, investments and delivery footprint. Everest Group's 2026 research places increasing emphasis on production-scale AI, governance, integration and measurable business outcomes as enterprises move beyond experimentation.

Fuel iX Puts Enterprise AI Into the CX Workflow

At the center of TELUS Digital's offering is its Fuel iX portfolio, which includes enterprise AI orchestration capabilities, Fuel iX Agent Assist for real-time agent support and Fuel iX Agent Trainer for AI-supported training and quality improvement.

The company also operates within a broad technology ecosystem that includes Google Cloud, Zendesk, Five9, Amazon Connect, Cresta, ElevenLabs, Verint, Automation Anywhere and Sanas. According to the Everest assessment, that ecosystem extends TELUS Digital's capabilities across contact-center-as-a-service (CCaaS), analytics, automation, conversational AI and agentic AI.

That ecosystem approach is important because enterprise CX rarely operates through a single platform. Customer interactions can span CRM systems, contact-center infrastructure, knowledge bases, workforce management and analytics. AI therefore needs to work across existing technology rather than operate as a standalone chatbot.

TELUS Digital says its delivery organization supports more than 50 languages and serves customers across multiple regions. Everest Group also cited the company's breadth across customer service, technical support, sales, compliance, retention, order fulfillment and transaction processing.

Enterprise AI Spending Is Moving Toward Customer Operations

The recognition arrives as customer service organizations increase investment in AI. Gartner reported in August 2026 that AI spending by customer service leaders had increased 38%, while overall service and support budgets grew by 2%. The research was based on a survey of 199 service and support leaders conducted in April and May 2026.

Yet investment does not automatically translate into successful deployment. Gartner also found that only 24% of service and support leaders in a separate 2026 survey demonstrated positive financial returns across their AI use cases.

That gap helps explain why CX service providers are increasingly positioning themselves around implementation, workforce adoption, governance and outcome measurement rather than AI software alone.

Everest Group's assessment similarly identifies a market moving toward integrated human-AI operating models, agentic workflows and production-ready AI across agent assistance, self-service, quality assurance, knowledge management, analytics and workflow automation.

What It Means for Enterprise Marketing and CX Teams

For enterprise organizations, the competitive issue is shifting from whether AI can assist customer service to how AI is incorporated into the broader customer journey.

TELUS Digital's outcome-based and hybrid commercial models are part of that change. Everest Group says enterprises are increasingly considering commercial flexibility and accountability alongside technology maturity, rather than evaluating providers solely on cost.

The company's positioning also reflects a larger convergence between MarTech, CRM, CCaaS and AI infrastructure. Marketing teams increasingly depend on service interactions for customer insights, while customer-service organizations need access to the same customer data and journey context used by marketing and sales.

There is also a human-experience consideration. Gartner found that 87% of surveyed B2B and B2C customers said companies using generative AI for customer service should provide access to a human agent.

For enterprises, that suggests AI-enabled CX will need to balance automation with escalation paths, human oversight and consistent customer context.

Market Landscape

The CXM services market is increasingly centered on integrating AI into existing enterprise operations rather than deploying isolated generative AI experiments. Everest Group's 2026 assessment covers 63 providers globally across its Americas, EMEA and APAC assessments and identifies production-ready AI, governance, integration and measurable returns as important market themes.

TELUS Digital's Americas recognition places its Fuel iX portfolio alongside a broader service-delivery model that combines AI capabilities, multilingual operations, technology partnerships and CX outsourcing. Everest Group also classified TELUS Digital as a Major Contender in its 2026 EMEA and APAC assessments.

The competitive landscape includes technology vendors, CCaaS providers, systems integrators and specialized CX outsourcing companies. As AI becomes embedded in contact-center workflows, differentiation is increasingly tied to integration capabilities, industry expertise, workforce transformation, governance and the ability to demonstrate business outcomes.

Top Insights

• CXM is moving into production AI: Everest Group identifies a shift from generative AI pilots toward integrated human-AI operations, affecting enterprise service and technology leaders.
• Fuel iX expands AI orchestration: TELUS Digital combines agent assistance, training and enterprise AI capabilities with a broader CCaaS technology ecosystem.
• AI spending is accelerating: Gartner reported a 38% increase in customer-service AI spending in 2026, putting greater pressure on organizations to demonstrate measurable returns.
• Human escalation remains important: Gartner found 87% of surveyed customers consider human-agent access essential when companies use GenAI for customer service.
• Commercial models are changing: Outcome-based and hybrid pricing models are becoming part of how enterprises evaluate AI-enabled CX providers alongside technology and delivery capabilities.

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