DRAFT Media Partners has been recognized by Manage Marketing as its Top Advertising Fulfillment Company of 2026, highlighting the growing importance of integrated media buying, creative production and performance reporting as advertisers manage increasingly fragmented campaigns across digital, traditional and programmatic channels.

The recognition places DRAFT Media Partners among the vendors featured in Manage Marketing’s 2026 list of advertising providers. According to the announcement, the company was selected by Manage Marketing’s editorial board for its approach to media buying, omnichannel campaign management, creative production and reporting.

DRAFT Media Partners operates across several parts of the advertising workflow, combining media strategy and purchasing with campaign execution and performance measurement. Its services span digital, traditional and programmatic advertising, reflecting the increasingly mixed-channel nature of media planning.

For advertisers, the operational challenge is no longer simply choosing individual media channels. Campaigns can involve programmatic advertising, paid digital media, traditional placements and creative assets that need to work together while producing comparable performance data. Vendors that can connect these functions are competing with both specialized agencies and technology platforms that increasingly automate parts of media buying and optimization.

That makes the company’s positioning notable, although an industry award itself does not establish campaign performance or market leadership. Manage Marketing’s recognition is an editorial distinction from the publication rather than an independent measurement of DRAFT Media Partners’ advertising results.

The company says its offering is designed to serve both brands and agencies that need support scaling advertising operations. Its reporting capabilities are positioned as a way to give clients greater visibility into campaign performance across different media environments.

The development comes as advertising technology continues to converge around data, automation and cross-channel measurement. Platforms from companies such as Google, Amazon, Microsoft and Adobe increasingly connect media activation with audience data, analytics and automated optimization. Agencies and fulfillment partners, meanwhile, continue to differentiate themselves through execution capabilities, creative services and access to multiple advertising channels.

DRAFT Media Partners’ model sits within that broader ecosystem. Rather than focusing exclusively on a single advertising format, the company emphasizes coordination between media buying, creative production and reporting.

For marketing teams, that integration can simplify campaign management when multiple channels and vendors are involved. It can also create a clearer operational connection between the creative assets being produced, the media inventory being purchased and the performance data used to evaluate campaigns.

The bigger industry question is how advertising fulfillment companies will evolve as more media-buying and reporting functions become automated. AI-powered campaign optimization, programmatic buying and increasingly sophisticated analytics are reducing the amount of manual work required in some advertising processes. Providers therefore face pressure to demonstrate value beyond basic execution.

DRAFT Media Partners’ recognition by Manage Marketing reflects one approach to that changing market: combining advertising execution with creative and reporting services under a broader omnichannel model. The award provides visibility for the company, while advertisers will ultimately need to assess vendors based on their specific channel requirements, measurement needs and campaign objectives.

Market Landscape

Advertising fulfillment is becoming more closely connected to the wider AdTech ecosystem. Programmatic platforms automate media transactions, while advertising platforms increasingly incorporate machine learning, audience targeting and automated campaign optimization.

This creates a more complex role for agencies and fulfillment partners. They must coordinate technology platforms, creative assets, media inventory and performance data rather than treating each advertising channel as a separate workflow.

DRAFT Media Partners’ recognition arrives against this backdrop, with its service model emphasizing integration across digital, traditional and programmatic advertising. The broader market is moving toward consolidated campaign operations while specialist platforms continue to compete on automation and data capabilities.

Strategic Outlook

The advertising services market is likely to place increasing emphasis on measurable execution, cross-channel reporting and the ability to integrate technology with human campaign management.

For enterprise marketing teams and agencies, the relevant consideration is not simply whether a provider offers multiple channels, but whether those channels can be managed through coherent planning, creative production and measurement processes.

As AI and automation take over more routine advertising functions, fulfillment companies may increasingly compete on strategic execution, data interpretation and the ability to connect fragmented media activity to business outcomes.

Top Insights

• DRAFT Media Partners received Manage Marketing’s 2026 advertising fulfillment recognition, highlighting integrated media buying and campaign execution.
• The company combines digital, traditional and programmatic advertising services, reflecting advertisers’ increasingly fragmented multichannel media requirements.
• Creative production and performance reporting are central to DRAFT Media Partners’ positioning alongside its media buying capabilities.
• Automation and AI are changing advertising operations, increasing pressure on fulfillment providers to deliver value beyond campaign execution.
• Brands and agencies increasingly need coordinated media workflows connecting creative, activation and measurement across advertising channels.

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PostcardMania is expanding its multichannel marketing platform into a rapidly changing part of the customer journey: AI-powered search. The company has launched Optimize Search + AI, a service designed to improve small and midsized businesses’ visibility in Google search while increasing their chances of appearing in answers generated by platforms such as ChatGPT and Claude.

PostcardMania’s new service extends its Everywhere Small Business campaign beyond direct mail and digital advertising into search engine optimization and AI search visibility. The company says the offering is aimed at businesses that often lack dedicated marketing teams but increasingly need to manage how their brands are represented across traditional search and generative AI.

Everywhere Small Business already combines targeted direct mail with advertising across Google, Facebook, Instagram, Gmail, YouTube and Connected TV, with campaign activity consolidated into a centralized reporting dashboard. Optimize Search + AI adds another layer by addressing the point in the buying journey when prospects actively research a company, product or service.

The timing reflects a broader change in search behavior. Google says AI Overviews now reach more than 2.5 billion monthly active users, while AI Mode has surpassed one billion monthly users. Google also says AI-powered Search is generating new types of queries and changing how people interact with search results.

That shift creates a problem for conventional SEO strategies. Pew Research Center found that users clicked a traditional Google result in only 8% of visits when an AI summary appeared, compared with 15% when no summary appeared. The finding does not mean every business will experience the same traffic decline, but it illustrates why visibility inside an answer can increasingly matter alongside conventional rankings.

AI visibility also extends beyond a company’s own website. An Ahrefs analysis of 75,000 brands found that the three strongest correlations with visibility in Google AI Overviews were off-site signals: branded web mentions, branded anchor text and branded search volume. The study explicitly notes that correlation does not establish causation, but it points to a broader difference between traditional SEO and AI search optimization.

PostcardMania’s service approaches the problem through a three-stage process. The first month focuses on auditing a business’s Google Business Profile, citations, website and local search presence. The second addresses off-site listings and homepage recommendations, while subsequent work expands into additional website pages and ongoing content development.

The offering enters a market that already includes specialized AI visibility and local-search platforms. Semrush tracks brand mentions and citations across ChatGPT, Gemini and Google AI experiences, while Yext focuses heavily on structured business information and distribution across search publishers and AI assistants. BrightLocal has also introduced local AI visibility tracking for platforms including ChatGPT and Google AI Overviews.

PostcardMania’s distinction is less about inventing a new category than integrating AI search optimization into an existing direct-mail and advertising workflow. For small businesses, that bundled approach could reduce the need to coordinate separate vendors for offline acquisition, digital retargeting, SEO and emerging AI visibility.

Market Landscape

Search is becoming a more distributed discovery environment. Google remains central, but AI-generated answers increasingly mediate how consumers research businesses, compare alternatives and decide which sources deserve attention.

For enterprise marketing organizations, this means SEO, content strategy, local listings, earned media and brand authority increasingly overlap with AEO and GEO. For SMBs, the operational challenge is even more pronounced because maintaining accurate listings, producing useful content and building third-party visibility can require capabilities that small teams do not have in-house.

PostcardMania is therefore positioning Optimize Search + AI as an extension of its existing multichannel demand-generation model rather than as a standalone SEO product. The longer-term question will be whether bundled services can demonstrate measurable business outcomes from AI visibility, rather than simply increasing mentions or citations.

Strategic Outlook

The significance of PostcardMania’s announcement lies in the direction of the market. Search optimization is moving from a page-ranking discipline toward a broader brand discoverability strategy spanning websites, directories, reviews, third-party publications and AI-generated answers.

That creates opportunities for marketing platforms that can connect visibility data with measurable leads and revenue. As Google, Microsoft and AI platforms continue changing how discovery works, marketers will increasingly need to understand not just where a business ranks, but how its brand is represented when a prospect asks an AI system what to buy or whom to contact.

Top Insights

• PostcardMania is bringing AI search optimization into SMB multichannel marketing, connecting traditional acquisition campaigns with emerging AI-driven discovery.
• Google’s AI Overviews and AI Mode are expanding rapidly, forcing marketers to consider visibility inside generated answers alongside conventional organic rankings.
• Off-site brand signals are becoming increasingly important to AI visibility, widening the role of listings, reviews, mentions and third-party content.
• Semrush, Yext and BrightLocal already offer AI visibility capabilities, making measurement and differentiation increasingly important for new SMB-focused services.
• Enterprise and SMB marketing teams will need unified search, content and brand-authority strategies as consumer research moves across search engines and AI assistants.

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AI video enhancement is moving from experimental post-production technology toward a more routine part of professional and creator workflows. Aiarty Video Enhancer V3.8 focuses on that transition by adding resumable exports, MKV support and improvements to GPU memory management, positioning AI enhancement as a more dependable stage within existing video editing pipelines.

Aiarty has released Aiarty Video Enhancer V3.8, an update aimed at improving the reliability of AI-powered video enhancement during longer and more demanding production jobs.

Rather than attempting to replace conventional editing software, the application is designed to operate alongside existing video workflows. Editors can complete cutting and sequencing in their preferred editing environment before sending footage to Aiarty for tasks such as upscaling, denoising, deblurring, frame interpolation and other quality improvements.

The distinction is increasingly relevant as AI becomes embedded in professional video production. Enhancement is rarely the entire workflow. A creator may process dozens of clips, work with long-form footage or repeatedly export material at higher resolutions. In those scenarios, processing reliability can become as important as the underlying AI model.

One of the main additions in V3.8 is export task and progress recovery.

If an export is interrupted before completion, unfinished tasks can be recovered and resumed rather than requiring the entire process to start again. For lengthy enhancement jobs, that can reduce the operational cost of interruptions and make batch processing more practical.

The update also targets GPU resource management. Aiarty says it has optimized CUDA encoding memory management to reduce VRAM usage during demanding exports and improve stability when processing long videos or larger batches.

That focus reflects a practical limitation of local AI video processing. Increasing resolution, generating intermediate frames and restoring fine details can place sustained demands on GPU resources. Memory management therefore affects not only processing speed but whether a workflow can complete reliably.

Format Compatibility Becomes Part of AI Workflows

V3.8 also introduces MKV support, expanding the formats that can move directly into the enhancement process.

Video projects frequently combine footage from cameras, screen-recording tools, archives and other sources. Format conversion can add another step between source material and enhancement, potentially introducing compatibility problems or additional processing.

Aiarty says it has also optimized decoding and export logic for certain formats to improve compatibility with problematic video files.

The broader objective is straightforward: reduce the number of technical interruptions between source footage, AI processing and final delivery.

AI Enhancement Extends Beyond Upscaling

The software's feature set goes beyond increasing video resolution.

Aiarty supports AI-based denoising, deblurring, detail restoration and frame interpolation, while also providing audio denoising, color adjustment and SDR-to-HDR conversion.

For low-resolution footage, upscaling can be used to produce higher-resolution output while attempting to reconstruct additional visual detail. Denoising targets luminance and color noise, while frame interpolation generates additional frames for smoother motion at outputs such as 60 or 120 frames per second.

These capabilities address different production problems rather than representing a single universal enhancement process.

The software also supports batch processing and GPU acceleration across compatible NVIDIA, AMD and Intel hardware, according to the company.

Another differentiating factor is that processing takes place locally on Windows and Mac, meaning source footage does not need to be uploaded to a cloud-based processing service.

For creators working with large video files, that can have practical implications for upload times, network dependency and control over source media.

V3.8 also adds Spanish and Italian user interfaces, extending the desktop application's localization.

Jack Han, CEO of Digiarty Software, said the objective is to make enhancement complement established editing processes rather than disrupt them.

Market Landscape

The AI video market is increasingly divided between platforms focused on generative video creation and tools designed to improve existing footage.

Companies such as Adobe, Topaz Labs and Blackmagic Design are part of a broader ecosystem where AI is being incorporated into video editing, restoration, upscaling and post-production workflows.

That creates different requirements for enhancement software than for generative video platforms. Users processing existing footage generally need predictable output, format compatibility, hardware efficiency and workflow continuity alongside visual quality.

Aiarty's V3.8 release emphasizes those operational considerations.

The addition of resumable exports is particularly notable because it addresses a problem that has little to do with AI quality itself: what happens when a long-running process is interrupted.

Strategic Outlook

As AI enhancement becomes more common in video production, software vendors will increasingly compete on the workflow around the AI, not just the model powering it.

For editors and creators, an enhancement tool that produces strong results but frequently fails during long exports can create additional production costs. Conversely, better recovery, hardware utilization and format support can make AI processing easier to incorporate into established pipelines.

That suggests the next stage of AI video software will be less about demonstrating that AI can improve footage and more about making that improvement predictable at production scale.

Aiarty Video Enhancer V3.8 is moving in that direction by combining enhancement capabilities with export recovery, format support and local processing.

Top Insights

• Resumable exports allow interrupted enhancement jobs to continue without restarting the entire export process.
• CUDA memory optimization targets GPU resource usage during long videos and demanding batch-processing workloads.
• MKV support expands format compatibility and can remove an additional conversion step from some video workflows.
• Local AI processing keeps source videos on Windows or Mac rather than requiring uploads to a cloud processing service.
• AI enhancement extends beyond upscaling, covering denoising, deblurring, frame interpolation, audio cleanup, color adjustment and HDR conversion.

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AI is changing the role of multichannel marketing platforms from campaign execution systems into decision-making infrastructure. Optimove is positioning its platform around that shift after Gartner's 2026 research placed the company first among evaluated vendors for the Real-Time Personalization and Decisioning use case and named it a Visionary in the companion Magic Quadrant for Multichannel Marketing Hubs for the third consecutive year.

Optimove said it was ranked first for Real-Time Personalization and Decisioning in Gartner's 2026 Critical Capabilities for Multichannel Marketing Hubs, receiving a score of 3.59 out of 5 in that use case.

The company was also named a Visionary in Gartner's 2026 Magic Quadrant for Multichannel Marketing Hubs, marking its third consecutive year in that category, according to Optimove. Gartner's Critical Capabilities report evaluates products against specific functional requirements, while the Magic Quadrant assesses vendors using Ability to Execute and Completeness of Vision.

The distinction matters because the two Gartner frameworks measure different aspects of a marketing platform. The Critical Capabilities research is more directly focused on how products perform against defined use cases, while the Magic Quadrant provides a broader view of vendor positioning.

Gartner's 2026 research reflects a market that has moved considerably toward AI. The analyst firm says AI assistants are now effectively standard features in multichannel marketing hubs, with differentiation increasingly centered on decisioning, context and platform ecosystems.

Optimove was among the three highest-scoring vendors across all four use cases evaluated in the 2026 Critical Capabilities report, according to the company.

Its reported scores were:

• 3.59/5 for Real-Time Personalization and Decisioning
• 3.58/5 for Multichannel Engagement
• 3.46/5 for Omnichannel Workflow Innovation
• 3.45/5 for Conversational and Agent-Augmented Marketing

Rather than treating personalization as a separate campaign function, Optimove is building it into a broader architecture spanning customer data, AI-powered decisioning, content creation, personalization and multichannel execution.

The company's platform includes Optimove Orchestrate for journey decisioning and orchestration, Optimove Engage for campaign execution, Optimove Personalize for real-time personalization and Optimove Gamify for loyalty and promotional experiences. Optimove says those products are powered by its Optimove AI layer.

The company's recent development of an Optimove MCP also illustrates how the interaction model for marketing technology is changing.

Through MCP support, Optimove says marketers can work with its capabilities through AI tools including ChatGPT, Claude and Microsoft Copilot. The intended workflow allows marketers to analyze performance, build audiences and develop campaign content without necessarily starting from the platform's traditional interface.

That approach reflects a wider industry movement toward AI assistants acting as an interface across enterprise marketing systems.

Personalization itself is also moving away from manually constructing large numbers of campaign variants. Optimove says its use of Liquid, Data Connections and Content Blocks allows marketers to create multiple personalized variations from a master template.

The strategic implication is significant. As customer journeys become more dynamic, marketers need systems that can determine not only what message to send, but which customer should receive it, through which channel and at what point in the journey.

Gartner's 2026 Magic Quadrant describes this as a market increasingly differentiated by advanced decision-making algorithms that optimize journey, offer and consent state at the moment of action.

That moves multichannel marketing hubs closer to real-time decision engines.

Market Landscape

The multichannel marketing hub market includes platforms from Adobe, Salesforce, Braze, Bloomreach, CleverTap, Iterable, Klaviyo, MoEngage, SAP and others, alongside Optimove. Gartner's 2026 Critical Capabilities research evaluates 12 vendors in the market.

The competitive landscape is increasingly shaped by the convergence of customer data, personalization, journey orchestration, AI agents and multichannel execution.

Gartner defines multichannel marketing hubs as primarily SaaS applications that orchestrate personalized campaigns and event-driven customer journeys using customer data, predictive models and real-time insights.

That definition places AI decisioning at the center of the category rather than treating it simply as a content-generation feature.

For enterprise marketing teams, the resulting evaluation criteria are becoming more operational: how quickly can a platform react to behavioral signals, how effectively can it determine the next action and how much of that process can be automated without sacrificing governance?

Strategic Outlook

Optimove's Gartner recognition arrives as marketing organizations are reassessing the role of AI inside campaign operations.

The next phase is unlikely to be defined solely by generative AI producing more copy or images. The larger opportunity is decision automation: using customer data and predictive intelligence to determine the next interaction while continuously adjusting the journey.

That also explains why real-time personalization is becoming strategically important.

A static campaign can personalize a message before launch. A decisioning system can potentially change the customer experience as new behavioral information arrives.

Optimove is attempting to build around that model through its combination of AI decisioning, journey orchestration, personalization and external AI interfaces.

The Gartner findings do not establish that one platform is universally superior for every organization. They provide a framework for evaluating vendors against specific capabilities and market criteria. For buyers, the more relevant question remains whether a platform's decisioning, data integration, orchestration and AI capabilities align with the complexity of their own customer journeys.

Top Insights

• Optimove ranked first for Real-Time Personalization and Decisioning, with a 3.59/5 score in Gartner's 2026 Critical Capabilities evaluation.
• AI decisioning is becoming central to multichannel hubs, with Gartner highlighting context, real-time optimization and control as key areas of differentiation.
• Optimove placed in the top three across all four evaluated use cases, according to the company.
• MCP support extends marketing workflows into external AI assistants, including ChatGPT, Claude and Microsoft Copilot.
• Personalization is shifting toward dynamic decisioning, connecting customer data, content, journeys and channel selection rather than relying solely on predefined campaign variants.

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For BlueFocus, artificial intelligence is moving beyond automation and productivity gains into a broader test of how a global marketing company operates. Its 2025 results show a business still dominated by media buying, but increasingly using AI agents, automated decision-making and technology infrastructure to reshape how client work is delivered and how future revenue could be generated.

BlueFocus reported $10.07 billion in revenue for 2025, including $546.05 million in what it classifies as AI-driven revenue. The company also reported more than one trillion tokens consumed during the year, an operating metric CEO Fei Pan describes as an indicator of how deeply AI has entered its business processes.

The numbers show a company in transition rather than one that has completed an AI transformation.

Global outbound media buying remains BlueFocus's largest business, generating $8.28 billion, or 82.25% of total revenue. AI-driven revenue represented 5.42% of the company's overall revenue.

That gap is important. BlueFocus is not replacing its existing marketing business with AI overnight. Instead, management is using AI to alter the underlying workflows supporting a large-scale global advertising operation.

In a letter to investors accompanying the annual report, Pan argued that companies need to move beyond using AI primarily to reduce costs and increase efficiency. BlueFocus's stated objective is to use AI-native methods to redesign business models, organizations and decision-making systems.

The company says that transformation is already visible across several marketing functions, including social media intelligence, creator analysis, advertising risk control, creative analysis, automated budget allocation and video production.

BlueFocus reported that its Blue AI system completed 146 million agent-to-agent collaborative tasks in 2025. The company also said that AI outperformed humans without manual intervention in 85% of relevant use cases across core scenarios including strategy formulation, budget allocation and delivery decisions.

Those figures are company-reported performance measures, but they point to a more consequential question than automation alone: how much of a marketing workflow can operate continuously without direct human intervention?

Pan's definition of an AI-native business centers on that distinction. In his view, a system becomes more than an AI-enabled tool when it can evaluate its own output, trace the execution process and adjust subsequent actions.

That model could change the economics of marketing services.

BlueFocus defines AI-driven revenue as revenue generated through highly automated operations requiring minimal human input. The company also emphasizes higher potential gross margins and systems capable of accumulating, annotating and learning from data over time.

At $546.05 million, that revenue stream remains a minority of the company's overall business. But its significance lies in the direction of the operating model rather than its current scale.

AI Is Also Changing the Organization

BlueFocus is investing in the organizational infrastructure required to support that model.

The company says it has nearly 500 AI professionals, including more than 350 employees in product and technology roles and more than 150 working at the intersection of business and technology.

It has also introduced an AI Business Partner mechanism intended to embed AI capabilities within individual business units.

AI-related technical talent investment reached $13.96 million in 2025, up 76.52% year over year, according to the company.

That investment reflects an important shift in enterprise AI adoption. Deploying generative AI tools is relatively straightforward; changing organizational workflows, decision rights and operating processes is considerably more complex.

Global Advertising Provides the Testing Ground

BlueFocus's global outbound business provides the scale on which its AI strategy is being tested.

The company says more than 80% of its revenue and more than half of its profit now come from global outbound operations. Its major media relationships include Meta, Google and TikTok for Business, while newer relationships with AppLovin, Uber Ads and Netflix are expanding.

The company is also increasing its physical presence outside China. BlueFocus currently operates seven overseas offices and expects that number to exceed ten during 2026.

Its strategy includes growing operations in markets such as Singapore, Vietnam, Thailand and Indonesia while increasing the contribution of mid-tier media partners and proprietary platforms including Blue X and Blue Turbo.

That connects AI with another major strategic priority: globalization.

Pan describes AI and globalization as closely connected because cross-border marketing introduces challenges involving language, localization, media fragmentation and execution at scale. AI systems can potentially automate parts of that complexity while allowing marketing operations to expand across markets without a proportional increase in human labor.

Market Landscape

BlueFocus's strategy reflects a broader shift in enterprise marketing from AI-assisted workflows toward AI-operated systems.

Platforms from Google, Meta, Adobe, Salesforce and other major technology companies are increasingly embedding AI into campaign optimization, creative production, customer data and marketing operations.

The competitive question is therefore moving beyond whether an agency or marketing platform uses AI. It is increasingly about where AI sits inside the operating model.

A company that uses AI to produce content faster retains essentially the same service structure. A company that allows AI agents to analyze data, allocate budgets, evaluate creative and adjust execution begins to change how work itself is organized.

BlueFocus is explicitly pursuing the second model.

Strategic Outlook

The company's financial results also provide a useful reality check on the transformation.

More than 80% of revenue still comes from the established global outbound business, while AI-driven revenue represents a relatively small portion of the total. That means BlueFocus's AI-native strategy remains an emerging component of a much larger marketing-services operation.

The next test will be whether AI-driven revenue can scale without simply becoming another technology-enabled service line.

If BlueFocus succeeds in shifting more decision-making and execution into autonomous systems, the resulting economics could differ from traditional agency models. Work that previously required incremental human capacity could potentially scale through software, data and agentic infrastructure.

But that outcome is not yet established by the current figures.

For enterprise marketing leaders, BlueFocus offers a useful case study in what a deeper AI transformation can look like: not simply adding AI tools to existing processes, but redesigning the processes, organizational structures and revenue models around increasingly autonomous systems.

Top Insights

• BlueFocus generated $546.05 million in AI-driven revenue in 2025, although global outbound media buying remains the company's dominant revenue engine.
• Blue AI completed 146 million agent-to-agent tasks, according to BlueFocus, showing the company's emphasis on autonomous marketing workflows.
• AI investment is moving into organizational design, with nearly 500 AI professionals and an AI Business Partner mechanism across business units.
• Globalization and AI are strategically connected in BlueFocus's model, particularly across multilingual, cross-border advertising and media operations.
• The transformation remains incomplete, with AI-driven revenue representing 5.42% of total revenue despite rapid growth.

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French marketing and data specialist Groupe DEKUPLE is putting digital marketing, technology and international expansion at the center of its next growth phase after reporting stronger profitability for the first half of 2026. The company says its digital marketing activities now account for nearly three-quarters of group revenue, while investments in artificial intelligence and marketing technology continue to reshape its service model.

Groupe DEKUPLE reported €121.9 million in consolidated revenue for the first half of 2026, up 3.9% year over year. Net revenue reached €90.3 million, an increase of 2.2%, while adjusted EBITDA climbed 11.7% to €11.9 million.

The improvement was more pronounced further down the income statement. Operating profit increased 25.3% to €8.1 million, while group net income rose 58.9% to €5.8 million, according to the company.

DEKUPLE's strongest growth engine was digital marketing, which represented 72.7% of consolidated revenue during the period, compared with 69.6% a year earlier. Net revenue from the segment increased 8.4%, including 6% growth on a like-for-like basis.

The company attributes that performance primarily to its Consulting & Technology and Agencies & Solutions activities. The combination reflects a broader strategy to bring marketing consulting, data, technology, creative services and artificial intelligence into a more integrated operating model.

That shift is becoming increasingly important across the marketing services industry. Enterprise advertisers are asking agencies and technology providers to connect strategy with execution while providing more precise measurement of marketing performance.

DEKUPLE is positioning its model around that convergence.

Rather than treating data, marketing technology, creative execution and AI as separate capabilities, the company says it is building a multi-expertise structure intended to connect those disciplines and industrialize its offerings.

International expansion is another significant part of the strategy. DEKUPLE's international net revenue increased 37.1% during the first half, with international activities representing 15.3% of group net revenue, up from 11.4% in the first half of 2025.

The company continued its international expansion during the period through the creation of Das Kapital DEKUPLE Group in the Middle East and the acquisition of Subko & Co in Poland. Subko's income statement will be consolidated from July 1, 2026.

The international growth comes while DEKUPLE continues investing in AI and technology. Those investments are weighing on profitability within the consulting business in the short term, particularly as the company expands in the United States and Spain.

That trade-off highlights an increasingly common challenge for marketing technology businesses: investment in AI capabilities and geographic scale can require significant upfront spending before the resulting services generate their full commercial contribution.

DEKUPLE's overall adjusted EBITDA margin nevertheless improved to 13.2% of net revenue, compared with 12% in the first half of 2025.

The company's traditional magazine business remains under pressure. Net revenue from the division declined 7% amid a structurally declining publishing market, leading DEKUPLE to focus investments selectively on activities it considers most capable of generating recurring value.

The insurance business is also undergoing a strategic review as part of the group's Ambition 2030 plan.

For DEKUPLE, the financial results therefore represent more than a recovery in profitability. They show an ongoing transition in the composition of the business, with digital marketing, technology and international operations becoming increasingly important relative to legacy activities.

Market Landscape

The marketing services industry is moving toward increasingly integrated models that combine data, AI, marketing technology, creative services and performance measurement.

That trend is visible across the wider enterprise ecosystem, where platforms such as Salesforce, Adobe and Microsoft increasingly connect customer data, automation, analytics and AI within broader technology stacks.

Agencies are responding to the same shift from the services side. Clients increasingly expect partners to connect strategic planning with execution and demonstrate measurable commercial outcomes.

DEKUPLE's results illustrate that transition at the company level. Digital marketing accounted for 72.7% of its consolidated revenue in the first half, while international operations delivered significantly faster growth than the group overall.

The company's challenge will be converting investments in AI, technology and international expansion into sustainable margins while continuing to scale its digital operations.

Strategic Outlook

DEKUPLE is continuing to execute its Ambition 2030 strategy with a focus on international scale and stronger connections between its different marketing capabilities.

The company's model reflects an industry moving away from isolated agency disciplines toward integrated marketing infrastructure. Data can inform strategy, AI can accelerate production and analysis, marketing platforms can automate execution, and performance measurement can connect those activities to commercial objectives.

For enterprise marketing teams, the development is significant because the agency relationship is increasingly becoming a technology and data relationship as well as a creative one.

DEKUPLE's first-half results suggest that digital marketing will remain central to that transformation. The next phase will depend on whether its investments in AI, technology and international markets can continue generating growth while maintaining the improvement in profitability recorded during the first half.

Top Insights

• Digital marketing generated 72.7% of DEKUPLE's consolidated revenue, reinforcing the segment's role as the group's primary growth engine.
• International net revenue increased 37.1%, highlighting Europe and other overseas markets as increasingly important components of DEKUPLE's expansion strategy.
• AI and technology investment is accelerating, although those investments continue to pressure consulting profitability during the international expansion phase.
• Adjusted EBITDA increased 11.7% to €11.9 million, while the corresponding margin improved by 111 basis points year over year.
• DEKUPLE's integrated model connects data, technology, consulting and creative services, reflecting broader changes in enterprise marketing procurement.

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For many businesses, lead generation gets most of the attention while the prospects generated months earlier quietly disappear into CRM records, spreadsheets and email threads. As customer acquisition costs rise and buying cycles become less predictable, lead follow-up automation is emerging as a way for companies to reconnect with prospects who were interested but not ready to buy.

Generating a new lead is only the beginning of the sales process. A prospect may request a quote, schedule a consultation or download information and still take weeks or months before deciding whether to purchase.

When there is no structured follow-up process, those prospects can become increasingly difficult to recover.

Rhino Precision Marketing, a New Orleans-based marketing automation company, is highlighting automated lead nurturing as one approach to keeping older inquiries visible and creating opportunities for businesses to reconnect with prospects over time.

The underlying problem is less about lead volume than lead management. New inquiries naturally receive immediate attention, while older prospects compete with current sales conversations, customer service requests and administrative work.

A contact that asked to be called back later may eventually be forgotten. Another prospect may have received an estimate but delayed the project because of budget constraints. A business customer may have postponed a purchase until the next quarter.

In each case, the original lead has not necessarily become irrelevant. The timing simply changed.

“Older leads are often treated as if the opportunity has expired, when the real issue may simply have been timing,” said Brett Thomas, owner of Rhino Precision Marketing. “Follow-up automation creates a structure for staying connected without requiring someone to manually remember every name, date, conversation, and next step.”

Buying Cycles Make Follow-Up More Complicated

Not every purchasing decision happens during the first interaction.

For a home-services company, a customer might request several estimates before deciding. A business buyer could need internal approval. A professional-services prospect may need to revisit a project after a budget cycle.

That makes the timing of follow-up important.

Marketing automation platforms can schedule communications based on elapsed time, lead status, previous interactions and other CRM information. Depending on the business, a workflow might trigger a reminder a few days after an estimate, another communication several weeks later and a longer-term check-in months afterward.

The objective is not to contact prospects as frequently as possible. It is to establish a repeatable process that matches the expected buying journey.

Segmentation Makes Automated Follow-Up More Relevant

A large database of inactive leads is not necessarily useful if every contact receives the same message.

Lead segmentation allows businesses to distinguish prospects according to factors such as service interest, location, sales stage, estimated value, appointment history and previous engagement.

Someone who requested pricing may need a different follow-up from a prospect who missed an appointment. A customer who specifically asked to reconnect in six months should also be treated differently from someone who stopped responding after an initial conversation.

That context can make automated communication less generic while giving sales teams a clearer picture of which prospects deserve immediate attention.

Automation Does Not Replace Sales Conversations

The most useful role for follow-up automation may be identifying when a previously inactive lead becomes active again.

A system can send a scheduled check-in, monitor the response and alert a salesperson when the prospect replies, requests an appointment or signals renewed interest.

That creates a division of labor between software and people. Automation handles scheduling, reminders and repetitive communications, while sales and service teams handle questions, negotiations and decisions requiring judgment.

“Automation works best when it supports the conversation rather than trying to replace it,” Thomas said. “The system can handle timing, reminders, and repetitive follow-up, while a person can handle questions, decisions, and the parts of the conversation that require judgment.”

For CRM and marketing teams, that distinction is increasingly important. The goal of automation is not simply to send more messages. It is to make sure potentially valuable conversations do not disappear because a salesperson was busy when the prospect was not ready.

Market Landscape

Lead nurturing has become increasingly connected to CRM automation, marketing automation and customer engagement platforms. Tools from Salesforce, HubSpot, Microsoft Dynamics 365 and other enterprise platforms already provide workflows for managing prospects across different stages of the customer journey.

The challenge for businesses is often not whether automation technology exists, but whether their processes contain enough useful information to make those workflows effective.

A CRM record with an accurate last-contact date, service interest and sales stage can support considerably more relevant automation than a database containing only a name, email address and phone number.

This is also where AI-powered marketing automation is beginning to change the workflow. AI can help summarize previous conversations, classify lead intent, identify engagement signals and assist teams in deciding which prospects should receive attention.

Automation therefore works best as part of a broader lead lifecycle management strategy, rather than as an isolated messaging tool.

Strategic Outlook

The economics of customer acquisition are making dormant leads increasingly relevant to marketing and sales teams. Businesses invest significant resources in generating inquiries, but the value of that investment can be reduced when prospects receive little attention after the initial interaction.

Long-term follow-up provides another route: instead of treating every non-conversion as a lost opportunity, businesses can maintain structured relationships until timing changes.

The next evolution will likely involve greater integration between CRM data, marketing automation and AI-driven lead scoring. Systems may increasingly determine not just when to send a message, but which prospects warrant human attention and why.

For enterprises, that creates an important distinction between automation that generates activity and automation that improves decision-making. The latter has the greater potential to turn dormant customer data into actionable sales opportunities.

Top Insights

• Lead follow-up automation keeps older prospects visible when buying decisions extend beyond the initial sales conversation.
• CRM segmentation can make automated messages more relevant by incorporating service interest, sales stage, timing and previous interactions.
• Longer buying cycles make scheduled follow-up particularly useful for industries where customers routinely delay purchasing decisions.
• Human sales teams remain essential, with automation handling repetitive timing while people manage complex questions, negotiations and decisions.
• AI-enabled CRM workflows can increasingly identify renewed buying signals and prioritize dormant prospects for human follow-up.

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AI search visibility is becoming harder to reduce to a single percentage. New campaign data from Zen Media shows that the same brand can have substantially different visibility across ChatGPT, Gemini, Claude, Perplexity and Grok, even when every engine is tested against the same buyer-intent prompts. The findings suggest that brands measuring AI search performance may need to look beyond an overall visibility score to understand where they are appearing, how prominently they are positioned and which sources are influencing the answers.

Zen Media's analysis of three 2026 AI visibility campaigns covering 900 buyer-intent prompts found significant variation in how often the same company appeared across leading AI engines.

In one corporate event planning campaign, the client appeared in 15.5% of prompts on the weakest-performing engine and 59.5% on the strongest. Across the five engines, the average visibility was 45.6%.

That difference illustrates a growing measurement problem for marketers. An aggregate score can provide a useful headline metric, but it can also conceal a platform where a brand is barely appearing.

“Clients usually want one number, but one number can hide the thing they actually need to fix,” said Sarah Evans, CEO of Zen Media.

The distinction becomes even more important when brands examine positioning rather than simple presence.

In Zen Media's oilfield supply campaign, two AI engines surfaced the client at comparable rates, at 45.4% and 40.6%. Yet the client appeared first in 57.6% of its appearances on one engine and 92.5% on the other.

For marketers, those outcomes represent different optimization challenges. A brand with low presence may need to improve the information available to AI systems about its products or expertise. A brand that already appears frequently but ranks behind competitors may face a different problem involving authority, relevance or the sources being used to construct answers.

The data also highlights the importance of non-branded AI search visibility.

In the corporate event planning campaign, 152 of the 175 prompts that surfaced the client were non-branded, representing 87% of appearances. In the enterprise AI readiness campaign, 110 of 130 appearances, or 85%, came from non-branded prompts.

That distinction matters because a prompt containing a company name already demonstrates some level of brand awareness. A buyer asking an AI system which provider can solve a particular problem gives the model a much broader choice of companies to recommend.

For B2B marketers, appearing in those unbranded answers can therefore become an important part of reaching prospects before they have established a shortlist.

Zen Media's analysis also found that the source influencing AI answers can vary by campaign.

In the enterprise AI readiness campaign, a GlobeNewswire press release was cited across 47 measured prompts, narrowly ahead of the client's own website at 46 citations. Another owned domain generated 41 citations. In the oilfield campaign, however, the client's website led the citation set.

That finding challenges the assumption that publishing more content on a company's own domain will automatically increase AI visibility.

The more useful question may be which sources AI systems actually retrieve and cite for a particular subject.

Competitive measurement introduces another layer. In the enterprise AI readiness campaign, Zen Media tested the client and competing firms against the same buyer-intent prompts. The client generated 1,026 mentions, compared with 631, 472 and 432 for three other named firms.

Those figures are campaign-specific rather than universal AI visibility rankings. Prompt selection, measurement period and competing answers can materially change the results.

For marketers building an AEO or GEO strategy, that makes consistency essential. Brands need to measure the same prompt set across engines and competitors if they want the resulting data to support meaningful content and communications decisions.

Market Landscape

The shift from traditional search rankings toward AI-generated answers is creating a new measurement layer for SEO, content marketing and digital PR.

Traditional search analytics can show rankings, impressions, clicks and traffic. AI visibility measurement introduces additional questions: Is the brand mentioned? How often does it appear? Where is it positioned? Which competitors appear alongside it? Which domains are cited?

Recent research across the broader AI-search industry has similarly highlighted that visibility in AI-generated results does not always map directly to conventional Google rankings. That makes AI visibility a complementary measurement discipline rather than simply another version of traditional SEO.

Zen Media's campaign data adds another practical consideration: AI visibility is not necessarily uniform across models. A strategy producing strong results in one engine may not deliver equivalent exposure elsewhere.

Strategic Outlook

For enterprise marketing teams, the emerging priority is likely to move from asking “What is our AI visibility score?” to understanding the components behind that score.

Engine-level visibility, position, non-branded discovery, competitor presence and citation sources can each point toward different actions.

That could influence editorial planning, digital PR, thought leadership, technical content, third-party publishing and distribution strategies. It also reinforces the importance of testing AI search performance against realistic buyer questions rather than relying exclusively on branded prompts.

The broader implication is that AEO and GEO measurement will need to become diagnostic. A single score can establish a baseline, but campaign-level data is more useful when it explains why visibility is changing and where the next content or authority-building opportunity exists.

Top Insights

• AI visibility varies by engine: Zen Media found one brand appearing in 15.5% of prompts on one engine and 59.5% on another.
• Presence and position differ: Similar appearance rates can produce very different outcomes when one engine places the brand first substantially more often.
• Non-branded discovery dominates: Up to 87% of measured appearances in one campaign came from prompts that did not mention the company.
• Citation sources can change: A distributed press release led citations in one campaign while the client's own website led in another.
• Competitive measurement requires consistency: Prompt sets, competitors and measurement periods need to remain consistent for meaningful comparisons.

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