Enterprise customer service is entering a new phase where messaging platforms are evolving from simple text-based support channels into interactive digital experience environments. As AI chat becomes more common across customer support operations, technology vendors are increasingly trying to differentiate not through automation alone, but through richer and more contextual customer interactions.

This week, Stackable Labs introduced a developer platform designed to extend Zendesk Messenger with interactive workflows, embedded actions, and AI-powered customer experiences that move beyond traditional chat interfaces.

For years, customer messaging platforms largely operated as lightweight communication layers — essentially digital chat windows connecting customers with support agents or automated bots.

Even as AI transformed the scale and speed of customer service operations, the user interface itself changed relatively little. Most interactions still revolve around sequential text exchanges with limited contextual awareness or workflow integration.

Stackable Labs is attempting to change that dynamic by introducing what it describes as an “AI experience layer” for customer service messaging.

Built on top of Zendesk Messenger, the platform enables developers and brands to embed real-time workflows, interactive experiences, customer data integrations, and operational actions directly into messaging conversations.

The goal is to transform messaging from a static communication channel into a dynamic customer interaction environment.

Instead of simply exchanging messages with a bot or support representative, customers could potentially interact with embedded workflows capable of retrieving order information, updating reservations, submitting requests, processing approvals, or surfacing personalized support experiences without leaving the messaging interface.

The launch reflects broader shifts occurring across enterprise customer experience infrastructure.

As generative AI becomes deeply integrated into customer service operations, enterprises are increasingly focused on balancing automation efficiency with user experience quality. Faster AI-generated responses alone are no longer viewed as sufficient differentiation.

“Messaging experiences should feel immersive, contextual, and native to the brand, not stuck inside a chat bubble,” said Adam Grohs, Co-Founder and CEO of Stackable and agnoStack.

That perspective aligns with a growing trend inside the customer experience industry where conversational interfaces are increasingly merging with application functionality.

Rather than redirecting users to external websites or backend systems, modern customer engagement platforms are beginning to surface workflows directly inside messaging environments. The approach mirrors broader enterprise software trends where interfaces become operational hubs rather than simple communication layers.

Major enterprise ecosystems including Salesforce, Microsoft, ServiceNow, and Zendesk are all investing heavily in AI-driven customer engagement infrastructure combining automation, workflow orchestration, and contextual assistance.

Stackable’s approach specifically targets Zendesk’s messaging ecosystem, positioning itself as an extension framework developers can use to create branded and industry-specific interaction experiences.

The platform supports integrations across sectors including eCommerce, SaaS, healthcare, travel, and financial services — industries where customer interactions increasingly require access to real-time operational systems rather than simple text support.

The product’s developer-centric architecture is also notable.

Stackable functions as a platform layer rather than a standalone application, allowing partners and technology vendors to build modular experiences on top of existing Zendesk environments. Launch partners include companies such as SnapCall, Cordial, and Optimate.me.

That ecosystem strategy reflects the broader platformization trend occurring across enterprise software.

Instead of competing solely through standalone products, vendors increasingly aim to become extensible infrastructure layers supporting partner-built applications and specialized workflows.

Research from Gartner has shown that enterprises are increasingly prioritizing composable customer experience architectures capable of integrating AI, automation, and operational systems within unified engagement environments. Meanwhile, Forrester analysts have highlighted conversational AI and digital customer engagement as major investment priorities across enterprise service operations.

The timing is significant.

Customer expectations around messaging experiences continue rising as consumers become accustomed to highly personalized interfaces across commerce, entertainment, and digital services. Traditional support chat windows increasingly appear outdated compared to interactive app-like experiences users encounter elsewhere online.

At the same time, enterprises face growing pressure to operationalize AI investments in ways that improve both efficiency and customer satisfaction.

That balancing act has become increasingly difficult as businesses deploy generative AI at scale. Many organizations have succeeded in automating responses but struggled to maintain brand personality, contextual understanding, and seamless workflow execution.

Stackable is betting that the next stage of customer service evolution will center on experiential messaging rather than conversational automation alone.

If that shift accelerates, messaging platforms may increasingly resemble lightweight operational interfaces where customer service, transactions, workflows, and AI-driven assistance converge inside a single interactive environment.

The broader implication is that customer messaging could evolve from a support channel into a fully integrated engagement layer embedded directly into enterprise operations.

Market Landscape

The customer experience and conversational AI market is rapidly evolving as enterprises modernize digital engagement strategies around AI-native interactions.

Organizations are increasingly investing in messaging infrastructure that combines automation, personalization, workflow orchestration, and real-time customer intelligence within unified support environments.

Major technology ecosystems including Zendesk, Salesforce, Microsoft, and ServiceNow continue expanding AI-powered customer engagement capabilities as conversational interfaces become central to enterprise service operations.

According to Gartner, enterprises are increasingly adopting composable customer experience architectures capable of integrating AI agents, workflows, and contextual engagement tools. Forrester research also points to rising investment in conversational AI and digital customer service transformation initiatives.

Top Insights

• Stackable Labs launched an AI experience layer designed to extend Zendesk Messenger with interactive workflows and contextual customer engagement capabilities.
• The platform enables brands to surface real-time data, execute workflows, and embed actions directly inside customer messaging environments.
• Stackable reflects a broader enterprise shift from text-based AI chat toward immersive and operationally integrated customer experience interfaces.
• Strategic partners including SnapCall and Cordial are building integrations on the platform’s developer ecosystem.
• Enterprises increasingly view conversational AI as a workflow and engagement layer rather than simply an automation tool for customer support responses.

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Artificial intelligence is rapidly becoming a competitive dividing line for small businesses, particularly in service-based industries where responsiveness, consistency, and operational efficiency directly shape customer retention. While many independent operators remain cautious about AI adoption, new research suggests the financial gap between AI adopters and non-adopters may already be widening.

A new study released by HoneyBook found that small businesses using AI tools report a median annual revenue of $500,000 — substantially higher than the $90,000 median reported by businesses slower to adopt automation and AI-driven workflows.

The findings arrive as small business owners increasingly navigate a difficult balancing act between maintaining authentic customer relationships and modernizing operations with AI-powered tools.

For many service professionals — including consultants, photographers, designers, marketers, event planners, coaches, and freelancers — concerns around AI have centered less on technology limitations and more on perception. Business owners have worried that customers might associate automation with impersonal service or lower-quality work.

The HoneyBook study suggests customer concerns may lie elsewhere.

According to survey results conducted with The Harris Poll, customers are far more likely to abandon businesses because of operational friction than because of AI usage. Among surveyed customers, 36% cited businesses being difficult to reach, while 32% pointed to lack of professionalism and 30% highlighted inconsistent service quality.

Those are precisely the categories where AI-enabled workflow systems increasingly promise measurable operational advantages.

“Customers care about getting fast, professional, consistent service,” said Oz Alon, Co-Founder and CEO of HoneyBook. “They do not care whether a person or a tool delivered it.”

The broader implication is significant for the evolving small business software market.

For years, AI adoption was largely concentrated among large enterprises with access to advanced analytics teams, automation infrastructure, and technical resources. But generative AI and low-code workflow platforms are now lowering implementation barriers for smaller organizations.

That democratization is reshaping how independent businesses manage customer communications, scheduling, invoicing, marketing, lead generation, and operational workflows.

The HoneyBook study surveyed 503 service-based small business owners and 1,002 customers across the United States. Among the company’s identified “Leader” segment — characterized as high-performing and risk-tolerant operators — 97% reported using AI tools and automation within their business operations.

The results align with broader market trends.

Research from McKinsey & Company has repeatedly shown that organizations adopting AI-driven operational workflows often experience productivity gains and improved customer responsiveness. Meanwhile, Gartner analysts have projected continued acceleration in SMB AI software adoption as vendors simplify implementation and embed AI into everyday business applications.

The competitive dynamics are also shifting.

Historically, many small businesses differentiated primarily through personalization and human relationships. AI is now changing how those businesses scale customer interactions without dramatically increasing staffing requirements.

Tools integrated into CRM platforms, marketing automation systems, scheduling applications, and customer support environments are increasingly handling repetitive administrative work once managed manually. That includes lead qualification, appointment scheduling, proposal generation, invoicing, customer follow-ups, and communication management.

Companies such as HubSpot, Salesforce, Intuit, and Adobe have all expanded AI capabilities targeted at SMB and midmarket users over the past two years.

HoneyBook’s findings suggest those investments may already be influencing revenue performance.

Notably, the study also indicates customers increasingly expect businesses to incorporate AI-enhanced experiences. Nearly half of surveyed customers said they expect small businesses to use AI to improve quality over the next five years, while 46% expect AI to accelerate turnaround times.

That signals a potentially important psychological shift in consumer expectations.

Earlier AI adoption cycles often focused on whether customers would tolerate automation. The emerging question appears to be whether customers will eventually penalize businesses that fail to modernize operational experiences.

The transition is particularly important for service-based industries where responsiveness and consistency are difficult to maintain during periods of growth.

Unlike product-based businesses that can scale inventory and fulfillment systems independently of customer interaction, service businesses often struggle to expand without operational bottlenecks. AI-enabled automation offers a potential path toward maintaining personalized experiences while improving efficiency and availability.

Still, adoption challenges remain.

Many small business owners continue to face uncertainty around implementation costs, workflow integration, AI accuracy, and maintaining authentic brand voice. Concerns around over-automation and customer trust also continue shaping adoption decisions.

Yet the broader market trajectory increasingly suggests AI is becoming operational infrastructure rather than experimental technology.

For service businesses competing in crowded digital marketplaces, the ability to respond quickly, communicate consistently, and maintain customer engagement outside traditional business hours may soon become baseline expectations rather than premium differentiators.

If that trend continues, AI adoption among small businesses may evolve from a strategic advantage into a competitive necessity.

Market Landscape

The small business AI software market is expanding rapidly as CRM, workflow automation, and customer engagement vendors embed generative AI into operational platforms.

AI adoption among SMBs is increasingly focused on productivity enhancement, customer communication, lead management, scheduling automation, and service delivery optimization rather than standalone experimental use cases.

Technology providers including HubSpot, Salesforce, Intuit, and Adobe are aggressively expanding AI capabilities aimed at independent businesses and service professionals.

According to Gartner and McKinsey & Company, AI-powered workflow automation and customer engagement technologies are expected to remain major drivers of small business digital transformation through the remainder of the decade.

Top Insights

• HoneyBook found that AI-using small businesses report median annual revenue nearly five times higher than slower AI adopters.
• Customers are more concerned about responsiveness, professionalism, and service consistency than whether businesses use AI-powered operational tools.
• Nearly half of surveyed consumers expect small businesses to use AI to improve service quality and accelerate turnaround times within the next five years.
• AI adoption is increasingly becoming operational infrastructure for service-based businesses managing communications, scheduling, customer engagement, and workflow automation.
• CRM and SMB software vendors are rapidly embedding AI into everyday business operations as competitive pressure around customer experience intensifies.

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As enterprises accelerate investments in AI agents and workflow automation, one operational bottleneck continues to persist across industries: document processing. Contracts, invoices, legal filings, compliance forms, and clinical records still move through fragmented systems that often rely heavily on manual intervention.

This week, Nitro Software launched Nitro Automate, a new intelligent document automation platform designed to embed document processing capabilities directly into enterprise workflows, applications, and AI agents.

The launch highlights a growing shift in enterprise automation strategy where organizations are no longer treating documents as isolated files, but as operational data streams that AI systems and workflows must actively process, interpret, and execute against.

For many enterprises, documents remain one of the last major barriers preventing fully automated business operations.

While AI agents can increasingly analyze and reason about content, they often lack the infrastructure necessary to manipulate files, extract structured information, convert formats, manage approvals, or execute document-centric workflows at scale.

Nitro Software says Nitro Automate is designed to bridge that gap by embedding document automation directly into enterprise systems already in use, including CRM, ERP, HR, and AI platforms.

“Most companies are still building their document workflows manually,” said Cormac Whelan, CEO of Nitro Software. “Nitro embeds wherever work happens—your agents, your platforms, your applications—and handles your documents automatically.”

The product arrives at a pivotal moment for enterprise AI adoption.

Across industries, organizations are deploying generative AI assistants, intelligent agents, and automation frameworks to streamline operational processes. Yet document-heavy workflows continue to slow many initiatives because business-critical information remains trapped inside PDFs, scanned records, contracts, forms, and unstructured files.

That challenge is particularly pronounced in regulated industries such as healthcare, financial services, legal operations, logistics, and government where document-intensive processes remain deeply embedded into day-to-day operations.

Nitro Automate positions itself as an infrastructure layer capable of integrating document operations directly into those workflows without requiring organizations to switch between disconnected tools.

The platform supports multiple deployment models.

Organizations can integrate Nitro through AI agents using the emerging Model Context Protocol (MCP), through low-code platforms such as Microsoft Power Automate and Zapier, or through direct API integrations embedded into custom enterprise applications.

The MCP integration is especially notable because it aligns Nitro with a rapidly growing ecosystem of AI agent interoperability frameworks.

MCP is increasingly emerging as a standardized method for connecting AI assistants to external tools and operational systems. By enabling AI agents to perform document actions programmatically, Nitro is effectively positioning document automation as a functional layer inside broader agentic AI environments.

That reflects a larger enterprise trend.

The market is quickly moving beyond AI systems that merely generate text toward operational AI agents capable of executing workflows, interacting with enterprise software, and performing multi-step tasks autonomously.

Major enterprise ecosystems including Microsoft, Google, Salesforce, and Adobe are all expanding investments in AI-powered workflow orchestration and enterprise productivity automation.

Document processing is becoming a critical component of that ecosystem because many enterprise operations still revolve around contracts, compliance forms, approvals, invoices, procurement records, and customer documentation.

Research from Gartner suggests intelligent document processing and AI-powered workflow automation remain among the fastest-growing enterprise software categories as organizations pursue operational efficiency initiatives. Meanwhile, Forrester analysts have highlighted agentic automation as a major evolution in enterprise digital transformation strategies.

Security and governance also remain central concerns.

Nitro emphasized that the platform operates within infrastructure certified for SOC 2 Type II, ISO 27001, and HIPAA compliance requirements. The company also stated that customer data processed through Nitro Automate is not used to train AI models — an increasingly important distinction as enterprises evaluate vendor trustworthiness and data governance policies.

That positioning reflects growing enterprise caution around generative AI deployments, particularly in industries handling regulated or highly sensitive information.

Rather than fully outsourcing workflows to public AI systems, many organizations are seeking infrastructure providers capable of integrating AI functionality while preserving operational control, compliance visibility, and auditability.

The competitive landscape is becoming increasingly crowded.

Document automation vendors, workflow orchestration platforms, RPA providers, and AI productivity companies are all converging around similar enterprise automation opportunities. The differentiation increasingly depends on interoperability, governance, scalability, and the ability to integrate into existing operational ecosystems.

Nitro’s broader strategy appears focused on embedding document intelligence into the infrastructure layer of enterprise automation rather than competing solely as a standalone PDF or eSignature provider.

If enterprise AI adoption continues accelerating, document automation may become one of the foundational operational capabilities enabling AI agents to move from conversational assistants to true workflow participants.

Market Landscape

The intelligent document processing market is evolving rapidly as enterprises modernize workflows around AI-powered automation and agentic operational systems.

Organizations are increasingly seeking platforms capable of integrating document extraction, workflow orchestration, eSignature management, and automation directly into enterprise applications and AI environments.

Technology ecosystems led by Microsoft, Google, Adobe, and Salesforce continue expanding AI-driven automation capabilities aimed at improving operational efficiency and enterprise productivity.

According to Gartner and Forrester, intelligent document processing and agentic automation are expected to remain major enterprise technology investment areas as organizations pursue scalable AI-enabled operations.

Top Insights

• Nitro Software launched Nitro Automate to embed intelligent document processing into AI agents, workflows, and enterprise applications.
• The platform supports integrations through MCP-enabled AI agents, low-code automation systems, and direct API deployment models.
• Nitro is targeting enterprise bottlenecks where contracts, invoices, records, and forms still require heavy manual processing across fragmented systems.
• The launch reflects broader enterprise movement toward agentic AI systems capable of executing operational workflows rather than simply generating responses.
• Security certifications including SOC 2 Type II, ISO 27001, and HIPAA compliance position Nitro Automate for regulated industries handling sensitive data.

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As marketers navigate a rapidly fragmenting digital advertising landscape, the ability to unify audience targeting across multiple platforms without relying on third-party cookies is becoming increasingly valuable. Brands are under pressure to reach consumers across both community-driven discovery environments and professional decision-making networks while maintaining measurable performance outcomes.

This week, Iridio expanded its social media marketing platform with new integrations for Reddit and LinkedIn, extending its multichannel advertising capabilities into two of the internet’s most distinct audience ecosystems.

The move reflects broader shifts underway in digital advertising as marketers search for alternatives to traditional cookie-based targeting while also adapting to increasingly fragmented consumer attention patterns.

For years, performance marketers concentrated budgets heavily across dominant social ecosystems such as Meta platforms and TikTok. But audience diversification, privacy regulations, and evolving platform behavior are pushing brands toward more distributed advertising strategies.

Iridio is positioning its expanded platform around that transition.

Powered by parent company RRD’s Consumer Graph and Household Connect technologies, the platform aims to help advertisers target audiences across online and offline environments using probabilistic identity mapping rather than traditional third-party cookies.

According to RRD, its Consumer Graph technology connects behavioral, transactional, and demographic intelligence layers across 130 million personas while maintaining privacy-focused targeting practices. Household Connect extends that framework by grouping devices within shared behavioral and geographic environments to create household-level audience profiles.

The Reddit integration is particularly notable given the platform’s growing importance within both consumer discovery and AI-driven search ecosystems.

Reddit now reports more than 190 million weekly active unique users, and its communities increasingly influence product research, purchasing decisions, and organic search visibility. The company cited internal audience overlap data suggesting sizable portions of Reddit users are not active on platforms such as Facebook, Instagram, or TikTok.

That audience differentiation has become increasingly attractive for marketers seeking incremental reach outside saturated social advertising environments.

More importantly, Reddit’s structure around topic-based communities creates contextual targeting opportunities that differ significantly from traditional interest-based advertising models. Brands can potentially align campaigns with active conversations occurring inside subreddits tied to specific industries, product categories, hobbies, or consumer intent signals.

The LinkedIn expansion addresses a different side of the advertising market.

While Reddit emphasizes community engagement and discovery, LinkedIn offers access to professional audiences and enterprise decision-makers. Iridio said it has already begun beta testing LinkedIn campaigns and plans a broader rollout later this year.

The addition reflects growing convergence between B2B and consumer-targeting strategies inside enterprise advertising infrastructure.

Modern marketers increasingly need unified audience frameworks capable of supporting full-funnel engagement across both consumer and professional ecosystems. Campaigns frequently span awareness, consideration, and conversion stages simultaneously across multiple channels.

Iridio’s broader strategy appears centered on performance orchestration across media formats.

The company highlighted internal campaign data from nearly 60 consumer packaged goods campaigns conducted between late 2023 and mid-2025. According to the study, campaigns combining display advertising with social media reportedly generated 67% higher average featured sales lift and four times greater incremental sales compared to single-channel social campaigns.

Those findings align with broader industry trends emphasizing cross-channel attribution and integrated media strategies.

Research from Gartner and Forrester has shown that advertisers are increasingly prioritizing unified identity frameworks and omnichannel measurement systems as signal loss from cookies and mobile identifiers continues affecting traditional targeting models.

The privacy-first positioning is also strategically important.

As regulatory scrutiny intensifies globally and browsers continue restricting third-party tracking technologies, ad platforms are racing to build alternative identity and targeting infrastructure. Probabilistic identity mapping, first-party data activation, contextual targeting, and household-level audience modeling are emerging as key components of the post-cookie advertising ecosystem.

Brand safety remains another critical issue, particularly for platforms driven by user-generated content and community conversations.

Iridio says campaigns include monitoring systems using sensitivity controls and keyword blocklists designed to prevent ads from appearing alongside potentially harmful or inappropriate content. That capability is becoming increasingly important as advertisers balance reach expansion with reputational risk management.

The broader market direction suggests digital advertising is moving toward a more distributed and intelligence-driven model.

Rather than relying on a handful of dominant social ecosystems, brands are increasingly building multichannel strategies spanning community platforms, professional networks, connected TV, retail media, search, and contextual engagement environments.

For marketers, the challenge is no longer simply reaching audiences — it is understanding how to connect fragmented consumer behaviors into measurable and privacy-compliant performance systems.

Market Landscape

The digital advertising industry is undergoing major structural changes as privacy regulations, signal loss, and AI-driven discovery reshape audience targeting strategies.

Advertisers are increasingly adopting identity-based and contextual targeting approaches that reduce reliance on third-party cookies while supporting omnichannel campaign measurement and attribution.

Platforms including Meta, LinkedIn, Reddit, Google, and Amazon continue competing for advertising budgets as brands diversify media investments across social, community, retail, and professional ecosystems.

According to Gartner and Forrester, identity resolution, privacy-first targeting, and multichannel performance measurement are expected to remain central priorities for enterprise marketing organizations.

Top Insights

• Iridio expanded its social advertising capabilities through new integrations with Reddit and LinkedIn.
• RRD’s Consumer Graph and Household Connect technologies enable privacy-focused audience targeting across 130 million personas without relying on third-party cookies.
• Reddit’s community-driven structure offers advertisers contextual engagement opportunities tied to active consumer research and discussion environments.
• Internal campaign analysis found combining display and social advertising generated significantly higher sales lift and incremental revenue than single-channel campaigns.
• The launch reflects broader advertising industry movement toward omnichannel identity resolution, contextual targeting, and privacy-first performance measurement.

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Marketing technology vendors are increasingly transforming customer conferences into year-round digital engagement ecosystems as competition intensifies around AI-driven advertising, measurement, and cross-channel orchestration. In a market where product innovation cycles move rapidly, extending event content beyond physical attendees has become a strategic way to influence global marketing audiences.

This week, StackAdapt announced the global rollout of its Conversion 2026 on-demand experience, giving marketers worldwide access to the company’s annual advertising and orchestration summit held earlier this month in Austin, Texas.

The expansion reflects broader shifts occurring across the advertising technology sector where AI, automation, and unified campaign orchestration are rapidly reshaping how brands manage media execution and performance measurement.

Originally held May 12–13 in Austin, Conversion 2026 brought together marketers, agencies, and technology executives to discuss the future of programmatic advertising, AI-powered creative systems, and cross-channel campaign management.

The event featured speakers from organizations including Spotify, PMG, and JetBlue, reflecting the increasingly interconnected nature of modern digital advertising ecosystems.

By extending the summit into an on-demand digital platform, StackAdapt is following a growing industry trend where conferences are evolving from limited-time events into scalable content and thought leadership channels.

That shift has accelerated significantly since hybrid and virtual engagement models became normalized across enterprise technology industries. Vendors increasingly use digital event ecosystems not only to showcase product launches, but also to reinforce platform positioning within highly competitive software categories.

At the center of StackAdapt’s event strategy is its broader push into AI-powered advertising orchestration.

During the summit, the company introduced several new platform capabilities designed to unify campaign execution, creative development, attribution, and media coordination across multiple channels.

Among the most notable additions were Command Center, Ivy Studio, AI Video Builder, expanded cross-channel attribution capabilities, and new programmatic direct mail functionality.

Together, the announcements reflect how advertising platforms are increasingly converging around orchestration rather than standalone media buying tools.

The advertising technology market is undergoing a structural transformation as marketers seek centralized systems capable of coordinating creative production, audience targeting, performance measurement, and media optimization simultaneously.

Historically, many organizations managed those functions through fragmented stacks involving separate demand-side platforms, analytics tools, creative systems, customer data platforms, and campaign management software.

AI is now accelerating efforts to consolidate those workflows.

Major ecosystems including Google, Adobe, Salesforce, and Amazon are all investing heavily in AI-driven advertising infrastructure capable of automating campaign planning, creative generation, optimization, and attribution.

StackAdapt’s platform updates appear aimed at positioning the company within that broader orchestration category rather than solely within traditional programmatic advertising.

The introduction of AI Video Builder is particularly notable as generative AI rapidly reshapes digital creative production. Marketers are increasingly exploring AI-generated ad assets to accelerate campaign deployment and reduce production costs across multiple channels and formats.

Attribution also remains a critical battleground.

As privacy restrictions continue disrupting third-party tracking and cookie-based measurement systems, advertisers are seeking more unified approaches to understanding campaign performance across fragmented digital environments.

Enhanced cross-channel attribution tools reflect industry demand for visibility into how different media formats collectively influence customer journeys and business outcomes.

The addition of programmatic direct mail capabilities also signals the continuing convergence between digital and offline marketing channels.

Rather than treating direct mail as a disconnected legacy tactic, advertisers are increasingly integrating physical media into data-driven omnichannel campaigns coordinated through centralized marketing platforms.

Research from Gartner suggests AI-powered marketing orchestration platforms are becoming strategic priorities for enterprise marketing teams seeking operational efficiency and measurable performance visibility. Meanwhile, Forrester analysts have emphasized growing demand for unified campaign management systems capable of supporting increasingly complex omnichannel customer journeys.

The event’s global digital rollout further highlights how enterprise software companies are increasingly treating content itself as a product layer.

On-demand conference ecosystems allow vendors to extend product education, community engagement, and thought leadership far beyond the constraints of physical attendance. That creates ongoing opportunities to influence buying cycles and strengthen platform visibility throughout the year.

For advertisers navigating rapidly changing AI, privacy, and media environments, those ecosystems are becoming important sources of both strategic guidance and competitive positioning.

As the advertising industry moves deeper into AI-native campaign infrastructure, vendors are increasingly competing not only on tools and features, but also on their ability to define the operational future of modern marketing.

Market Landscape

The advertising technology industry is rapidly evolving around AI-powered orchestration, unified measurement, and cross-channel campaign automation.

Marketers are increasingly seeking centralized platforms capable of coordinating media buying, creative generation, audience targeting, attribution, and workflow automation across fragmented digital ecosystems.

Technology leaders including Google, Adobe, Salesforce, and Amazon continue investing heavily in AI-driven marketing infrastructure designed to improve efficiency, personalization, and campaign performance visibility.

According to Gartner and Forrester, AI-enabled marketing orchestration and attribution systems are expected to remain major enterprise investment priorities as brands adapt to privacy changes and increasingly complex customer journeys.

Top Insights

• StackAdapt expanded its Conversion 2026 summit into a global on-demand experience for marketers worldwide.
• The company introduced platform updates including AI Video Builder, cross-channel attribution tools, and programmatic direct mail capabilities.
• Advertising platforms are increasingly shifting from standalone media buying tools toward AI-powered orchestration ecosystems coordinating creative, measurement, and execution.
• On-demand conference platforms are becoming strategic content ecosystems for enterprise technology vendors competing in crowded software markets.
• AI-generated creative production and unified attribution remain major priorities as marketers adapt to fragmented media environments and privacy-driven measurement changes.

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The race to operationalize AI across the mobile advertising ecosystem is entering a new phase as companies shift focus from experimentation to real-time intelligence infrastructure. Digital Turbine announced a strategic partnership with Databricks aimed at accelerating AI-driven mobile experiences using large-scale behavioral data collected across apps and connected devices.

The partnership brings Databricks’ enterprise AI and analytics stack into Digital Turbine’s advertising and mobile growth platform, allowing the company to process and operationalize data signals from more than 80,000 mobile apps and over one billion devices globally. The companies say the integration is designed to improve predictive modeling, ad targeting, and automated decision-making while maintaining privacy-conscious data governance.

At the center of the announcement is Digital Turbine’s Ignite Graph and DT iQ infrastructure, which collectively aggregate and analyze real-time device interactions. The company has increasingly positioned these assets as foundational components of an AI-first mobile advertising ecosystem, particularly as advertisers seek alternatives to third-party cookies and legacy identity tracking systems.

The partnership arrives at a time when enterprise AI deployments are rapidly moving beyond chatbot experimentation into operational systems embedded within marketing, advertising, and customer engagement platforms. According to Gartner, more than 80% of enterprises are expected to deploy generative AI-enabled applications by 2026, up sharply from less than 5% in 2023. Meanwhile, IDC projects worldwide AI infrastructure spending will surpass $200 billion within the next several years as enterprises modernize data pipelines for machine learning workloads.

For Digital Turbine, the integration appears to focus on turning massive behavioral datasets into operational intelligence at scale. Databricks’ Genie Spaces will allow employees and analysts to query complex datasets using natural language prompts rather than traditional SQL workflows. The approach reflects a broader enterprise trend toward conversational analytics systems that simplify data access for non-technical teams.

Databricks Apps, another key component of the partnership, gives Digital Turbine a serverless framework for building and deploying AI applications directly within its governed data environment. That capability could prove significant as advertising platforms increasingly require near real-time decisioning across fragmented mobile ecosystems.

The mobile advertising market has become heavily dependent on first-party data strategies following privacy policy changes introduced by platforms including Apple and Google. Those shifts have reduced visibility into user-level tracking while increasing demand for contextual intelligence, predictive analytics, and consent-driven engagement models.

Digital Turbine’s scale gives it a potentially differentiated position in this environment. The company operates across device distribution, app monetization, and advertising infrastructure layers, creating access to large volumes of mobile interaction data. By combining that reach with Databricks’ AI architecture, the company is attempting to create a feedback loop where real-time signals continuously improve targeting and automation models.

Ben John, CTO of Digital Turbine, said the partnership helps unify the company’s data architecture while improving collaboration between engineering and analytics teams. According to John, the integration is intended to accelerate the deployment of next-generation AI capabilities capable of delivering more precise mobile intelligence to advertisers and brand partners.

Databricks is simultaneously expanding its footprint within the advertising and media sector, an industry increasingly dependent on scalable AI infrastructure. The company has been competing with cloud-native AI and analytics ecosystems from Microsoft, Amazon, Adobe, and Salesforce as enterprise buyers consolidate data management and AI operations under unified platforms.

Tony LaVasseur, RVP of Media and Advertising at Databricks, described the implementation as an example of enterprise AI built on governed and trusted datasets. He noted that Genie allows teams to retrieve insights directly from enterprise data while Databricks Apps operationalizes those insights into production-ready AI systems without requiring data movement across disconnected environments.

The broader significance of the partnership extends beyond advertising optimization. The integration signals how mobile ecosystem companies are increasingly treating AI infrastructure as a competitive differentiator rather than an experimental layer. Real-time personalization, predictive engagement, and AI-assisted app discovery are becoming central to mobile monetization strategies.

Industry analysts expect this trend to intensify as advertisers demand measurable performance improvements tied to AI-driven automation. Platforms capable of combining large-scale first-party data, privacy governance, and real-time inference models are likely to gain strategic advantages in both ad targeting and customer acquisition efficiency.

For enterprise marketing teams, the announcement highlights a growing convergence between customer data infrastructure, AI orchestration, and mobile engagement systems. Rather than operating separate analytics, advertising, and activation platforms, companies are increasingly seeking unified ecosystems capable of transforming behavioral signals into immediate business actions.

Digital Turbine’s partnership with Databricks reflects that shift. The companies are positioning AI not simply as an analytics enhancement, but as the operational layer powering the next generation of mobile growth infrastructure.

Market Landscape

The Digital Turbine–Databricks partnership underscores several major shifts shaping the MarTech and AdTech industries:

• Enterprise AI platforms are moving toward governed, real-time data architectures capable of powering automated decision systems.
• Mobile advertising companies are prioritizing first-party behavioral intelligence as privacy regulations reshape targeting capabilities.
• Conversational analytics tools such as Databricks Genie are reducing dependency on technical data teams and democratizing enterprise intelligence.
• AI-native infrastructure is becoming central to programmatic advertising, predictive engagement, and mobile app monetization strategies.
• The convergence of AI, cloud analytics, and customer data platforms is accelerating across enterprise marketing ecosystems.

Top Insights

• Digital Turbine is integrating Databricks AI infrastructure to operationalize real-time behavioral data from over one billion mobile devices and 80,000 apps globally.
• Databricks Genie Spaces enables Digital Turbine teams to query complex mobile datasets using natural language, accelerating analytics and AI-driven decision-making workflows.
• The partnership strengthens first-party data strategies as mobile advertisers adapt to privacy-driven changes introduced by Apple and Google ecosystems.
• Databricks Apps provides a serverless environment for deploying enterprise AI applications directly within governed data infrastructure without moving sensitive datasets.
• The collaboration reflects a broader enterprise trend toward AI-native advertising infrastructure combining predictive analytics, automation, and privacy-conscious customer intelligence.

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Artificial intelligence may be dominating marketing technology investment strategies, but most enterprise marketing teams still lack the data infrastructure needed to make AI effective at scale. That is the central finding from GrowthLoop’s newly released 2026 AI and Marketing Performance Index, a study examining how marketers and data leaders across North America are operationalizing AI inside modern marketing organizations.

The report, conducted with research firm Ascend2, surveyed more than 300 marketing and data professionals in the U.S. and Canada. Its conclusions point to a widening gap between enterprise AI ambitions and the underlying customer data infrastructure required to support real-time personalization, experimentation, and measurable business outcomes.

While 87% of surveyed marketers said they have implemented AI into at least part of their workflows, the majority still depend heavily on fragmented historical data and disconnected measurement systems. According to the report, only 23% of organizations can reliably connect marketing actions to actual business outcomes, a limitation that continues to undermine personalization and campaign optimization efforts.

The findings reinforce a broader shift taking place across the MarTech ecosystem. Enterprises are increasingly discovering that AI alone does not solve operational inefficiencies if customer data remains siloed across advertising platforms, CRM systems, analytics environments, and cloud infrastructure.

GrowthLoop’s research suggests organizations with a fully centralized “single source of truth” (SSOT) are significantly outperforming competitors still operating fragmented marketing stacks. Companies with centralized customer data environments reported substantially higher revenue growth rates than organizations without unified infrastructure, with 44% of SSOT-enabled companies reporting stronger revenue performance compared to just 8% among those lacking centralized systems.

The report arrives as enterprises accelerate investments in cloud-native data environments from providers including Google, Microsoft, and Amazon. At the same time, marketing organizations are rethinking how AI models interact with customer data platforms, analytics pipelines, and activation systems.

Anthony Rotio, co-founder and co-CEO of GrowthLoop, argued that many organizations mistakenly equate experimentation volume with data maturity. According to Rotio, running more tests does not necessarily improve marketing performance unless companies understand the causal relationship between campaigns and customer behavior.

That distinction is becoming increasingly important as enterprise marketing teams face mounting pressure to justify AI spending with measurable ROI. According to McKinsey & Company, organizations effectively integrating AI into operational decision-making can improve marketing productivity by up to 30%. However, those gains often depend on clean, unified, and continuously updated datasets.

The study also highlights growing skepticism around so-called “real-time personalization” capabilities marketed across the advertising and customer engagement sectors. Despite years of industry messaging around instantaneous customer targeting, only 12% of surveyed organizations reported primarily using real-time signals to execute campaigns. Most teams continue relying on historical or partially delayed data inputs.

That gap between marketing narratives and operational reality reflects one of the largest challenges facing enterprise AI adoption today: data latency.

Many enterprise marketing stacks still operate on batch-based architectures where customer signals take hours or days to process across platforms. As a result, personalization engines often optimize campaigns using outdated behavioral patterns rather than live customer intent.

The report found organizations operating customer data infrastructure within cloud data lakes or modern enterprise data clouds performed better across several operational categories. Those companies reported fewer challenges related to impact measurement, manual workflows, and experimentation bottlenecks compared to teams relying primarily on traditional marketing automation suites.

The implications extend beyond campaign execution. Industry analysts increasingly view centralized data infrastructure as foundational for the next generation of AI agents, predictive analytics systems, and autonomous marketing decision engines.

That transition is already reshaping the competitive landscape for vendors across the MarTech and AdTech industries. Platforms such as Salesforce, Adobe, and composable customer data platform providers are racing to position themselves as AI-ready infrastructure layers capable of unifying customer intelligence and activation workflows.

The report’s conclusions also align with growing enterprise interest in composable marketing architectures. Rather than moving data across multiple disconnected systems, organizations are increasingly bringing AI models directly to centralized cloud environments where customer data already resides.

Phil Gamache, founder of Humans of Martech, said the findings mirror conversations taking place across the industry. While AI tools continue becoming more sophisticated, he noted that many enterprise teams remain constrained by outdated data infrastructure that limits execution speed and experimentation quality.

The broader market trend points toward a future where AI success depends less on standalone applications and more on how effectively organizations integrate cloud data infrastructure, measurement frameworks, and decisioning systems into a unified operational model.

For enterprise marketing leaders, the message from GrowthLoop’s report is increasingly difficult to ignore: AI may accelerate campaign execution, but without centralized and actionable customer data, automation alone cannot deliver meaningful performance gains.

Market Landscape

The GrowthLoop report highlights several important developments shaping enterprise marketing technology strategies in 2026:

• AI adoption across marketing organizations is accelerating faster than enterprise data modernization efforts.
• Real-time personalization remains difficult because many organizations still operate on delayed or fragmented customer data pipelines.
• Centralized customer data environments are becoming critical for AI-driven experimentation, attribution, and predictive marketing analytics.
• Composable MarTech architectures are gaining momentum as enterprises seek flexibility beyond legacy marketing suites.
• Cloud-native AI decisioning systems are increasingly replacing disconnected analytics and campaign optimization workflows.

Top Insights

• GrowthLoop’s survey found that companies with centralized customer data infrastructure report significantly stronger revenue growth and operational efficiency than fragmented marketing organizations.
• Despite widespread AI adoption, only 23% of marketers can directly connect campaign actions to measurable business outcomes using reliable causal measurement.
• Most enterprise marketing teams still rely heavily on historical customer data, limiting real-time personalization and AI-driven decision-making capabilities.
• Organizations operating cloud-based data lakes and modern data clouds experience fewer operational bottlenecks than companies dependent on legacy marketing automation platforms.
• The report underscores how AI success increasingly depends on unified enterprise data infrastructure rather than standalone automation or experimentation tools.

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Enterprise conversational AI is rapidly evolving beyond customer service chatbots and into autonomous operational systems capable of handling HR, IT, procurement, finance, and internal enterprise workflows. That shift is central to a new industry assessment from IDC, which named Aisera, an Automation Anywhere company, a Leader in its inaugural Worldwide Conversational AI Platforms for Back-Office Use Cases 2026 Vendor Assessment.

The report marks IDC’s first MarketScape focused specifically on conversational AI platforms designed for internal enterprise operations rather than customer-facing engagement. The evaluation reflects how AI adoption is expanding deeper into enterprise infrastructure as organizations increasingly deploy AI agents capable of automating workflows, synthesizing business intelligence, and executing operational tasks autonomously.

According to IDC, conversational AI vendors are moving well beyond traditional FAQ bots and help desk assistants. Modern enterprise AI platforms are now expected to support complex back-office functions including IT service management, employee onboarding, procurement analysis, enterprise research, and workflow orchestration.

Aisera’s placement as a Leader highlights growing demand for AI systems that combine conversational interfaces with enterprise automation capabilities. The company’s platform was recognized for supporting multiple large language model deployment options, including proprietary domain-specific models as well as integrations with foundation models from OpenAI, Anthropic, Google via Vertex AI, Meta through Llama 3, and Amazon through Bedrock.

That flexibility has become increasingly important for enterprises attempting to balance AI performance, governance, compliance, and cost management across diverse operational environments.

IDC’s assessment also emphasized Aisera’s workflow integration capabilities and low-code deployment framework. Customers cited the platform’s prebuilt connectors and “plug-and-play” integrations as a major operational advantage, particularly for organizations seeking to deploy AI across fragmented enterprise systems without relying heavily on developer resources.

The broader market context is significant. Enterprises are now facing pressure to operationalize generative AI investments while simultaneously improving workforce productivity and reducing operational complexity. According to Gartner, by 2027 more than half of enterprise knowledge workers are expected to rely on AI assistants or AI agents as part of daily workflows. Meanwhile, McKinsey & Company estimates generative AI could contribute trillions of dollars in annual productivity gains across business operations, customer support, and enterprise services.

That opportunity is accelerating convergence between conversational AI platforms and robotic process automation (RPA) ecosystems.

Automation Anywhere has increasingly positioned Aisera within this broader enterprise automation strategy, combining AI-driven conversational intelligence with workflow automation and autonomous task execution. Derek Toone, SVP of Agentic AI Solutions at Automation Anywhere, said enterprises are no longer looking for AI systems that simply answer questions. Instead, organizations increasingly want AI capable of making decisions, initiating workflows, and producing measurable operational outcomes.

The concept of “agentic AI” has quickly become one of the most closely watched trends across enterprise software markets. Unlike traditional AI assistants, agentic systems are designed to complete multi-step tasks autonomously using integrated enterprise data, APIs, workflow systems, and business logic.

That evolution is reshaping competition across the enterprise AI sector. Vendors including Microsoft, Salesforce, Adobe, and enterprise workflow providers are aggressively integrating conversational AI into broader automation ecosystems.

IDC’s report suggests back-office use cases may become one of the fastest-growing areas of enterprise AI investment over the next several years. Internal operations environments often contain highly structured workflows, repeatable processes, and rich enterprise datasets — conditions well suited for AI automation.

The report also highlights how enterprises are increasingly prioritizing interoperability and deployment flexibility when selecting AI platforms. Organizations want systems capable of integrating across existing cloud environments, identity frameworks, ERP systems, HR platforms, and collaboration tools rather than deploying isolated AI applications.

For enterprise technology leaders, the MarketScape findings underscore a broader transition underway in AI adoption strategies. The conversation is moving from standalone generative AI experimentation toward operational AI infrastructure embedded directly into enterprise workflows.

In that environment, conversational AI platforms are becoming less about chat interfaces and more about orchestrating enterprise actions across complex digital ecosystems.

Aisera’s recognition in IDC’s first dedicated back-office conversational AI MarketScape reflects how quickly that market is maturing — and how central AI agents are becoming to the future of enterprise operations.

Market Landscape

The conversational AI market is entering a new enterprise phase centered on operational automation and AI agents:

• Enterprises are increasingly deploying conversational AI for HR, IT, finance, procurement, and knowledge work automation rather than customer support alone.
• Agentic AI platforms are converging with robotic process automation to create autonomous workflow execution systems.
• Multi-model AI environments are becoming critical as enterprises seek flexibility across OpenAI, Anthropic, Google, Meta, and Amazon ecosystems.
• Low-code AI deployment frameworks are gaining traction as organizations attempt to scale AI without large engineering dependencies.
• Enterprise demand is shifting toward integrated AI infrastructure capable of combining conversational intelligence, automation, governance, and analytics.

Top Insights

• IDC named Aisera a Leader in its first MarketScape dedicated specifically to conversational AI platforms for enterprise back-office operations and workflow automation.
• Aisera’s platform supports multiple foundation models and enterprise AI ecosystems, including OpenAI, Anthropic, Google Vertex AI, Meta Llama 3, and Amazon Bedrock.
• The report highlights growing enterprise demand for AI agents capable of executing operational tasks autonomously across HR, IT, procurement, and knowledge management systems.
• Customers cited Aisera’s low-code workflows, prebuilt integrations, and plug-and-play deployment capabilities as major advantages for enterprise AI adoption.
• The conversational AI market is rapidly converging with robotic process automation as enterprises prioritize operational AI platforms over standalone chatbot tools.

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