As enterprises race to deploy AI agents across customer service operations, a new challenge has emerged: building an AI agent is easy, but building one that can reliably handle real customers, complex workflows, and production-scale demands is far harder.

Cresta aims to solve that problem with the launch of Conductor, a developer-focused agentic engine designed to automate much of the AI agent development lifecycle while maintaining the governance and oversight enterprises require.

The company claims Conductor can help engineering teams deploy production-ready AI agents twice as fast by using natural language, real customer conversations, and enterprise workflow intelligence to design, build, test, and optimize AI-powered customer experience agents.

The launch comes as businesses increasingly move beyond AI experimentation and into large-scale deployment. While countless platforms can generate agent prototypes and demonstrations, enterprises are discovering that production-grade AI requires extensive testing, system integrations, workflow orchestration, and ongoing optimization.

Cresta believes that gap between prototype and production is where most organizations struggle—and where Conductor is designed to help.

"Building production-ready AI agents is one of the hardest engineering challenges in the enterprise right now," said Ping Wu, CEO of Cresta.

Rather than functioning as another AI assistant, Conductor acts as what Cresta calls an "agent-building agent"—an AI system designed specifically to create and improve other AI agents.

Moving Beyond AI Demos

The rise of generative AI has dramatically lowered the barrier to creating conversational agents. However, enterprise customer experience environments introduce a level of complexity that many low-code or no-code agent builders cannot handle.

Customer service agents frequently need access to proprietary systems, payment platforms, reservation engines, CRM environments, internal APIs, and custom business logic. They also require strict governance, compliance controls, and extensive testing before interacting with customers.

Many organizations discover that creating a proof of concept takes days, while making that agent production-ready can take months.

Conductor is designed to automate much of that process.

Instead of starting with prompts alone, the platform begins with discovery. It reviews documentation, analyzes platform insights, examines customer interactions, and gathers business context before proposing a structured blueprint for the AI agent.

Developers then review and approve that blueprint before development begins.

The approach mirrors software engineering best practices, where architecture and requirements are validated before code is written.

According to Cresta, this reduces development errors and creates a more predictable path to deployment.

How Conductor Works

At the core of Conductor is a workflow that spans the entire AI agent lifecycle, from planning to post-launch optimization.

The first stage focuses on discovery and blueprint creation.

Conductor reviews enterprise documentation, knowledge bases, customer conversations, and existing system data to understand the intended use case. It can also ask developers clarifying questions to gather additional context before generating a comprehensive development plan.

Once approved, Conductor automatically generates the components needed to build the agent.

This includes prompt logic, sub-agent orchestration, configurations, integrations, and custom code required for deterministic actions such as payment processing, account updates, or reservation management.

That distinction is important because enterprise AI agents increasingly rely on more than conversational capabilities. They must execute real business actions safely and reliably.

Rather than simply generating responses, modern customer service agents are expected to complete tasks.

Conductor's architecture reflects that reality.

Testing Before Customers Ever See It

One of the biggest challenges facing enterprise AI deployments is quality assurance.

Unlike traditional software, AI systems can behave unpredictably when exposed to new customer inputs or edge-case scenarios.

To address that issue, Conductor integrates directly with Cresta's Testing Suite and Synthetic Customers platform.

The system automatically generates testing scenarios based on the approved blueprint and runs simulations before deployment.

If failures occur, Conductor identifies the underlying issue, proposes fixes, and re-tests the agent until it reaches predefined performance thresholds.

This automated feedback loop could significantly reduce the time developers spend manually validating AI behaviors.

As enterprises become more cautious about deploying customer-facing AI, testing infrastructure is increasingly becoming a competitive differentiator among AI platform vendors.

Companies are realizing that deployment speed matters far less than deployment reliability.

Post-Launch Optimization Built In

The challenge does not end once an AI agent goes live.

Customer interactions constantly evolve, products change, and new edge cases emerge over time.

Conductor includes post-launch monitoring and diagnostics designed to address those realities.

When issues surface in production, the system reviews customer transcripts, identifies root causes, and generates a prioritized list of recommendations.

For routine issues, Conductor can autonomously implement fixes, validate the results, and present proposed updates for developer approval before changes are deployed.

The approach resembles emerging AI-assisted software development workflows, where AI systems not only generate code but also participate in debugging, testing, monitoring, and optimization.

In effect, Conductor functions as both an AI developer and an AI operations assistant.

Why This Matters

The launch highlights a broader trend in enterprise AI: the rise of AI systems designed to create and manage other AI systems.

As organizations scale their AI investments, manually building and maintaining thousands of specialized agents becomes increasingly impractical.

Industry leaders including OpenAI, Microsoft, Salesforce, Google, and Anthropic have all emphasized agentic AI as the next major phase of enterprise adoption. However, creating reliable, business-ready agents remains a significant bottleneck.

The market is now shifting from agent creation to agent operations.

Questions around governance, testing, monitoring, orchestration, and optimization are becoming just as important as model performance itself.

Cresta's Conductor enters the market at a time when enterprises are searching for ways to accelerate AI deployment without sacrificing control.

By combining blueprint generation, automated development, testing, diagnostics, and optimization into a single workflow, the company is attempting to reduce the complexity associated with enterprise AI rollouts.

The Bigger Picture

The emergence of platforms like Conductor signals a new phase in enterprise AI adoption.

The first wave focused on building AI assistants. The second wave focused on deploying AI agents. The next wave may focus on AI systems that build, govern, and optimize those agents automatically.

For enterprises facing pressure to deploy customer-facing AI at scale, that evolution could prove essential.

As organizations increasingly treat AI agents as part of their operational infrastructure, the tools used to build and maintain those agents may become just as valuable as the agents themselves.

With Conductor, Cresta is positioning itself squarely in that emerging category—where AI doesn't just assist developers but actively helps create the next generation of enterprise AI systems

Get in touch with our MarTech Experts

As AI agents move from experimental tools to operational systems, marketing platforms are facing a critical question: should they build proprietary AI assistants or become the infrastructure those assistants rely on?

Cordial is betting on the latter.

The enterprise marketing platform, used by brands including Levi's, Tapestry, L.L.Bean, and Boot Barn, has launched a new AI-focused headless infrastructure designed to expose every major platform capability as a service that AI agents can access and execute. Rather than introducing another chatbot-style interface, Cordial is opening its underlying marketing engine through standards-based integrations, positioning itself as a foundational layer for the emerging agentic AI ecosystem.

The launch includes support for Model Context Protocol (MCP), command-line tools, APIs, and developer resources that allow organizations to connect both internal and external AI agents directly to Cordial's marketing infrastructure.

The announcement reflects a broader shift underway across enterprise software. While many marketing technology vendors are racing to add generative AI features, a growing number of organizations are looking beyond simple content generation toward autonomous systems capable of executing workflows across multiple platforms.

Cordial's latest move suggests the company believes the future belongs not to standalone AI assistants, but to interoperable AI systems that can operate across an organization's entire technology stack.

A Different Take on AI in Marketing

Most marketing technology vendors have approached AI by layering conversational experiences on top of existing products. Users ask questions, generate content, or receive recommendations through a chat interface.

Cordial argues that approach only addresses part of the problem.

Marketing operations remain highly fragmented. Customer data lives in one platform, campaign execution in another, analytics elsewhere, and loyalty or commerce systems in separate environments. AI may automate tasks within each platform, but without shared infrastructure, coordination challenges remain.

According to Cordial CEO Jeremy Swift, accelerating isolated systems simply creates bottlenecks faster.

"The next era of marketing won't be won by whoever ships the most agents. It'll be won by the platform agents can actually build on," Swift said.

Instead of creating another closed AI ecosystem, Cordial is exposing audience management, content generation, campaign execution, reporting, and brand governance as reusable services accessible through standard interfaces.

In practical terms, that means AI agents built inside Cordial, as well as agents developed externally on commerce platforms, customer data systems, data warehouses, or custom applications, can interact with the same capabilities.

The goal is to turn Cordial into a connected node within a broader AI-driven marketing architecture rather than a destination where all work must occur.

What Cordial Is Launching

At the center of the announcement is support for Model Context Protocol (MCP), an increasingly popular framework emerging as a standard for connecting AI agents with enterprise systems and external tools.

Through MCP integration, AI agents can directly access Cordial services regardless of where they are built or deployed.

The company is also launching a Command Line Interface (CLI), giving developers a programmable way to run marketing operations from scripts, automation frameworks, and existing engineering workflows.

Another major component is Context Services, which provides AI agents with access to organizational knowledge, brand guidelines, customer data, product information, and creative assets.

Rather than relying on generic prompts or disconnected data sources, agents can operate with business-specific context from the start.

The result, according to Cordial, is AI-generated output that is grounded in actual business rules and customer understanding instead of generalized assumptions.

The platform launch also includes reporting capabilities designed specifically for AI-assisted analysis.

Marketing teams can use natural-language queries to retrieve campaign performance insights, understand audience behavior, validate segmentation assumptions, and analyze customer overlap without relying on technical teams or manual reporting processes.

For enterprise marketers increasingly expected to make real-time decisions, this could reduce the lag between campaign execution and performance analysis.

Building for the Agent Economy

Perhaps the most significant aspect of the launch is Cordial's decision to remain LLM-agnostic.

The company says its infrastructure is designed to work across AI models rather than being tied to a single provider or ecosystem.

That flexibility is becoming increasingly important as enterprises seek to avoid vendor lock-in while experimenting with multiple foundation models from providers such as OpenAI, Anthropic, Google, Meta, and others.

By abstracting infrastructure from the underlying model layer, Cordial hopes customers can continue evolving their AI strategies without rebuilding workflows whenever a new model gains traction.

This architecture aligns with a growing trend across enterprise technology, where organizations are shifting focus from individual AI models to orchestration frameworks capable of coordinating multiple models, tools, and data sources.

In that environment, infrastructure often becomes more valuable than any single model.

Real-World Agents Already in Production

To demonstrate the capabilities of its new architecture, Cordial highlighted two AI agents already operating on the platform.

The first, Email Production Agent, automates campaign execution from personalization and audience selection to orchestration and performance measurement. Before execution, outputs are validated against real customer profiles and business rules.

The second, Data Intelligence Agent, continuously monitors audience and campaign performance, identifies emerging issues, and recommends corrective actions while campaigns remain active.

Unlike traditional reporting systems that surface insights after a campaign concludes, the agent is designed to support in-flight optimization.

Both agents operate within what Cordial describes as a governed execution framework, incorporating quality controls, retry mechanisms, compliance safeguards, and brand-specific rules.

That emphasis on governance reflects a growing concern among enterprise marketers. While AI agents promise efficiency gains, organizations remain cautious about granting autonomous systems unrestricted access to customer communications and revenue-generating workflows.

The company says these safeguards are powered by its proprietary Context Graph, which combines customer, product, and messaging intelligence to provide the contextual understanding needed for accurate decision-making.

Why This Matters for MarTech

The launch highlights an important evolution in marketing technology.

For years, vendors competed primarily on features, channels, and user interfaces. AI is changing that equation.

Increasingly, competitive advantage may come from how well platforms integrate into agent-driven ecosystems rather than how many standalone features they offer.

Companies such as Salesforce, Adobe, HubSpot, Oracle, and Braze have all expanded their AI investments over the past year. Many have introduced agents, copilots, and autonomous workflow capabilities designed to automate marketing operations.

Cordial's approach differs by focusing on accessibility and infrastructure rather than exclusively on proprietary AI experiences.

If the broader AI market continues moving toward interconnected agent networks, open standards such as MCP could become as important to marketing technology as APIs became during the cloud computing era.

The Bigger Picture

Cordial's headless infrastructure launch represents more than a new developer toolkit. It signals a strategic shift toward a future where AI agents become first-class users of enterprise software.

Rather than forcing organizations to work inside a predefined AI environment, the company is positioning its marketing capabilities as modular services that can be orchestrated by any agent, application, or workflow.

For enterprises building long-term AI strategies, that flexibility could prove valuable as agent ecosystems continue evolving.

The marketing platforms that thrive in the next phase of AI adoption may not be the ones with the flashiest assistants. They may be the ones that become indispensable infrastructure for every assistant that follows.

Get in touch with our MarTech Experts

As generative AI rapidly reshapes creative workflows, Shutterstock is betting that creators and brands want more than another AI image generator. The company has unveiled a major overhaul of its platform, transforming its vast stock content marketplace into what it calls a human-led, AI-powered creative ecosystem.

The update combines Shutterstock’s extensive library of licensed, contributor-created content with integrated AI generation, editing, discovery, and workflow tools designed to help marketers, designers, and content teams move from concept to finished asset without jumping between multiple applications.

The move reflects a broader shift across the creative technology industry. While companies such as Adobe, Canva, Getty Images, and OpenAI continue expanding AI-powered creative offerings, enterprise customers increasingly want unified platforms that balance speed, creative control, legal protection, and brand consistency.

Rather than positioning AI as a replacement for human creativity, Shutterstock is presenting it as an enhancement layer built on top of authentic, rights-cleared content.

“Businesses do not need disconnected AI tools. They need creative systems that work together,” said Paul Teall, Vice President of Marketplace Strategy at Shutterstock.

A Creative Workflow Built Around AI

At the center of the launch is a redesigned creative workflow that merges content discovery, AI generation, editing, and asset refinement into a single environment.

Users can start with Shutterstock’s existing content library, then modify, enhance, or transform assets using integrated AI tools. The company says creators can adapt existing content instead of generating everything from scratch, a workflow that could appeal to brands seeking efficiency while maintaining visual consistency.

One of the standout additions is Model Match, Shutterstock’s proprietary technology that automatically routes prompts to the generative AI model best suited for a particular task. As the number of AI models continues to multiply, choosing the right one has become a growing challenge for creative teams. Shutterstock aims to remove that complexity by handling model selection behind the scenes.

The platform also introduces conversational AI search, allowing users to discover images, videos, and creative assets through natural-language queries rather than traditional keyword searches.

For marketers and creative professionals managing brand consistency, Shutterstock has added content reference and first-frame reference capabilities. These tools allow users to build AI-generated outputs from existing assets, helping maintain visual continuity across campaigns and creative projects.

Additional features include:

• AI-powered image and video generation

• Prompt enhancement tools for richer and more detailed queries

• Integrated AI editing capabilities

• Natural-language content discovery

• Human creative support services

• Commercial licensing and indemnification protections for AI-generated content

Why Shutterstock’s Contributor Model Matters

One of the more notable aspects of Shutterstock’s AI strategy is its continued emphasis on creator compensation.

As debates around AI training data, copyright protection, and creator rights intensify, Shutterstock says contributors will continue earning royalties when their content is modified through AI tools and licensed via the platform.

That approach differs from some AI providers that have faced criticism and legal scrutiny over how training data is sourced. Shutterstock has spent the past several years positioning itself as a rights-cleared AI partner, arguing that commercially viable AI systems require transparent licensing and clear data provenance.

For enterprise customers concerned about legal exposure, that distinction could become increasingly important as AI-generated content moves from experimentation into mainstream marketing and advertising campaigns.

Competing in an Increasingly Crowded AI Creative Market

The launch comes at a time when nearly every major creative software vendor is racing to integrate generative AI.

Adobe continues expanding Firefly across Creative Cloud applications. Canva has embedded AI generation throughout its design suite. Getty Images has introduced its own commercially safe AI offerings, while OpenAI, Google, and numerous startups are pushing increasingly capable image and video generation models.

Shutterstock’s differentiator appears to be integration rather than model development alone.

Instead of competing solely on AI output quality, the company is focusing on workflow efficiency, trusted content, licensing protection, and access to multiple AI models through a single interface.

That strategy may resonate with enterprise marketing teams that care less about experimenting with the newest model and more about producing campaign-ready assets quickly and safely.

Beyond Content Creation

The announcement also highlights Shutterstock’s broader ambitions in the AI ecosystem.

Alongside its creative platform, the company continues to expand its Data Licensing & AI Services business, which provides training data, model evaluation, fine-tuning, and human-in-the-loop services for AI developers.

The business gives Shutterstock exposure to two rapidly growing AI markets: creative production and AI infrastructure.

The company says it offers access to one of the world’s largest rights-cleared multimodal datasets, alongside data curation, evaluation tools, preference modeling, benchmarking, and continuous model improvement services.

As AI developers face increasing pressure to improve model performance while maintaining compliance and transparency, demand for high-quality licensed datasets and human evaluation services is expected to grow.

The Bigger Picture

Shutterstock’s latest platform overhaul signals a significant evolution from stock media provider to AI-powered creative technology company.

The company is attempting to solve one of the biggest challenges facing modern marketing and creative teams: how to harness AI’s speed without sacrificing authenticity, brand control, or legal confidence.

Whether that approach proves more compelling than standalone AI tools remains to be seen. But as organizations increasingly move AI projects from experimentation to production, platforms that combine trusted content, workflow integration, and commercial safeguards may gain an advantage over tools focused solely on generation.

For Shutterstock, the goal is clear: become the bridge between human creativity and artificial intelligence rather than choosing one over the other.

Get in touch with our MarTech Experts

As community banks and credit unions seek new ways to deepen customer relationships and compete against larger financial institutions, digital marketing agency BankBound has announced a strategic partnership with BOND.AI. The collaboration will combine BankBound's financial services marketing expertise with BOND.AI's artificial intelligence-powered customer intelligence platform, enabling financial institutions to transform transaction data into highly personalized marketing campaigns designed to drive deposits, lending growth, and customer engagement.

Personalization has become one of the most important priorities in modern financial services marketing. Yet many banks and credit unions continue to struggle with a common challenge: they possess vast amounts of customer transaction data but lack the technology and operational frameworks needed to convert those insights into meaningful marketing actions.

A new partnership between BankBound and BOND.AI aims to solve that problem.

The companies announced a strategic alliance that integrates BOND.AI's Autopilot platform and patented Empathy Engine® into BankBound's marketing services portfolio. The partnership is designed to help financial institutions move beyond traditional demographic-based marketing and toward AI-driven engagement strategies powered by real-time customer behavior and transaction intelligence.

The announcement reflects a broader transformation occurring across the banking industry.

Financial institutions increasingly recognize that customers expect the same level of personalization from banks that they receive from digital-native companies such as Amazon, Netflix, and Spotify. As consumer expectations evolve, banks are investing heavily in data analytics, AI-driven personalization, and customer engagement technologies to remain competitive.

However, community banks and credit unions often face unique challenges compared to large national institutions.

Many smaller financial organizations have access to extensive customer data through core banking systems and transaction histories, but limited resources to operationalize those insights across marketing channels. As a result, marketing campaigns frequently rely on broad audience segments and generalized messaging rather than individualized customer experiences.

The BankBound-BOND.AI partnership seeks to bridge this gap.

At the center of the collaboration is BOND.AI's Autopilot platform, which analyzes transaction-level behavior to identify customer needs, financial goals, and product opportunities. Powered by the company's Empathy Engine®, the system is designed to detect signals that may indicate a customer's readiness for products such as loans, deposit accounts, savings solutions, or other financial services.

These insights can then be activated through the marketing channels managed by BankBound, including email marketing automation, digital advertising campaigns, and customer engagement programs.

The approach aligns with a growing industry trend toward predictive and intent-based marketing.

Rather than targeting customers based solely on demographics or historical account information, financial institutions are increasingly using behavioral data and AI models to anticipate customer needs and deliver more relevant communications. Industry analysts at Gartner and Forrester have identified hyper-personalization and predictive engagement as key strategic priorities for financial services organizations seeking to improve customer retention and lifetime value.

For banks, the business case is compelling.

Personalized marketing not only improves customer experience but can also increase cross-sell opportunities, strengthen loyalty, and improve marketing efficiency. By delivering relevant offers at the right moment, financial institutions can reduce wasted advertising spend while increasing conversion rates and product adoption.

The partnership also highlights the growing role of artificial intelligence in banking marketing operations.

Across the industry, institutions are deploying AI to enhance customer segmentation, automate marketing workflows, improve campaign optimization, and uncover growth opportunities hidden within customer data. Major financial institutions and technology providers such as Salesforce, Microsoft, and Adobe continue to expand AI-powered capabilities aimed at helping organizations deliver more contextual customer experiences.

What differentiates the BankBound partnership is its focus on community financial institutions.

Unlike large enterprises with dedicated analytics teams and sophisticated martech infrastructures, many regional banks and credit unions require practical solutions that can be integrated into existing marketing programs without major operational disruption.

By combining AI-driven customer intelligence with managed marketing execution, the partnership creates a model that allows institutions to benefit from advanced personalization without building extensive internal technology resources.

As competition for deposits intensifies and customer acquisition costs continue to rise, data-driven engagement strategies are becoming increasingly important for financial institutions seeking sustainable growth.

The collaboration between BankBound and BOND.AI illustrates how AI-powered customer intelligence is evolving from an emerging technology into a practical business tool capable of helping banks strengthen relationships, improve marketing performance, and deliver more relevant financial experiences at scale.

Market Landscape

The financial services marketing sector is rapidly embracing artificial intelligence, customer data analytics, and predictive engagement technologies. According to research from Gartner, Forrester, and McKinsey & Company, personalization has emerged as one of the strongest drivers of customer satisfaction, retention, and revenue growth in banking.

At the same time, community banks and credit unions face increasing competition from digital-first banks, fintech providers, and large national institutions. This environment is accelerating investment in marketing technologies that help organizations leverage existing customer data to create more relevant, timely, and measurable customer interactions.

AI-powered customer intelligence platforms are expected to play an increasingly central role in helping financial institutions deliver personalized experiences while improving operational efficiency and marketing ROI.

Top Insights

• BankBound and BOND.AI formed a strategic partnership focused on AI-powered customer personalization for banks and credit unions.
• The collaboration integrates BOND.AI's Autopilot platform and Empathy Engine® with BankBound's marketing services.
• Financial institutions can leverage transaction-level data to identify customer needs and deliver more relevant offers.
• The partnership supports predictive marketing strategies aimed at increasing deposits, lending activity, and customer engagement.
• AI-driven personalization is becoming a key competitive differentiator across the banking and financial services sector.

Get in touch with our MarTech Experts

As marketing leaders face increasing pressure to justify spending and demonstrate business impact, marketing operations platform Uptempo is using the Gartner Marketing Symposium/Xpo 2026 stage to address one of the industry's most persistent challenges: proving marketing ROI in the age of artificial intelligence. The company's panel discussion, featuring executives from IBM, Indeed, and AT&T, will explore why fragmented marketing data remains a major barrier to AI adoption and measurable business outcomes.

Artificial intelligence has become the centerpiece of modern marketing transformation strategies, yet many organizations continue to struggle with a challenge that predates generative AI: proving marketing's contribution to business growth.

At the Gartner Marketing Symposium/Xpo 2026, marketing operations provider Uptempo is bringing together senior leaders from some of the world's largest organizations to discuss how marketers can rebuild data foundations, improve decision-making, and unlock more reliable ROI measurement in an increasingly AI-driven environment.

The panel session, titled "The ROI Blueprint: Rebuilding Marketing for the AI Era," comes at a time when chief marketing officers face growing scrutiny from boards, CEOs, and CFOs regarding marketing efficiency, budget allocation, and revenue contribution.

While AI tools promise unprecedented levels of automation, predictive analytics, and operational intelligence, industry experts argue that many organizations are attempting to build AI capabilities on top of fragmented and disconnected data ecosystems.

According to Uptempo Chief Marketing Officer Marie Bahl, AI alone cannot solve marketing's accountability challenges.

The company's central argument is that trustworthy AI outcomes require trustworthy data foundations. Without unified planning, budgeting, performance, and spend management systems, AI models may generate insights that lack context, consistency, and strategic value.

This concern is increasingly echoed across the marketing technology industry.

Research from Gartner consistently identifies data quality, governance, and integration challenges as major obstacles to successful AI implementation. Similarly, analysts at Forrester have emphasized that organizations must establish strong data governance frameworks before expecting meaningful returns from AI investments.

The panel brings together executives from some of the world's most influential enterprise brands, including IBM, Indeed, and AT&T. Their participation reflects the growing importance of marketing operations, performance management, and data-driven decision-making across industries.

Among the topics expected to dominate the discussion is the challenge of reconciling multiple marketing data streams without requiring costly infrastructure replacements.

For many enterprises, marketing information remains distributed across customer relationship management systems, analytics platforms, advertising networks, budgeting applications, content management systems, and campaign execution tools. This fragmentation often makes it difficult to create a unified view of performance or establish clear attribution models.

The emergence of agentic AI introduces both opportunities and risks.

Proponents argue that AI agents can automate analysis, identify optimization opportunities, and accelerate decision-making. However, many experts caution that AI systems are only as effective as the data they access. When underlying datasets are incomplete, duplicated, or inconsistent, AI-driven recommendations may amplify existing inefficiencies rather than resolve them.

The panel will also address a broader strategic issue: marketing's role within executive leadership teams.

Despite decades of investment in analytics, attribution modeling, and performance measurement, many marketing leaders continue to struggle to communicate their business impact in financial terms. This challenge has contributed to ongoing debates regarding marketing's influence within the C-suite compared to functions such as finance, operations, and technology.

Industry analysts suggest that solving this problem requires more than better dashboards.

Increasingly, organizations are moving toward decision intelligence frameworks that combine data integration, business context, predictive analytics, and AI-driven recommendations to support strategic planning. These systems aim to move beyond reporting what happened and instead help leaders understand why outcomes occurred and what actions should follow.

Uptempo's vision aligns with this trend through its focus on Decision Intelligence, a marketing-specific approach that integrates planning, budgeting, spend management, and performance data into a unified decision-making environment.

The concept reflects a broader evolution within marketing technology.

As AI adoption accelerates, competitive advantage is shifting away from simply deploying AI tools and toward building the information architecture necessary to support intelligent decision-making. Many industry observers now argue that organizations that prioritize data governance, integration, and operational alignment will be better positioned to realize AI's full value.

For marketing leaders attending Gartner Marketing Symposium/Xpo 2026, the discussion represents an opportunity to examine a question that continues to define the future of the profession: how to transform marketing from a cost center frequently defending its budget into a strategic growth function capable of demonstrating measurable business value.

Market Landscape

The marketing technology industry is entering a new phase where AI adoption is increasingly dependent on data quality and operational maturity rather than tool availability. According to Gartner, Forrester, and IDC, enterprises are prioritizing data governance, measurement frameworks, and cross-functional integration as foundational requirements for AI success.

At the same time, growing economic uncertainty and budget scrutiny are increasing demand for marketing accountability. Organizations are investing in marketing operations platforms, decision intelligence solutions, and performance management technologies that can provide greater visibility into ROI and business impact.

The convergence of AI, data governance, and financial accountability is expected to become one of the defining themes shaping marketing leadership over the next decade.

Top Insights

• Uptempo is leading a Gartner Marketing Symposium panel focused on marketing ROI, AI readiness, and data governance.
• Executives from IBM, Indeed, and AT&T will discuss how fragmented marketing data limits AI effectiveness.
• Marketing leaders face increasing pressure from boards and executive teams to demonstrate measurable business impact.
• Agentic AI adoption is accelerating, but organizations continue to struggle with disconnected data ecosystems.
• Decision Intelligence is emerging as a potential framework for connecting planning, budgeting, spend, and performance data into actionable insights.

Get in touch with our MarTech Experts

As organizations seek to modernize enterprise resource planning (ERP) environments without undertaking costly system overhauls, ERPVAR and Zaptiva have announced a strategic partnership aimed at simplifying ERP integration and workflow automation. The alliance combines ERPVAR's extensive ERP consulting ecosystem with Zaptiva's low-code, AI-driven automation platform, enabling mid-market businesses to automate complex operational processes while reducing dependency on custom development and legacy middleware.

Enterprise resource planning systems remain the operational backbone of many organizations, yet integrating them with modern business applications continues to be a significant challenge for mid-market companies. Legacy ERP environments often require expensive custom coding, fragmented middleware solutions, and extensive manual intervention to support evolving business workflows.

A newly announced partnership between ERPVAR and Zaptiva seeks to address this challenge through a combination of low-code automation, artificial intelligence, and streamlined ERP connectivity.

The alliance brings together ERPVAR's network of ERP consultants and value-added resellers (VARs) with Zaptiva's automation and data transformation platform. The goal is to help organizations modernize operational workflows while enabling ERP consultants to expand their role beyond implementation services into broader digital transformation and automation advisory engagements.

The announcement reflects a growing trend across the enterprise software market. Organizations increasingly expect ERP platforms to function as connected ecosystems rather than standalone systems of record. As businesses adopt cloud applications, digital workflows, and AI-powered business processes, seamless data movement between systems has become a strategic requirement.

According to industry analysts at Gartner, integration complexity remains one of the primary barriers to successful digital transformation initiatives. Many organizations continue to struggle with disconnected systems, duplicate data entry, inconsistent workflows, and delayed business processes.

Zaptiva's platform is designed to simplify these challenges through a low-code architecture that enables organizations to automate transaction-level workflows without extensive development resources. The company combines automation capabilities with AI-powered data extraction, transformation, and validation tools that can process information from multiple formats, including PDFs, spreadsheets, emails, and business applications.

One of the most significant aspects of the partnership is its focus on operational efficiency.

According to benchmarks shared by Zaptiva, organizations can significantly reduce transaction processing times by automating data validation, mapping, and synchronization tasks that traditionally require manual intervention. The company reports that workflows previously taking up to seven days to complete can potentially be compressed into less than 24 hours through automated processing and validation mechanisms.

This aligns with broader enterprise technology trends where AI is increasingly being used to improve operational resilience and eliminate repetitive manual processes.

Technology leaders such as Microsoft, Salesforce, Oracle, and SAP continue to expand AI-powered automation capabilities across enterprise ecosystems as businesses seek greater efficiency and operational visibility.

For ERP consulting firms, the partnership introduces additional revenue opportunities beyond software implementation services.

ERPVAR's network includes more than 2,000 consulting firms that support organizations across various ERP environments. Through the partnership, these consultants gain access to Zaptiva's partner ecosystem, recurring revenue opportunities, and automation-focused service offerings that align with growing customer demand for business process modernization.

The collaboration also emphasizes education and market enablement. Both companies plan to deliver webinars, technical training sessions, and educational content focused on ERP optimization, workflow automation, AI adoption, and digital transformation strategies for mid-market businesses.

Industry experts note that the demand for automation is particularly strong among mid-market organizations. Unlike large enterprises that often have dedicated development teams and extensive technology budgets, mid-sized businesses frequently require solutions that can deliver enterprise-grade capabilities without significant implementation costs.

Low-code platforms have emerged as a popular solution to this challenge because they reduce development complexity while accelerating deployment timelines. When combined with AI-powered automation, these platforms can help organizations improve operational efficiency, data quality, and business agility without replacing existing ERP investments.

The partnership between ERPVAR and Zaptiva reflects a larger movement across enterprise technology toward composable business architectures, where organizations extend and enhance existing systems through integrations, automation layers, and intelligent workflows rather than pursuing costly full-system replacements.

As AI adoption accelerates and businesses seek greater return on ERP investments, partnerships that simplify automation and integration are likely to play an increasingly important role in helping organizations modernize operations while maintaining cost efficiency.

Market Landscape

The global ERP market continues to evolve as organizations seek ways to connect traditional systems with modern cloud applications, automation platforms, and AI-powered workflows. According to Gartner and IDC, integration and workflow automation remain among the top priorities for enterprise technology leaders pursuing digital transformation initiatives.

Simultaneously, the low-code and no-code market is experiencing rapid growth as businesses seek faster and more cost-effective approaches to application development and process automation. AI-powered automation platforms are becoming a critical component of modern enterprise architectures, enabling organizations to improve efficiency while reducing manual workloads.

For mid-market organizations, the combination of ERP modernization, workflow automation, and AI-driven data processing represents one of the most significant opportunities for operational improvement over the next several years.

Top Insights

• ERPVAR and Zaptiva announced a strategic alliance focused on ERP integration and AI-powered workflow automation.
• The partnership combines ERPVAR's consultant ecosystem with Zaptiva's low-code automation and data transformation platform.
• Organizations can automate transaction-level workflows without relying on extensive custom development or traditional middleware solutions.
• AI-powered data extraction and validation capabilities help improve data quality and reduce manual processing requirements.
• The collaboration supports growing demand for ERP modernization, digital transformation, and operational automation among mid-market businesses.

Get in touch with our MarTech Experts

Public relations and legal marketing agency Furia Rubel Communications has announced that Jennifer Simpson Carr, Vice President of Strategic Development, has been recognized in the 2026 PRNEWS Top Women in PR & Communications Awards. Named a "Change-Maker," Carr was honored for her contributions to business development, strategic communications, and reputation management at a time when organizations are increasingly navigating complex market shifts, digital transformation, and evolving stakeholder expectations.

Recognition programs across the public relations industry increasingly highlight professionals who are driving innovation, leadership, and measurable business impact. This year's PRNEWS Top Women in PR & Communications Awards spotlighted individuals helping shape the future of communications, marketing, and reputation management, including Jennifer Simpson Carr of Furia Rubel Communications.

Carr, who serves as Vice President of Strategic Development at the agency, was recognized in the "Change-Maker" category during an awards ceremony held in New York City. The distinction acknowledges communications leaders whose work is influencing organizational growth, strategic transformation, and industry advancement.

The recognition comes as public relations professionals face a rapidly evolving landscape shaped by artificial intelligence, digital media fragmentation, changing client expectations, and heightened demands for reputation resilience. Business leaders increasingly rely on strategic communications partners not only for brand visibility but also for crisis preparedness, stakeholder engagement, and long-term reputation management.

At Furia Rubel Communications, Carr plays a central role in connecting market intelligence with business strategy. Her responsibilities include managing client relationships, identifying emerging industry trends, supporting business development initiatives, and helping organizations align communications efforts with broader corporate objectives.

The growing importance of strategic development within communications agencies reflects wider industry trends. According to research from Gartner and Forrester, organizations increasingly view brand reputation, customer trust, and thought leadership as critical drivers of competitive differentiation. As a result, communications leaders are being asked to contribute directly to business growth strategies rather than operating solely as marketing support functions.

Carr's professional background spans business development, digital marketing, legal industry communications, and content strategy. In addition to leading strategic development efforts, she serves as executive producer of Furia Rubel's award-winning podcast, On Record PR, which explores communications, reputation management, and legal industry topics.

Her recognition also highlights the growing intersection between public relations and digital transformation. Today's communications professionals must navigate a landscape where earned media, content marketing, social engagement, search visibility, executive thought leadership, and crisis response increasingly operate as interconnected disciplines.

Industry observers note that modern public relations success depends on the ability to combine traditional reputation-building expertise with data-driven decision-making and digital communications strategies. This shift has elevated the role of communications leaders who can bridge business objectives with emerging technologies and changing audience behaviors.

Carr is also widely recognized within the legal marketing sector, where she regularly contributes insights on business development and industry trends. Her involvement with the Legal Marketing Association reflects the growing importance of specialized communications expertise within highly regulated and professional services industries.

The legal sector, in particular, has become increasingly competitive as firms invest in brand differentiation, thought leadership, client experience, and digital marketing initiatives. Strategic communications professionals play a critical role in helping organizations navigate these shifts while maintaining credibility and trust among clients and stakeholders.

Educationally, Carr has pursued advanced studies across communications, marketing, digital transformation, and executive leadership. Her credentials include programs from Rutgers Business School, Columbia Business School, and The University of Texas at Dallas, reflecting the increasing demand for multidisciplinary expertise within modern communications leadership roles.

For Furia Rubel Communications, the recognition reinforces the agency's position within the legal marketing and corporate communications sector. The firm specializes in crisis communications, litigation communications, business development strategy, content marketing, and public relations services for professional services organizations, government entities, financial institutions, and legal technology companies.

More broadly, the award underscores an ongoing industry evolution in which communications professionals are becoming key strategic advisors within organizations. As business environments become more complex and reputation risks emerge more rapidly, communications leaders are expected to contribute not only to visibility and awareness but also to growth, resilience, and long-term business success.

The PRNEWS recognition reflects this shift, highlighting professionals whose influence extends beyond traditional public relations functions and into the broader strategic direction of organizations and industries.

Market Landscape

The global public relations and communications industry continues to evolve as organizations increase investments in reputation management, executive visibility, crisis preparedness, and digital engagement. According to industry research from Gartner, Forrester, and the Public Relations Society of America, communications teams are increasingly integrating data analytics, AI-driven insights, and content strategies into broader business operations.

Professional services sectors such as legal, financial, and consulting organizations are among the fastest adopters of strategic communications programs, reflecting growing competition and heightened emphasis on trust, expertise, and brand authority.

Top Insights

• Jennifer Simpson Carr was recognized as a 2026 PRNEWS Top Women in PR & Communications "Change-Maker."
• Carr leads strategic development, business growth initiatives, and market intelligence efforts at Furia Rubel Communications.
• The recognition highlights the growing strategic role communications professionals play in business development and reputation management.
• Legal marketing and professional services communications continue to evolve as firms invest in brand differentiation and thought leadership.
• Modern PR leadership increasingly combines traditional communications expertise with digital marketing, analytics, and strategic business planning.

Get in touch with our MarTech Experts

Healthcare marketing platform Doceree is introducing a new intelligence layer designed to help pharmaceutical brands engage healthcare professionals based on real-time clinical behavior rather than historical prescribing patterns. Called Clinical Intent Signals (CIS), the technology aims to provide healthcare marketers with a new way to identify and activate physician intent across digital channels, potentially reshaping how omnichannel healthcare campaigns are planned, targeted, and measured.

The healthcare marketing industry has long faced a fundamental challenge: understanding what healthcare professionals are considering today rather than what they prescribed weeks or months ago.

Doceree believes it has a solution.

The company announced the launch of Clinical Intent Signals (CIS), a real-time intelligence layer designed to detect, interpret, and activate physician intent signals across healthcare engagement channels. The technology will become commercially available through Doceree's upcoming Daily Command Marketplace, scheduled to launch publicly on July 14, 2026.

The announcement represents an important development in healthcare marketing technology because it introduces intent-based marketing capabilities to a sector that has historically relied on retrospective data, static audience segmentation, and delayed prescribing insights.

In consumer and B2B marketing, intent data has become a foundational component of audience targeting and campaign orchestration. Marketers routinely use behavioral signals such as search activity, content consumption, website engagement, and purchase research patterns to identify buyers who are actively evaluating products or services.

Healthcare marketing has largely been excluded from this transformation.

Regulatory requirements, privacy constraints, fragmented data ecosystems, and the complexity of clinical workflows have traditionally limited marketers' ability to identify real-time physician decision-making signals.

Doceree's Clinical Intent Signals platform seeks to bridge that gap.

According to the company, CIS analyzes digital interactions associated with healthcare professional research and clinical evaluation processes, including medical content consumption, guideline lookups, journal engagement, workflow interactions, and therapeutic-area research activity. These signals are then translated into actionable intent stages that can be activated across advertising, email marketing, field sales engagement, physician education initiatives, and patient support programs.

The company emphasizes that the system operates within privacy-first and PHI-compliant frameworks, relying on verified healthcare professional-level signals rather than patient-level information.

For healthcare marketers, the implications could be significant.

Traditional healthcare marketing often relies heavily on prescription data, claims information, and historical physician behavior to identify potential audiences. While valuable, those datasets frequently reflect actions that have already occurred. Clinical Intent Signals is designed to identify decision-making activity earlier in the process, potentially enabling pharmaceutical brands to engage physicians before prescribing patterns become visible through conventional reporting systems.

This shift reflects a broader trend occurring across enterprise marketing technology.

Major platforms from Salesforce, Adobe, Google, and Microsoft have increasingly focused on intent-driven customer engagement models that enable organizations to personalize messaging based on real-time behavioral signals.

Doceree is applying a similar philosophy to healthcare marketing.

The launch is also closely tied to Daily Command, the company's new operational platform designed to serve as a centralized command layer for healthcare marketers. Through the Daily Command Marketplace, organizations will be able to integrate Clinical Intent Signals with existing demand-side platforms (DSPs), customer relationship management systems, marketing automation platforms, sales engagement tools, and omnichannel orchestration environments.

According to Doceree, early pilot results suggest measurable performance improvements.

The company reported findings from a 12-week pilot involving approximately 36,000 matched healthcare professionals across multiple therapeutic categories. Campaigns leveraging Clinical Intent Signals achieved a 38% faster progression through physician decision stages, a 27% increase in contextual engagement rates, and a 21% improvement in media efficiency compared with control groups.

While these results were generated under pilot conditions, they highlight growing interest in intent-based healthcare engagement strategies.

Industry analysts have increasingly pointed to personalization and real-time engagement as major priorities for life sciences organizations. Research from Forrester indicates that organizations leveraging behavioral intelligence and contextual engagement strategies often achieve stronger campaign performance and more efficient customer acquisition outcomes. Similarly, Gartner has identified AI-powered decision intelligence as a critical area of investment across marketing and commercial operations.

What makes the announcement particularly notable is its potential impact on omnichannel healthcare marketing.

Many pharmaceutical organizations continue to operate separate systems for digital advertising, field sales engagement, physician education, email marketing, and patient support programs. By introducing a unified intent signal that can operate across these channels, Doceree is attempting to create a more coordinated approach to healthcare engagement.

The broader opportunity extends beyond campaign performance.

As healthcare marketers seek more precise, privacy-compliant ways to engage physicians, intent intelligence could become a foundational layer within future healthcare marketing stacks. Similar to how customer data platforms transformed audience management in enterprise marketing, clinical intent platforms may emerge as a new category focused on understanding physician decision-making in real time.

If adoption grows, Clinical Intent Signals could mark the beginning of a larger shift from audience-based healthcare marketing to intent-driven healthcare engagement.

Market Landscape

Healthcare marketing technology is rapidly evolving as pharmaceutical companies seek more personalized and measurable engagement strategies. Traditional audience segmentation models are increasingly being supplemented by behavioral intelligence, AI-powered analytics, and omnichannel orchestration platforms.

According to research from Gartner and Forrester, investments in AI-enabled decision intelligence, predictive analytics, and real-time customer engagement technologies continue to accelerate across life sciences organizations. Meanwhile, the growing adoption of digital physician engagement channels is creating demand for more sophisticated targeting and measurement capabilities.

The emergence of clinical intent intelligence platforms reflects a broader industry movement toward contextual engagement models that prioritize timing, relevance, and behavioral signals over static audience classifications.

Top Insights

• Doceree launched Clinical Intent Signals, a new intelligence layer designed to identify and activate physician intent across healthcare marketing channels in real time.
• The platform enables pharmaceutical marketers to move beyond retrospective prescribing data and engage healthcare professionals during active clinical decision-making processes.
• Clinical Intent Signals integrates with advertising platforms, CRM systems, marketing automation tools, and sales engagement environments through the Daily Command Marketplace.
• Pilot programs involving 36,000 healthcare professionals demonstrated improvements in engagement, media efficiency, and progression through physician decision journeys.
• The launch introduces intent-based marketing principles to healthcare, a capability long established in B2B and consumer marketing but historically difficult to implement in regulated healthcare environments.

Get in touch with our MarTech Experts