Digital experience platform provider Optimizely has once again secured a leadership position in the 2026 Magic Quadrant for Content Marketing Platforms, published by Gartner. The recognition marks the ninth consecutive year the company has been placed in the Leaders quadrant, highlighting its continued influence in the evolving enterprise content marketing technology landscape.

Enterprise marketing teams are under mounting pressure to produce more content across more channels while maintaining consistency, governance, and measurable impact. In this environment, technology platforms that unify planning, production, and distribution have become foundational to modern marketing operations.

Against this backdrop, Optimizely announced it has been named a Leader in the 2026 Magic Quadrant for Content Marketing Platforms by Gartner—a milestone that extends the company’s leadership streak in the category to nine consecutive years.

The Magic Quadrant is one of the technology industry's most closely watched evaluations, assessing vendors on their ability to execute and completeness of vision. Sustained recognition in the Leaders quadrant signals strong product capabilities, consistent innovation, and broad enterprise adoption.

For Optimizely, the recognition reflects a broader shift occurring across marketing technology stacks: the move toward AI-driven content operations platforms that automate large portions of the marketing workflow.

AI Agents Move Into Marketing Workflows

At the center of Optimizely’s strategy is Optimizely Opal, the company’s AI orchestration platform designed to operate directly inside content marketing workflows.

Unlike earlier generations of AI tools that acted primarily as assistants or standalone generators, Opal embeds AI agents directly into the Content Marketing Platform (CMP) environment. These agents can draft content, localize assets for global markets, chain together multi-step marketing workflows, and enforce governance rules for brand consistency.

The approach reflects a growing industry trend toward autonomous marketing operations, where AI systems manage operational tasks while marketers focus on strategy and creativity.

“AI is shifting from something marketers consult to something that performs work inside the system,” said Rupali Jain, Chief Product Officer at Optimizely, describing how the platform integrates automation with enterprise governance.

The company's vision for “Autonomous Ops” aims to remove friction across the entire content lifecycle—from campaign planning and editorial collaboration to production and distribution.

Why Content Marketing Platforms Are Becoming Strategic Infrastructure

Content marketing platforms have evolved far beyond editorial planning tools. Today they function as central orchestration systems for enterprise marketing operations, integrating with analytics platforms, CRM systems, and digital experience stacks.

Major technology ecosystems—including Salesforce, Adobe, Microsoft, and Google—have increasingly embedded AI capabilities into their marketing clouds. As a result, CMP vendors are racing to differentiate through automation, workflow intelligence, and deeper data integration.

Optimizely’s platform is designed for large global enterprises across industries such as banking, healthcare, and technology. Its system consolidates content planning, collaboration, creation, and publishing into a unified workflow layer.

That integration is becoming essential as marketing teams manage an expanding number of digital touchpoints—from websites and mobile apps to social media, email campaigns, and paid advertising channels.

A Competitive and Expanding Market

The content marketing platform market is growing quickly as enterprises invest in scalable marketing infrastructure.

According to research from Gartner, marketing leaders are increasing spending on content supply chain technologies to manage complex content ecosystems. Meanwhile, Statista estimates global spending on marketing automation platforms could exceed $25 billion by 2030, fueled by AI-driven campaign management and personalization.

Within this competitive landscape, Optimizely competes with vendors offering specialized content orchestration, marketing automation, and digital experience solutions.

The company strengthened its CMP capabilities after acquiring Welcome in 2021, a platform that had already appeared in previous Magic Quadrant reports. That technology was later rebranded as Optimizely CMP, forming the backbone of the company’s content operations strategy.

The latest Gartner recognition also builds on a series of analyst acknowledgments for the company. In recent months, Optimizely was also named a Leader in the 2026 Magic Quadrant for Personalization Engines and recognized in The Forrester Wave: Digital Experience Platforms, Q4 2025 by Forrester.

What This Means for Enterprise Marketing Teams

For enterprise marketing organizations, the shift toward AI-driven content operations represents a structural change in how campaigns are executed.

Traditional marketing teams often rely on disconnected tools for planning, asset management, collaboration, and distribution. That fragmentation can slow campaign launches and introduce governance risks, especially in regulated industries.

Platforms like Optimizely’s CMP aim to consolidate these processes into a single environment where AI assists with operational execution.

In practice, that means marketers can plan campaigns, generate content drafts, coordinate global localization, and manage approvals within one platform while automated workflows handle routine tasks.

The broader implication is that content supply chains are becoming automated digital infrastructure, similar to how DevOps transformed software development pipelines.

For organizations managing high volumes of digital content across markets and channels, the ability to orchestrate these operations through AI-enabled platforms could become a competitive advantage.

As marketing technology stacks continue to consolidate, vendors capable of combining AI orchestration, governance, and enterprise workflow automation are likely to shape the next phase of the MarTech ecosystem.

Market Landscape

The content marketing platform sector sits at the intersection of marketing automation, digital experience platforms (DXPs), and AI-driven content operations. Vendors such as Adobe and Salesforce integrate content workflows into broader marketing clouds, while specialized CMP vendors focus on workflow orchestration and content supply chain management.

Industry analysts increasingly view AI-powered marketing operations as the next evolution of enterprise MarTech stacks. Research from IDC suggests that by 2027, more than 60% of enterprise marketing workflows will incorporate AI-assisted automation, accelerating campaign execution and improving personalization capabilities.

Platforms capable of combining content lifecycle management, AI orchestration, and enterprise governance are expected to become core infrastructure for global marketing organizations.

Top Insights

• Optimizely secured a Leader position in the 2026 Gartner Magic Quadrant for Content Marketing Platforms, extending a nine-year leadership streak and reinforcing its role in enterprise marketing infrastructure.

• The company’s Optimizely Opal platform introduces AI agents embedded directly into marketing workflows, enabling automated content drafting, localization, governance, and campaign orchestration.

• Content marketing platforms are evolving into enterprise content supply chain systems that unify planning, collaboration, and distribution across digital channels and global markets.

• Analysts say AI-driven marketing operations are becoming central to modern MarTech stacks as enterprises scale content production and campaign management across complex customer journeys.

• Enterprise marketing teams increasingly rely on integrated platforms that combine automation, governance, and AI orchestration to manage growing content demands across digital channels.

Get in touch with our MarTech Experts.

Enterprise data infrastructure company Nasuni has introduced a broader platform strategy aimed at helping organizations unlock the value of unstructured file data for artificial intelligence and distributed collaboration. The announcement includes new platform capabilities—Active Everywhere and AI Activate—designed to allow enterprise teams and AI systems to access governed data directly from a unified cloud-based file infrastructure.

Enterprise organizations are rapidly adopting artificial intelligence across operations, but many still struggle with a fundamental challenge: most enterprise data remains locked in unstructured files scattered across global systems.

To address this gap, Nasuni unveiled an expanded product and brand strategy focused on what it calls file data activation—the ability to turn large volumes of enterprise file data into a usable foundation for both human collaboration and AI-driven workflows.

The move signals a shift in positioning for Nasuni, which historically focused on cloud-based file storage. The company now describes its platform as a broader unstructured data infrastructure for enterprise teams and AI systems, reflecting the growing importance of operational file data in modern digital transformation initiatives.

Why File Data Is Becoming Critical for AI

While enterprises increasingly deploy generative AI and automation platforms, the underlying data needed to power these systems often remains fragmented across legacy file systems.

Operational assets such as engineering designs, financial documents, project files, media content, and research data typically live in distributed file environments. These repositories represent some of the most valuable corporate information but are often difficult for AI systems to access securely.

According to research from Gartner, more than 80% of enterprise data is unstructured, stored in files, documents, and media assets rather than structured databases. As AI adoption accelerates, unlocking this data layer has become a top priority for CIOs and data leaders.

Nasuni’s platform attempts to solve this challenge by creating a global file data layer that centralizes governance, access controls, and versioning while enabling distributed teams to work with data stored in the cloud.

Active Everywhere: Edge Access Without Hardware

One of the company’s key product announcements is Resilio Active Everywhere v6, a technology that enables distributed teams to access file data at local network speeds while maintaining centralized governance.

The feature builds on Nasuni’s acquisition of data synchronization provider Resilio and integrates it more deeply into the Nasuni platform.

Active Everywhere allows edge offices and remote teams to access shared file data directly without relying on traditional WAN optimization appliances or proprietary caching hardware. Instead, the solution uses software-based synchronization built into the platform’s global namespace.

This approach addresses a growing enterprise challenge: the cost and complexity of maintaining physical infrastructure across geographically distributed operations.

Companies operating in industries such as manufacturing, architecture, engineering, construction (AEC), energy, and life sciences often rely on large file assets that must be accessed across multiple locations. As file sizes increase and collaboration expands globally, infrastructure bottlenecks can slow workflows.

Nasuni’s strategy is to replace these hardware-heavy architectures with a software-defined file infrastructure model built on cloud storage.

AI Activate: Giving AI Direct Access to File Data

The second major announcement is AI Activate, a new capability that enables AI agents and large language models to interact directly with enterprise file data stored within the Nasuni platform.

Through integration with Model Context Protocol (MCP), AI Activate allows authorized AI tools to discover, read, and act on file data while respecting existing permissions and governance controls.

This design addresses a common challenge in enterprise AI deployments: the need to create separate data pipelines or duplicate datasets before AI models can use them.

By enabling AI to operate directly on file data stored in its platform, Nasuni aims to reduce the need for additional infrastructure while maintaining enterprise security controls.

The approach aligns with broader industry trends in AI-ready data infrastructure, where platforms are evolving to support AI-native workflows.

Technology ecosystems including Microsoft, Amazon, and Google are increasingly embedding AI capabilities into their cloud platforms, prompting infrastructure vendors to ensure enterprise data can be accessed safely by these systems.

A Growing Market for Unstructured Data Platforms

The rise of generative AI is reshaping enterprise data strategies, particularly around unstructured content.

According to IDC, the global datasphere will reach 175 zettabytes by 2025, with the majority of that growth coming from unstructured data sources such as documents, images, videos, and design files.

Organizations that can operationalize this data—by making it searchable, governed, and AI-accessible—are expected to gain competitive advantages in automation, analytics, and innovation.

Nasuni already serves more than 1,300 enterprise customers across industries including manufacturing, media, life sciences, and energy. These sectors often generate large volumes of file-based operational data that must be shared across global teams.

The company has also expanded its cloud ecosystem in recent years, supporting multi-cloud deployments across platforms such as Microsoft Azure and Amazon Web Services.

What It Means for Enterprise IT and Data Leaders

For enterprise CIOs and infrastructure leaders, Nasuni’s expanded strategy highlights a broader industry shift: file infrastructure is becoming part of the AI data pipeline.

Traditional file storage solutions were designed primarily for archiving and collaboration. In the AI era, however, file systems must support real-time access, governance, and integration with intelligent systems.

Platforms capable of unifying file storage, collaboration, governance, and AI access may play an increasingly central role in enterprise technology stacks.

As organizations invest heavily in generative AI and automation, the ability to activate previously untapped file data could determine how effectively those AI systems deliver business value.

Market Landscape

The enterprise file data platform market is evolving as organizations move away from hardware-heavy storage architectures toward cloud-native, software-defined data infrastructure.

Major cloud providers such as Microsoft, Amazon, and Google continue expanding storage and AI services, while specialized vendors like Nasuni focus on operational file systems that integrate governance, collaboration, and AI access.

Analysts at McKinsey & Company estimate that generative AI could add $2.6 trillion to $4.4 trillion annually to the global economy. Unlocking enterprise data—particularly unstructured content—will be critical to capturing that value.

As a result, platforms capable of activating file-based data for AI workflows are emerging as a new category within enterprise data infrastructure.

Top Insights

• Nasuni expanded its enterprise platform strategy to focus on file data activation, helping organizations unlock unstructured data for AI systems and distributed teams.

• The new Active Everywhere v6 capability enables edge teams to access governed file data at LAN speeds without relying on WAN optimization hardware or proprietary caching infrastructure.

• AI Activate introduces AI-ready access to enterprise file data, allowing large language models and AI agents to work directly on governed datasets using Model Context Protocol.

• Enterprise organizations are increasingly prioritizing unstructured data platforms as AI adoption accelerates across global operations and collaborative workflows.

• Analysts say activating file-based operational data could become critical for enterprises seeking to maximize ROI from generative AI investments.

Get in touch with our MarTech Experts.

Enterprise data engineering firm Hoonartek has introduced ClearView, an agentic decisioning platform designed to help enterprises translate large-scale data investments into automated business execution. The new platform sits above existing data infrastructure—such as lakehouses and cloud data warehouses—and deploys AI agents capable of making governed, traceable decisions across business operations.

Enterprises have spent the past decade building large-scale data platforms—modernizing infrastructure with lakehouses, cloud warehouses, and advanced analytics pipelines. Yet many organizations still struggle to convert these data investments into real-time operational decisions.

That gap is precisely what Hoonartek is targeting with its newly launched ClearView platform, an AI-driven decision layer designed to connect enterprise data estates directly to business execution.

Rather than adding another analytics tool or SaaS application, ClearView introduces what Hoonartek describes as an agentic decisioning layer. The system deploys autonomous AI agents that interact directly with an organization’s existing data infrastructure to execute operational decisions in real time.

The idea is simple but increasingly relevant in modern enterprise IT: if data platforms contain valuable insights, those insights should directly trigger business actions.

Why Enterprises Are Struggling to Activate Data

Over the last decade, companies have invested heavily in enterprise data architecture. Technologies such as cloud data warehouses, distributed lakehouses, and real-time analytics pipelines have become central components of digital transformation initiatives.

Yet despite these investments, many organizations still rely on fragmented SaaS tools for operational decision-making. Marketing teams use separate platforms for campaign optimization, finance departments rely on forecasting software, and supply chain teams deploy independent analytics tools.

The result is what many technology leaders describe as SaaS sprawl—a rapidly expanding stack of niche applications that operate independently from the underlying enterprise data platform.

According to research from Gartner, organizations now manage hundreds of SaaS applications on average, creating governance challenges, rising licensing costs, and fragmented decision workflows.

ClearView attempts to address this issue by shifting the architecture away from tool-centric automation toward decision-centric automation.

Instead of deploying individual SaaS tools for specific functions, the platform embeds AI agents that act directly on enterprise data to perform business decisions such as pricing adjustments, fraud detection, operational alerts, or customer engagement triggers.

An Architecture Built Around Autonomous Decisioning

The ClearView platform operates across three core layers that together create an enterprise AI decision engine.

The first layer focuses on decision governance, defining how AI agents operate, what authority they hold, and how their actions align with enterprise policy.

The second layer is RealizeAI, Hoonartek’s AI development framework designed to scale machine learning models and analytics use cases across the organization.

The final component, BlueFoundry, functions as the operational execution engine. It converts business intent—such as a rule or optimization objective—into automated agent workflows capable of executing real-time decisions.

Every action generated by the system remains traceable from intent to outcome, creating an audit trail designed for enterprise governance and regulatory compliance.

For enterprise leaders, traceability has become a critical requirement as AI moves from experimental analytics into operational systems.

AI Moves Closer to Business Execution

The broader shift toward agentic systems reflects a growing trend in enterprise technology: AI is moving beyond analysis and into autonomous operational execution.

Large technology ecosystems—including Microsoft, Google, and Amazon—have increasingly invested in AI agent frameworks that automate tasks previously handled by human operators.

However, most enterprise deployments still rely on human-in-the-loop workflows where AI generates insights but stops short of making decisions.

ClearView attempts to close that gap by enabling AI agents to execute actions directly within business systems while maintaining governance oversight.

Industry experts say this shift may become increasingly important as organizations look to scale AI beyond isolated use cases.

“Enterprises don’t fail at AI because of poor models,” said Dejan Deklich, former CTO of Aisera, in reference to the platform announcement. “They fail because no one connected the data platform to decisions.”

Growing Pressure to Rationalize SaaS and AI Spending

The launch of ClearView also reflects broader economic pressures shaping enterprise technology strategies.

Chief financial officers and chief data officers are increasingly tasked with reducing SaaS complexity while accelerating AI adoption.

According to IDC, worldwide spending on AI technologies is expected to exceed $500 billion by 2027, while organizations simultaneously attempt to consolidate software vendors and simplify digital infrastructure.

Platforms that enable AI-driven automation directly on existing data environments could help organizations achieve both goals: activating AI capabilities while reducing reliance on specialized SaaS tools.

Hoonartek says ClearView is already being deployed in sectors such as financial services, telecommunications, and manufacturing—industries where operational decisions often depend on real-time data signals.

The company recently received recognition for AI Service Excellence at the NASSCOM Inspire Awards 2026, highlighting growing interest in its enterprise AI services.

What This Means for Enterprise Technology Strategy

For enterprise IT leaders, the concept of a decision layer above the data platform represents a new architectural approach.

Instead of building separate applications for every operational function, organizations may increasingly adopt AI-driven orchestration layers capable of executing decisions across systems.

If successful, this model could reshape how companies design enterprise technology stacks—placing autonomous agents at the center of operational workflows rather than traditional SaaS applications.

As AI infrastructure matures, the next competitive frontier may not be data collection or analytics alone, but how quickly organizations can translate data into automated decisions that drive business outcomes.

Market Landscape

The emergence of agentic AI platforms reflects a broader evolution in enterprise software architecture. Vendors across the technology ecosystem—including Microsoft, Google, and Amazon—are investing heavily in AI systems capable of automating complex workflows.

Meanwhile, analysts at McKinsey & Company estimate that generative AI could generate $2.6 trillion to $4.4 trillion in annual economic value, much of it tied to automation of operational decision-making.

As enterprises mature their data platforms, technologies that connect data infrastructure directly to autonomous decision systems are expected to become a new layer of enterprise AI architecture.

Top Insights

• Hoonartek launched ClearView, an AI-powered decisioning layer designed to activate enterprise data platforms by deploying autonomous agents capable of executing real-time business decisions.

• The platform addresses growing enterprise concerns around SaaS sprawl by enabling decision-centric automation directly on top of existing lakehouse and cloud data infrastructure.

• ClearView’s architecture combines governance, machine learning development, and workflow orchestration to create traceable AI-driven decision systems for enterprise operations.

• Industry experts say the future of enterprise AI will depend less on model accuracy and more on how effectively organizations connect data infrastructure to operational decision workflows.

• As AI spending grows globally, enterprises are increasingly seeking platforms that activate existing data investments while reducing dependence on fragmented SaaS applications.

Get in touch with our MarTech Experts.

Enterprise IT services provider DXC Technology has deepened its long-standing collaboration with ServiceNow through a new multi-year agreement aimed at accelerating AI-driven enterprise transformation. The partnership will integrate ServiceNow’s AI platform and agentic automation capabilities into DXC’s global operations, enabling the company to deploy AI-powered workflows across complex enterprise environments while building repeatable automation solutions for customers.

Artificial intelligence has dominated enterprise technology roadmaps for years, yet many organizations remain stuck in pilot projects rather than achieving measurable operational impact.

A new partnership between DXC Technology and ServiceNow seeks to bridge that gap by turning experimental AI deployments into scalable operational systems.

The companies announced a multi-year collaboration designed to modernize enterprise operations using AI-driven automation, agentic workflows, and unified service management platforms. The initiative will focus on embedding AI into everyday business processes, from IT and finance to HR and operational support functions.

For DXC, which provides enterprise technology services across industries, the project represents both an internal transformation effort and a blueprint for delivering AI solutions to clients worldwide.

From AI Experimentation to Enterprise Execution

Many organizations have invested heavily in AI tools but struggle to deploy them across complex enterprise infrastructures that include legacy systems, multiple cloud providers, and large vendor ecosystems.

According to research from Gartner, more than 80% of AI projects fail to move beyond pilot phases, largely due to integration challenges and organizational complexity.

The DXC–ServiceNow partnership is designed to address this challenge by combining DXC’s consulting and implementation expertise with ServiceNow’s workflow automation platform.

At the center of the initiative is the ServiceNow AI Platform, which acts as a centralized orchestration layer for enterprise operations. The platform enables organizations to automate workflows, integrate data across systems, and deploy digital agents capable of executing routine tasks.

By embedding AI into operational workflows rather than standalone analytics tools, the partners aim to enable AI-driven execution at scale.

DXC Becomes Early Adopter of Agentic AI

A key element of the partnership involves DXC serving as Customer Zero for ServiceNow’s new Core Business Suite, a platform designed to automate enterprise service functions using AI agents.

As the first large enterprise to deploy these capabilities globally, DXC will integrate agentic AI workflows into its Global Business Services (GBS) operating model.

The GBS approach consolidates traditionally siloed back-office functions—such as finance, procurement, HR, and IT—into a centralized operating structure that can be managed across regions and departments.

By embedding AI automation into these processes, DXC aims to reduce manual work, improve cross-functional visibility, and accelerate decision-making.

Digital agents built on the ServiceNow platform can monitor operational activity, identify anomalies, surface insights, and resolve routine service issues automatically.

For employees, the goal is to shift work away from repetitive tasks and toward higher-value analysis and innovation.

Building a Library of Enterprise AI Use Cases

Another strategic element of the partnership is the creation of a catalog of validated AI use cases.

As DXC deploys AI automation internally, the company will document workflows, best practices, and automation patterns. These will then be packaged as ready-to-deploy solutions for enterprise clients.

The approach reflects a broader trend in enterprise technology consulting: transforming internal digital transformation initiatives into repeatable market offerings.

DXC’s consulting teams—including AI architects, automation engineers, and adoption specialists—will work with customers to identify high-impact automation opportunities and deploy solutions across existing technology environments.

Extending a Long-Standing Partnership

The announcement builds on a relationship that already spans more than 17 years between the two companies.

In 2024, the partners established a joint AI Innovation Center of Excellence (CoE) designed to accelerate enterprise AI adoption.

The CoE focuses on developing standardized AI blueprints, automation accelerators, and implementation frameworks that help organizations deploy AI responsibly and at scale.

As an Elite ServiceNow Partner, DXC already employs more than 1,800 certified ServiceNow consultants who implement digital workflow solutions for global enterprises.

The expanded collaboration deepens this integration while positioning DXC as an early validator of ServiceNow’s newest AI capabilities.

Why Enterprise Workflow Platforms Are Becoming AI Control Towers

The partnership highlights the growing importance of workflow platforms as central control layers for enterprise automation.

ServiceNow has increasingly positioned its platform as a digital control tower for enterprise operations, coordinating tasks across systems and departments.

This model competes with automation ecosystems built by major technology vendors such as Microsoft, Google, and Amazon, all of which are expanding AI-driven automation capabilities within their cloud platforms.

Enterprise workflow orchestration tools are becoming critical because modern organizations operate across multiple software environments and cloud providers.

Platforms capable of unifying these environments—and enabling AI to automate processes across them—are emerging as the backbone of AI-driven enterprise operations.

What It Means for Enterprise Technology Leaders

For CIOs and digital transformation leaders, the DXC-ServiceNow initiative reflects a broader shift in how organizations approach AI adoption.

Instead of building isolated AI applications, enterprises are increasingly embedding AI directly into core operational workflows.

This approach allows organizations to automate high-volume processes, reduce operational friction, and generate measurable productivity improvements.

Research from IDC predicts that by 2027, over 60% of enterprise service workflows will include AI-driven automation, driven by demand for faster decision-making and improved operational efficiency.

Partnerships like the one between DXC and ServiceNow suggest that the next phase of enterprise AI adoption will focus less on experimentation and more on industrial-scale execution across global business operations.

Market Landscape

Enterprise workflow platforms are increasingly evolving into AI orchestration layers that coordinate operations across digital ecosystems.

Companies including ServiceNow, Microsoft, and Salesforce are embedding AI agents and automation tools directly into enterprise platforms.

Analysts at McKinsey & Company estimate that automation powered by generative AI could deliver productivity gains of up to 40% in certain operational roles, particularly in customer service, IT operations, and administrative functions.

As AI capabilities mature, enterprises are expected to adopt platform-centric automation strategies that unify workflows, analytics, and AI decision systems.

Top Insights

• DXC Technology and ServiceNow expanded their long-term partnership to accelerate enterprise AI adoption using automation and agentic workflows across global operations.

• DXC will act as Customer Zero for ServiceNow’s Core Business Suite, deploying AI-driven automation across its Global Business Services model.

• The collaboration aims to transform internal deployments into repeatable enterprise AI solutions that DXC can deliver to customers worldwide.

• ServiceNow’s AI platform will orchestrate digital agents that monitor operations, resolve issues automatically, and improve enterprise workflow efficiency.

• Analysts expect enterprise workflow platforms to evolve into central AI control layers as organizations automate operational processes at scale.

Get in touch with our MarTech Experts.

Enterprise automation vendor Automation Anywhere reported that 61% of its fourth-quarter software bookings came from AI-powered solutions, reflecting a broader shift in enterprise technology adoption—from experimental AI pilots to production-scale automation running core business operations.

Artificial intelligence adoption across enterprises is entering a new phase. After years of experimentation, organizations are now deploying AI systems that automate operational workflows, resolve service requests, and execute business decisions.

New financial results from Automation Anywhere highlight this transition. The company reported that AI-driven products accounted for 61% of its software bookings in the fourth quarter, signaling growing enterprise demand for automation solutions capable of delivering measurable operational impact.

The company, which focuses on Agentic Process Automation (APA), said the results reflect a broader shift toward what it calls the “autonomous enterprise”—a model where AI-powered agents run business processes alongside human teams.

“Companies are moving past pilots and into production,” said Mihir Shukla, CEO and chairman of Automation Anywhere. “Organizations are deploying AI solutions by department—from autonomous IT service management to finance and customer support—on the path to becoming autonomous enterprises.”

AI Automation Moves Into Core Business Functions

Enterprise adoption of AI-powered automation has accelerated as organizations attempt to increase efficiency, reduce operational costs, and respond faster to changing market conditions.

Automation Anywhere’s results indicate that large enterprises are beginning to scale automation across entire departments rather than deploying isolated AI tools.

The company reported a 23% increase in enterprise customers generating more than $1 million in annual recurring revenue (ARR), suggesting expanding usage among large organizations.

Its base of agentic AI customers more than doubled during the quarter, driven in part by forward-deployed engineering teams that help convert AI pilot projects into full enterprise deployments.

Agentic automation differs from earlier robotic process automation (RPA) technologies by incorporating AI models capable of reasoning, decision-making, and multi-step workflow execution.

Instead of simply automating repetitive tasks, agentic systems can analyze incoming data, determine the appropriate action, and execute workflows across enterprise applications.

Expanding Use Cases Across Enterprise Operations

Automation Anywhere said demand for its AI solutions is growing across a wide range of business functions.

Organizations are increasingly deploying automation across areas such as:

• Customer service operations
• IT service management (ITSM)
• Finance and accounting workflows
• Human resources operations

For example, the company recently signed a multi-million-dollar agreement with a U.S. healthcare system to automate patient data management, scheduling, and administrative tasks while maintaining regulatory compliance.

Highly regulated sectors such as healthcare and financial services are emerging as key adopters of AI-driven process automation because they manage large volumes of repetitive workflows that require strict governance.

Strategic Partnerships and Product Expansion

Automation Anywhere has expanded its automation capabilities through acquisitions and strategic partnerships.

The company recently acquired Aisera, a provider of AI-driven service management tools, to strengthen its ability to deliver autonomous solutions for IT support and customer service operations.

It also announced a collaboration with OpenAI to combine Automation Anywhere’s enterprise workflow insights with advanced reasoning models.

The partnership aims to enable AI systems that can interpret business requests, make operational decisions, and execute complex processes across multiple enterprise systems.

These capabilities represent an evolution of traditional automation platforms into AI-powered operational orchestration systems.

Financial Momentum and Enterprise Demand

Beyond AI adoption metrics, Automation Anywhere reported strong financial performance.

The company said it exceeded its EBITDA guidance and recorded its 10th consecutive quarter of non-GAAP profitability and positive free cash flow.

Remaining Performance Obligations (RPO) and annual recurring revenue also grew at double-digit rates year over year, indicating sustained demand for enterprise automation solutions.

Industry analysts note that automation platforms are increasingly becoming foundational infrastructure for digital transformation.

According to Gartner, by 2027 over 50% of enterprise organizations are expected to deploy some form of AI-driven process automation to support operational decision-making and workflow orchestration.

The Rise of the Autonomous Enterprise

Automation Anywhere’s strategy centers on the idea of the autonomous enterprise, where AI systems handle large portions of operational workloads.

In this model, digital agents continuously monitor systems, resolve routine issues, and escalate exceptions to human employees when needed.

Technology providers across the ecosystem—including Microsoft, Google, and Amazon—are also investing heavily in AI agents capable of performing complex enterprise workflows.

The shift has gained momentum among global business leaders. Discussions at the World Economic Forum in Davos earlier this year highlighted the growing role of AI in operational decision-making and enterprise productivity.

Automation Anywhere’s leadership has been involved in those conversations as part of broader industry efforts to define responsible AI adoption strategies.

Skills and Workforce Transformation

As automation expands across enterprises, companies are also investing in workforce development.

Automation Anywhere said it plans to train two million individuals in automation and AI technologies by 2030, with more than 650,000 learners already participating in its training programs.

Reskilling initiatives like these are becoming essential as organizations adapt to hybrid work environments where human employees collaborate with AI-powered systems.

What It Means for Enterprise Technology Strategy

For enterprise CIOs and digital transformation leaders, Automation Anywhere’s results provide evidence that AI automation is moving beyond experimentation.

Organizations are increasingly seeking platforms that combine AI reasoning, workflow orchestration, and enterprise governance.

As companies scale automation across departments—from IT service management to finance and customer support—AI-driven platforms are likely to play a central role in enterprise operations.

Industry observers say the next phase of digital transformation will not focus solely on AI models themselves, but on how effectively organizations integrate those models into real-world business workflows.

Market Landscape

The global automation software market is rapidly expanding as enterprises invest in AI-driven operational efficiency.

Research from IDC estimates that global spending on AI technologies will surpass $500 billion by 2027, much of it tied to automation and decision systems embedded in enterprise applications.

Meanwhile, McKinsey & Company estimates generative AI could deliver productivity improvements of up to 40% in certain operational functions, including customer support and administrative workflows.

Automation platforms that combine agentic AI, , and enterprise governance are increasingly emerging as foundational components of modern digital operations.

Top Insights

• Automation Anywhere reported that 61% of its fourth-quarter software bookings came from AI-powered solutions, highlighting growing enterprise demand for agentic automation technologies.

• The company’s enterprise customer base expanded significantly, with a 23% increase in organizations generating more than $1 million in annual recurring revenue.

• Strategic initiatives, including the acquisition of Aisera and collaboration with OpenAI, aim to accelerate the development of autonomous enterprise workflows.

• Enterprises across sectors such as healthcare are deploying AI automation to streamline operational processes while maintaining governance and regulatory compliance.

• Industry analysts expect AI-driven process automation platforms to become core infrastructure as organizations scale automation across business functions.

Get in touch with our MarTech Experts.

FinTech data platform provider SalesFocus Solutions has introduced an expanded Distribution Intelligence and Master Data Management (MDM) solution aimed at helping asset management firms unify fragmented distribution data and accelerate assets under management (AUM) growth. The company says its MARS platform provides a “golden copy” of distribution data, enabling more accurate sales insights, advisor intelligence, and marketing decisions.

For asset management firms, growth often depends on one critical factor: the ability to understand where assets originate and how financial advisors distribute investment products. Yet the data required to generate those insights is often scattered across multiple intermediaries, custodians, and legacy systems.

To address this challenge, SalesFocus Solutions has introduced a Distribution Intelligence and Master Data Management (MDM) solution designed specifically for asset management organizations. The platform, called MARS, consolidates distribution data from multiple financial sources into a unified environment that supports sales analytics, advisor intelligence, and operational reporting.

The goal is to provide firms with a single trusted data layer, often referred to as a “golden copy,” enabling investment managers to make faster, more informed decisions about distribution strategies and client engagement.

Why Distribution Data Is Critical for AUM Growth

Asset managers rely heavily on complex distribution ecosystems that include broker-dealers, financial advisors, transfer agents, and custodial institutions. Each participant generates transactional and relationship data that can influence sales strategy.

However, this information is rarely centralized.

Data from sources such as the National Securities Clearing Corporation (NSCC), transfer agents, and financial intermediaries often arrives in different formats and timelines, creating data fragmentation across the enterprise.

This fragmentation can limit a firm’s ability to analyze sales performance, identify advisor opportunities, or track investment flows accurately.

According to research from McKinsey & Company, data quality issues remain one of the biggest obstacles to digital transformation in financial services, particularly when organizations attempt to unify information across legacy systems and external partners.

Platforms like MARS aim to address that problem by standardizing distribution data and providing traceable data lineage.

Turning Fragmented Data Into Advisor Intelligence

The MARS platform aggregates data from multiple distribution channels and transforms it into advisor and advisor-team intelligence.

This includes mapping relationships between financial institutions, branch offices, advisors, and client accounts. By linking these data points, asset managers can analyze sales activity across territories, product lines, and distribution networks.

The platform also supports multi-product reporting, enabling firms to track investment flows across various asset types, including:

• Mutual funds
• Exchange-traded funds (ETFs)
• Managed accounts
• Model portfolios
• Alternative investments

The ability to unify these datasets provides investment managers with deeper visibility into how advisors allocate assets and how product demand evolves over time.

For marketing and sales teams, that insight can translate into targeted outreach strategies and improved advisor engagement.

Integrating Data Into Enterprise CRM Systems

A critical component of the platform involves integration with customer relationship management systems.

MARS includes bi-directional integration with Salesforce, enabling asset management firms to keep CRM data synchronized with distribution intelligence insights.

This connection allows sales teams to access real-time advisor information directly within CRM workflows, reducing reliance on manual data updates or disconnected reporting systems.

Enterprise software ecosystems—including those built by Salesforce, Microsoft, and Adobe—have increasingly emphasized unified customer and data platforms that integrate analytics directly into operational tools.

For financial services firms, integrating distribution data into CRM environments allows teams to track advisor relationships, monitor asset flows, and identify cross-selling opportunities more efficiently.

Advanced Analytics for Distribution Strategy

Beyond core data management capabilities, the MARS platform includes analytical features designed to help asset managers optimize distribution strategies.

These include:

• Advisor segmentation and lead scoring
• Cross-sell opportunity identification
• Automated insights based on transaction data
• Time-based sales metrics for trend analysis

Such capabilities extend beyond traditional MDM systems, which typically focus only on data governance and record consolidation.

Instead, platforms like MARS aim to combine data management with operational intelligence, enabling firms to move from static reporting to actionable insights.

The Growing Importance of Data Platforms in FinTech

The asset management industry is increasingly investing in data infrastructure as firms seek to improve operational efficiency and client engagement.

Research from Statista estimates the global financial analytics and business intelligence market will exceed $20 billion by 2028, driven by demand for real-time data platforms and advanced analytics capabilities.

At the same time, asset managers face growing competition from digital investment platforms and fintech-driven advisory services.

To remain competitive, firms are investing in technologies that provide deeper insight into distribution networks and advisor behavior.

Platforms capable of consolidating and analyzing distribution data may therefore play an increasingly central role in modern asset management operations.

What It Means for Asset Management Firms

For investment firms seeking to grow assets under management, data visibility has become a strategic advantage.

Centralized distribution intelligence platforms allow organizations to track advisor performance, identify growth opportunities, and streamline sales operations across regions and product lines.

As financial institutions continue modernizing their technology stacks, solutions that combine master data management, analytics, and CRM integration are likely to become critical infrastructure for sales and marketing teams in asset management.

Market Landscape

The intersection of financial data platforms, CRM systems, and analytics tools is rapidly expanding within the FinTech ecosystem.

Technology vendors such as Salesforce and Microsoft are integrating AI-driven analytics into enterprise platforms, while specialized providers like SalesFocus Solutions focus on industry-specific data intelligence solutions.

Analysts at Gartner predict that data and analytics platforms will remain one of the fastest-growing enterprise software segments, particularly in regulated industries such as financial services where governance and traceability are critical.

As asset managers seek deeper insights into advisor networks and product distribution, platforms that unify data across financial intermediaries are likely to gain increasing adoption.

Top Insights

• SalesFocus Solutions introduced the MARS Distribution Intelligence and MDM platform designed to unify fragmented financial distribution data across asset management organizations.

• The platform creates a centralized “golden copy” of advisor and distribution data, enabling more accurate sales reporting and faster marketing decisions.

• Integration with Salesforce allows asset managers to synchronize CRM records with real-time distribution insights across advisors and financial intermediaries.

• Advanced analytics features such as advisor segmentation, lead scoring, and cross-sell identification help investment firms optimize distribution strategies.

• Growing demand for financial data intelligence platforms reflects broader digital transformation efforts across the global asset management industry.

Get in touch with our MarTech Experts.

AI productivity startup Zenfox has announced the public launch of its AI operating system for professionals, positioning the platform as an alternative to fragmented productivity tools and chat-based AI interfaces. The company says the agentic platform connects directly with enterprise workflows, enabling autonomous AI agents to operate across email, calendars, CRM systems, and collaboration tools without requiring professionals to switch contexts.

Despite billions invested in enterprise productivity tools, many professionals report feeling more overwhelmed than empowered by technology.

According to new insights shared during the launch of its AI operating system, Zenfox highlights a growing paradox in enterprise AI adoption: organizations have spent an estimated $15 billion on productivity platforms, yet 73% of professionals say these tools increase their cognitive load.

The company argues that the issue lies not in the capabilities of artificial intelligence itself, but in how it integrates—or fails to integrate—with everyday workflows.

The Problem With Chat-Based AI Interfaces

Modern AI assistants such as ChatGPT Enterprise and Claude are designed primarily for conversational interactions.

While powerful for research, writing, and brainstorming, these tools often operate separately from the software ecosystems where professionals actually perform their work.

Tasks like managing emails, updating CRM records, coordinating schedules, and accessing internal documents still require users to move between multiple platforms.

Common tools in the enterprise productivity stack include:

• Gmail for email communication
• Google Calendar for scheduling
• Slack for collaboration
• HubSpot for customer relationship management

According to Zenfox, this fragmentation forces professionals to copy sensitive information between systems, maintain multiple AI subscriptions, and manage complex workflows manually.

From Chatbots to Autonomous AI Agents

Zenfox aims to address these challenges through an agentic AI architecture that operates directly across connected enterprise tools.

Instead of requiring explicit prompts in chat windows, the platform deploys autonomous agents capable of executing tasks across the user’s digital environment.

The system uses a two-tier agent orchestration model, where a central “meta-agent” coordinates specialized sub-agents that handle specific tasks such as research, scheduling, reporting, or CRM updates.

This architecture enables AI to perform multi-step workflows without requiring constant supervision from users.

For example, an AI agent could gather research, draft an email response, update CRM records, and schedule follow-up meetings across multiple platforms automatically.

Core Platform Capabilities

The Zenfox platform combines several technologies designed to support autonomous workflows:

2-Tier Agent Architecture
A meta-orchestrator assigns tasks to specialized agents that execute multi-step processes across integrated software platforms.

Autonomous Workflow Execution
Agents interact directly with enterprise tools such as Gmail, Google Calendar, Slack, and HubSpot to perform tasks without requiring manual prompts.

Deep Research Engine
The system can break down complex queries, search multiple sources, and generate synthesized reports with citations.

Retrieval-Augmented Generation (RAG)
Zenfox uses RAG architecture to ground AI outputs in the user’s own documents and internal knowledge bases rather than relying solely on training data.

Proactive Intelligence
By analyzing contextual patterns, the system anticipates tasks and recommends actions within a user’s workflow.

Addressing Security and Data Sovereignty

Security and data control have become central concerns for organizations adopting AI platforms.

Many AI tools rely heavily on external APIs or third-party models, which can introduce potential data exposure risks when sensitive information is transmitted outside company systems.

Zenfox claims its architecture maintains greater control over data flow and model integration, reducing reliance on external “black-box” AI services.

The platform processes data in Europe while allowing users to select storage locations across several global regions, including the United States, United Kingdom, Canada, and Singapore.

This flexibility is designed to help organizations comply with regional data protection regulations such as the European Union’s General Data Protection Regulation (GDPR).

Early Adoption and Performance Results

According to the company, early adopters report workflow acceleration of up to 40%, primarily due to reduced context switching and automated task execution.

The platform’s ability to operate across multiple enterprise tools simultaneously allows professionals to focus on higher-value work rather than administrative tasks.

While AI-powered productivity tools have been widely adopted across industries, many organizations are now exploring agentic AI systems that can execute workflows independently rather than simply assisting users through chat interfaces.

The Rise of the AI Operating System

The concept of an AI operating system is gaining traction across the technology industry.

Rather than functioning as standalone tools, these platforms aim to become a central orchestration layer for digital work environments, coordinating applications, data sources, and automated processes.

Companies such as Microsoft and Google are already integrating AI copilots into productivity platforms, while enterprise software providers like Salesforce are embedding autonomous AI agents into CRM systems.

However, Zenfox argues that many of these tools remain tied to individual applications rather than serving as cross-platform intelligence layers.

By positioning itself as infrastructure rather than an additional productivity app, the company hopes to unify fragmented enterprise software stacks under a single AI orchestration layer.

What It Means for the Productivity Software Market

The global market for productivity and collaboration software is expected to continue growing rapidly as enterprises digitize workflows and adopt AI technologies.

Research from Gartner indicates that AI-enabled productivity platforms will become a core component of enterprise software ecosystems, particularly as organizations seek automation beyond traditional task management tools.

If agentic AI platforms can successfully integrate across enterprise systems, they may reshape how professionals interact with technology—moving from manual task management to autonomous digital operations.

Top Insights

• Zenfox launched an AI operating system designed to unify enterprise workflows across email, calendars, CRM platforms, and collaboration tools.

• The company highlights a paradox in the productivity software market: despite billions invested, many professionals report increased cognitive load from fragmented tools.

• The platform uses a two-tier agent architecture that enables autonomous AI agents to execute multi-step workflows across enterprise systems.

• Built-in retrieval-augmented generation and proactive intelligence capabilities allow the system to analyze documents, gather research, and anticipate workflow needs.

• Early adopters report workflow acceleration of up to 40% due to reduced context switching and automated task execution.

Get in touch with our MarTech Experts.

A new consumer study commissioned by Idomoo reveals that advanced video technologies—including personalization and AI-generated video—are dramatically influencing purchasing behavior. The research shows consumers are twice as likely to purchase from brands that use personalized or AI-powered video, highlighting a growing gap between consumer expectations and the video experiences most brands currently deliver.

Video has rapidly evolved from a marketing enhancement into a central pillar of digital engagement.

According to a new study conducted by Atomik Research and commissioned by Idomoo, 80% of consumers want more video from brands, yet 46% report never receiving video communications at all. The findings reveal a widening “video gap” between what consumers expect and what many businesses deliver.

The research is part of the State of Video Technology report, now in its fifth year, based on insights from 2,500 adults in the United States and United Kingdom, including both consumers and business owners.

Video’s Growing Influence on Purchase Decisions

The report highlights the growing influence of advanced video technology on consumer behavior.

Consumers are twice as likely to buy from brands that use personalized or AI-powered video, while personalized video content is four times more likely to be preferred over generic video messaging.

These results suggest that video—especially personalized video—has become a powerful tool for driving engagement, loyalty, and conversions.

However, failing to meet these expectations may carry risks. According to the study, 52% of consumers say they would consider leaving a brand that does not deliver modern video experiences.

Personalization Becomes the New Standard

The research indicates that personalization is no longer optional in digital communication strategies.

Nearly half of consumers (45%) say they become frustrated when video content lacks personalization, while 52% believe brands that fail to personalize communications do not respect their time.

This reflects a broader trend in digital marketing where tailored content—driven by data and AI—has become a key factor in customer experience strategies.

Companies such as Netflix, Amazon, and Spotify have already demonstrated how personalized recommendations can drive engagement and loyalty in digital environments.

Marketing leaders are now exploring how similar personalization principles can be applied to video communications.

Gen Z and Millennials Lead Demand

The study also reveals generational differences in video expectations.

Gen Z consumers show the highest demand, with 92% saying they want more video communication from brands. Close behind are millennials at 91%, indicating that younger audiences strongly favor video-first engagement strategies.

Among Gen Z respondents, 89% also expect video to be personalized, reflecting their preference for digital experiences tailored to their interests and behaviors.

Interestingly, high-income consumers share similar preferences, with 92% wanting more video and 91% expecting personalization, suggesting that demand for advanced video communication extends across demographic segments.

Advanced Video and Inclusive Engagement

Another notable finding from the report is the strong demand for advanced video technologies among minority consumer groups.

The research shows that these audiences are particularly receptive to next-generation video experiences, including:

• Personalized video (87%)
• Interactive video (88%)
• AI-generated video (84%)

Additionally, 58% say they would consider switching to a competitor if another brand offered more advanced video communication experiences.

These findings highlight the role that personalized digital content can play in creating more inclusive and engaging customer experiences.

AI Video Gains Momentum

One of the most significant shifts in the report involves consumer attitudes toward AI-generated video.

Interest in AI video content increased to 74% of consumers in 2026, up from 65% the previous year, representing the largest single-year increase recorded in the study.

Businesses appear equally enthusiastic about the technology.

The report indicates that 88% of executives would increase video production if they had access to AI tools capable of generating videos within minutes.

This shift reflects the growing role of generative AI in marketing and content creation.

Technology companies such as OpenAI, Google, and Adobe have introduced AI-powered tools that enable marketers to generate images, video, and multimedia content at scale.

Closing the Video Gap

Despite strong consumer demand, the report suggests many organizations are still early in their adoption of advanced video technology.

The “video gap” highlighted in the research suggests that brands risk falling behind if they do not invest in personalized, interactive, and AI-driven video communication strategies.

For marketers, the findings reinforce a key lesson: video innovation is quickly becoming a competitive differentiator in digital engagement.

As consumer expectations evolve, companies that successfully integrate personalized video, AI-driven production, and interactive experiences into their communication strategies may see stronger engagement and higher conversion rates.

Top Insights

• 80% of consumers want more video from brands, yet 46% say they never receive video communications.

• Personalized video is four times more likely to be preferred than generic video content.

• Consumers are twice as likely to purchase from brands that use personalized or AI-powered video.

• Demand for video is highest among Gen Z (92%) and millennials (91%).

• Interest in AI-generated video rose to 74% of consumers in 2026, marking the largest annual increase in the study.

Get in touch with our MarTech Experts.