Enterprise marketing platforms are entering a new phase—one where content isn’t just published on websites but discovered across AI assistants, search engines, social feeds, and marketplaces long before a customer reaches a brand’s digital property.

That shift is forcing marketing teams to rethink how content is created, governed, and personalized at scale. Against that backdrop, enterprise digital experience vendor Sitecore is highlighting a wave of industry recognition that suggests its AI-led platform strategy is resonating with both customers and the broader marketing technology ecosystem.

The company announced that it swept all eight categories it was nominated for in the 14th Annual CMS Critic Awards, including Best Enterprise CMS, Best Headless CMS, Best Digital Experience Platform (DXP), Best Content Marketing Platform (CMP), Best Digital Asset Management (DAM) System, Best Customer Data Platform (CDP), Best e-Commerce Solution, and Best AI Solution.

The recognition adds to a broader run of accolades for the company’s AI platform, SitecoreAI with Studio, which also received a Two-Star Technology DaVinci Award and landed among G2’s 2026 Best Web CMS Products rankings.

For enterprise marketing teams evaluating long-term technology stacks, such recognition often serves as an early signal of which platforms are gaining real traction—and which are merely riding the hype cycle.

Why These Awards Matter in Today’s MarTech Landscape

Awards don’t make a product successful—but they can reflect momentum.

In this case, the recognition comes from three different directions: practitioners, customers, and industry experts.

The CMS Critic Awards rely on community voting from developers, marketers, and digital practitioners who build and manage customer experiences every day. G2’s annual rankings, meanwhile, are based on verified user reviews and market presence data across more than 179,000 software vendors, with just 0.63% making the final award lists for 2026.

The DaVinci Awards, judged by a global panel of technology experts, evaluate enterprise platforms on factors such as architectural maturity, integration capabilities, and business impact.

Taken together, those perspectives provide a multi-angle snapshot of how a platform is performing in the market—from real-world usability to enterprise readiness.

For Sitecore, the results reinforce a broader message: that its AI strategy isn’t just experimental—it’s becoming embedded across the marketing technology stack.

A Platform Play Across the Entire Content Lifecycle

The eight award categories Sitecore won align with the core systems that modern marketing teams rely on.

Those include:

• Enterprise content management systems

• Headless content delivery architectures

• Digital experience platforms

• Content marketing platforms

• Digital asset management systems

• Customer data platforms

• Commerce infrastructure

• AI-driven content and personalization tools

In other words, nearly the entire lifecycle of digital experience delivery—from content planning and creation to customer data activation and omnichannel engagement.

That breadth is significant because enterprise marketing stacks have grown increasingly fragmented over the past decade.

Organizations often juggle separate tools for content creation, analytics, personalization, and customer data management. Vendors that can unify those capabilities into a single platform—or at least orchestrate them effectively—are gaining attention from CIOs and CMOs looking to simplify their technology ecosystems.

Sitecore’s pitch is that AI acts as the connective tissue across those systems, helping marketing teams automate workflows, generate and personalize content, and optimize campaigns based on real-time customer signals.

The Rise of AI-Driven Content Operations

The timing of Sitecore’s recognition also reflects a broader industry trend: the rapid adoption of AI in marketing operations.

Brands are increasingly experimenting with generative AI for everything from content creation to customer segmentation. But many early experiments have remained isolated pilots rather than production-ready systems.

Mary Ellen Grom, executive director of global marketing and corporate communications at AFL, said her organization initially faced that exact challenge.

“Like a lot of companies, we were testing AI adoption in several areas of our global business,” she explained. “But without governance and executive buy-in it remained experimental.”

The company turned to Sitecore to operationalize those experiments into repeatable marketing workflows.

According to Grom, the platform now allows teams to build personalized content, test performance, and scale marketing output while maintaining oversight and quality control.

That governance layer is becoming a critical differentiator as enterprises move from AI experimentation to full operational deployment.

Expansion Signals Global Demand for Enterprise AI Platforms

Recognition is arriving alongside Sitecore’s continued global expansion.

Since launching SitecoreAI in late 2025, the company says major global brands—including G4S, Nord Anglia, Colt Data Centre Services, Beyond Now, and PulteGroup—have adopted the platform.

In March 2026, Sitecore also announced sovereign deployments of SitecoreAI in Singapore, Saudi Arabia, and the United Arab Emirates.

Those deployments allow organizations to run AI workloads and cloud services within national borders, helping companies meet increasingly strict data residency and regulatory requirements.

For multinational enterprises operating in regulated sectors—such as finance, telecommunications, and government services—that capability can be a deciding factor when selecting cloud and AI platforms.

It also reflects a broader shift in enterprise infrastructure, where regional data sovereignty has become a top priority for CIOs.

The “Answer Moment” and the New Discovery Layer

Another factor driving enterprise interest in AI-enabled content platforms is the changing nature of online discovery.

Customers increasingly research products through AI assistants, search engines, and social feeds before ever visiting a brand’s website.

Godard Abel, co-founder and CEO of G2, described this shift as the rise of the “answer moment”—when software buyers rely on AI-driven platforms to surface recommendations instantly.

“As buyers increasingly shift to AI-driven research to discover software solutions, being recommended in the ‘answer moment’ must be earned with credible proof,” Abel said.

Because many AI search tools rely on structured data and verified customer reviews when generating responses, recognition from platforms like G2 can directly influence how vendors appear in AI-generated results.

That dynamic creates a feedback loop: strong user reviews and industry validation increase visibility in AI answers, which in turn drives further adoption.

Agentic AI and the Next Phase of Marketing Automation

At the technical level, SitecoreAI’s architecture emphasizes agentic AI workflows—systems that can execute marketing tasks autonomously while still operating within governance frameworks.

Annabelle Whittall, chief operating officer of the DaVinci Awards, said that capability played a role in the platform’s evaluation.

“The submission clearly articulated how agentic AI workflows and governed extensibility can deliver meaningful operational gains for global enterprises,” she noted.

The judges were also impressed by the platform’s integration within existing cloud ecosystems, a factor that has become increasingly important as enterprises try to embed AI into complex IT environments rather than replacing them entirely.

From Awards to Real-World Results

Recognition may boost credibility, but ultimately enterprise buyers want measurable outcomes.

Some early adopters report tangible performance gains.

GoTo Technologies, for example, has reported a 78% increase in organic trial conversions and expects 60–70% reductions in infrastructure costs after implementing Sitecore’s platform.

While individual results vary, metrics like those highlight the potential business case for AI-driven content orchestration.

And for large enterprises managing thousands of content assets and customer segments, even incremental improvements in efficiency or personalization can translate into substantial revenue impact.

The Bigger Picture: A Consolidation Moment for MarTech

The broader marketing technology industry is currently navigating two major shifts at once.

First, generative AI is rapidly transforming how marketing content is created and distributed. Second, enterprises are under pressure to simplify their sprawling technology stacks.

Platforms that combine content management, data orchestration, and AI-powered automation are increasingly positioned as the solution to both challenges.

That dynamic has fueled competition among major enterprise vendors—including Adobe, Salesforce, and Sitecore—to define the next generation of digital experience platforms.

Sitecore’s sweep at the CMS Critic Awards doesn’t settle that competition. But it does highlight how quickly AI-led experience platforms are gaining ground with enterprise marketing teams.

As digital discovery moves beyond traditional websites and into AI-driven channels, the platforms that can orchestrate content, data, and personalization across those environments may define the next era of marketing technology.

For now, Sitecore is signaling that it intends to be among them.

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Customer service agents today often spend as much time navigating software as they do helping customers. With enterprise tech stacks growing increasingly fragmented, agents frequently jump between CRM dashboards, messaging platforms, ticketing tools, and internal knowledge systems just to resolve a single inquiry.

Conversational AI provider LivePerson is trying to eliminate that friction.

The company announced the launch of LivePerson Sync, a new integration framework designed to pull CRM data, workflows, and automation directly into the agent workspace. Built in partnership with enterprise integration specialist Coral Active, the new capability aims to create a unified environment where agents can access customer information and manage conversations without constantly switching applications.

The release underscores a broader industry push to simplify customer service operations while layering in AI-powered automation.

“LivePerson Sync is the answer brands have been looking for to improve agent productivity and experience by providing agents with the customer information they need in a single view,” said John Sabino, CEO of LivePerson. “By further connecting our agent workspace with critical information from across systems, we’re removing the friction of disconnected systems.”

The Growing Problem of Fragmented Contact Center Tech

Modern customer service operations rely on a patchwork of tools.

Agents might handle conversations through messaging platforms while referencing customer records in CRMs, updating tickets in service management systems, and checking order histories or billing information in separate databases. Each step adds time and complexity to the interaction.

For enterprises handling thousands—or millions—of customer conversations each month, those inefficiencies can translate into higher operating costs, longer handle times, and frustrated customers.

The rise of digital channels has only intensified the problem. Messaging, social media, and AI-powered chatbots now sit alongside traditional phone support, increasing the number of platforms agents must manage.

LivePerson Sync targets that operational gap by bringing key systems together inside the company’s Conversational Cloud platform.

Instead of toggling between multiple screens, agents can view and act on CRM data, service tickets, and conversation history from within a single interface.

A “Single Pane of Glass” for Customer Conversations

At the center of LivePerson Sync is a concept many enterprise software vendors have been chasing for years: the “single pane of glass.”

The idea is simple but difficult to execute—consolidate multiple operational systems into one unified workspace where users can access everything they need without leaving the environment.

LivePerson Sync integrates with major enterprise platforms including Salesforce, Microsoft applications, and ServiceNow. The system synchronizes customer data and workflows in real time, allowing agents to see relevant records, update cases, and trigger automated actions during conversations.

This integration layer is powered by an event-driven architecture that treats each customer interaction as a trigger for downstream workflows.

For example, a customer conversation could automatically pull up the corresponding CRM profile, update service tickets, or initiate follow-up actions based on the content of the interaction.

That orchestration layer becomes particularly valuable when combined with conversational AI systems that generate summaries, extract insights, and automate routine updates.

Four Deployment Models for Different Enterprise Workflows

LivePerson Sync introduces four primary deployment models designed to accommodate different enterprise environments and workflows.

CRM in LivePerson

In this configuration, CRM functionality is embedded directly within the LivePerson agent workspace. Agents can access customer profiles, update records, and manage leads or service tickets without leaving the conversation interface.

The integration also supports bidirectional data synchronization, ensuring that updates made in either system are reflected across both platforms.

LivePerson in CRM

For organizations that prefer to keep their existing CRM as the central workspace, LivePerson offers the inverse deployment model.

Here, conversational capabilities are embedded directly within the CRM desktop. Agents can handle messaging conversations inside their familiar CRM interface while still benefiting from LivePerson’s AI-driven capabilities.

This approach also provides a pathway for organizations transitioning from older chat connectors or legacy integrations.

Context Synchronization

Context synchronization allows enterprise teams to automate how customer records appear during live conversations.

When an agent switches between customer chats, the relevant CRM record can automatically appear on a secondary monitor or dashboard. That reduces the time agents spend searching for the right customer profile or case history.

AI Enrichment and Automation

The final model focuses on post-conversation automation.

After a conversation ends, LivePerson Sync can automatically ingest transcripts, generate summaries, and update CRM records using AI-powered workflows. That eliminates the manual documentation tasks that typically follow customer interactions.

For high-volume support operations, those automated updates can significantly reduce after-call work.

A Partnership Built Around Contact Center Integration

LivePerson developed Sync in partnership with Coral Active, a company that has specialized in contact center integrations since 2011.

Coral Active’s technology focuses on simplifying agent workspaces by connecting CRMs, legacy systems, and modern customer engagement platforms. By combining that integration expertise with LivePerson’s conversational AI infrastructure, the two companies aim to streamline how enterprise agents interact with multiple systems.

The partnership reflects a broader shift in the contact center industry toward interoperability.

Instead of replacing existing enterprise software, many modern platforms are designed to act as orchestration layers that connect and automate workflows across existing systems.

Event-Driven Architecture Meets Conversational AI

Technically, LivePerson Sync operates through an event-driven orchestration model.

Every customer interaction—whether it’s a message, chatbot conversation, or escalation to a human agent—generates an event that can trigger workflows across integrated systems.

That architecture enables automation scenarios such as:

• Updating CRM records in real time during conversations

• Triggering service tickets when certain customer intents are detected

• Automatically summarizing conversations and logging them in enterprise systems

• Launching follow-up workflows based on customer sentiment or issue type

This event-driven model is becoming increasingly common in enterprise software because it allows organizations to connect multiple systems without building custom integrations for every use case.

Why Agent Experience Is Becoming a Competitive Priority

While much of the customer experience conversation focuses on automation and chatbots, many enterprises are now turning their attention back to human agents.

The reason is simple: even as AI handles routine inquiries, complex issues still require human expertise.

Improving the tools available to agents can have a direct impact on both operational efficiency and customer satisfaction.

If agents spend less time navigating software and more time resolving issues, companies can reduce handle times and improve service quality simultaneously.

LivePerson’s strategy reflects that shift. Rather than focusing solely on AI automation, the company is positioning its platform as a bridge between conversational AI and human support workflows.

The Bigger Picture: Simplifying the Enterprise AI Stack

LivePerson Sync arrives at a time when many enterprises are reassessing their customer engagement technology stacks.

Over the past decade, companies have layered messaging tools, CRM platforms, chatbot frameworks, analytics systems, and automation engines on top of each other. The result is often a fragmented environment that requires complex integrations and custom workflows.

As AI adoption accelerates, organizations are looking for ways to consolidate and orchestrate those systems more effectively.

Platforms that can unify customer conversations, enterprise data, and automation workflows are increasingly attractive to CIOs and customer experience leaders.

By positioning Sync as an integration layer between conversational AI and enterprise systems, LivePerson is betting that the future of customer service will depend as much on orchestration as it does on automation.

Availability

LivePerson Sync is available immediately for organizations looking to modernize their customer service operations, unify agent workflows, and reduce handle times through AI-powered automation.

As enterprises continue to balance automation with human expertise, tools that simplify how agents interact with complex tech stacks could become a defining feature of the next generation of customer engagement platforms.

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Customer experience outsourcing is entering a new phase—one where AI augmentation, global talent networks, and governance frameworks must work together rather than operate as separate strategies.

Atento, a major provider of customer experience (CX) and business transformation outsourcing (BTO), is leaning into that shift with a new three-way collaboration aimed at reshaping how global CX delivery is built and scaled.

The company announced a strategic partnership with Sanas, a provider of real-time speech understanding technology, and Thrivin, a Kenya-based impact sourcing platform focused on developing high-quality talent pipelines. Together, the companies say they are creating a unified delivery model that blends AI-enabled communication tools with a disciplined outsourcing framework and a rapidly growing African workforce.

The move reflects a broader industry trend: outsourcing providers are increasingly combining artificial intelligence with distributed global talent pools to meet rising enterprise demand for scalable, high-quality customer engagement.

Atento describes the approach with a simple premise: CX augmented by AI but driven by people.

A New Operating Model for BTO

At the core of the collaboration is a unified business transformation outsourcing model that integrates governance, AI enablement, and workforce development.

Atento will act as the orchestrator of the overall framework, overseeing security, compliance, performance management, and enterprise transformation processes across the delivery network.

Within that structure, the other partners contribute specialized capabilities.

Sanas provides an AI layer designed to improve communication in voice-based customer interactions. Its real-time speech understanding technology helps agents overcome accent barriers and communication friction, making it easier for international support teams to interact with customers in markets like the United States.

Thrivin contributes a talent pipeline built around highly educated, English-proficient professionals in Kenya, a country increasingly recognized as a rising hub for digital services and outsourcing.

By combining these capabilities under Atento’s governance framework, the partners aim to create an outsourcing model that supports global expansion while maintaining enterprise-grade operational standards.

The Rise of AI-Augmented Customer Experience

Customer experience outsourcing has traditionally been driven by geography—companies moved operations to lower-cost regions while attempting to maintain service quality.

But the equation is changing as enterprises adopt AI technologies that can enhance agent performance, automate repetitive tasks, and improve communication across global teams.

Sanas’ technology plays a key role in that transformation.

Its speech understanding system works in real time during voice interactions, helping reduce misunderstandings between agents and customers. By smoothing communication barriers, the technology allows companies to expand support operations internationally without sacrificing customer experience consistency.

In practice, this means organizations can tap into broader talent pools while maintaining the clarity and responsiveness customers expect from voice-based support.

The approach aligns with a growing industry trend toward AI-augmented agents, where human representatives remain central to service delivery but operate with AI tools that enhance speed, accuracy, and communication.

Operationalizing Impact Sourcing

Another major element of the partnership is impact sourcing, a workforce development model that prioritizes creating economic opportunities in emerging markets while delivering enterprise-quality services.

Thrivin specializes in building talent pipelines across Africa, focusing on recruiting and training professionals who meet the technical and linguistic requirements of global enterprises.

Through the collaboration, Thrivin’s talent network will integrate directly into Atento’s global delivery framework, operating under the same governance and performance management standards used across the company’s existing operations.

This approach allows enterprises to expand into new delivery geographies without the operational uncertainty that can sometimes accompany new outsourcing locations.

It also reflects the increasing strategic importance of Africa in the global outsourcing market.

Countries such as Kenya, Rwanda, and South Africa have invested heavily in digital infrastructure and education programs aimed at positioning the region as a competitive destination for business services and technology operations.

Total Experience as the Organizing Principle

The partnership is also built around the concept of Total Experience (TX)—an operational model that aligns customer experience, employee experience, and operational efficiency.

Rather than treating customer satisfaction, workforce engagement, and cost management as separate priorities, the TX framework attempts to optimize them together.

Within this model:

• Customer experience improves through AI-enabled communication tools and trained support agents.

• Employee experience benefits from structured career pathways and performance frameworks.

• Operational efficiency increases through standardized governance and automation.

By aligning these elements, Atento aims to create a delivery model that is both scalable and sustainable over the long term.

Africa’s Growing Role in Global CX Delivery

Africa’s emergence as a new outsourcing frontier is a key element of the strategy.

The continent has one of the world’s youngest populations, along with a rapidly expanding pool of digitally skilled, English-speaking professionals. For global companies seeking to diversify their support operations beyond traditional outsourcing hubs, this workforce represents a significant opportunity.

However, scaling operations in new regions also requires strong governance and operational frameworks—areas where established outsourcing providers like Atento play a crucial role.

By integrating Thrivin’s local talent development capabilities with its own transformation methodologies, Atento aims to create a model that balances expansion with operational discipline.

Enterprise Governance Still Takes Center Stage

Even as AI and new talent markets reshape the CX outsourcing landscape, governance remains a critical factor for enterprise buyers.

Large organizations must meet strict requirements around data security, regulatory compliance, and performance management—particularly when customer interactions involve sensitive personal or financial information.

In the new collaboration, Atento maintains responsibility for those enterprise-grade standards.

The company’s global delivery framework ensures that all participating partners operate within established security and compliance guidelines, giving enterprise clients confidence that expansion into new geographies won’t introduce unnecessary risk.

Industry Shift: From Cost Optimization to Transformation

The partnership also reflects a broader shift underway across the outsourcing industry.

For decades, business process outsourcing was largely defined by cost savings. Companies moved operations to lower-cost locations while attempting to maintain service quality.

Today, the conversation is increasingly about transformation rather than cost reduction.

Enterprises want outsourcing partners that can help them modernize operations, integrate AI technologies, and deliver better customer experiences.

By combining AI augmentation, workforce development, and governance frameworks, Atento is positioning its new model as an example of that evolution.

Brent Bush, executive vice president of sales and business development at Atento, said the collaboration brings together complementary strengths across technology, talent, and operational expertise.

“Together, we’re creating new opportunities to deliver AI-augmented customer experience at scale while helping enterprises expand into new markets with the governance, performance, and quality they expect from Atento,” Bush said.

Chris Condon, president and general manager of Atento’s U.S. Nearshore operations, added that the approach demonstrates how responsible global expansion can coexist with enterprise performance standards.

“By integrating AI-enabled voice technology with a disciplined, governed impact sourcing delivery model, we’re demonstrating that global expansion, operational rigor, and workforce development can coexist within a high-performance BTO framework,” he said.

The Future of CX Outsourcing

As customer expectations rise and AI technologies reshape service delivery, outsourcing providers are under pressure to rethink how their operations work.

The next generation of CX delivery models will likely rely on a combination of:

• AI-enhanced agents

• globally distributed talent networks

• enterprise-grade governance frameworks

• integrated customer and employee experience strategies

Atento’s partnership with Sanas and Thrivin represents an early attempt to bring those elements together into a single operating model.

If successful, it could provide a blueprint for how outsourcing firms evolve in an era where AI and human expertise are increasingly intertwined.

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Media planning has long been a fragmented process. Strategy teams analyze audiences, planners build targeting frameworks, and activation teams translate those plans into live campaigns across multiple platforms.

Seedtag wants to collapse those steps into a single AI-driven workflow.

The company, known for its neuro-contextual advertising technology, announced the launch of Liz Agent, an agentic AI platform designed to streamline media planning and campaign activation for brands and agencies. Acting as an AI consultant, the system combines real-time contextual intelligence, audience insights, and competitive analysis to guide marketers from campaign brief to execution through a conversational interface.

In practical terms, the platform lets marketing teams interact with Seedtag’s data ecosystem the same way they might consult a strategist—asking questions, exploring audience insights, and refining campaigns before activating them across Seedtag’s global advertising inventory.

The release reflects a growing trend across marketing technology: AI agents are increasingly moving beyond analytics tools to become decision-making partners in campaign strategy and execution.

Turning Context Into the Core of Media Planning

Seedtag has built its reputation around neuro-contextual advertising, a methodology that analyzes signals such as content context, audience interests, emotional tone, and user intent across the open web.

Rather than relying on third-party cookies or behavioral tracking, the company uses AI to understand the environment surrounding digital content and the mindset of audiences consuming it.

Liz Agent brings that contextual intelligence directly into the media planning workflow.

Powered by Seedtag’s proprietary neuro-contextual engine, the platform functions as a strategic interface where marketers can analyze market signals, explore audience segments, and build campaign strategies based on contextual insights.

Instead of simply retrieving data, the AI agent generates strategic recommendations—suggesting targeting parameters, creative messaging angles, and campaign structures aligned with specific marketing objectives.

The goal is to close the gap between planning and activation, a step that often slows down campaign execution in traditional advertising workflows.

From Campaign Brief to Activation in One Conversation

Liz Agent is designed to guide marketers through the entire campaign development process.

A typical workflow might begin with a campaign brief—such as launching a new product or increasing brand awareness among a specific demographic.

Using conversational prompts, marketers can ask the system to:

• Identify contextual audience segments aligned with campaign goals

• Analyze cultural or content trends across the open web

• Evaluate competitors’ messaging and positioning

• Suggest creative angles and campaign messaging

• Build a media plan optimized for contextual engagement

Once the strategy is finalized, the campaign can be activated directly through Seedtag’s advertising network.

This direct path from analysis to execution is a key differentiator for the platform.

Many marketing AI tools focus solely on insights or analytics. Liz Agent attempts to connect those insights directly to media buying and campaign activation.

Inside the Multi-Agent Architecture

Technically, Liz Agent runs on a multi-agent orchestration architecture that blends large language models with Seedtag’s proprietary datasets and advertising infrastructure.

The system coordinates multiple specialized AI agents, each responsible for different tasks such as data analysis, audience mapping, contextual interpretation, and campaign planning.

That orchestration layer allows Liz Agent to move beyond simple chat interfaces and perform more complex strategic analysis.

Four core components underpin the platform’s capabilities.

Direct Integration With Proprietary Data

One of the biggest challenges with AI-driven marketing tools is data accuracy.

Many systems rely heavily on general knowledge from large language models, which can produce insights disconnected from real advertising performance.

Liz Agent addresses this by connecting directly to Seedtag’s proprietary neuro-contextual datasets. That integration ensures recommendations are grounded in real campaign data and contextual intelligence rather than generic AI assumptions.

Proactive Intelligence

Unlike traditional planning tools that respond only to user queries, Liz Agent can proactively surface insights.

The system continuously analyzes the open web and Seedtag’s internal knowledge base to detect emerging cultural trends, shifts in audience interest, and competitive activity.

Those insights can help marketers identify campaign opportunities before they appear in standard analytics dashboards.

Conversational Strategy Interface

The conversational interface is central to Liz Agent’s design.

Instead of navigating dashboards or running complex queries, marketing teams interact with the system through natural language prompts.

This approach lowers the technical barrier to advanced analytics and allows planners, strategists, and brand managers to collaborate more easily around campaign strategy.

From Insight to Activation

The final step is execution.

Strategies developed through the AI interface can be activated directly across Seedtag’s global inventory, eliminating the traditional handoff between strategy and media buying teams.

That end-to-end workflow is designed to reduce the time it takes to move from campaign concept to live activation.

The Rise of AI Agents in Advertising

Liz Agent arrives at a time when AI agents are beginning to reshape how marketing technology works.

For years, AI tools in advertising focused primarily on optimization—automatically adjusting bids, testing creatives, or improving targeting algorithms.

But the latest generation of AI systems is expanding into earlier stages of the marketing workflow.

Instead of optimizing campaigns after they launch, these tools help marketers design campaigns from the ground up.

Industry analysts expect this shift to accelerate as AI models become better at interpreting complex datasets and generating strategic recommendations.

Platforms that combine proprietary data with agentic AI capabilities may gain a significant advantage in this environment.

Privacy-Friendly Contextual Advertising Gains Momentum

Seedtag’s focus on contextual intelligence also reflects a broader shift in digital advertising.

With the decline of third-party cookies and increasing privacy regulations worldwide, advertisers are searching for alternatives to traditional behavioral targeting.

Contextual advertising—targeting ads based on the content environment rather than user tracking—has regained popularity as a privacy-friendly approach.

Seedtag’s neuro-contextual technology attempts to take that model further by analyzing emotional signals, audience intent, and semantic meaning across web content.

By embedding that intelligence into Liz Agent, the company aims to help marketers build campaigns rooted in contextual understanding rather than surveillance-based targeting.

Seedtag’s Vision: AI as the Interface to Advertising Intelligence

Seedtag executives see the launch of Liz Agent as a broader shift in how marketers interact with advertising technology.

Rather than navigating multiple dashboards, analytics tools, and planning platforms, marketing teams may increasingly rely on AI agents as their primary interface to campaign intelligence.

“Liz Agent represents a major step forward in how our clients can interact with Seedtag’s intelligence and use it to think through and strategize their campaigns,” said Kartal Goksel, the company’s chief technology officer.

According to Goksel, the agent allows brands to plan and activate campaigns through natural conversation while ensuring recommendations remain grounded in Seedtag’s proprietary data.

Seedtag CEO Brian Gleason echoed that vision, describing AI agents as the next major interface layer in marketing technology.

“We are entering a new era where agents are the primary interface to intelligence,” Gleason said. “Liz Agent puts Seedtag’s AI directly into the hands of our clients, enabling them to interact with Liz through natural conversation.”

What It Means for Brands and Agencies

For media planners and agencies, tools like Liz Agent could significantly change how campaigns are built.

Traditionally, campaign planning involves multiple teams, long research cycles, and numerous software platforms.

By centralizing insights, analysis, and activation into a single AI-driven workflow, platforms like Liz Agent promise to reduce complexity and accelerate campaign timelines.

That could be particularly valuable for brands running global campaigns across fast-moving digital environments where cultural trends shift rapidly.

Seedtag says clients can begin using Liz Agent immediately to gain deeper audience insights and streamline campaign development.

Whether the platform ultimately transforms media planning as promised will depend on how effectively it integrates into agency workflows.

But its launch highlights a broader reality in advertising technology: the next generation of marketing tools may not just assist marketers—they may act as strategic partners in building campaigns from the ground up.

Get in touch with our MarTech Experts.

As generative AI increasingly becomes the first place people turn for answers, organizations are facing a new communications challenge: ensuring their expertise is visible, credible, and easily surfaced by AI-driven systems.

Knowledge management platform ExpertFile is aiming to address that shift with a major expansion of its product suite. The company announced the launch of ExpertFile Studio, alongside two additional platform capabilities—ExpertFile Search and ExpertFile SignalsAI—designed to help organizations publish, distribute, and optimize expert-led content in environments shaped by AI search.

The expanded platform targets a growing concern among marketing and communications leaders: how to ensure institutional expertise is properly represented in generative AI results, media coverage, and search-driven discovery.

Rather than relying on scattered bios, static directories, or loosely structured web pages, ExpertFile’s platform focuses on creating structured expert content that can be easily surfaced, cited, and recommended by both human researchers and AI systems.

The Growing Importance of AI-Readable Expertise

In the past, organizations often showcased their expertise through faculty directories, executive bios, or occasional thought leadership articles.

But the rise of generative AI tools—such as ChatGPT, Google’s AI Overviews, and Claude—has changed how audiences discover information and evaluate credibility.

Instead of browsing websites directly, users increasingly rely on AI-generated answers that synthesize information from multiple sources.

For organizations hoping to appear in those responses, visibility now depends on whether their expertise is structured in ways that AI systems can interpret and trust.

ExpertFile’s latest platform update is designed to address exactly that problem.

By structuring expert information into clearly defined profiles, topic hubs, and content frameworks, the company aims to help organizations become more “AI-legible”—making it easier for AI systems and media professionals to identify authoritative sources.

Introducing ExpertFile Studio

At the center of the announcement is ExpertFile Studio, a no-code publishing environment built to help communications teams create structured expert content.

The platform allows organizations to build and manage a range of expert-focused content experiences, including:

• Expert profiles and biographies

• Topic pages that highlight subject-matter expertise

• Speaker bureau directories

• Research showcases and academic highlights

• Expert answers addressing emerging topics

The system is designed to help communications teams publish this content quickly while maintaining governance standards around accuracy, brand consistency, and reputational safeguards.

Structured content formats also improve how information is indexed by search engines and interpreted by generative AI models.

According to ExpertFile, the goal is to transform expert knowledge into decision-ready content that can be easily referenced by journalists, researchers, and AI systems alike.

Extending Discoverability With ExpertFile Search

Publishing expert content is only part of the equation. Ensuring that expertise is discoverable beyond an organization’s website is equally important.

To address that challenge, the company has expanded ExpertFile Search, a global expert discovery engine used by journalists, media producers, and event organizers to locate credible sources.

The platform allows professionals to search across more than 50,000 topics, connecting them with experts who can provide insights, commentary, or speaking engagements.

In addition to its web-based search engine, ExpertFile also offers mobile apps for iOS and Android that extend expert discovery into mobile workflows commonly used by media professionals.

For organizations participating in the network, this distribution layer can significantly increase exposure, helping their experts appear in media opportunities that might otherwise go unnoticed.

SignalsAI Adds Analytics to Expert Content Strategy

The third component of the platform expansion focuses on analytics.

ExpertFile SignalsAI provides reporting tools designed to help organizations track how their expert content performs across media, search, and AI-driven discovery environments.

The system surfaces insights such as:

• Emerging topics that may require expert commentary

• Media coverage trends and visibility signals

• Alignment between expert content and search demand

• Opportunities to expand authority in specific subject areas

For communications teams, these insights can guide editorial planning and help organizations respond quickly to evolving news cycles or emerging industry trends.

The analytics layer also reflects a broader shift in content strategy: organizations increasingly treat expertise as a strategic asset that requires measurement, optimization, and ongoing management.

Why AI Discovery Is Changing Communications Strategy

The launch of the expanded ExpertFile platform comes at a time when the role of AI in information discovery is expanding rapidly.

Search engines are integrating generative AI summaries into results pages, and conversational AI platforms are becoming primary research tools for students, journalists, and business professionals.

In this environment, organizations that fail to structure their expertise clearly risk losing visibility to competitors whose information is easier for AI systems to interpret.

That dynamic is pushing communications teams to rethink how knowledge is published and governed.

Instead of simply hosting information on static webpages, organizations must ensure their expertise is:

• Structured and easily interpretable by machines

• Attributed to credible experts

• Governed for accuracy and consistency

• Updated regularly to reflect new insights

Platforms like ExpertFile aim to provide the infrastructure needed to support that approach.

Building Authority in the AI Information Economy

According to ExpertFile leadership, the ultimate goal is to help organizations maintain control over how their expertise is represented in AI-driven information ecosystems.

“Organizations do not want competitors and algorithms defining how their expertise is represented in AI search,” said Robert Carter, vice president of product and co-founder at ExpertFile.

By giving communications teams tools to structure, govern, and distribute expert content, the company aims to help institutions position themselves as credible sources in environments where AI-generated answers often shape public perception.

The Bigger Picture: Expertise as a Strategic Asset

The expansion of the ExpertFile platform highlights a broader shift in marketing and communications.

As AI becomes a primary gateway to information, institutional expertise is becoming a strategic asset that must be actively managed and optimized.

Universities, healthcare institutions, corporations, and research organizations all rely on subject-matter experts to build credibility and influence public conversations.

But without structured systems for publishing and distributing that expertise, those voices can be overlooked by search engines, journalists, and AI models.

By combining publishing tools, discovery platforms, and analytics capabilities into a single system, ExpertFile hopes to help organizations ensure their experts remain visible—and trusted—in an increasingly AI-mediated information landscape.

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Document translation has long been one of those deceptively simple tasks that can quietly derail productivity.

For professionals working across languages—whether reviewing contracts, preparing investor reports, or analyzing research documents—the process typically involves jumping between multiple tools. A file must be uploaded to a translation platform, processed externally, downloaded again, and then painstakingly reformatted after tables, numbering systems, or tracked changes break in the conversion.

Bluente, an AI-powered document translation platform used by more than 40,000 professionals globally, is trying to eliminate that workflow disruption.

The company announced the release of its Model Context Protocol (MCP) server, an open-source integration that allows AI assistants to translate documents directly within existing AI-powered work environments such as Claude Desktop and Cursor.

By embedding translation capabilities directly into AI agent workflows, Bluente aims to remove the need for context switching—and preserve document formatting in the process.

The Bluente Translate MCP Server is now available on GitHub under the MIT open-source license, allowing developers to integrate or modify the tool for their own environments.

Why Translation Workflows Are Still Broken

Despite advances in AI translation quality, the practical workflow around document translation has remained stubbornly inefficient.

Most translation tools operate as standalone services. Users upload files, wait for processing, and download a translated version—often only to discover that formatting has been disrupted.

This is particularly problematic in professional environments where formatting is critical.

Legal contracts rely on strict numbering systems and clause structures. Financial reports depend on intact tables and formatting. Investor presentations require visual consistency across slides.

Traditional translation processes frequently strip away these structures, forcing users to manually reconstruct the document afterward.

Bluente’s MCP integration aims to address both the workflow interruption and the format preservation problem simultaneously.

Translation Without Leaving the AI Workspace

The Model Context Protocol is an emerging open standard designed to allow AI assistants to interact directly with external software tools.

By publishing an MCP server, Bluente enables AI systems such as Claude Desktop or developer environments like Cursor to access its translation engine as a native capability.

In practice, this means a user can translate a document from within the same AI conversation or coding environment they are already working in.

For example:

• A developer using Cursor could translate a client’s PDF contract without switching applications.

• A legal analyst using Claude Desktop could upload a scanned Arabic contract and receive a translated, formatted version in the same chat interface.

Instead of juggling multiple platforms, the translation process becomes a simple command executed by the AI assistant.

The result is delivered in the original document format—tables, numbering, and layout preserved.

Six Core Tools Power the MCP Server

Bluente’s MCP server exposes six tools that handle the full lifecycle of document translation.

Language Discovery

The system can query supported languages and translation pairs across more than 120 languages, helping users identify available translation options.

File Upload

Documents—including PDFs, Word files, spreadsheets, presentations, and images—can be uploaded directly to Bluente’s translation engine.

Translation Execution

Once uploaded, the system processes the document while preserving formatting and applying integrated optical character recognition (OCR) for scanned files.

Status Tracking

For large documents or complex files, the server provides real-time progress monitoring.

File Download

Once processing is complete, users can retrieve the translated document with the original structure intact.

End-to-End Workflow Command

For simplicity, the server also supports a single command that handles upload, translation, and download in one automated step.

Together, these capabilities allow AI agents to manage the entire translation process without requiring manual intervention.

Built for AI Development Environments

Technically, the MCP server runs on Node.js version 20 or later and communicates with AI clients through standard input/output (stdio).

The translation engine itself connects to Bluente’s APIs over HTTPS, ensuring compatibility with secure enterprise workflows.

This architecture allows developers to integrate translation capabilities directly into their AI-driven applications, tools, or automation pipelines.

For engineering teams building AI-powered workflows, the integration replaces a common workaround: stitching together multiple services for OCR, translation, and formatting preservation.

With the MCP server, a single tool call can handle the entire process.

Enterprise-Grade Security Built In

Security is often a critical concern when translating sensitive business documents.

Bluente says the MCP server adheres to enterprise-grade security standards, including:

• End-to-end encryption for document processing

• Zero data retention policies

• Automatic file deletion after processing

These safeguards are particularly important for professionals in regulated industries such as finance, legal services, and life sciences, where document confidentiality is essential.

Why MCP Is Gaining Momentum in AI Ecosystems

The Model Context Protocol is quickly emerging as a key infrastructure layer in the rapidly expanding ecosystem of AI assistants.

Instead of building monolithic AI systems with every capability embedded internally, MCP allows developers to connect AI models to specialized tools that handle specific tasks.

This modular approach enables AI assistants to interact with real-world systems—databases, APIs, and software platforms—while maintaining conversational interfaces.

For companies like Bluente, publishing an MCP server effectively turns their product into an AI-native service.

Rather than requiring users to visit a standalone website or application, the functionality becomes accessible wherever AI agents operate.

Open Source for Developer Adoption

Bluente has also chosen to release the MCP server as open-source software under the MIT license.

This allows developers to inspect the code, customize integrations, and contribute improvements back to the project.

Open sourcing the tool could accelerate adoption across developer communities experimenting with AI-powered workflows.

It also aligns with a broader trend in the AI ecosystem, where many infrastructure components—protocols, frameworks, and integrations—are being developed collaboratively through open-source projects.

Translation as an AI-Native Capability

Bluente’s release highlights a broader transformation in professional software.

Instead of standalone applications, many tools are evolving into AI-native capabilities embedded directly inside conversational workflows.

Tasks that once required switching between platforms—writing code, analyzing data, generating images, or translating documents—can increasingly be executed through AI agents connected to specialized services.

Bluente CEO Daphne Tay said eliminating context switching was a key motivation behind the project.

“Professionals already work inside AI-powered environments,” Tay said. “They shouldn’t have to leave those environments, upload a file somewhere else, wait, download the result, and then spend an hour fixing broken formatting.”

By bringing translation directly into those environments, the company hopes to remove a long-standing productivity bottleneck for professionals working across languages.

The Bigger Picture

As AI assistants evolve into full productivity platforms, integrations like Bluente’s MCP server may become increasingly common.

Rather than launching separate apps for every task, professionals could rely on AI agents that orchestrate multiple services behind the scenes—handling everything from document processing to analytics and translation within a single interface.

Bluente’s move suggests that document translation, once a disconnected workflow step, may soon become just another native capability inside the expanding ecosystem of AI-powered work environments.

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Video translation has improved dramatically in recent years, but a critical piece of the localization puzzle has often remained overlooked: the text embedded directly inside videos.

Subtitles and AI dubbing can translate what viewers hear, but many videos also rely heavily on visual elements—slides, labels, diagrams, and callouts—to communicate key information. When those elements remain in the original language, global audiences can miss important context even if they understand the narration.

To address this gap, Vozo AI has introduced Visual Translate, a new generative AI capability designed to automatically translate on-screen text within videos while preserving the original layout, design, and animations.

The feature, currently available in beta, aims to bring fully localized video experiences to global audiences without requiring creators to manually rebuild video content.

The Hidden Challenge in Video Localization

Traditional video localization focuses primarily on speech.

Tools can generate subtitles, perform voice translation, or create AI-generated dubbing tracks. However, videos frequently contain important visual information that these tools do not address.

Examples include:

• Slide text in presentation-style videos

• Labels in product demonstrations

• Callouts highlighting key features

• Charts and diagrams explaining processes

• Instructional overlays in training materials

When these elements remain untranslated, viewers may understand the narration but struggle to fully grasp the message.

For organizations producing training materials, marketing content, or educational videos, this creates a serious barrier to global communication.

Automating On-Screen Text Translation

Vozo AI’s Visual Translate technology is designed to automatically detect and translate visual text directly within video files.

Unlike traditional workflows that require access to the original editing project or design files, the system works directly from the video itself.

This allows organizations to localize videos even when the original production assets are unavailable.

Visual Translate performs several steps automatically:

• Detects on-screen text within video frames

• Translates the text into the selected target language

• Recreates the text within the original visual layout

• Maintains fonts, positioning, colors, and animations

The result is a localized video where both narration and visuals are translated cohesively.

This approach ensures that international viewers receive the same visual clarity and context as the original audience.

Preserving Design and Layout

One of the biggest challenges in translating visual content is maintaining the integrity of the original design.

Text overlays in videos often interact with animated transitions, visual elements, and spatial layouts. Simply replacing text with a translated version can disrupt formatting or cause layout issues.

Visual Translate addresses this by preserving:

• Original design structure

• Text positioning

• Font styles and sizes

• Color schemes

• Animated effects

Users can also manually adjust the translated text, allowing further customization if necessary.

This flexibility ensures the final localized video remains visually consistent with the original production.

Dramatic Efficiency Gains in Early Testing

During its alpha testing phase, Visual Translate was used by a multinational manufacturing company to localize training content for global teams and distributors.

The organization relied heavily on slide-based training videos where key information appeared directly within the visuals.

Previously, the company’s localization process required manually editing video assets to replace text in each language version.

By using Visual Translate, the company was able to automatically translate visual content into nine languages, dramatically reducing production time.

According to Vozo AI, the process was shortened from two days to approximately 30 minutes, representing a 96% reduction in localization time.

A Shift Toward Complete Video Localization

The launch of Visual Translate reflects a broader evolution in AI-powered video localization.

Until recently, AI tools focused mainly on speech-based translation—subtitles, voiceovers, and dubbing.

However, fully localized video experiences require translating both what viewers hear and what they see.

For industries such as:

• Corporate training

• Education and e-learning

• Product marketing and demos

• Technical instruction

visual content often carries critical information that cannot be conveyed through narration alone.

By addressing this missing layer, Visual Translate aims to make video localization more comprehensive and scalable.

Expanding Global Communication Through Video

As video continues to dominate digital communication, organizations increasingly rely on visual content to educate, train, and engage audiences worldwide.

However, language barriers remain one of the biggest obstacles to global video distribution.

Automating the translation of visual elements could significantly reduce the time and cost required to adapt content for international audiences.

According to Vozo AI founder and CEO Dr. CY Zhou, solving this problem requires rethinking how translation tools handle video.

“Most video translation tools focus on speech,” Zhou said. “But in many videos, meaning is conveyed visually—through slides, diagrams, and on-screen text.”

Visual Translate aims to bridge that gap by enabling videos to carry their full meaning across languages.

Beta Launch and Future Development

Visual Translate is currently available in beta, allowing users to experiment with the technology while Vozo AI continues expanding its capabilities.

Future updates are expected to broaden support for additional visual formats and more complex video structures.

As AI continues to reshape media production workflows, tools like Visual Translate could play a key role in making global video communication faster, easier, and more accessible.

For organizations producing multilingual video content, the ability to localize visuals automatically may represent a major step toward truly global storytelling.

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Customer experience (CX) is no longer confined to contact centers.

Today, customer interactions happen everywhere—on retail floors, in service workshops, at healthcare facilities, and across distributed field teams. Every one of those interactions can influence brand perception, customer loyalty, and ultimately revenue.

To address this shift, 8x8 has announced the global general availability of 8x8 Engage, a new capability designed to bring enterprise-grade customer engagement tools to frontline teams across the organization.

The platform expands the reach of CX technology beyond traditional service departments, enabling employees outside the contact center to communicate with customers using the same intelligence, automation, and governance frameworks typically reserved for dedicated support teams.

By embedding these capabilities into its broader 8x8 Platform for CX, the company is betting that the future of customer experience will depend on empowering every customer-facing employee—not just call center agents.

Customer Experience Moves Beyond the Contact Center

For years, companies treated customer experience primarily as a function of customer service departments.

But as digital transformation reshaped business operations, customer interactions increasingly occur outside formal support channels.

A service technician speaking with a customer in a repair workshop, a retail associate answering questions in-store, or a healthcare administrator coordinating appointments can all influence the customer journey.

These decentralized interactions present both an opportunity and a challenge.

Organizations want to empower frontline employees to respond quickly and effectively, but they also need visibility, consistency, and governance across every interaction.

That balance is what 8x8 Engage is designed to provide.

“The way organizations deliver customer experience has fundamentally changed,” said Hunter Middleton, chief product officer at 8x8. “They need every customer-facing team to engage with consistency, intelligence, and accountability.”

Bringing Contact Center Tools to Frontline Teams

Traditionally, advanced engagement tools—such as call routing, analytics dashboards, and AI-powered insights—have been limited to contact center environments.

8x8 Engage extends those capabilities across the entire enterprise.

Frontline teams can access communication tools, customer data, and AI-powered insights from mobile devices or desktop environments, allowing them to interact with customers regardless of where they are working.

The platform enables employees to handle calls and messages while moving between locations or working remotely, helping organizations avoid missed interactions and maintain service continuity.

For companies with distributed operations, this flexibility can significantly improve responsiveness.

Strong Early Adoption Signals Growing Demand

8x8 reports significant growth momentum since the initial introduction of Engage.

According to the company:

• Customer adoption has increased by more than 150% year over year

• Daily active new customers have grown nearly fivefold

• Daily active users have increased more than four times compared to the previous year

These numbers suggest that enterprises are actively seeking ways to extend CX capabilities beyond centralized contact centers.

As organizations expand their customer engagement strategies across multiple departments, the need for unified platforms that maintain visibility and accountability becomes more pressing.

Designed for Mobile and Distributed Workforces

One of the defining characteristics of modern customer-facing roles is mobility.

Employees often move between different environments—workshops, retail locations, client sites, or hospital departments—while still needing access to communication tools.

8x8 Engage addresses this reality with mobile-ready engagement features that allow teams to interact with customers regardless of location.

For example, a technician working on a service floor could receive or return customer calls without needing to return to a desk. Similarly, field teams can maintain communication continuity while traveling between job sites.

This mobility helps organizations respond faster to customer inquiries and maintain service levels even in dynamic work environments.

AI Adds Context to Every Interaction

In addition to communication capabilities, the platform incorporates artificial intelligence features designed to improve both efficiency and customer outcomes.

Among the AI-powered tools included in 8x8 Engage are:

• AI-generated conversation summaries that capture key details from customer interactions

• Sentiment analysis to help identify customer satisfaction levels during conversations

• CRM-integrated context, providing employees with relevant customer information before and during interactions

Together, these capabilities allow frontline teams to respond more effectively while reducing the need for manual documentation after conversations.

AI-generated summaries also help ensure important details are captured consistently across teams.

Intelligent Routing and Workload Visibility

Another feature borrowed from traditional contact center environments is intelligent routing.

8x8 Engage uses routing and queue management tools to ensure that customer interactions reach the most appropriate team member.

For example, inquiries can be directed to specialists with the right expertise or routed to available staff based on real-time workload conditions.

Managers also gain visibility into team activity through analytics dashboards that track interactions across departments.

This oversight helps organizations maintain accountability while allowing teams to operate with greater autonomy.

Unified Analytics Across the Customer Journey

As customer interactions spread across multiple teams, tracking the entire customer journey becomes increasingly difficult.

8x8 Engage addresses this challenge with unified governance and analytics capabilities that provide end-to-end visibility into interactions.

Organizations can monitor engagement across departments, identify patterns in customer behavior, and measure performance metrics such as response times and customer satisfaction.

This centralized oversight allows CX leaders to maintain strategic control while enabling decentralized teams to manage day-to-day interactions independently.

Industry Analysts See a Broader CX Shift

Industry analysts say the expansion of customer engagement technology beyond contact centers reflects a broader transformation in how organizations approach CX.

“Customer engagement is increasingly happening across all parts of the enterprise,” said Zeus Kerravala, founder and principal analyst at ZK Research.

According to Kerravala, enterprises are searching for flexible engagement models that give frontline teams more control without creating additional complexity.

That shift is pushing technology vendors to rethink how communication platforms are designed.

Rather than focusing solely on contact center environments, vendors are building platforms that integrate communications, analytics, and AI capabilities across the entire organization.

Real-World Impact for Distributed Businesses

Some early adopters say the platform has already improved operational flexibility.

Motus Commercials, a commercial vehicle dealer group, implemented 8x8 Engage to support teams working across service locations.

According to Jake Blowers, the company’s head of projects and innovation, the platform allows employees to take calls wherever they are working—whether at a desk, in a workshop, or on the move.

This flexibility has reduced missed customer interactions while improving responsiveness across the business.

The result, he said, is a more agile operation and a better overall customer experience.

A Platform Strategy for the Future of CX

The release of 8x8 Engage also reflects the company’s broader strategy of building a unified communications and customer experience platform.

Rather than offering standalone tools for messaging, voice, analytics, and automation, the company integrates these capabilities into a single platform designed to support both internal collaboration and customer engagement.

By extending CX capabilities across all customer-facing roles, 8x8 hopes to help organizations eliminate communication silos and manage interactions more efficiently.

The Bigger Picture

The global launch of 8x8 Engage highlights a growing reality in modern business: customer experience is no longer owned by a single department.

Every employee who interacts with a customer plays a role in shaping that experience.

As organizations adopt distributed work models and digital communication channels, the technology supporting those interactions must evolve as well.

Platforms like 8x8 Engage represent an attempt to bring enterprise-grade CX tools to the entire workforce—giving organizations the ability to deliver consistent, intelligent engagement wherever customer conversations happen.

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