South Africa is rapidly emerging as a leading hub for customer experience excellence and digital transformation in Africa. As organizations shift from traditional contact center models to intelligent, AI-driven customer engagement ecosystems, the need for secure, compliant, and locally hosted platforms has intensified. Addressing this demand, NICE has officially launched a dedicated local instance of CXone Mpower, its AI-powered customer experience platform, hosted with full redundancy across Cape Town and Johannesburg.

This launch not only ensures data sovereignty and compliance with South African regulatory frameworks but also establishes a strategic foundation for NICE’s broader expansion across Africa. It brings advanced agentic AI, unified customer journey orchestration, and integrated digital and voice channels to enterprises across regulated industries, financial institutions, and large-scale customer operations.

Growing Demand for AI-Driven Customer Engagement in South Africa

Market Shift Toward Modern CX Ecosystems

• Organizations are transitioning away from legacy contact center models.

• Real-time decisioning and operational resilience are becoming essential.

• Regulated sectors need platforms that ensure strict data governance.

• Businesses seek measurable, outcome-driven customer engagement strategies.

Why Local Data Residency Matters

• Ensures all CXone Mpower applications and data remain within South Africa.

• Supports compliance with stringent data protection and governance laws.

• Helps organizations minimize regulatory risk while improving customer trust.

• Enables secure deployment models for financial institutions and government agencies.

Introducing NICE CXone Mpower: A Unified, Cloud-Native CX Platform

1. Local Hosting with Redundancy for Reliability

• CXone Mpower is now fully hosted within South Africa.

• Redundant data centers in Cape Town and Johannesburg enhance resilience.

• Supports continuous operations with high availability for enterprise workloads.

• Provides a scalable foundation for broader African deployment.

2. Enterprise-Ready, AI-Powered Experience Platform

• Enables end-to-end customer journey orchestration across channels.

• Integrates digital and voice interactions in a unified cloud environment.

• Supports real-time AI-driven insights and automated workflows.

• Delivers measurable improvements in operational efficiency and CX outcomes.

3. Native Agentic AI Following Cognigy Acquisition

• Combines automated and agent-assisted conversations.

• AI can understand context, listen, analyze intent, and execute tasks.

• Enables organizations to merge human empathy with intelligent automation.

• Enhances accuracy, speed, and consistency of customer interactions.

4. Complete Workforce Augmentation Suite

• Provides a 360° view of customer journeys across touchpoints.

• Improves agent performance, satisfaction, and productivity.

• Empowers supervisors with AI-powered insights and decisioning tools.

• Supports front, middle, and back-office alignment for seamless operations.

Telecommunications Investment to Improve Voice Quality

Enhancing Regional Infrastructure for Better CX

• NICE has strengthened telecom infrastructure to keep voice traffic within the region.

• Ensures low latency for real-time interactions.

• Improves clarity, call reliability, and overall customer experience.

• Supports higher-volume enterprise and BPO operations across Africa.

Strategic Leadership Perspectives

NICE’s Commitment to Growth Across Africa

Darren Rushworth, President of NICE International, emphasized South Africa’s importance as a global CX leader and a market with strong potential.

Key Takeaways:

• Local hosting supports AI-assisted, outcome-driven service innovation.

• The launch reflects NICE’s long-term regional strategy.

• Positioned to scale across Africa as enterprises modernize CX environments.

• Provides the technological foundation for South African organizations to compete globally.

Driving Global Adoption of CX AI

1. Real-Time Orchestration and Automation

• Automates routine customer interactions with AI-driven workflows.

• Supports agents with intelligent assistance and dynamic guidance.

• Enhances efficiency and reduces handling times across channels.

2. Unified Experiences Across the Customer Lifecycle

• Integrates systems across the front, middle, and back offices.

• Provides seamless journeys with consistent, personalized experiences.

• Enables organizations to measure real-time outcomes for continuous improvement.

3. Strengthening Local Collaboration

• NICE will work with local technology partners and systems integrators.

• Plans to host executive briefings, workshops, and industry roundtables.

• Supports customer enablement and knowledge transfer for regional success.

Conclusion

NICE’s launch of a dedicated, locally hosted CXone Mpower instance in South Africa marks a significant advancement for the region’s customer experience industry. By integrating agentic AI, unified omnichannel capabilities, and robust data governance, NICE empowers organizations to modernize their engagement strategies while meeting strict regulatory requirements.

With enhanced telecommunications infrastructure, regional hosting, and scalable cloud services, enterprises in South Africa and beyond can deliver high-quality, resilient, and personalized customer experiences. As part of NICE’s global strategy to accelerate the adoption of CX AI, this launch sets the stage for long-term growth, innovation, and competitive advantage across Africa.

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Brick-and-mortar retail is in the midst of a transformation. Once considered at a disadvantage to e-commerce due to limited shopper data, physical retail environments are now becoming just as measurable, responsive, and insight-driven as digital channels. Advances in artificial intelligence, sensor-based technologies, and computer vision have enabled retailers to capture and act on real-time data that was previously inaccessible.

At the forefront of this shift is Sensormatic Solutions, the global retail solutions portfolio of Johnson Controls. By integrating AI-enabled sensors and cameras into retail environments, Sensormatic Solutions is helping retailers unlock critical business metrics tied to shopper behavior, store performance, and operational efficiency.

Its Store Guest Behaviors Analytics, powered by AI and built on Re-Identification (Re-ID) technology, gives retailers unprecedented visibility into how shoppers move, dwell, and engage inside physical stores. Delivered through Orbit AI overhead people counters, Video AI cameras, and ShopperTrak Analytics, the platform turns raw activity into actionable insights, enabling faster decision-making and measurable sales growth—all while maintaining shopper privacy.

This evolution represents a major moment for MarTech and retail analytics, where offline behavior is no longer a blind spot but a strategic growth lever.

Why In-Store Data Now Rivals E-Commerce Insights

Traditionally, e-commerce platforms have dominated when it comes to shopper analytics. Retailers could track clicks, conversion paths, dwell time, abandonment, and revenue attribution with precision. Physical stores, by contrast, relied on sales totals and foot traffic, offering limited context into shopper intent or engagement.

That gap is rapidly closing.

According to Sensormatic Solutions leadership, today’s brick-and-mortar retailers now have access to data that rivals, and in some cases complements, digital insights.

Key developments making this possible include:

• AI-powered computer vision that tracks movement without personal identification.

• Advanced sensors capable of distinguishing shoppers from staff and passersby.

• Real-time analytics platforms that transform foot traffic into business intelligence.

• Privacy-first design that excludes personally identifiable information.

The result is not just more data, but better data—information that directly influences store layout, staffing decisions, merchandising strategy, and revenue optimization.

Understanding Store Guest Behaviors Analytics

At the core of Sensormatic Solutions’ innovation is Store Guest Behaviors Analytics, a suite designed to convert in-store movement into measurable and actionable insights.

What Powers the Platform

Store Guest Behaviors Analytics is driven by a combination of hardware and software innovations:

• Orbit AI Overhead People Counters

  • AI-enabled sensors mounted overhead

  • Designed to accurately count shoppers

  • Exclude staff, children in strollers, and non-shopping traffic

• Video AI Cameras

  • Capture anonymized movement patterns

  • Enable behavioral attribution without facial recognition

• Re-Identification (Re-ID) Technology

  • Assigns anonymized IDs to track movement journeys

  • Identifies repeat behavior patterns during a visit

• ShopperTrak Analytics Platform

  • Centralized dashboard for data visualization

  • Translates raw inputs into actionable metrics

Together, these components create a complete view of shopper behavior across the store.

Privacy-First Design as a Core MarTech Principle

One of the biggest challenges in modern analytics is balancing personalization and insight with consumer privacy. Sensormatic Solutions has prioritized privacy by design.

Key privacy measures include:

• No collection of personally identifiable information (PII)

• No facial recognition or biometric capture

• Anonymized behavior tracking only within a single visit

• Exclusion of staff and irrelevant traffic from analytics

This approach allows retailers to benefit from high-quality behavioral insights while staying compliant with evolving global privacy regulations and maintaining shopper trust.

Driving Higher Conversion Through Shopper Journey Insights

One of the most valuable applications of Store Guest Behaviors Analytics is its ability to reveal the path to purchase inside physical stores.

By understanding how shoppers move through a space, retailers can identify:

• Entry points with the highest engagement

• High-traffic versus low-traffic aisles

• Bottlenecks that prevent smooth navigation

• Drop-off zones where shoppers disengage

How Path-to-Purchase Analytics Improves Conversion

Retailers can use journey data to:

• Optimize store layouts based on real movement patterns

• Reposition high-margin or promotional products

• Improve signage and wayfinding

• Reduce friction in high-traffic zones

Rather than relying on assumptions or outdated floor plans, retailers gain data-backed clarity on how store design directly impacts sales conversion.

Boosting Merchandising Impact With Dwell and Zone Analytics

Beyond movement, understanding where shoppers spend their time is critical to merchandising success.

Store Guest Behaviors Analytics provides insights into:

• Zone preference

• Dwell time by display or category

• Engagement levels across store sections

Turning Dwell Time Into Revenue

With these insights, retailers can:

• Identify top-performing displays that capture attention

• Compare engagement across multiple product categories

• Test and validate new merchandising strategies

• Quickly adjust underperforming displays

This level of real-time feedback allows merchandising teams to move from static setups to dynamic, data-driven optimization.

Improving Operational Efficiency Through Smarter Staffing

Labor optimization remains one of the most complex challenges in retail. Overstaffing increases costs, while understaffing hurts customer experience and sales.

AI-powered traffic analytics offer a more precise approach.

How Real-Time Traffic Data Supports Staffing Decisions

Retailers can use traffic and behavioral data to:

• Identify peak shopping hours with higher accuracy

• Align staff scheduling with real demand

• Adjust floor coverage dynamically throughout the day

• Reduce wait times during high-traffic periods

By matching labor allocation to actual in-store activity, retailers improve both efficiency and shopper satisfaction without increasing labor spend.

Real-World Validation: LIDS Expands Its Insights With Orbit AI

Sensormatic Solutions’ innovations are already delivering measurable value for retail brands.

LIDS, a long-time partner of Sensormatic Solutions, has deepened its collaboration with the introduction of Orbit AI technology.

How LIDS Is Using AI-Powered Insights

According to LIDS leadership, the new technology provides:

• Clearer visibility into shopper flow

• Better understanding of in-store engagement

• Stronger decision-making around layout and merchandising

• Improved overall shopper experience

These insights demonstrate how AI-powered analytics can transition from experimentation to everyday operational value.

MarTech Meets Retail: A Broader Strategic Shift

Store Guest Behaviors Analytics represents a broader trend in MarTech: the blending of physical and digital intelligence.

Key implications for MarTech leaders include:

• Offline data is becoming first-class marketing intelligence

• Attribution is expanding beyond clicks to physical experiences

• Customer journey mapping is now omnichannel by default

• Real-time decision-making is extending into physical environments

Retailers that embrace this convergence gain a competitive advantage by aligning digital strategy with in-store realities.

NRF 2026: Bringing AI-Powered Retail Analytics to Life

Sensormatic Solutions will showcase Store Guest Behaviors Analytics at the 2026 NRF Big Show, taking place January 11–13 at the Jacob K. Javits Convention Center in New York City.

Attendees can expect:

• Live demonstrations of AI-powered analytics

• Hands-on walkthroughs of Orbit AI sensors

• Insights into upcoming retail technology innovations

• Direct discussions with Sensormatic Solutions experts

This event provides a tangible look at how AI-backed MarTech is reshaping physical retail.

Conclusion: Turning In-Store Behavior Into Strategic Advantage

The future of retail is no longer a debate between online and offline. It is about how effectively brands can connect data, insights, and action across every customer touchpoint.

Sensormatic Solutions’ Store Guest Behaviors Analytics shows what’s possible when AI, sensors, and MarTech principles converge. By transforming in-store activity into real-time, privacy-safe intelligence, retailers can:

• Increase conversion rates

• Strengthen merchandising performance

• Optimize staffing and operations

• Deliver better shopper experiences

As physical retail becomes more measurable and responsive, solutions like these will define the next era of data-driven growth. For retailers looking to compete smarter, faster, and more efficiently, AI-powered in-store analytics are no longer optional—they are foundational.

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Connected TV (CTV) has rapidly become one of the most important digital advertising channels, but it has also introduced new complexities around targeting, measurement, and relevance. As streaming ecosystems grow more fragmented, advertisers are searching for smarter ways to align their messages with meaningful content moments—without relying on personal data.

Historically, contextual targeting in video advertising has stopped at broad classifications such as genre, show title, or channel. While useful, these approaches lack the precision required to reflect how viewers actually experience content. A comedy show can contain intense moments. A live sports broadcast can shift tone in seconds. Advertisers need intelligence that operates at the same speed and nuance as streaming content itself.

That gap is now closing.

Anoki, a leader in contextual video intelligence for CTV, has announced its integration with Index Marketplaces, Index Exchange’s omnichannel platform for curated, signal-rich supply activation and sell-side decisioning. With this integration, Index becomes one of the first global ad exchanges to provide buyers and sellers with scalable access to AI-powered, scene-level contextual intelligence across streaming TV.

This collaboration marks a significant advancement in how CTV advertising is planned, activated, and optimized—bringing real-time contextual precision into programmatic workflows at scale.

The Limitations of Traditional Contextual Targeting in CTV

For years, marketers have depended on contextual tools that classify content at a high level. While these methods helped ensure brand adjacency and general relevance, they fall short in today’s dynamic streaming environment.

Common limitations include:

• Context classification limited to genre or program level

• Lack of emotional or tonal understanding within content

• Inability to adapt ads to real-time content shifts

• Missed opportunities during high-impact scenes

• Inefficient spend due to overly broad targeting

As a result, advertisers have struggled to fully capitalize on CTV’s storytelling power, often delivering creative messages that are misaligned with the actual moment viewers are experiencing.

What Scene-Level Intelligence Changes for Marketers

Scene-level intelligence represents a step change in contextual advertising. Instead of treating a program as a single environment, it analyzes content moment by moment.

With Anoki’s AI-powered approach, marketers gain visibility into:

• Scene-level tone and sentiment

• Subject matter within individual moments

• Emotional context of live and on-demand programming

• Shifts in content during live events

• Nuanced storytelling elements that influence viewer attention

This intelligence allows advertisers to move from static adjacency to dynamic contextual alignment, where creative messaging can be activated around the most relevant moments in real time.

How Anoki and Index Marketplaces Work Together

The integration connects Anoki’s ContextIQ platform with Index Marketplaces, combining deep content intelligence with curated, high-quality supply.

Key Components of the Integration

• Anoki ContextIQ

  • AI-powered scene-level contextual analysis for streaming TV

  • Categorizes scenes by tone, sentiment, and subject matter

  • Enables real-time activation without personal data

• Index Marketplaces

  • Omnichannel platform for curated, signal-rich inventory

  • Designed for scalable activation and sell-side decisioning

  • Supports premium streaming and live event supply

• Index Exchange Infrastructure

  • Efficient, transparent, and DSP-agnostic activation

  • Seamless integration with existing programmatic workflows

Together, these components allow scene-level intelligence to be operationalized across the open internet at scale.

Unlocking Precision Through Dynamic Ad Podding

A major strength of Index Exchange lies in its leadership in dynamic ad podding, particularly across streaming and live events.

By pairing Anoki’s intelligence with dynamic ad podding, the integration enables:

• Scene-aware decisioning at the pod level

• Creative alignment with real-time content shifts

• Reduced ad waste through more precise placements

• Improved transparency across supply and delivery

• Greater control over how ads appear within content experiences

Rather than treating every ad break equally, advertisers can now align creative messaging with the emotional and contextual state of the content at that exact moment.

Real-Time Relevance for Live and Streaming Content

Live events present some of the most valuable but challenging inventory in CTV. Content tone, momentum, and emotional intensity can change rapidly, making traditional targeting ineffective.

This integration enables advertisers to:

• Activate ads around key live moments

• Maintain brand safety even in unpredictable environments

• Respond to content changes as they happen

• Deliver more emotionally aligned messaging

• Enhance viewer experience without disrupting engagement

For live sports, news, and events, scene-level intelligence ensures ads remain relevant without sacrificing scalability.

What This Means for Marketers

From a buyer perspective, the Anoki–Index integration delivers immediate and measurable benefits.

Key Advantages for Advertisers

• Access to scene-level contextual targeting without personal data

• Curated, high-performance deal packages in Index Marketplaces

• Improved alignment between creative and content moments

• Enhanced brand safety, including live event environments

• Reduced inefficiencies from overly broad contextual buys

• No disruption to existing DSP or SSP workflows

Marketers gain more control and confidence in where and how their ads appear—driving stronger performance outcomes.

New Opportunities for Media Owners

Media owners also stand to benefit significantly from this integration, particularly those with premium CTV and live event inventory.

Value Delivered to Media Owners

• AI-powered intelligence to enhance inventory valuation

• Greater transparency into content-level performance

• Improved yield through smarter contextual alignment

• Control over how inventory is packaged and sold

• Ability to compete beyond basic contextual signals

• No additional platform fees or workflow changes

By activating scene-level insights through Index Marketplaces, media owners can unlock the full contextual value of their content without increasing complexity.

Privacy-First Contextual Intelligence at Scale

As privacy regulations evolve and third-party data becomes less reliable, contextual advertising is emerging as a core MarTech strategy.

This integration reinforces that shift by:

• Eliminating dependence on personal or identity-based data

• Preserving viewer privacy across streaming environments

• Supporting compliance with global regulatory standards

• Delivering relevance through content, not consumers

For MarTech leaders, this represents a sustainable model for performance-driven advertising in a privacy-first future.

Industry Perspectives on the Integration

Industry leaders see this collaboration as a meaningful step forward for CTV advertising.

Key themes highlighted by executives include:

• A new level of precision in contextual activation

• Stronger emotional alignment between content and creative

• Smarter decisioning across fragmented streaming platforms

• Improved outcomes for both buyers and sellers

• Expanded creative opportunities not possible in legacy programmatic models

The consensus is clear: scene-level intelligence is reshaping what’s possible in CTV.

MarTech Implications: From Programmatic to Moment-Based Activation

This integration reflects a broader MarTech evolution toward moment-based marketing, where relevance is defined by context, timing, and emotional resonance rather than static segments or assumptions.

Implications for MarTech teams include:

• Context becoming a primary optimization signal

• Creative strategy increasingly tied to content moments

• Programmatic buying shifting toward curated intelligence

• Measurement expanding beyond impressions to impact

• CTV emerging as a premium, intelligent channel—not just a scaled one

Conclusion: Setting a New Standard for CTV Contextual Advertising

The integration between Anoki and Index Marketplaces represents a meaningful leap forward for connected TV advertising. By bringing AI-powered scene-level intelligence into curated, scalable programmatic workflows, the partnership addresses one of CTV’s biggest challenges: delivering relevance without sacrificing privacy or efficiency.

For marketers, it unlocks real-time alignment with meaningful content moments. For media owners, it enhances inventory value and transparency. For the broader MarTech ecosystem, it signals a future where contextual intelligence operates at the same depth and speed as streaming content itself.

As CTV continues to grow, solutions that connect intelligence, efficiency, and relevance will define the next phase of digital advertising and this integration sets a strong benchmark for what’s possible.

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Modern enterprises are operating in an environment where sensitive data is no longer confined to a single system, platform, or location. Data is spread across cloud storage, SaaS applications, on-prem systems, and increasingly, automated and AI-driven processes. This distribution has unlocked efficiency and scale but has also introduced new security, governance, and compliance risks.

When incidents occur—whether it’s a file deletion, unauthorized access, suspicious downloads, or misuse by automated systems—security teams often face a critical challenge: they cannot quickly answer the most basic investigative questions. Who accessed the data? What was touched? When did it happen? And was it a human user, a service account, or an AI-driven process?

Traditional audit logs were not designed for today’s hybrid, AI-enabled environments. They are often fragmented, inconsistent, difficult to search, or entirely unavailable. As a result, investigations slow down, insider risks go undetected, and organizations face increased exposure to regulatory, operational, and reputational damage.

BigID, a leader in data security, privacy, compliance, and AI governance, is addressing this gap with the launch of Activity Explorer—a new capability that delivers centralized auditability and granular activity investigation across distributed environments. By unifying activity signals into a single interface, BigID enables security teams to move faster, investigate smarter, and strengthen their overall data security posture.

The Growing Challenge of Insider Risk in Hybrid Environments

Insider risk has evolved beyond malicious employees acting alone. Today’s environments include:

• Human users with legitimate access

• Privileged service accounts running automated processes

• AI agents interacting with sensitive data at scale

• Hybrid data estates spanning cloud, SaaS, and on-prem systems

When combined with limited visibility, this complexity increases exposure.

Key challenges facing security teams today include:

• Incomplete audit trails across systems

• Lack of context around what data was accessed

• Difficulty correlating identity activity across platforms

• Manual, time-consuming investigations

• Limited support for AI and non-human identities

Without unified visibility, organizations often discover issues too late—after damage has already occurred.

Introducing BigID Activity Explorer

Activity Explorer is designed to provide auditability and investigative clarity across modern data ecosystems. It centralizes activity events from cloud and on-prem environments into a single, searchable, and filterable interface.

What Activity Explorer Delivers

• Unified activity visibility across hybrid data environments

• Granular tracking of human, service, and AI-driven activity

• Faster investigations through centralized search and filtering

• Reliable audit trails for compliance and forensics

• Context-rich insights tied to data sensitivity and risk

This capability extends BigID’s platform beyond discovery and classification into actionable monitoring and response.

Unified Activity Auditing Across Hybrid Data Environments

One of the core strengths of Activity Explorer is its ability to remove blind spots created by fragmented logging systems.

Supported Environments

BigID Activity Explorer consolidates activity across:

• AWS S3

• SharePoint

• OneDrive

• Google Drive

• NetApp

• Cloud, SaaS, and on-prem data stores

By centralizing these activity signals, security teams no longer need to hunt across multiple tools or inconsistent logs to reconstruct events.

Benefits of Unified Auditing

• Complete visibility across distributed data sources

• Faster triage and investigation

• Reduced operational complexity

• Improved confidence in audit accuracy

This unified approach is essential for modern data security operations.

Visibility Across All Identity Types

A defining challenge in today’s environments is the explosion of non-human identities. Service accounts, scripts, automation tools, and AI agents now access sensitive data as frequently as human users.

Activity Explorer extends visibility across all identity types, including:

• Individual user accounts

• Privileged and non-privileged service accounts

• Automated workflows and system processes

• AI-driven agents interacting with data

This ensures that no identity operating within the environment is invisible to security teams.

Fast and Flexible Activity Investigation

When incidents arise, speed and precision are critical. Activity Explorer provides security teams with a powerful investigative experience designed for real-world response scenarios.

Investigation Capabilities

Security teams can:

• Search activity by date, user, operation, or resource

• Filter events using multiple criteria simultaneously

• Quickly answer questions such as:

  • Who deleted this file?

  • What data did this account access yesterday?

  • Which identities performed downloads during a specific window?

This flexibility significantly reduces investigation time and improves response accuracy.

Building a Trusted Audit History

Compliance and governance demands require disciplined record-keeping. Activity Explorer maintains a comprehensive activity history across sensitive data environments.

Compliance and Forensics Support

Activity Explorer supports:

• Long-term audit logging

• Forensic analysis during investigations

• Regulatory requirements such as:

  • HIPAA

  • GLBA

  • GDPR

Having trustworthy, centralized activity records enables organizations to respond confidently to auditors, regulators, and internal governance teams.

Breach Investigation and Blast-Radius Analysis

When accounts are compromised, understanding the scope of exposure becomes urgent. Activity Explorer enables security teams to analyze the blast radius of an incident.

Key Capabilities

• Identify all data accessed during a breach window

• Trace compromised account activity across systems

• Determine what sensitive data was touched

• Prioritize containment and remediation

This accelerates incident response and reduces the likelihood of prolonged exposure.

Accelerating Insider Risk Detection

Activity Explorer is not only reactive—it also enables proactive risk detection.

Insider Risk Indicators

The platform helps surface patterns associated with:

• Unauthorized access attempts

• Mass downloads

• Suspicious file deletions

• Unusual behavior by privileged accounts

• Anomalies involving service accounts or AI agents

By identifying risky behavior early, organizations can intervene before incidents escalate.

Adding Context to Activity With Data Intelligence

Activity logs alone only tell part of the story. BigID enhances Activity Explorer by pairing activity events with data context and sensitivity classification.

Why Context Matters

With this combined view, security teams can understand:

• What type of data was accessed

• Whether it included regulated or sensitive information

• The potential risk level of the activity

• The business impact of the event

This context-driven approach allows teams to focus on what truly matters, rather than chasing low-risk noise.

Executive Perspective: Visibility as the Foundation of Security

According to BigID leadership, visibility is the cornerstone of effective data protection.

Security leaders emphasize that without the ability to see and trace activity across environments, organizations cannot:

• Protect sensitive data

• Investigate incidents effectively

• Maintain compliance

• Secure AI-driven processes

Activity Explorer is positioned as a foundational capability that strengthens every layer of data security operations.

Expanding BigID’s Data Security Platform

The launch of Activity Explorer builds on BigID’s broader leadership across:

• Data Security Posture Management (DSPM)

• Data Detection and Response (DDR)

• Insider risk management

• Cloud Data Loss Prevention (Cloud DLP)

• AI governance and oversight

Together, these capabilities help organizations reduce risk, accelerate investigations, and maintain confidence in their security controls.

MarTech and Data Governance Implications

For MarTech and data-driven organizations, Activity Explorer addresses a growing concern: ensuring accountability as AI and automation become embedded into data workflows.

Key implications include:

• Stronger governance for AI-enabled data access

• Improved auditability for marketing and analytics systems

• Greater alignment between data security and compliance teams

• Reduced operational risk tied to sensitive customer data

As MarTech stacks continue to expand, audit visibility becomes as critical as activation and performance.

Conclusion: From Fragmented Logs to Confident Investigations

As data ecosystems grow more distributed and AI-driven, organizations can no longer rely on fragmented, incomplete audit logs to protect sensitive information. Insider risk, whether human or automated, demands unified visibility and rapid investigative capabilities.

With Activity Explorer, BigID delivers a centralized, context-rich, and scalable approach to activity auditing and investigation. By bringing together identity activity, data sensitivity, and historical records into a single experience, the platform enables security teams to move faster, smarter, and with greater confidence.

In an era where understanding who accessed data, when, and why is non-negotiable, Activity Explorer sets a new standard for modern data security and governance.

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Enterprise communication is undergoing a structural shift. Businesses are moving away from fragmented, transactional messaging models toward conversational, engagement-driven interactions that align with how consumers actually communicate. At the center of this transformation is WhatsApp, which has rapidly evolved from a consumer messaging app into a critical enterprise engagement channel.

As customer expectations increase and digital interactions become the default, organizations are rethinking how they connect with users across sales, support, and operations. Messaging channels that support rich media, real-time conversations, and high delivery reliability are now essential to customer experience strategies.

Within this context, Messangi—a leader in enterprise messaging and CPaaS solutions—has been promoted to Select Partner in Meta’s Partner Performance Accelerator for Business Messaging Program. This milestone reflects sustained growth in WhatsApp adoption among Messangi’s customers and highlights a broader industry transition toward conversational messaging at scale.

The promotion underscores not only strong performance metrics but also Messangi’s growing role in helping enterprises modernize their communication infrastructure across the Americas.

The Evolution From SMS to Conversational Messaging

For years, SMS served as the backbone of enterprise messaging. While reliable and ubiquitous, SMS is inherently limited in its ability to support rich, interactive experiences.

As organizations began prioritizing engagement, personalization, and two-way communication, the limitations of SMS became increasingly clear.

Key challenges of legacy SMS-only platforms include:

• Limited support for rich media

• Lack of structured conversational flows

• Lower engagement rates for complex interactions

• Reduced ability to personalize experiences

• Minimal support for automation and AI-driven journeys

Conversational channels like WhatsApp address these challenges by enabling branded messaging, media-rich interactions, verified business identities, and real-time responses—all within a user experience that customers already trust and use daily.

Messangi’s Role in Driving WhatsApp Adoption

Over the past several years, Messangi has worked closely with enterprises, telecom operators, aggregators, and independent software vendors to help them transition from SMS-centric messaging models to modern, conversational platforms.

This transition has not been theoretical. Messangi has supported real-world deployments where WhatsApp became a core communication channel for customer engagement, notifications, and support.

Key factors driving WhatsApp adoption among Messangi’s customers include:

• Growing consumer preference for messaging-based interactions

• Demand for richer, more interactive customer experiences

• Need for faster response times and two-way communication

• Higher reliability compared to traditional SMS in certain markets

• Better alignment with omnichannel engagement strategies

As a result, WhatsApp usage among Messangi’s customer base has grown rapidly, directly contributing to its promotion within Meta’s partner ecosystem.

What Select Partner Status Means in Meta’s Program

Meta’s Partner Performance Accelerator for Business Messaging Program evaluates partners based on performance, scale, adoption success, and customer outcomes. Promotion to Select Partner reflects sustained excellence across these dimensions.

Key Implications of Select Partner Status

Messangi’s Select-level recognition provides:

• Increased access to Meta enablement resources

• Participation in incentive and growth programs

• Closer collaboration with Meta teams

• Early insight into product updates and best practices

• Enhanced ability to support large-scale WhatsApp deployments

This strengthened position enables Messangi to better serve organizations seeking to operationalize WhatsApp as part of their long-term messaging strategy.

WhatsApp Performance Compared to SMS: What the Data Shows

Through deployments across multiple industries, Messangi has consistently observed WhatsApp outperform SMS across key performance indicators.

Reported Performance Gains Include

• Higher conversion rates on customer journeys

• Stronger engagement and response rates

• Improved message delivery reliability

• Faster customer response times

• More effective two-way communication

These outcomes validate the broader shift in enterprise communication behavior and confirm that conversational messaging is not just a trend, but a performance-driven evolution.

Industry Use Cases Driving WhatsApp Growth

Messangi’s WhatsApp implementations span a wide range of industries, each with distinct communication needs but shared performance goals.

Banking and Financial Services

• Transactional notifications with two-way engagement

• Customer service automation

• Secure and trusted messaging channels

• Faster response handling and issue resolution

Telecommunications

• Service notifications and plan updates

• Customer onboarding and retention messaging

• Conversational support at scale

• Reduced reliance on call centers

Retail and E-commerce

• Order updates and delivery notifications

• Promotional messaging with rich media

• Conversational commerce experiences

• Increased engagement across the customer lifecycle

Insurance

• Policy updates and claims communication

• Customer education and support workflows

• Faster resolution through conversational interactions

Across these sectors, WhatsApp has emerged as a preferred channel for delivering timely, relevant, and interactive communication.

Helping Organizations Modernize Messaging Infrastructure

Messangi’s expanded role as a Select Partner enhances its ability to support organizations at different stages of messaging transformation.

Supported Use Cases Include

• Upgrading legacy SMS-only infrastructure

• Integrating native WhatsApp capabilities

• Embedding messaging into enterprise software platforms

• Supporting ISVs building messaging-driven solutions

• Scaling conversational strategies across regions

For organizations navigating messaging modernization, this support reduces complexity and accelerates time to value.

Executive Perspective: Industry Direction and Customer Trust

Messangi leadership views the Select Partner promotion as a reflection of customer trust and market momentum.

Key themes emphasized by executives include:

• Clear growth trajectory for WhatsApp adoption

• Measurable performance improvements for customers

• Increasing demand for conversational engagement

• Long-term shift away from transactional-only messaging

These signals reinforce the idea that messaging is no longer just a utility—it is a strategic channel for customer experience and brand engagement.

Beyond WhatsApp: A Multichannel Messaging Strategy

While WhatsApp plays a central role, Messangi continues to support a broad messaging ecosystem designed for flexibility and scale.

Supported Channels and Capabilities

• WhatsApp

• SMS

• RCS

• Email

• AI-powered chatbots

• Intelligent workflows and automation

• Scalable CPaaS infrastructure

This multichannel approach allows organizations to meet customers where they are while maintaining consistency, control, and efficiency.

MarTech Implications: Messaging as a Core Engagement Layer

From a MarTech perspective, Messangi’s Select Partner status highlights a larger trend: messaging platforms are becoming foundational engagement layers within modern marketing and customer experience stacks.

Key implications for MarTech leaders include:

• Messaging is evolving from support to engagement

• Conversational channels drive higher lifecycle value

• Automation and AI are essential for scale

• Channel strategy must reflect customer preference

• Platform partners play a critical role in execution

As brands compete on experience, messaging performance is increasingly tied to business outcomes.

Conclusion: Select Partner Status Signals the Future of Business Messaging

Messangi’s promotion to Select Partner in Meta’s Business Messaging Program reflects more than a partner milestone—it signals where enterprise communication is headed.

As WhatsApp adoption accelerates across industries, organizations are prioritizing channels that deliver higher engagement, faster interactions, and richer customer experiences. Messangi’s proven ability to help customers transition from legacy SMS platforms to conversational messaging has positioned it as a key enabler of this shift.

With expanded access to Meta’s resources and continued investment in multichannel, AI-enabled messaging, Messangi is well-positioned to help organizations scale modern communication strategies that meet the expectations of today’s connected customers.

Get in touch with our MarTech Experts.

Artificial intelligence is reshaping marketing at every level, from audience targeting and personalization to budgeting, forecasting, and creative optimization. Yet for many enterprises, AI progress is constrained by a stubborn underlying problem: fragmented, unreliable, and inflexible data infrastructure.

While AI-powered tools continue to proliferate, most organizations still rely on legacy marketing measurement systems that were never designed for real-time decisioning, generative models, or autonomous agents. Data is siloed across channels, vendors, and teams, making it difficult to govern, unify, or activate intelligence at scale.

Marketing Evolution, long recognized as a pioneer in marketing analytics, is positioning itself at the center of this transformation. The company has announced new funding led by Insight Partners, a global software investor and existing backer. The investment is focused on accelerating product innovation and go-to-market expansion for Marketing Evolution’s AI-ready data infrastructure, purpose-built to unify, govern, and activate enterprise marketing data across AI-driven and agentic systems.

This milestone marks a strategic shift: from analytics provider to foundational data infrastructure platform designed for the next generation of marketing intelligence.

From Analytics Leader to AI-Ready Infrastructure Provider

Founded in 2000, Marketing Evolution built its reputation by helping brands measure and improve marketing ROI through advanced data science. Over time, it became clear that measurement alone was no longer sufficient for enterprises navigating increasingly complex and automated marketing ecosystems.

Customers needed:

• Unified data across channels and platforms

• Reliable, governed inputs for AI systems

• Faster insights and continuous learning

• Explainable intelligence, not black-box outputs

• Infrastructure that could support agentic decision-making

In response, Marketing Evolution began evolving its platform beyond analytics into a domain-specific, AI-ready data infrastructure for marketing.

This latest round of funding accelerates that transformation.

Why Marketing’s AI Transformation Depends on Data Infrastructure

AI adoption in marketing is often framed as a tooling problem. In reality, it is a data architecture problem.

Without adaptable and trustworthy data infrastructure:

• AI models produce inconsistent or biased results

• Automation is limited to narrow use cases

• Insights cannot be operationalized in real time

• Marketing systems fail to learn and improve continuously

Marketing Evolution is addressing what it describes as the most critical barrier in marketing’s AI transformation: building infrastructure that can reliably operationalize AI across the marketing ecosystem.

Rather than focusing solely on performance dashboards or attribution models, the company is building the data backbone and connective tissue required for an always-on, learning-based marketing system.

Insight Partners’ Strategic Support and Board Expansion

The funding round is led by Insight Partners, reinforcing long-standing confidence in Marketing Evolution’s direction.

As part of the investment:

• Rajiv Gihwala, Principal at Insight Partners, joins the Board of Directors

• He will serve alongside George Mathew, Managing Director at Insight Partners and an AI-focused leader

The board also includes experienced industry leaders such as:

• Andy Frawley, CEO of Data Axle and former CEO of Epsilon

• Lindsay Luger, Founding Partner at EIP

• Brett Marchand, CEO of Plus Company

This composition reflects the company’s positioning at the intersection of data, AI, and enterprise marketing transformation.

The Shift From Legacy Measurement to an Agentic Future

Traditional measurement systems were built for retrospective analysis. They explain what happened, but not what to do next, and they rarely improve themselves over time.

Marketing Evolution’s current strategy is designed for a different future:

• AI-driven decision-making

• Predictive and generative intelligence

• Autonomous and semi-autonomous agents

• Continuous learning from outcomes and feedback

This shift requires infrastructure that can ingest, unify, augment, and govern data at scale, while remaining flexible enough to support evolving AI use cases.

Mevo: The Decision-Ready Intelligence Layer for Marketing

At the center of Marketing Evolution’s offering is Mevo, its domain-specific analytics and intelligence platform. Mevo is not just a reporting tool—it functions as a decision-ready intelligence layer for modern marketing organizations.

Core Characteristics of Mevo

• Built on a marketing-specific data model (ontology)

• Designed to unify fragmented data across the enterprise

• Continuously reconstructs customer and media journeys

• Delivers explainable intelligence in real time

• Supports predictive and generative AI use cases

Mevo represents the operational expression of Marketing Evolution’s AI-ready infrastructure vision.

How Mevo Transforms Marketing Data Into Action

Mevo enables marketing organizations to move from fragmented inputs to unified intelligence through three core capabilities.

Unified Data Structure

Mevo delivers:

• A standardized data structure across sources and channels

• Alignment between audiences, tactics, outcomes, and spend

• Consistent definitions across teams and systems

• Trustworthy inputs for analytics and AI models

This unified structure reduces the friction that typically prevents enterprise-scale AI adoption.

Continuous Learning and Intelligence Compounding

Unlike static analytics systems, Mevo is designed to learn continuously.

Key learning capabilities include:

• Reconstructing journeys across complex touchpoints

• Generating synthetic data to fill gaps and improve models

• Feeding predictive and generative AI systems

• Improving accuracy as new outcomes are observed

This approach enables intelligence to compound over time, rather than reset with each analysis cycle.

Real-Time Decisioning and Interaction

Mevo introduces conversational access to intelligence, enabling:

• Simulation of scenarios before decisions are made

• Forecasting of outcomes across channels and budgets

• Real-time recommendations based on live data

• Faster alignment between insights and execution

This moves marketing intelligence closer to operational decision-making rather than post-hoc analysis.

Proven ROI Impact From Marketing Evolution’s Foundation

Marketing Evolution’s infrastructure is not theoretical. It is already delivering measurable benefits to customers.

Organizations using its analytics suite have reported:

• An average 35% increase in marketing ROI

• Improved confidence in data-driven decisions

• Faster insights across fragmented environments

• Stronger alignment between spend and outcomes

These results demonstrate the tangible value of building intelligence on a strong data foundation.

What’s Coming Next: The 2026 Enterprise Data Platform

In Q1 2026, Marketing Evolution plans to launch a next-generation enterprise data platform designed to extend its infrastructure vision further.

Platform Goals

The upcoming platform aims to help brands:

• Unify AI-ready marketing data across their ecosystems

• Govern and control intelligence at the enterprise level

• Activate insights across AI-driven and agentic systems

• Own and compound the value of their data assets

This launch represents the next phase of the company’s evolution from analytics provider to infrastructure leader.

MarTech Implications: Infrastructure as Competitive Advantage

Marketing Evolution’s announcement reflects a broader MarTech trend: competitive advantage is shifting from tools to infrastructure.

Key implications for MarTech leaders include:

• AI success depends more on data readiness than algorithms

• Domain-specific ontologies outperform generic data models

• Continuous learning systems outperform static dashboards

• Enterprises must own and govern their intelligence

• Agentic systems require reliable, explainable data inputs

As marketing becomes increasingly autonomous, infrastructure will define which organizations can scale AI responsibly and effectively.

Executive Perspective on a Generational Platform Shift

Industry leaders see this moment as part of a larger platform transition driven by AI-native companies.

Core themes emphasized include:

• AI is redefining how industries operate, not just tools

• Foundational data platforms will outlast point solutions

• Marketing analytics is evolving into intelligence systems

• Long-term value lies in adaptable infrastructure, not features

Marketing Evolution’s strategy aligns directly with this generational shift.

Conclusion: Building the Intelligence Backbone for Modern Marketing

Marketing Evolution’s latest funding round signals a clear strategic ambition: to become foundational infrastructure for the AI-driven future of marketing.

By moving beyond legacy measurement and investing in AI-ready data infrastructure, the company is addressing the core challenge that blocks meaningful AI adoption across enterprises. With Mevo as its intelligence layer and a next-generation data platform on the horizon, Marketing Evolution is positioning itself to help brands build adaptive marketing systems that learn, explain, and optimize continuously.

As AI transforms marketing from analysis to autonomy, the organizations that succeed will be those that own their data, govern their intelligence, and invest in infrastructure designed for the future. Marketing Evolution is betting that this is where the next era of MarTech leadership will be defined.

Get in touch with our MarTech Experts.

The marketing technology industry is at a turning point. After decades of platform sprawl, fragmented tools, and increasing complexity, marketing and customer engagement teams are demanding something fundamentally different. Insider One’s rebrand from Insider signals more than a name change—it marks a strategic declaration that the MarTech status quo is no longer sustainable.

As organizations struggle with bloated tech stacks, rising total cost of ownership, and diminishing returns on innovation, Insider One is positioning itself at the center of what it calls The Great Reset in MarTech. The company’s renewed mission is clear: bring everything marketing and customer engagement teams need into one unified, intelligent platform—so teams can focus less on managing tools and more on creating meaningful customer connections at scale.

This shift reflects a wider industry reckoning. Traditional MarTech promised acceleration but often delivered friction. Insider One’s rebrand is a challenge to that model and a roadmap toward a new category where AI sits above MarTech, reshaping customer engagement into what the company defines as AITech.

The Great Reset in MarTech: Why the Industry Reached a Breaking Point

Insider One’s rebrand is rooted in a direct critique of how MarTech evolved—and where it went wrong.

How MarTech Lost Its Way

Over the past two decades, marketing technology expanded rapidly but unevenly:

• Vendors positioned themselves as “missing pieces” rather than cohesive solutions

• Platforms added features without removing complexity

• Integration challenges became the norm rather than the exception

• Teams were forced to rebuild their stacks every 2–3 years

Instead of enabling growth, MarTech often became a blocker.

The Cost of Digital Pollution

Insider One defines this overload as digital pollution—a state where:

• Marketing teams feel overworked and overwhelmed

• Tool management consumes more time than customer engagement

• Innovation slows under the weight of operational complexity

• Teams settle for partial solutions out of necessity

This cycle has led to widespread dissatisfaction, with marketing leaders recognizing that incremental fixes are no longer enough.

A New Path Forward

Insider One argues that the future of customer engagement is not about adapting to broken systems but replacing them altogether. The Great Reset represents a move toward:

• Fewer platforms, but deeper capabilities

• AI-native systems instead of AI add-ons

• Technology that accelerates human creativity rather than replacing it

This reset forms the foundation of Insider One’s new identity and product vision.

Insider One’s Vision: Everything Marketing Teams Need, In One Place

At the heart of Insider One’s rebrand is a commitment to delivering the ultimate vendor experience—a unified platform that removes friction instead of adding it.

What Insider One Stands For

The Insider One platform is designed around a simple but ambitious promise:

• Everything teams need to engage customers across channels

• Nothing redundant or unnecessary

• A single system that scales with business complexity

This vision reframes how brands think about customer engagement—not as a collection of disconnected tools, but as a continuous, intelligent journey.

The Three Core Promises That Define Insider One

Insider One’s new identity is anchored in three strategic promises that define how it serves modern marketing and customer engagement teams.

Be First: Leading the Shift from MarTech to AITech

Insider One positions itself as a first mover in the transition from MarTech to AITech.

Why AI No Longer Belongs Inside MarTech

According to Insider One:

• AI is no longer just a feature embedded in tools

• It now operates above the tech stack, orchestrating it

• Human intelligence and artificial intelligence must work together

This marks a fundamental reset in how customer engagement platforms are designed and deployed.

What Being “First” Means in Practice

Insider One commits to:

• A unified, AI-powered customer engagement platform

• Continuous innovation driven by an ambitious product and AI roadmap

• Omnichannel engagement at scale without added complexity

For customers, the benefit is clear:

• Brands react to change while Insider One users shape it

• Engagement strategies evolve faster than competitors

• Innovation becomes proactive rather than reactive

Building on more than a decade of AI leadership, Insider One aims to redefine what modern customer engagement can achieve.

Be Focused: Eliminating Friction So Teams Can Execute

Technology should enable focus, not divert it. Insider One’s second promise targets one of marketing’s biggest pain points: operational drag.

The Insider One Advantage™

The Insider One Advantage™ is a customer-obsessed operating model refined over more than a decade. It spans:

• 30+ global markets

• 15 industries

• 2,000+ enterprise customers

This experience informs how Insider One designs, deploys, and supports its platform.

What “Be Focused” Delivers

Insider One helps brands:

• Migrate from legacy systems with white-glove support

• Onboard rapidly without operational disruption

• Reduce tech-stack complexity and total cost of ownership

• Accelerate time to value

By removing fear, cost, and complexity, Insider One enables teams to focus on what matters most: building lasting customer relationships and driving growth.

Be Progressive: Powering Growth Through Community

Beyond technology, Insider One emphasizes the power of shared learning and collective intelligence.

The Growth Makers™ Club

The Growth Makers™ Club is a global community of:

• Marketing leaders

• Innovators and strategists

• Industry disruptors

This community exists to:

• Share proven strategies

• Accelerate learning across markets

• Multiply impact through collaboration

Why Community Matters

Insider One believes that progress accelerates when:

• Organizations learn from real-world outcomes

• Best practices are shared, not siloed

• Networks compound value over time

For customers, this means access not just to a platform—but to a living ecosystem of expertise.

What’s Next: Scaling Through M&A and Global Expansion

Under its new identity, Insider One plans to accelerate growth across multiple fronts.

Strategic Expansion Priorities

Insider One will focus on:

• Acquiring best-in-class product companies

• Integrating breakthrough technologies into its platform

• Expanding its global footprint

Today, Insider One already operates at significant scale:

• 1,500+ team members

• 50+ nationalities

• 30+ offices across six continents

This foundation positions the company for aggressive expansion in both capability and market reach.

From MarTech Leader to AITech Pioneer

Insider One’s leadership points to its first decade as proof of its ability to anticipate and lead industry shifts.

A Decade of Transformation

Over the years, the company:

• Navigated the shift from mobile-first to AI-driven engagement

• Earned recognition from analysts and customers alike

• Built trust with over 2,000 global brands

The Next Decade

Looking ahead, Insider One aims to:

• Lead the industry’s shift from MarTech to AITech

• Deliver not just great technology, but a superior vendor experience

• Empower teams to operate at peak potential

The company’s message is clear: marketing teams deserve more than fragmented tools—they deserve systems that learn, explain, and scale with them.

Conclusion: A New Standard for Customer Engagement

Insider One’s rebrand represents a decisive statement about the future of marketing technology. As AI reshapes how brands engage with customers, the industry is moving beyond point solutions toward unified, intelligent platforms.

The Great Reset in MarTech is not about incremental improvement—it is about replacing friction with focus, complexity with clarity, and tools with true systems of intelligence. By positioning itself at the intersection of AI, customer engagement, and operational simplicity, Insider One is setting a new standard for what marketing technology can and should deliver.

As MarTech gives way to AITech, Insider One is betting that the brands who thrive will be those who embrace fewer platforms, deeper intelligence, and experiences designed for humans first.

Get in touch with our MarTech Experts.

EPAM Systems, Inc. (NYSE: EPAM) has taken a significant step in advancing enterprise AI adoption with the launch of multiple production-ready AI agents on Google Cloud Marketplace. Known for its deep expertise in digital, cloud, and AI transformation, EPAM is leveraging an engineering-first approach to deliver secure, compliant, and scalable AI solutions designed for real-world enterprise environments.

This launch strengthens EPAM’s strategic collaboration with Google Cloud and underscores its growing role in shaping the future of Gemini Enterprise. By making these AI agents readily available through Google Cloud Marketplace, EPAM enables organizations to deploy trusted AI solutions faster, reduce operational complexity, and maximize the value of their cloud investments.

The move reflects a broader industry shift toward agent-based AI systems that can automate workflows, optimize data usage, and deliver measurable business outcomes across regulated and complex enterprise settings.

Deepening EPAM’s Strategic Collaboration With Google Cloud

The availability of EPAM’s AI agents on Google Cloud Marketplace represents a milestone in the company’s 360-degree partnership with Google Cloud.

A Multi-Dimensional Collaboration

EPAM’s relationship with Google Cloud spans:

• Advanced engineering and cloud-native architecture

• AI solution design and deployment

• Joint go-to-market strategies for enterprise customers

This expansion builds on years of collaboration and signals EPAM’s growing influence as an enterprise AI delivery partner within the Google Cloud ecosystem.

Aligning With Gemini Enterprise

Support for Gemini Enterprise ensures EPAM’s AI agents are:

• Built on interoperable, enterprise-grade AI foundations

• Secure by design and compliant with industry standards

• Scalable across diverse enterprise workloads

By contributing its domain expertise, EPAM has helped customers adopt Gemini Enterprise as part of robust, production-ready AI workflows.

Production-Ready AI Agents Designed for Enterprise Impact

EPAM’s newly launched AI agents are engineered to address some of the most pressing challenges enterprises face today—from regulatory compliance to data optimization and operational inefficiency.

Core Design Principles

Across the portfolio, EPAM emphasizes:

• Enterprise-grade security and compliance

• Production readiness, not experimental prototypes

• Rapid deployment through Google Cloud Marketplace

• Tangible ROI through automation and performance optimization

This approach enables organizations to go beyond pilots and move directly into scaled AI execution.

Overview of EPAM’s AI Agent Portfolio

The initial release includes seven high-impact AI agents, each tailored for specific industry and functional use cases.

Agentic Know Your Customer (KYC)

Designed for financial services institutions, this agent delivers end-to-end automation of KYC processes.

Key capabilities include:

• Automated data aggregation from multiple sources

• Screening and validation of customer information

• AI-driven risk assessment and compliance checks

• Reduced manual effort and faster onboarding cycles

This solution helps financial institutions improve compliance while reducing operational cost and friction.

Streamlined Document Authoring for Clinical Trials

This agent focuses on accelerating document-heavy workflows in life sciences.

Key benefits include:

• Automated creation of complex clinical trial documentation

• Improved consistency and regulatory readiness

• Faster time-to-market for research and development initiatives

By streamlining documentation, organizations can reallocate resources toward innovation rather than administrative overhead.

AI for Drug Discovery (Cloud Pipeline)

Built for research-intensive environments, this agent orchestrates advanced workflows across life sciences data sets.

Capabilities include:

• Workflow orchestration for genomics and multiomics data

• Integration of cheminformatics pipelines

• Faster hypothesis testing and drug discovery cycles

This approach enables pharmaceutical and biotech organizations to shorten discovery timelines through AI-driven automation.

Agentic AI Query Optimization

This agent targets inefficiencies in data analytics and cloud spend.

Core features include:

• Automated analysis of complex SQL queries

• Optimization and refactoring for performance improvements

• Reduced query execution time and infrastructure costs

By improving query efficiency, enterprises can optimize analytics workflows while controlling cloud expenses.

Retail Media Orchestration Toolkit

Designed for modern retail and media environments, this toolkit enables advanced cross-platform coordination.

Primary functions include:

• Unified management of media and data across platforms

• Enhanced targeting and performance measurement

• Improved efficiency in retail media execution

This solution supports data-driven marketing and monetization strategies across retail ecosystems.

Video Processing Suite

As video data volumes surge, this agent transforms unstructured content into usable intelligence.

Key capabilities include:

• Processing and indexing large video libraries

• Making video assets searchable and actionable

• Unlocking new value from existing media content

This agent helps organizations extract insights from video at scale, supporting analytics, training, and content discovery.

Talk to Your Data

This agent democratizes data access through natural language interaction.

Benefits include:

• AI-powered data analysis without technical queries

• Natural language questions and conversational insights

• Faster decision-making across business teams

By lowering the barrier to data exploration, enterprises can enable broader, real-time access to insights.

Enterprise Foundations: Security, Compliance, and Interoperability

A defining element of EPAM’s AI agents is their enterprise readiness.

Built for Trust and Scale

EPAM ensured that its AI agents are:

• Secure and compliant with enterprise requirements

• Interoperable across varied IT environments

• Designed for long-term scalability

The company has also collaborated closely with Google Cloud on key initiatives such as:

• The Agent-to-Agent (A2A) protocol

• The Agent Developer Kit (ADK)

These efforts ensure that EPAM’s AI agents can operate reliably across different enterprise systems and use cases.

Accelerating AI Adoption Through Google Cloud Marketplace

Launching these agents on Google Cloud Marketplace simplifies how enterprises adopt AI.

Key advantages include:

• Faster procurement and deployment

• Centralized management within the Google Cloud ecosystem

• Trusted global infrastructure for scaling AI initiatives

For enterprises navigating digital transformation, this approach reduces friction and accelerates time to value.

Conclusion: A Practical Path to Enterprise AI at Scale

EPAM’s launch of enterprise-grade AI agents on Google Cloud Marketplace reflects a broader evolution in how AI is designed, deployed, and scaled across industries. Rather than experimental tools, these agents are built to solve real business problems—securely, compliantly, and at production scale.

By combining advanced engineering expertise with Google Cloud’s trusted infrastructure and Gemini Enterprise capabilities, EPAM is positioning itself as a key enabler of agent-based AI adoption. From financial services and life sciences to retail, media, and data analytics, the new AI agents offer enterprises a practical pathway to operationalizing AI while maximizing cloud investments.

As organizations move toward autonomous, AI-driven workflows, EPAM’s collaboration with Google Cloud signals how enterprise AI is transitioning from vision to execution.

Get in touch with our MarTech Experts.