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.

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IFS, a global leader in Industrial AI software, has announced that Dixstone has selected IFS Cloud to modernize and unify its operations across ten countries and a workforce of more than 2,200 professionals. The decision marks a major milestone in Dixstone’s digital transformation journey, reinforcing its commitment to operational excellence, safety leadership, and long-term sustainability across the offshore oil and gas value chain.

As offshore energy services become increasingly complex, organizations like Dixstone require digital platforms that can scale globally, integrate deeply across projects and assets, and provide real-time intelligence. By adopting IFS Cloud, Dixstone positions itself to leverage Industrial AI to manage high-risk, asset-intensive operations while supporting innovation in areas such as carbon capture, offshore construction, and decommissioning.

This move demonstrates how Industrial AI and unified enterprise platforms are becoming strategic enablers—not just operational tools—for energy and infrastructure-focused organizations navigating transformation at scale.

Dixstone’s Role in the Offshore Energy Ecosystem

Dixstone was created to consolidate Perenco’s offshore services capabilities into a single, integrated organization capable of delivering end-to-end offshore solutions.

A Broad Offshore Services Portfolio

Dixstone provides fully integrated services across:

• Offshore construction and installation

• Drilling and oilfield services

• Workover operations

• Marine services

• Offshore decommissioning

This breadth positions Dixstone as a critical partner across the offshore energy lifecycle, from exploration and production to end-of-life asset management.

Driving Innovation in Offshore Energy

Dixstone is already recognized for breakthrough initiatives, including:

• The United Kingdom’s first CO2 injection test for Carbon Capture and Storage (CCS)

• The Obana decommissioning platform, the world’s largest self-elevating heavy-lift jack-up vessel of its type

These projects highlight the company’s need for a digital backbone capable of supporting innovation, compliance, and safety at an unprecedented scale.

Why Dixstone Chose IFS Cloud

As Dixstone continues to scale operations across geographies and offshore environments, legacy systems were no longer sufficient to support its ambitions.

Key Drivers Behind the Decision

The selection of IFS Cloud was driven by:

• Deep industry-specific functionality for energy and asset-intensive operations

• Modular architecture supporting rapid scaling and adaptation

• Unified platform capabilities across projects, assets, and finance

• Built-in Industrial AI to enhance decision-making and predictability

These attributes align with Dixstone’s need to manage complex, multi-site operations without introducing fragmentation or operational silos.

Unifying Global Operations Across Ten Countries

Operating across multiple countries introduces significant complexity in governance, compliance, financial oversight, and execution.

A Single Source of Operational Truth

With IFS Cloud, Dixstone can:

• Standardize processes across regions and business units

• Maintain consistent operational visibility across global projects

• Improve collaboration between onshore and offshore teams

This unification is particularly critical in offshore energy, where delays, budget overruns, or safety incidents can have substantial operational and environmental consequences.

Strengthening Project Financial Control and Governance

One of the most significant benefits Dixstone will gain from IFS Cloud is enhanced project financial control across its diverse offshore portfolio.

Real-Time Project Oversight

IFS Cloud enables:

• Live tracking of project budgets and costs

• Real-time monitoring of timelines and milestones

• Accurate forecasting and resource allocation

This level of transparency allows Dixstone to ensure that complex offshore projects are delivered:

• On schedule

• Within budget

• In alignment with stringent safety and quality standards

For large-scale offshore conversions, marine operations, and decommissioning programs, such control is essential to minimizing risk and maximizing margin.

Enabling Predictive Asset Operations and Maintenance

In asset-heavy industries, maintenance efficiency directly impacts safety, uptime, and profitability.

Managing a Global Fleet With Confidence

Dixstone operates a global fleet of:

• Offshore rigs

• Specialized marine vessels

• Platforms and heavy-lift assets

IFS Cloud provides a unified framework to manage these assets across their full lifecycle.

Leveraging Predictive Maintenance With Industrial AI

Using centralized asset data and AI-driven insights, Dixstone can:

• Predict equipment failures before they occur

• Schedule maintenance proactively

• Extend asset lifespan

• Reduce unplanned downtime

This proactive approach enhances operational reliability while supporting safety-first offshore operations.

Supporting Safety and Sustainability Objectives

Safety and sustainability are not optional in offshore energy—they are foundational requirements.

Enhancing Safety Through Visibility and Intelligence

IFS Cloud supports safety goals by:

• Providing real-time operational insights

• Reducing reliance on manual processes

• Improving compliance monitoring across projects and assets

By minimizing blind spots, Dixstone can better protect its workforce and offshore environments.

Advancing Sustainable Offshore Operations

Digital integration across the E&P value chain enables Dixstone to:

• Reduce environmental impact across offshore projects

• Optimize resource usage

• Support low-carbon initiatives such as CCS and decommissioning

This aligns technology investment directly with sustainability outcomes.

Executive Perspectives on the IFS Cloud Partnership

Leadership from both organizations emphasized the strategic importance of the deployment.

Dixstone’s View

Jean-Christophe Le Gal, General Manager at Dixstone, highlighted the platform’s role beyond IT modernization:

• IFS Cloud delivers agility across global operations

• The platform supports sustainability alongside growth

• It acts as a long-term strategic enabler

This signals a shift from technology as infrastructure to technology as a driver of competitive advantage.

IFS’ Perspective

Simon Niesler, Chief Revenue Officer at IFS, emphasized how Dixstone’s decision reflects broader industry priorities:

• Unified project control is essential for complex global operations

• Integrated asset management drives safety and efficiency

• Predictive maintenance sets new standards for operational excellence

The partnership showcases how Industrial AI can be operationalized at scale within the offshore energy sector.

Aligning Digital Transformation With Long-Term Strategy

The IFS Cloud implementation supports Dixstone’s long-term strategic objectives.

End-to-End Value Chain Integration

By integrating processes across the exploration and production value chain—from early-stage operations through decommissioning—Dixstone can:

• Deliver modular, fit-for-purpose offshore solutions

• Reduce operational and environmental risk

• Create sustained value for global customers

This approach ensures digital transformation is directly tied to business outcomes rather than isolated system upgrades.

Conclusion: Industrial AI as a Foundation for Offshore Excellence

Dixstone’s selection of IFS Cloud reflects a clear understanding that modern offshore operations demand more than disconnected systems and manual oversight. By adopting an Industrial AI-powered, unified cloud platform, Dixstone is building a digital foundation capable of supporting safety-critical, asset-intensive operations at global scale.

From real-time project financial control to predictive asset maintenance and sustainability-driven innovation, IFS Cloud becomes a central enabler in Dixstone’s journey toward operational excellence. As the offshore energy sector evolves—balancing growth, decarbonization, and safety—this partnership demonstrates how Industrial AI and enterprise platforms are redefining what best-in-class operations look like.

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On The Go TV has announced the launch of its new streaming platform, marking another step in the ongoing evolution of the digital entertainment landscape. As streaming continues to reshape how audiences consume content, user expectations have shifted toward flexibility, simplicity, and consolidation. Viewers increasingly want access to diverse entertainment options without the friction of managing multiple subscriptions, devices, and interfaces.

The platform’s introduction reflects broader industry trends, where streaming providers are competing not only on content libraries but also on experience, usability, and service transparency. For consumers fatigued by fragmented streaming ecosystems, unified platforms are emerging as a compelling alternative—one that prioritizes convenience, choice, and ease of access.

The Changing Dynamics of the Streaming Market

The global streaming market has matured significantly in recent years, moving from rapid adoption to intense competition and differentiation.

Subscription Fatigue and Platform Fragmentation

A growing challenge for consumers is subscription overload. Many viewers now subscribe to multiple services to access movies, television series, live sports, and continuous channels. This fragmentation has led to:

• Increased monthly entertainment costs

• Difficulty discovering content across platforms

• Frustration with managing multiple user accounts and interfaces

As a result, consumers are actively seeking consolidated platforms that reduce complexity while expanding viewing options.

Demand for Simplicity and Transparency

Industry analysts note that modern viewers prioritize:

• Clear pricing models

• Straightforward user interfaces

• Flexible subscription or trial options

• Minimal onboarding friction

Streaming solutions that fail to address these expectations risk losing relevance in a crowded market.

On The Go TV’s Response to Shifting Consumer Preferences

On The Go TV’s new streaming platform is designed specifically to align with these changing consumer behaviors.

A Unified Entertainment Experience

The platform offers a single application that brings together a broad range of content types, reducing the need to switch between services. This approach aims to streamline content discovery and viewing, particularly for households with diverse entertainment preferences.

Key content offerings include:

• Movies available on demand

• Television series across genres

• Live sports programming

• Continuous and scheduled channels

By centralizing these options, On The Go TV positions itself as a flexible alternative to fragmented streaming ecosystems.

Key Features of the On The Go TV Streaming Platform

Beyond content aggregation, the platform focuses on functional features that enhance usability and personalization.

Live Sports and Real-Time Viewing

Live sports remain one of the most valuable drivers of streaming engagement. On The Go TV includes live sports coverage designed to appeal to viewers seeking real-time experiences alongside on-demand content.

This combination allows users to:

• Watch live events without switching platforms

• Balance scheduled viewing with on-demand flexibility

On-Demand Movies and Series

The platform supports on-demand entertainment, allowing users to watch content at their convenience. This aligns with viewer expectations for control and flexibility, particularly among audiences who prefer non-linear viewing experiences.

Customizable Viewer Profiles

To support household usage, On The Go TV includes customizable profiles. These profiles enable:

• Personalized content recommendations

• Individual watch histories

• Separate preferences within a shared account

This functionality improves discovery and long-term engagement.

Simplifying Onboarding and User Support

As competition intensifies, customer experience has become a critical differentiator in the streaming sector.

Trial Access for New Users

On The Go TV offers a trial period, allowing potential subscribers to explore the platform before committing. Trial access serves several strategic purposes:

• Reduces barriers to entry

• Builds trust with first-time users

• Encourages exploration of platform features

Free or limited trials have become an expected part of modern subscription models, especially in entertainment and media.

Dedicated Customer Support

The platform also emphasizes customer support to assist with:

• Account setup and onboarding

• Technical troubleshooting

• General usage inquiries

Consumer feedback across streaming services consistently highlights reliable support as a key factor influencing satisfaction and retention.

Streaming Competition and Market Differentiation

The launch of platforms like On The Go TV underscores the increasingly competitive nature of the streaming market.

Competing Beyond Content Libraries

While exclusive content remains important, providers are now competing on additional dimensions, including:

• Ease of use and navigation

• Performance reliability

• Customer service responsiveness

• Pricing clarity

For newer platforms, matching or exceeding established players in experience is critical to gaining traction.

Accessibility as a Growth Lever

Accessibility—both in terms of interface design and content availability—plays a growing role in adoption. Platforms that simplify discovery and minimize friction are better positioned to attract users overwhelmed by complexity in the streaming ecosystem.

Consumer Feedback and the Importance of User Experience

Third-party review sites and user forums consistently emphasize usability as a central concern for streaming subscribers.

What Viewers Value Most

Common themes in consumer feedback include:

• Easy navigation and intuitive layouts

• Reliable playback performance

• Responsive customer support

• Clear communication around pricing and features

Platforms that neglect these fundamentals often face higher churn rates, regardless of content depth.

On The Go TV’s Experience-Focused Approach

By prioritizing ease of use, onboarding support, and consolidated access, On The Go TV aligns with the expectations shaping current and future streaming adoption.

The Role of Consolidation in the Future of Streaming

The introduction of consolidated platforms reflects a broader shift toward simplification in digital entertainment.

Fewer Apps, Broader Access

As households reassess entertainment spending, services that reduce the need for multiple subscriptions are becoming increasingly attractive. Consolidated platforms can offer:

• Cost efficiency

• Simplified billing

• Easier content management

This trend mirrors similar shifts in other digital sectors, where aggregation and interoperability drive user preference.

Adapting to Evolving Viewing Habits

Viewer habits continue to evolve, influenced by mobile viewing, shared households, and demand for instant access. Streaming platforms that remain adaptable and user-centric are more likely to thrive in this environment.

Industry Outlook: What Platforms Must Do to Stay Relevant

As the streaming landscape continues to develop, platforms must focus on long-term differentiation.

Core Areas of Focus

To remain competitive, streaming providers must invest in:

• User experience design

• Scalable customer support

• Content diversity

• Flexible pricing models

Platforms that successfully balance these elements can respond more effectively to changing consumer expectations.

Conclusion: On The Go TV Enters a Market Defined by Choice and Convenience

The launch of On The Go TV’s streaming platform highlights how the digital entertainment industry continues to adapt to evolving viewer demands. As subscription fatigue and platform fragmentation challenge traditional models, consolidated solutions that emphasize accessibility, usability, and flexibility are gaining momentum.

By delivering a unified application that combines live sports, on-demand entertainment, customizable profiles, and customer support, On The Go TV positions itself within a growing category of platforms built around user experience rather than complexity. As competition intensifies, platforms that listen closely to consumer needs and evolve accordingly are likely to shape the next phase of streaming innovation.

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Adstra, a global leader in enterprise identity resolution across media and marketing touchpoints, has announced the launch of Conexa Workspace, the latest evolution of its Conexa™ Composable Identity Platform. The new Workspace introduces a turnkey, self-service experience through an updated user interface, enabling brands to unify fragmented customer data, identify high-value audiences across the web, and activate insights that drive measurable marketing and business outcomes.

As identity challenges intensify amid signal loss, walled gardens, and rising acquisition costs, brands are seeking more control over how identity data is unified, accessed, and activated. Conexa Workspace reflects this shift by delivering flexible, scalable identity capabilities in a composable framework designed for modern marketing teams.

The Growing Complexity of Identity in Modern Marketing

Identity resolution has become a foundational challenge for marketers navigating a privacy-first, omnichannel ecosystem.

Fragmentation Across Touchpoints

Brands today manage customer data across:

• Websites and mobile apps

• CRM and CDP platforms

• Paid media, owned channels, and partners

• Logged-in and anonymous environments

Without a unified identity layer, these data sources create disconnected views of customers, limiting personalization, measurement, and growth.

Pressure Beyond Walled Gardens

Walled ecosystems restrict transparency, scale, and portability. As a result, brands—especially direct-to-consumer and challenger brands—are increasingly prioritizing identity solutions that provide:

• Independent audience control

• Higher match rates across channels

• Reduced reliance on closed platforms

Conexa was built specifically to address these challenges through composability and interoperability.

What Is Conexa Workspace?

Conexa Workspace is the newest interface layer for Adstra’s Conexa™ Composable Identity Platform, delivering direct, turnkey access to identity capabilities in a self-service environment.

A Turnkey Identity Experience

The Workspace enables brands to:

• Access the full Conexa identity network

• Activate modules without heavy integration overhead

• Manage identity workflows through an intuitive UI

This approach lowers operational friction while maintaining enterprise-grade scale and precision.

Built on a Composable Identity Framework

Conexa is positioned as the industry’s first Composable Identity Platform. Rather than forcing brands into a rigid system, Conexa allows organizations to:

• Activate individual modules to enhance existing stacks

• Deploy a full end-to-end identity solution

• Adapt identity strategies as needs evolve

This flexibility ensures brands retain control while future-proofing their marketing infrastructure.

Key Capabilities Available Through Conexa Workspace

Conexa Workspace unlocks several high-impact identity capabilities that support data unification, audience expansion, and performance optimization.

First-Party Data Unification and Enrichment

Disparate customer data remains one of the biggest barriers to actionable insights.

Creating a Persistent Customer View

With Conexa Workspace, brands can:

• Securely ingest fragmented first-party data

• Unify records into a single, persistent identity

• Enrich customer profiles with additional attributes

This unified view supports more informed decisions across marketing, product development, and customer engagement.

Strategic Value for Enterprise Teams

By consolidating identity at the foundation, organizations can move beyond reactive campaigns toward proactive, insight-driven strategies.

Online Visitor Intelligence for Anonymous Audiences

A significant portion of web traffic remains anonymous, limiting addressability and measurement.

Resolving Unknown Visitors

Conexa Workspace enables brands to:

• Identify anonymous website visitors

• Resolve them against Adstra’s identity graph

• Convert unknown traffic into addressable audiences

This capability increases reach without relying on third-party cookies.

Driving Smarter Activation

By unlocking visibility into anonymous behaviors, brands can better tailor messaging, optimize journeys, and improve attribution accuracy.

Total Addressable Market (TAM) Expansion

Scale remains a constant challenge in modern digital acquisition.

Expanding Reach With Addressable Identity

Through Conexa Workspace, marketers gain:

• Direct access to Adstra’s consumer attributes and identifiers

• Greater control over audience construction

• Improved precision across activation channels

This enables brands to expand their total addressable market while maintaining relevance and performance.

Reducing Acquisition Costs

Higher match rates and better targeting help lower cost-per-acquisition and mitigate audience fatigue over time.

Supporting DTC and Challenger Brand Growth

Conexa Workspace is particularly impactful for emerging brands competing against larger incumbents.

Combatting Audience Fatigue

By unlocking scalable, identity-driven audiences outside walled ecosystems, brands can:

• Refresh acquisition pipelines

• Improve performance consistency

• Reduce dependency on saturated channels

Enhancing Media Efficiency

Improved identity resolution leads to:

• Better campaign measurement

• Real-time optimization

• Increased confidence in media investment decisions

Customer Validation: InsightsRx Perspective

Early adopters are already highlighting the value of Conexa Workspace.

Egbavwe Pela, CEO and Co-Founder of InsightsRx, emphasized the platform’s ability to improve audience quality and confidence in activation:

• Turnkey access reduces operational friction

• Direct, non-transcoded distribution preserves data fidelity

• Advanced tagging enables real-time validation and optimization

These capabilities help agencies and brands deliver more predictable and measurable outcomes for clients.

Executive Vision From Adstra

Adstra leadership positions Conexa Workspace as a direct response to market demand.

Rick Erwin, CEO of Adstra, highlighted the growing need for self-service access to composable identity:

• Brands want faster access to identity infrastructure

• Flexibility and control are now essential

• Unified identity drives scalable, measurable growth

Workspace simplifies how brands tap into Conexa’s network while preserving the platform’s modular advantages.

Composability as the Future of Enterprise Identity

Identity strategies are no longer static.

Why Flexibility Matters

As privacy rules, identifiers, and channels evolve, brands need identity solutions that can:

• Scale without replatforming

• Integrate into existing ecosystems

• Support future innovation

Conexa’s composable approach ensures identity adapts to business needs—not the other way around.

Interoperability Without Compromise

Conexa Workspace allows customization without sacrificing:

• Control

• Portability

• Transparency

This balance is increasingly critical as marketing stacks grow more complex.

Conclusion: Conexa Workspace Redefines How Brands Control Identity

The launch of Conexa Workspace represents a pivotal step in Adstra’s vision for enterprise identity. By delivering turnkey, self-service access to its composable identity platform, Adstra empowers brands to unify data, unlock anonymous audiences, and scale activation beyond traditional limitations.

As identity becomes the connective tissue of modern marketing, platforms that prioritize flexibility, interoperability, and control will define the next era of growth. Conexa Workspace positions Adstra—and its customers—at the center of that evolution, where brands dictate identity strategy and technology adapts to meet their needs.

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Holiday retail has always been a stress test. But according to Netcore Cloud, the 2025 season may be the toughest—and most revealing—yet.

The global customer engagement company has released The 2025 Holiday Marketing Guide: Tested Strategies to Convert Peak-Season Demand, a data-driven playbook built on millions of shopper interactions across markets. The message is blunt: peak-season growth is no longer about how loud your promotion is, but how precisely and quickly you respond to intent.

Traffic spikes are sharper. Prices change faster. Shopper patience is thinner than ever—especially on mobile. Retailers that cling to broad discounts and last-minute campaign blasts risk higher cart abandonment, lower margins, and exhausted customers. Those that win, Netcore argues, follow a simpler but more disciplined model: Engage → Convert → Retain, powered by real-time intelligence rather than guesswork.

In other words, holiday success is becoming less about marketing muscle and more about decision quality at speed.

A Behavioral Shift That Retailers Can’t Ignore

If there’s one underlying insight driving the guide, it’s this: festive-season shoppers don’t browse the way they used to.

According to Netcore’s analysis, modern shoppers—particularly mobile-first audiences—make buying decisions in seconds once the right signal appears. That signal might be price, trust, availability, or recognition. But when it’s missing or delayed, hesitation sets in fast. The result is decision fatigue, abandoned carts, and deferred purchases that may never return.

This creates a dangerous mismatch. Many retailers still respond to holiday pressure with mass promotions, generic emails, and late-stage discounts. Netcore’s data suggests that approach increasingly backfires, overwhelming shoppers right when clarity matters most.

The implication is clear: peak-season marketing has shifted from volume to velocity and relevance.

Three Shoppers, One Common Demand: Clarity

Rather than segmenting audiences by demographics, the guide distills holiday behavior into three dominant shopper mindsets. Each behaves differently, but all demand instant relevance.

The Deal Hunter is motivated by visible value. Price drops, low-stock warnings, and time-bound incentives move them quickly—but only if the message is unambiguous. Flood them with noise, and they disappear.

The Quality Seeker is more deliberate, comparing options and scanning for reassurance. Detailed product information, social proof, and consistent experience across channels are what convert this group, not surprise discounts.

The Loyal Regular, often overlooked during peak sales pushes, values recognition over price. Early access, seamless reordering, and frictionless checkout matter more than aggressive couponing.

Netcore’s guide maps how each shopper type moves from consideration to checkout, highlighting where high-performing brands intervene: personalized home feeds, smarter internal search, contextual product recommendations, and one-tap nudges timed to moments of intent.

The takeaway isn’t that personalization is optional—it’s that generic engagement is now actively harmful.

Record Demand, Shrinking Margins

The stakes have never been higher. The guide points to a telling paradox in holiday commerce.

On one side, consumer demand keeps hitting new highs. Cyber Monday alone generated $13.3 billion in online sales globally, with roughly half coming from mobile devices. On the other, profitability is under pressure. Advertising costs during peak periods can rise by as much as 140 percent. Cart abandonment hovers near 70 percent. Last-minute buying compresses fulfillment windows and magnifies operational risk.

This widening gap between demand and profit is forcing a rethink across retail and ecommerce. Throwing more budget at ads or deeper discounts at checkout no longer guarantees returns. In many cases, it accelerates margin erosion.

Netcore’s argument is that retailers must optimize experience efficiency, not promotional intensity.

Why Agentic AI Moves From “Nice to Have” to Necessary

Compressed timelines and volatile demand make manual optimization nearly impossible at scale. That’s where Netcore positions Agentic AI as the backbone of modern holiday marketing.

Rather than treating AI as a campaign tool, the guide frames it as a continuous decision engine. Predictive segmentation identifies which shoppers are close to buying. Timing models decide when to intervene. Frequency controls prevent fatigue. Recommendation systems adjust dynamically as behavior changes.

The emphasis is not automation for its own sake, but orchestration—multiple AI systems working together to respond to intent without overwhelming the customer.

This approach reflects a broader industry trend. As third-party tracking degrades and customer journeys fragment across channels, real-time, first-party intelligence has become the most defensible advantage retailers can build.

What Retailers Gain When Precision Replaces Push

Netcore outlines tangible outcomes from brands using these AI-driven frameworks. The benefits skew toward efficiency rather than theatrics.

Retailers see higher returning-visitor rates because experiences feel consistent and intentional. More shoppers progress from product detail pages to checkout as friction is removed at key moments. Abandoned-cart recovery accelerates because follow-ups are contextual instead of repetitive. The time between first and second purchase shortens, strengthening lifetime value rather than one-time conversions.

Perhaps most tellingly, repeat-purchase rates improve without increasing message volume, reinforcing the idea that restraint can outperform aggression during peak periods.

Inside the Playbook

Beyond strategy, the guide serves as a practical checklist for retail and ecommerce teams preparing for 2025’s holiday rush.

It covers how to personalize homepages, search, and discovery feeds based on real behavior rather than static segments. It emphasizes mobile-first checkout flows that eliminate unnecessary steps. It details how contextual nudges—applied sparingly—can rescue high-intent sessions before abandonment sets in.

Post-purchase journeys get equal attention. Netcore highlights how replenishment reminders, predictive churn signals, and early-access workflows can convert seasonal buyers into year-round customers—a critical shift as acquisition costs continue to climb.

Importantly, the guide also underscores governance. Frequency caps, consent management, and clean data practices are not compliance footnotes but trust-building tools, especially when customer attention is scarce.

The Bigger Implication for MarTech Leaders

Read between the lines, and Netcore’s holiday guide is about more than Q4 tactics. It reflects how quickly retail marketing is maturing under pressure.

As AI becomes table stakes and consumers demand relevance without intrusion, success increasingly belongs to brands that treat engagement as a system, not a series of campaigns. Holiday season simply amplifies the cost of getting that system wrong.

For MarTech leaders, the message is sharp: peak-season performance in 2025 will expose whether your stack drives decisions—or just delivers messages.

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Monks is doubling down on a simple but increasingly unavoidable reality of modern marketing: media is no longer bought—it’s computed.

The S4 Capital–owned, digital-first services company has appointed Thiago Correa as Senior Vice President of Media for EMEA, tasking him with helping brands recalibrate their media strategies for an era defined by algorithms, automation, and AI-led decisioning. It’s a hire that reflects a broader shift underway in the media industry, where the biggest ad platforms are also the most advanced AI companies—and where traditional agency models are starting to show their age.

Correa will report to Linda Cronin, EVP of Media at Monks, and joins at a moment when brands are grappling with fragmented journeys, opaque measurement, and platforms that increasingly operate as black boxes. His remit: future-proof client media strategies by leaning into the same forces reshaping the platforms themselves.

When Media Channels Become AI Companies

One of the more pointed observations behind Correa’s appointment is that the leaders of the AI revolution—Google, Meta, Amazon—are also the world’s dominant media ecosystems. These platforms no longer reward manual optimization or siloed planning. They reward clean data, high-quality signals, and creative scale.

Monks has built close partnerships with these platforms, positioning itself as an automation-first media partner rather than a traditional programmatic buyer. Correa is expected to help clients navigate what Google famously calls the “messy middle”: the unpredictable, non-linear path between discovery and conversion that algorithms now mediate.

In practice, this means shifting away from channel-by-channel decision-making toward systems that integrate creative, data, and measurement into a single operating model—something Correa argues is no longer optional.

“AI rewrites the economics of media, stripping out the manual middle where most of the margin sat,” Correa says. “The winners will be integrated, automation-first partners that connect creative, data, and measurement into one system.”

That statement could just as easily be read as a critique of legacy agency structures as it is a pitch for Monks’ model.

Growth Engineering Over Last-Click Thinking

Correa’s arrival lines up neatly with Monks’ increasing emphasis on Growth Engineering, a framework designed to replace legacy performance models that still lean heavily on last-click attribution.

Rather than chasing conversions at the end of the funnel, Growth Engineering focuses on improving the inputs platforms use to make decisions. This includes fixing data pipelines, enriching first-party signals, and feeding higher-quality information directly into platforms like Google and Meta. The goal is immediate performance lift—without forcing brands to rip and replace their existing media operations.

This approach reflects a growing realization across MarTech and AdTech: as platforms move further toward AI-driven optimization, signal quality matters more than bid tweaks. If the algorithm is the buyer, then your job is to train it well.

Monks’ pitch is that this can be done incrementally, proving value within the first year while laying the groundwork for deeper transformation. It’s a pragmatic stance at a time when many marketers feel overwhelmed by the pace of AI change.

Creative Meets Algorithmic Demand

Another pillar of Monks’ strategy—and a core focus for Correa—is tighter integration between creative and media, an area where many organizations still struggle.

Under its “Fuel & Freedom” methodology, Monks treats creative output as fuel for algorithmic systems. Platforms like Meta Advantage+ and Google Performance Max thrive on volume, variation, and velocity of creative assets. When creative supply can’t keep up, performance stalls, regardless of media spend.

By aligning creative production with algorithmic demand, Monks aims to remove the friction created by traditional agency silos. It’s a shift away from linear campaign planning toward continuous experimentation, where creative and media evolve together.

The framework also extends into search. Correa will help clients move beyond classic SEO models toward Answer Engine Optimization (AEO)—ensuring brands are visible not just on search results pages, but wherever AI assistants surface responses. As conversational interfaces reshape discovery, this evolution is quickly becoming a competitive necessity.

A Measured Approach to an Industry in Flux

Despite the ambition of the strategy, Correa is vocal about avoiding “AI theater.” The focus, he says, is on maturity and sequencing, not disruption for its own sake.

“We are providing a clear maturity roadmap that proves value in year one while identifying the next best step,” he explains. “Our goal is to ensure clients aren’t just surviving the shift to AI, but using it to future-proof their entire media business.”

That framing matters. Many brands are under pressure to adopt AI quickly but lack a clear commercial case. By tying AI adoption directly to measurable performance improvements, Monks is positioning itself as a translator between hype and operational reality.

A Proven Operator, Not Just a Strategist

Correa’s background supports that positioning. Before joining Monks, he held senior leadership roles across Publicis Groupe, including serving as Global Client Lead for H&M and as Chief Digital, Data and Technology Officer at Zenith. During his tenure, Zenith was named Campaign’s Media Agency of the Year in 2022.

He also played a key role in building Publicis’ Performics operation in the UK, which went on to win PMW’s Performance Agency of the Year in 2024. The throughline in his career is clear: scaling digital performance capabilities inside complex, global organizations.

That experience is especially relevant as brands seek partners who can operationalize AI across regions, not just pilot it in innovation labs.

Why This Matters for the Media Industry

Correa’s appointment is less about a single executive move and more about what it signals for the media sector at large.

As automation accelerates, the value of agencies shifts from execution to systems design—how data flows, how creative scales, how measurement adapts. Margins once hidden in manual optimization are disappearing, forcing agencies to reinvent their business models.

Juanita Draude, EVP EMEA at Monks, framed the hire in precisely those terms, noting that the industry has an opportunity to reinvent “practice, processes, and business models” rather than simply adding AI on top of legacy structures.

In that light, Monks’ bet on algorithmic performance and growth engineering looks less like a trend chase and more like an attempt to stay structurally aligned with where platforms—and budgets—are headed.

The Bigger Picture

Media has always followed technology. What’s different now is the speed and depth of change. Algorithms don’t just optimize campaigns; they shape visibility, influence creative formats, and redefine how success is measured.

By bringing Thiago Correa into a senior EMEA role, Monks is making a clear statement: the future of media belongs to organizations that can operate at the intersection of AI, creativity, and engineering—with less manual effort, and more strategic intent.

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Black Friday and Cyber Monday have become a stress test not just for ecommerce infrastructure, but for performance marketing itself. Budgets spike, competition explodes, and the margin for slow decision-making collapses to near zero. MAI.co believes it’s cracked that problem—not with bigger teams or smarter dashboards, but with autonomous AI agents.

The company, which provides AI-driven performance marketing for direct-to-consumer brands, says customers using its platform saw an average 63% increase in revenue during the BFCM period year over year, with some brands recording more than six-times growth compared to last holiday season.

Those are bold numbers in a period where many brands struggle simply to hold ground as ad costs surge. MAI’s wager is that continuous, machine-speed optimization—not manual media management—is the only way to compete during peak moments.

Why BFCM Exposes the Limits of Traditional Performance Marketing

For years, agencies have promised hands-on optimization during major shopping events. In reality, Black Friday weekends expose the constraint no one likes to admit: humans can’t keep up.

According to MAI, its AI agents reviewed an average of 39.1 Google Ads campaigns per client per day, executing 32.4 optimizations daily. That level of iteration—budget shifts, bid adjustments, signal interpretation, and anomaly detection—would be nearly impossible for a human team to manage in real time, especially across dozens or hundreds of accounts.

The comparison matters. During peak periods, performance gaps are rarely caused by poor strategy. They’re caused by delays. By the time a human notices an issue, debates its cause, and implements a change, the opportunity window has already closed.

MAI’s system aims to remove that lag entirely.

Optimization at Algorithm Speed

At the core of MAI’s platform is a network of autonomous AI agents designed specifically for Google Ads. These agents continuously evaluate performance signals, testing changes and measuring their impact through reinforcement learning and a fast feedback loop tied directly to ecommerce data.

Rather than automating a single action, the agents manage the system end-to-end: monitoring spend efficiency, reallocating budget, and responding to shifts in demand or conversion rates as they happen.

That architecture reflects a broader trend in MarTech. As platforms like Google Ads become increasingly opaque and algorithm-driven, success depends less on manual tweaking and more on feeding the system clean, timely signals—and reacting instantly when those signals change.

MAI is positioning itself as the connective tissue between ecommerce systems and Google’s AI-led buying engine.

What That Looks Like for Brands on the Ground

For brands, the impact shows up as scale without destabilization—a rare combination during holiday spikes.

Boring Mattress CEO Daehee Park says the company onboarded MAI ahead of Black Friday with one clear objective: scale profitable spend without sacrificing efficiency.

The result: stable performance while tripling daily ad spend, a scenario that often breaks traditional account structures. Park also highlighted MAI’s daily transparency updates, which explain not just what the agents are doing, but why—an important counterbalance to the “black box” criticism often associated with AI marketing tools.

That theme appears repeatedly in customer feedback. While the agents operate autonomously, MAI emphasizes visibility into decision logic to keep operators confident in high-stakes moments.

From Manual Monitoring to Continuous Vigilance

Beyond optimization, MAI pitches its agents as always-on watchdogs—something most internal teams can’t realistically maintain.

During BFCM, the agents flagged problems like sudden conversion-rate drops caused by broken website elements faster than customers’ own monitoring systems. In a compressed buying window, detecting those issues minutes or hours earlier can be the difference between a record day and a lost one.

That kind of vigilance reframes performance marketing from campaign management into operational insurance. When revenue velocity peaks, the cost of downtime spikes alongside it.

For brands like Italic, that responsiveness stood out. COO Avi Arora described MAI as feeling like an extension of the internal team during Black Friday, helping identify when to push spend and when to pull back—decisions that are easy in hindsight and brutally hard in real time.

Scaling Where Google Is the Only Channel

For some D2C brands, the stakes were even higher. NutritionFaktory, which relies exclusively on Google as its marketing channel, credits MAI with delivering 110% year-over-year revenue growth over Black Friday.

In catalogs with thousands of SKUs, human-led budget allocation becomes guesswork under pressure. MAI’s agents continuously redistributed spend across products based on performance signals, scaling what worked and throttling what didn’t without waiting for intervention.

This use case underscores where AI agents may be most disruptive—not as assistants to media buyers, but as primary operators in environments where speed beats intuition.

The “Toothbrush Problem” of Performance Marketing

MAI co-founder and CEO Yuchen Wu summarizes the company’s mission with a metaphor that resonates with any performance marketer: the constant urge to check numbers.

Wu describes this as the “toothbrush problem”—the need to manually review performance metrics multiple times a day just to maintain confidence that nothing is breaking. It’s a cognitive tax that grows heavier during peak periods.

By handing that layer of vigilance and adjustment to autonomous agents, MAI aims to free human teams to focus on strategy, product, and messaging—the areas where human judgment still carries the most value.

It’s also part of a larger democratization story. Advanced reinforcement learning and real-time optimization were once the domain of large enterprises with in-house data science teams. MAI’s pitch is that growth-stage brands can now access the same capabilities without building them internally.

What This Signals for the Performance Marketing Industry

MAI’s BFCM results point to a broader inflection point.

Manual optimization—whether in-house or at agencies—was designed for a slower era of digital advertising. Today’s platforms reward those who respond fastest to volatility, not those with the largest teams. As ad ecosystems consolidate around AI-driven buying, the advantage tilts toward systems that can operate continuously, learn autonomously, and execute instantly.

That doesn’t eliminate the need for marketers. But it does shift their role from operators to architects—defining strategy, constraints, and success metrics while machines handle execution at scale.

If MAI’s reported results hold across more categories and longer timeframes, autonomous agents could move from experimental add-on to baseline expectation, especially during revenue-critical moments like BFCM.

For now, the message is clear: in peak commerce, speed isn’t a nice-to-have—it’s the strategy.

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ServiceNow is placing a sizable, long-term bet on Canada’s public sector—and on the idea that AI adoption at government scale requires more than just software licenses.

The company announced a CA$110 million multi-year investment aimed at helping Canadian public sector organizations adopt AI securely and at scale. The commitment includes expanding Canadian-hosted, AI-ready digital infrastructure, strengthening data residency and security controls, and significantly increasing local expertise through a new Canada Centre of Excellence and approximately 100 high-skilled, Canada-based jobs.

For ServiceNow, which positions itself as the “AI control tower for business reinvention,” the move signals a deeper shift: governments are no longer experimenting with AI on the margins. They’re demanding production-ready platforms that meet sovereignty, privacy, and operational requirements from day one.

Why This Investment Matters Now

Public sector AI adoption has reached an inflection point. While governments globally have piloted automation and analytics initiatives for years, scaling AI across citizen services brings a new level of scrutiny around data location, security, and accountability.

Canada is no exception. Federal departments, crown corporations, provincial governments, and major municipalities are under pressure to modernize services while respecting strict regulatory environments. ServiceNow has already established a footprint across these organizations, and this investment builds on that groundwork rather than starting from scratch.

The message is clear: AI for government can’t be bolted on from outside the country. It needs local infrastructure, local people, and local governance.

Canadian-Hosted Infrastructure, Built for AI

A core pillar of the investment is expanding Canadian-hosted infrastructure designed specifically for AI workloads in the public sector.

Running the ServiceNow AI Platform in a secure, domestic environment allows government organizations to automate workflows and improve service delivery without compromising on data residency or privacy requirements. Advanced operational controls are intended to ensure public sector customers can meet compliance obligations while still moving faster than traditional IT modernization cycles allow.

This approach reflects a broader trend in government tech procurement: cloud-first is no longer enough. For sensitive workloads, governments increasingly require sovereign cloud capabilities that provide the flexibility of modern platforms without exporting data or control beyond national borders.

ServiceNow’s strategy aligns with that shift, positioning the company as a long-term infrastructure partner rather than just an application vendor.

From Platforms to People: Building Local Expertise

Technology alone doesn’t drive transformation—execution does. That’s where the company’s new Canada Centre of Excellence comes in.

The Centre will expand ServiceNow’s in-country expertise with roughly 100 new high-skilled roles, focused on helping Canadian public sector customers accelerate deployments, apply AI effectively, and realize value faster. These roles are designed to support implementation, optimization, and ongoing evolution of ServiceNow environments, not just initial rollouts.

This emphasis on people is notable. Many public sector modernization efforts stall after deployment, when internal teams struggle to operationalize new capabilities. By investing directly in local delivery and advisory capacity, ServiceNow is addressing one of the most persistent friction points in government digital transformation.

Chris Ellison, Group Vice President and General Manager of ServiceNow Canada, framed the move as both an economic and technological commitment.

“This is a major investment in Canada’s digital future,” Ellison said. “We’re creating high-skilled jobs, expanding our local footprint, and helping the Canadian public sector modernize how it serves citizens.”

AI at Scale, Without Sacrificing Trust

One of the consistent challenges facing public sector AI initiatives is trust—both internal and public-facing. Citizens expect faster, more responsive services, but they’re equally concerned about how their data is handled and how automated decisions are made.

ServiceNow’s pitch is that centralized workflow automation and AI-powered decisioning can actually increase transparency and accountability when implemented correctly. By standardizing processes, improving visibility across departments, and embedding controls into workflows, agencies can reduce manual errors and make outcomes more predictable.

That balance—efficiency with oversight—is increasingly critical as governments move from pilot projects into large-scale AI adoption.

Evan Solomon, Canada’s Minister of AI and Digital Innovation, highlighted that tension in welcoming the announcement.

“Advancing secure AI adoption and digital sovereignty is essential to building a resilient Canadian economy,” Solomon said, pointing to the importance of partnerships between public institutions and industry in driving trusted innovation.

A Strategic Bet on Public Sector Modernization

While the announcement is framed around AI, the implications extend beyond machine learning alone. ServiceNow’s platform is fundamentally about workflow orchestration—connecting people, systems, and data across large organizations.

For government entities, that means rethinking how work moves between departments, how cases are managed, and how services are delivered to citizens. AI accelerates those workflows, but the underlying transformation is organizational.

By committing capital, infrastructure, and talent locally, ServiceNow is positioning itself as a strategic modernization partner rather than a point solution provider. It’s a move that mirrors how hyperscalers and enterprise software vendors increasingly approach government markets: slow to enter, expensive to sustain, but sticky once embedded.

The Competitive Landscape

ServiceNow isn’t alone in targeting public sector AI adoption. Large cloud providers, systems integrators, and enterprise software companies are all racing to define their role in government AI strategies.

What differentiates ServiceNow’s approach is its focus on control, governance, and orchestration rather than raw compute or analytics. In heavily regulated environments, those traits often matter more than cutting-edge model performance.

By anchoring its Canadian strategy around digital sovereignty and local capability, ServiceNow is responding directly to concerns that have slowed adoption for some global vendors.

A Long-Term Commitment, Not a One-Off Announcement

Importantly, ServiceNow is framing this investment as part of a long-term commitment to Canada, not a standalone initiative tied to near-term revenue goals.

As public sector needs evolve—from service automation to predictive insights and cross-agency coordination—the company says it will continue investing in people, technology, and partnerships to support that evolution.

For Canadian government organizations, the significance is less about the headline dollar amount and more about what it enables: the ability to move forward with AI initiatives confidently, without waiting for regulatory clarity or infrastructure readiness to catch up.

The Bigger Picture for Government AI

ServiceNow’s CA$110 million investment underscores a reality that’s becoming harder to ignore: AI-driven government transformation requires industrial-scale commitment.

It’s not enough to pilot chatbots or automate isolated processes. Real impact comes from re-architecting how work flows across institutions—and doing so in a way that’s secure, compliant, and trusted.

By putting infrastructure, talent, and governance on Canadian soil, ServiceNow is betting that the future of public sector AI will be built locally, even if the platforms are global.

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