TikTok may be where trends are born, but increasingly it’s also where customers are found—especially for local businesses trying to stay relevant in a video-first internet. Scorpion, a major digital marketing and technology provider for local and SMB-focused brands, is making a direct play into that shift. The company announced it has joined the TikTok Marketing Partners Program as an officially badged Marketing Technology Partner, deepening its integration with the platform and expanding what its customers can do on TikTok.

The move positions Scorpion as a conduit between TikTok’s fast-moving ad ecosystem and the small and mid-sized businesses that often lack the time, expertise, or internal teams to manage it effectively. For TikTok, it’s another step toward making its ad platform more accessible beyond large brands and agencies.

Why TikTok Matters for Local Businesses Now

With more than 1 billion monthly active users globally, TikTok has evolved from an entertainment app into a full-fledged discovery and commerce engine. For local businesses—law firms, home services, healthcare providers, franchises, and regional brands—TikTok offers something other platforms increasingly struggle to deliver: attention.

But attention alone isn’t enough. TikTok advertising requires constant iteration, creative testing, and performance monitoring, all of which can be daunting for SMBs already stretched thin.

“It’s no secret that TikTok is a very unique space. It’s where culture often happens and is being created. And local businesses should be a part of that,” said Ashlie Kim, Senior Vice President of Advertising at Scorpion. “This partnership gives our customers the ability to easily show up, reach more of their audience, and improve their performance.”

The subtext is clear: TikTok is no longer optional, but it needs to be simplified if local businesses are going to use it consistently and effectively.

What the TikTok Marketing Partners Badge Signals

TikTok’s Marketing Partners Program is designed to connect advertisers with vetted technology providers that meet the platform’s standards for integration quality, scale, and innovation. For Scorpion, earning the Marketing Technology Partner badge signals TikTok’s confidence in the company’s ability to help businesses activate, manage, and optimize campaigns without unnecessary friction.

Rather than forcing SMBs to learn TikTok Ads Manager from scratch, Scorpion enables them to advertise through tools and workflows they already use. This “meet businesses where they are” approach mirrors a broader trend in martech: platforms win adoption not by adding features, but by reducing complexity.

For TikTok, partners like Scorpion extend the reach of its ad solutions into local and regional markets that are difficult to serve directly at scale.

What Scorpion Brings to TikTok Advertising

As a TikTok Marketing Technology Partner, Scorpion now offers a tighter, more automated connection between its marketing platform and TikTok’s ad infrastructure. The focus is on operational efficiency and performance, rather than flashy features.

Key capabilities include:

• Streamlined campaign creation and management, reducing setup time and technical barriers

• Optimization tools tailored for SMB performance, not enterprise-only use cases

• Integrated reporting and insights, enabling faster, data-driven decisions

• Automated workflows that minimize manual effort and operational overhead

• Scaled support for local and multi-location businesses, a core Scorpion audience

For SMBs that typically juggle marketing alongside daily operations, automation and simplification can be the difference between running TikTok ads sporadically—or not at all—and making them a repeatable growth channel.

TikTok’s Push to Broaden Its Advertiser Base

TikTok has been steadily investing in partnerships to lower the barrier to entry for advertisers. While major brands have already embraced the platform, the next phase of growth depends on attracting and retaining SMBs at scale.

“Businesses of every size are looking for trusted, efficient ways to activate on TikTok,” said Lorry Destainville, Head of Product Partnerships at TikTok. “Our Channel Sales Partners bring the technology, automation, and expertise needed to meet that demand.”

This reflects a familiar strategy seen at Google, Meta, and Amazon: partner ecosystems do the heavy lifting of onboarding and servicing smaller advertisers, while the platform focuses on reach, formats, and measurement.

How This Fits Into the Broader MarTech Landscape

Scorpion’s TikTok partnership highlights a larger shift in marketing technology. SMB-focused platforms are no longer just managing search, display, or social ads in isolation—they’re becoming centralized operating systems for customer acquisition.

At the same time, TikTok is evolving from a standalone channel into a must-have line item in local marketing mixes. The challenge is execution. Unlike search ads, TikTok requires creative experimentation, cultural awareness, and frequent optimization.

By embedding TikTok more deeply into its platform, Scorpion is effectively abstracting that complexity away from its customers. That approach aligns with where martech is heading: fewer dashboards, more automation, and tighter integrations between platforms and execution tools.

The Bottom Line

Scorpion joining TikTok’s Marketing Partners Program isn’t just a badge—it’s a signal that TikTok advertising is entering a more mature, SMB-friendly phase. For local businesses, it means easier access to one of the most influential platforms in digital culture today. For TikTok, it means broader adoption through trusted intermediaries that understand the realities of small and mid-sized teams.

As short-form video continues to shape how consumers discover brands, partnerships like this may determine which businesses show up—and which ones get left scrolling past.

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Bloomreach is making a decisive move to help brands stay visible—and in control—as shopping behavior shifts into conversational AI. The company announced Loomi Connect, a new capability that makes Bloomreach’s product discovery technology available through the Model Context Protocol (MCP), allowing retailers to bring their ecommerce search intelligence directly into ChatGPT and other conversational AI platforms.

In practical terms, Loomi Connect lets the same algorithms, ranking logic, and performance signals that drive conversions on a retailer’s website also determine how products surface inside AI-powered conversations. As more consumers turn to ChatGPT as a shopping assistant, Bloomreach is positioning conversational AI not as a threat to ecommerce, but as its next major channel.

Conversational Commerce Is No Longer Theoretical

Bloomreach’s launch is grounded in a sharp shift in consumer behavior. According to a company survey of more than 1,000 U.S. consumers, nearly half now shop with ChatGPT several times a week or more. More striking: when asked to choose between ChatGPT and a traditional ecommerce site, 41% said they would pick ChatGPT.

That data underscores a growing reality for retailers. Product discovery is no longer confined to search bars and category pages. It’s happening inside conversations—often before a shopper ever lands on a brand-owned site.

For years, brands invested heavily in on-site search optimization, merchandising rules, and personalization engines to control how products are discovered. Conversational AI threatens to bypass much of that work unless brands can inject their own intelligence into those experiences. Loomi Connect is Bloomreach’s answer to that problem.

What Loomi Connect Actually Does

Loomi Connect integrates Bloomreach’s product discovery stack directly into ChatGPT apps built on the OpenAI marketplace. Instead of relying on generic recommendations or static product feeds, ChatGPT can surface products using the same AI models and behavioral data that power a retailer’s ecommerce search.

That includes signals such as:

• Historical conversion performance

• Product availability and relevance

• Customer behavior patterns

• Merchandising logic refined over years of optimization

The result is product recommendations that are not only relevant to consumers, but also aligned with business goals such as profitability, inventory management, and conversion efficiency.

Equally important, Loomi Connect captures interaction data from conversational channels and feeds it back into Bloomreach’s customer profiles. This allows personalization to flow both ways—between a brand’s ecommerce site and AI-driven conversations—rather than fragmenting customer data across disconnected touchpoints.

Why MCP Matters for Commerce

The launch also highlights the growing role of the Model Context Protocol (MCP) as a bridge between enterprise systems and AI platforms. MCP enables structured, governed access to proprietary data and logic, making it possible for brands to expose intelligence to AI systems without relinquishing control.

For commerce teams, this is critical. Letting conversational AI “freestyle” product discovery can lead to inconsistent recommendations, margin erosion, or compliance risks. By plugging into MCP, Bloomreach allows brands to define how their data is used, which rules apply, and how recommendations are generated—even inside third-party AI environments.

This mirrors how brands approached earlier channels like email, SMS, and social media: initial experimentation followed by a push for control, consistency, and measurement.

ChatGPT As A Channel, Not A Competitor

Bloomreach is explicit about how it views the role of conversational AI in ecommerce.

“ChatGPT isn’t the competitor to the brand experience—it’s the next channel within it,” said Raj De Datta, co-founder and CEO of Bloomreach. “Brands need to take control of their presence on conversational channels, just as they did with email, SMS, and social media.”

That framing is significant. Rather than resisting AI-driven discovery, Bloomreach is encouraging brands to treat ChatGPT as an extension of their owned experience—one that should reflect the same intelligence, personalization, and strategic intent.

This stance aligns with a broader MarTech trend: brands increasingly accept that discovery happens off-site, but they still want to influence how it happens. Loomi Connect offers a way to project brand-specific intelligence into environments that retailers don’t directly own.

The Stakes For Retailers And Brands

The implications go beyond better recommendations. As conversational AI becomes a primary interface for shopping research and decision-making, brands that fail to integrate risk losing visibility—or worse, losing control over how their products are positioned.

Generic AI recommendations can prioritize popularity over profitability, relevance over compliance, or convenience over brand strategy. Bloomreach’s approach aims to ensure that when products appear in AI conversations, they do so on the brand’s terms.

For retailers already using Bloomreach for onsite search and personalization, Loomi Connect offers continuity. Years of tuning algorithms, refining relevance models, and analyzing shopper behavior don’t disappear in the age of conversational commerce—they extend into it.

How This Fits Into The Broader MarTech Landscape

Loomi Connect lands at the intersection of three major trends reshaping marketing and commerce technology:

1. Conversational interfaces replacing traditional search

2. AI platforms becoming new discovery gateways

3. Brands demanding governance and measurement in AI-driven channels

While many vendors are racing to “add AI,” Bloomreach is addressing a more nuanced challenge: how to operationalize AI across channels without fragmenting data, logic, or control.

In that sense, Loomi Connect isn’t just a feature—it’s infrastructure for a world where product discovery is increasingly conversational, decentralized, and mediated by large language models.

The Bottom Line

Bloomreach’s Loomi Connect signals a shift in how ecommerce leaders should think about AI-driven shopping. Conversational platforms like ChatGPT are no longer experimental—they’re becoming default entry points for discovery.

By enabling brands to bring their proven search intelligence into those conversations, Bloomreach is helping retailers protect relevance, consistency, and performance as commerce moves beyond the website. For brands navigating the next phase of digital commerce, that control may prove as important as reach itself.

Get in touch with our MarTech Experts.

In an industry known for rapid churn, short-lived agencies, and relentless platform upheaval, longevity is rare. Wonderful Websites & SEO, a U.S.–based digital marketing agency founded by Chris Moreno, is entering its 17th year in business—a milestone that stands out in a sector where most firms never make it past year three.

The timing is notable. Artificial intelligence is once again rewriting the rules of digital marketing, from search and content to customer engagement and operations. For Moreno, the anniversary isn’t just a celebration of survival. It’s a marker of relevance—and a signal that small businesses need steady guidance more than ever.

Founded nearly two decades ago, Wonderful Websites & SEO has grown into a multi-location agency serving more than 300 small businesses across the United States and Canada. Its core offerings—custom WordPress websites, local SEO, social media management, and paid advertising on Google and Facebook—have remained largely unchanged. Moreno calls them “the core four,” and says that focus has been key to the company’s endurance.

Surviving Where Most Agencies Don’t

Digital marketing is notorious for high failure rates. Algorithm updates, shifting ad platforms, client churn, and commoditized services have pushed countless agencies out of business. Against that backdrop, 17 years is an outlier.

“We’re entering our 17th year in business,” Moreno said. “We’ve earned over 100 five-star Google reviews, and to this day nobody has ever left us a review under five stars. But more importantly, we’ve stayed consistent in what we do best—bringing customers to small businesses.”

That consistency has carried the firm through multiple waves of disruption: the rise of Google Ads, the dominance of Facebook advertising, mobile-first search, and now AI-driven marketing tools. Rather than chasing every trend, Moreno says the agency’s strategy has been to master fundamentals and adapt carefully.

It’s a philosophy that resonates with small businesses, many of which don’t have the margin for experimentation or costly missteps.

AI Becomes the Defining Question

If there is a single force reshaping the marketing landscape today, Moreno says, it’s artificial intelligence.

“The elephant in the room is artificial intelligence,” he said. “Everyone has a different opinion of it, but at the very least it’s changing the market for small business dramatically—and therefore it’s changing the environment for marketing agencies like us.”

Unlike previous technology shifts, AI’s impact extends far beyond marketing execution. Moreno sees it touching nearly every operational layer of a business—from emails and spreadsheets to calls, meetings, and administrative work.

“Businesses are going to be blown away by what AI can do to make their lives easier,” he said. “But AI also introduces a wave of tools and services that aren’t ready for prime time.”

That tension—between genuine productivity gains and unproven hype—is where Wonderful Websites & SEO sees its role evolving. Rather than selling AI as a silver bullet, the agency is positioning itself as a filter and advisor, helping clients decide what’s worth adopting and what should be avoided.

Trusted Advisor in a Time of Noise

Moreno says many of the agency’s long-term clients are already feeling pressure to “do something with AI,” often without clarity on what that means.

“We work with over 300 small businesses, and many have been with us for a decade or more,” he said. “We’ve become a trusted advisor. When AI hits their industry, we’re usually their first call.”

That trust didn’t emerge overnight. It was built through years of consistent delivery, transparent communication, and a business model that minimizes friction for clients.

The agency operates without long-term contracts—a rarity in digital marketing. Retention, Moreno says, is earned month by month.

“Our retention rate is ridiculous,” he said. “If you deliver results, you never need to worry about people leaving.”

In an AI-driven future, that trust may become even more valuable. As automation lowers the barrier to entry for marketing services, differentiation increasingly comes down to judgment, accountability, and human guidance.

A People-First Approach in a Tech-Heavy Industry

Despite operating in a technology-driven sector, Moreno attributes the company’s longevity less to tools and more to people.

“The team I’ve put together I would put against any agency in the country,” he said. “I was inspired years ago by the show The Profit and its focus on people, product, and process—with people being the most important. From that moment on, I only hired the very best.”

Wonderful Websites & SEO now employs 23 team members, and Moreno says hiring discipline has shaped both culture and client outcomes. That people-first philosophy extends beyond internal operations to client relationships.

Many businesses, he notes, come to the agency after negative experiences elsewhere—locked into rigid contracts, underserved, or disappointed by results.

“We relieve that pain,” Moreno said. “We return calls and emails promptly. We do what we say we’re going to do. And we produce results.”

In a market increasingly flooded with AI-generated promises and automated services, that emphasis on responsiveness and accountability stands out.

Marketing Through Economic Uncertainty

The agency’s milestone also comes amid broader economic uncertainty, as many small businesses reassess spending. Marketing budgets are often first on the chopping block—a move Moreno believes is short-sighted.

“Most marketers and business coaches will tell you that marketing and advertising is the last thing you should cut—even though it’s often the first,” he said.

For businesses relying on lead flow, visibility, and customer acquisition, effective marketing can become a lifeline rather than a cost center.

“When we’re generating leads, calls, and new customers, we become their lifeline for growth,” Moreno added.

That argument is becoming more pointed as AI reshapes competitive dynamics. Businesses that disappear from search results, paid ads, or social platforms risk being replaced by more visible, tech-savvy competitors.

What 17 Years Signals for the Future

Wonderful Websites & SEO’s anniversary is less about nostalgia and more about positioning. As AI accelerates change across marketing and commerce, small businesses are likely to face increasing complexity—not less.

Tools will become more powerful, but also more confusing. Automation will promise efficiency, but not always deliver clarity. In that environment, agencies that combine technical competence with restraint and judgment may hold an advantage.

For Moreno, the next chapter is about applying the same principles that carried the company through previous shifts: focus on fundamentals, invest in people, and act as a long-term partner rather than a short-term vendor.

Seventeen years in, that approach appears to be resonating—and may prove even more relevant as AI pushes marketing into its next era.

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Cordial is taking a clear position in the increasingly crowded AI-for-marketing landscape: if AI can’t do the work, it’s not solving the problem.

The enterprise messaging platform announced the launch of two new AI agents—the Email Production Agent and the Data Intelligence Agent—built to automate real, production-grade marketing execution inside live workflows. Together, they form the first release of Cordial Agents, a governed agent system designed to close one of marketing’s most persistent gaps: the distance between insight and action.

While many martech vendors are racing to add AI assistants that generate ideas, drafts, or recommendations, Cordial is betting on something more operational—and more controversial. Instead of dozens of narrow agents, it’s launching fewer, deeper agents that are designed to eliminate manual, duplicative work across core marketing operations.

Why “Insights” Aren’t Enough Anymore

Marketing teams aren’t short on data. They’re short on the ability to act on it.

According to Cordial’s own research, 100% of marketers still rely on behavioral signals like clicks and opens to infer intent, yet nearly two-thirds say those insights are only used during campaign planning—not during live execution. The result is a familiar disconnect: campaigns run on assumptions formed days or weeks earlier, while customer intent changes in real time.

That disconnect shows up on the consumer side as well. Only 34% of consumers feel brands truly understand their needs, and 43% of marketers report losing customer trust when intent is misread.

As AI compresses the time between signal and action, this lag becomes harder to justify. In an environment where personalization, timing, and relevance increasingly determine performance, post-campaign insights are no longer enough.

Cordial’s response is to move AI directly into execution.

From “AI Assistants” to Production Systems

“Most AI tools stop at suggestions,” said Matt Howland, Chief Product Officer at Cordial. “We built Cordial Agents to do the work itself.”

That distinction matters. Typical AI assistants live outside production systems, generating copy or ideas that still require human translation into live campaigns. Cordial Agents, by contrast, operate inside real marketing systems, with access to live data, enforced rules, and production-grade tooling.

They don’t just advise. They execute.

Cordial describes its agents as systems designed to ground, govern, execute, and coordinate marketing work end to end. The emphasis on governance is deliberate. As AI-generated outputs move closer to live customer interactions, the risk of broken logic, brand violations, or misfired campaigns increases.

Cordial’s approach assumes that AI must be constrained, validated, and measurable if it’s going to operate at scale.

The Email Production Agent: Automating the Heavy Lifting

The first of the two agents, the Email Production Agent, targets one of the most execution-heavy areas of enterprise marketing: email.

Rather than generating a draft and handing it off, the agent handles the full production workflow, including:

• Personalization logic

• Audience definitions

• Message orchestration

• Campaign measurement

Crucially, it builds emails using production-grade tools that run inside live campaigns—not simplified prompts or static templates. Before anything is deployed, outputs are validated against real customer profiles to ensure correctness at scale.

This validation step addresses a common failure mode of AI-generated marketing: logic that looks right in isolation but breaks when exposed to real data. By checking outputs before execution, Cordial aims to prevent errors from ever reaching customers.

The Data Intelligence Agent: Acting While Campaigns Are Live

If the Email Production Agent executes, the Data Intelligence Agent observes—and intervenes.

Working from the same shared understanding of customer intent, the agent continuously monitors campaign and audience performance in real time. Instead of surfacing insights after a campaign ends, it identifies emerging trends and issues while there’s still time to act.

That includes flagging underperforming segments, detecting shifts in engagement, and recommending next actions while campaigns are still running. The goal is not just awareness, but timely response.

In practice, this moves analytics closer to operations, reducing the lag between detection and decision that has long defined marketing execution.

Governed AI, Not Autonomous Chaos

Cordial is careful to frame these agents as governed systems, not autonomous actors.

Each agent operates within a defined framework that includes explicit tools, built-in quality checks, controlled retries, and enforceable guardrails tied to brand and campaign standards. Outputs are continuously checked and corrected, allowing the agents to improve results without introducing operational risk.

Execution happens through specialized tools that operate directly inside live workflows, ensuring everything an agent produces is executable, measurable, and safe to run at enterprise scale.

This focus on governance reflects a broader shift in how serious martech buyers are evaluating AI. As experimentation gives way to production use, control, auditability, and predictability are becoming non-negotiable.

Agents That Work Together—and With Humans

Another notable design choice is that Cordial Agents are built to collaborate.

Agents share context and communicate with one another, allowing insights from one area—such as performance data—to inform execution elsewhere. Humans remain part of the loop as well, contributing briefs, artifacts, and direction that improve shared understanding.

Rather than replacing marketers, Cordial positions its agents as force multipliers that remove manual bottlenecks while keeping strategic oversight with human teams.

A Contrarian Bet in a Crowded AI Market

Cordial’s “fewer, deeper agents” philosophy stands in contrast to much of the current AI marketing narrative, which often emphasizes breadth over depth. Many platforms are adding AI features rapidly, but stopping short of execution.

Cordial is betting that marketers don’t need more assistants—they need fewer steps.

By embedding AI directly into production workflows, the company is addressing a harder problem: not generating ideas, but turning intent into action without friction.

The Bigger Picture for Enterprise Marketing

As AI becomes embedded across the marketing stack, the winners are likely to be platforms that reduce operational drag rather than add new layers of abstraction.

Cordial Agents reflect that shift. They’re not positioned as experimental tools, but as infrastructure—designed to remove manual, cumbersome, and duplicative work from marketing operations altogether.

For enterprise teams struggling to act on real-time signals at scale, that may be a more compelling promise than another AI assistant offering suggestions no one has time to implement.

Get in touch with our MarTech Experts.

CloudMasonry, a Salesforce-focused consulting firm, has formally launched its Data & AI Practice, signaling a strategic expansion beyond implementation services into advanced analytics, artificial intelligence, and enterprise data strategy. The company also announced the appointment of Landon Harris as Practice Lead, tasking him with building and scaling the new offering.

The move reflects growing enterprise demand for AI capabilities that extend beyond experimentation and into operational impact—particularly within Salesforce-led environments where data, automation, and customer engagement increasingly converge.

From Salesforce Expertise to Data-Led Intelligence

CloudMasonry has built its reputation around deep expertise across the Salesforce ecosystem, including Sales Cloud, Service Cloud, Marketing Cloud, Agentforce, and Data 360 (Data Cloud). As generative AI, embedded analytics, and real-time data orchestration reshape how businesses operate, the firm is positioning data and intelligence as the foundation of its next growth phase.

According to CEO Peter Ryan, the new practice is designed to help clients move from fragmented data initiatives to intelligence-driven execution.

“As businesses look to stay ahead in an AI-driven world, data and intelligence are at the core of transformation,” Ryan said. “Our Data & AI Practice empowers clients to unlock the value of data and turn AI innovation into real-world impact.”

What the Data & AI Practice Delivers

The newly launched practice brings together strategy, architecture, and execution across several core areas:

• Data strategy, governance, and architecture
  Helping organizations define how data is structured, secured, and activated across systems.

• Cross-cloud integration
  Connecting Salesforce with modern data platforms such as Data Cloud and Snowflake to enable unified, AI-ready workflows.

• Advanced analytics and business intelligence
  Transforming raw data into dashboards, decision platforms, and operational insights that support faster, smarter decisions.

• AI and machine learning advisory and implementation
  Designing AI use cases, building and operationalizing models, and embedding predictive intelligence directly into business processes.

• Intelligent Document Processing (IDP)
  Using tools like MuleSoft Intelligent Document Processing to automate extraction and transformation of unstructured data into usable insights.

• Client enablement and training
  Supporting long-term adoption through upskilling, governance frameworks, and operational best practices.

Together, these services reflect CloudMasonry’s intent to act not just as an implementation partner, but as a long-term advisor throughout the data and AI transformation lifecycle.

Leadership Spotlight: Landon Harris

As Practice Lead, Landon Harris brings more than 15 years of experience in CRM, analytics transformation, data strategy, and AI enablement. During his time at CloudMasonry, Harris has helped clients design intelligent data platforms that link insights directly to execution.

In his expanded role, Harris will define the practice’s vision, develop go-to-market offerings, and guide enterprise clients in applying AI to achieve measurable business outcomes.

“Our clients aren’t just looking to adopt AI—they’re looking to achieve meaningful outcomes,” Harris said. “We’re focused on connecting strategy, data, and technology to transform how organizations operate and compete.”

Why This Matters for Enterprises

As Salesforce continues to embed AI across its platform—from Agentforce to Data Cloud—many organizations struggle to operationalize intelligence across sales, service, and marketing. Data remains fragmented, analytics disconnected from execution, and AI initiatives often stall at the pilot stage.

CloudMasonry’s Data & AI Practice aims to close that gap by aligning data foundations, analytics, and AI execution within the systems enterprises already rely on.

What’s Next

CloudMasonry plans to roll out new data and AI frameworks, accelerate client delivery, and publish thought leadership to help enterprises navigate the rapidly evolving AI landscape. Early engagements will focus on industries including financial services, technology, and consumer brands, leveraging the firm’s cross-cloud and vertical experience.

As enterprises move from AI experimentation to production-scale intelligence, CloudMasonry is betting that integrated data strategy—anchored in Salesforce—will be the differentiator.

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Five9 (Nasdaq: FIVN) has expanded its partnership with Google Cloud, unveiling a new joint Enterprise Customer Experience (CX) AI solution designed to help large organizations deliver more intelligent, personalized, and seamless customer interactions at scale.

The integrated offering combines the Five9 AI-Infused Intelligent CX Platform with Google Cloud’s Gemini Enterprise for Customer Experience (GECX), alongside advanced AI services including Gemini models and Vertex AI. Together, the platforms aim to unify data, AI, and human workflows across digital and voice channels.

Connecting Data, AI, and Human Engagement

As enterprises accelerate CX modernization, many continue to struggle with fragmented systems and disconnected AI initiatives. Five9 and Google Cloud position their joint solution as an end-to-end CX platform that bridges that gap—enabling organizations to move from isolated automation to coordinated, AI-driven engagement.

“Enterprises today are looking for an end-to-end platform that connects data, AI, and humans to turn every interaction into a meaningful outcome,” said Mike Burkland, Chairman and CEO of Five9. “By combining Five9’s AI-driven platform with Google Cloud’s leadership in AI and data innovation, we’re making it easier for businesses to deliver smarter, more personalized customer experiences.”

A Unified Experience for Agents and Customers

The Enterprise CX AI solution is designed to deliver a seamless experience for agents, supervisors, and administrators, integrating contact center workflows, analytics, and real-time AI assistance into a single environment.

For customers, this translates into faster, more proactive, and more personalized interactions. For enterprises, the platform promises greater agility—enabling teams to innovate quickly, scale operations efficiently, and manage CX capabilities with increased confidence.

Gemini-Powered CX at Enterprise Scale

At the core of the solution is Gemini Enterprise for Customer Experience, which brings Google’s generative AI models into live customer engagement workflows. When combined with Five9’s intelligent CX capabilities, the solution enables AI-assisted conversations, deeper contextual understanding, and real-time decision support across channels.

“Digital transformation requires technology that helps businesses solve complex challenges unique to their industry, especially in customer engagement,” said Kevin Ichhpurani, President, Global Ecosystem and Channels at Google Cloud. “By utilizing Gemini Enterprise for Customer Experience with Five9’s intelligent CX platform, Five9 is delivering a unified, AI-led CX solution that moves the contact center industry forward.”

Industry Focus and Go-To-Market Expansion

The expanded partnership includes a strengthened go-to-market strategy targeting industries such as retail, financial services, healthcare, and other customer-intensive sectors.

As part of the agreement, Five9 is now available through the Google Cloud Marketplace, allowing customers and partners to simplify procurement, consolidate billing, and apply purchases toward existing Google Cloud spend commitments.

Five9 Deepens Its Own Use of Google Cloud AI

Beyond customer-facing solutions, Five9 is also expanding its internal use of Google Cloud’s AI infrastructure. The company is running key enterprise workloads on Google Cloud and leveraging Gemini Enterprise to drive efficiency across sales, legal operations, customer success, and business operations.

This internal adoption underscores Five9’s broader strategy to operationalize AI not just as a product feature, but as a core business capability.

Why This Matters

As contact centers evolve into intelligence-driven engagement hubs, enterprises are demanding platforms that unify AI, analytics, and human workflows rather than layering point solutions. The Five9–Google Cloud collaboration reflects a broader industry shift toward enterprise-grade, AI-native CX platforms built for scale, governance, and real-world execution.

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Cast AI is betting that the next major bottleneck in AI infrastructure isn’t algorithms—it’s access to compute. The Application Performance Automation company today unveiled OMNI Compute, a unified compute control plane designed to let enterprises tap into available cloud capacity across providers and regions as if it were native infrastructure. At the same time, Cast AI announced a strategic investment from Pacific Alliance Ventures (PAV), the U.S.-based corporate venture arm of South Korea’s Shinsegae Group, pushing the company’s valuation beyond $1 billion.

The dual announcement underscores Cast AI’s growing influence as enterprises struggle with GPU shortages, cloud lock-in, and rising infrastructure costs driven by AI workloads.

Why OMNI Compute Matters Now

AI adoption has exposed a structural weakness in cloud computing: capacity is fragmented, region-bound, and often unavailable when demand spikes. Enterprises may have Kubernetes clusters running efficiently—until they need GPUs in a region where supply is constrained or pricing becomes prohibitive.

OMNI Compute is Cast AI’s answer. The platform automatically discovers external compute resources—including GPUs—across cloud providers and regions, and extends existing Kubernetes clusters to consume them transparently. No code changes. No reconfiguration. No operational overhaul.

In practice, that means teams can run workloads where compute is actually available, rather than where their cloud contracts or regions force them to be.

“Enterprises don’t just need cheaper infrastructure—they need infrastructure that adapts automatically as workloads and constraints change,” said Yuri Frayman, Co-Founder and CEO of Cast AI. “That is what our automation agents were built to do.”

Turning GPUs Into a Shared, Global Resource

A central promise of OMNI Compute is fungibility—making GPUs interchangeable at the infrastructure layer. Instead of capacity being trapped inside a single hyperscaler or geography, Cast AI allows workloads to move across clouds while remaining governed and predictable.

According to Laurent Gil, Cast AI President and Co-Founder, the goal is to remove artificial barriers that slow AI deployment. “OMNI Compute makes GPUs fungible so capacity isn’t trapped inside a single cloud or region. Teams can run production workloads wherever compute is actually available.”

This is especially relevant for AI inference, the first workload Cast AI is prioritizing with OMNI Compute. Unlike training, inference must run continuously and close to users, making regional shortages and pricing volatility particularly painful.

Oracle Joins the Multi-Cloud Push

One of the first major providers making GPU capacity available through OMNI Compute is Oracle Cloud Infrastructure (OCI). Through the integration, enterprises running on any hyperscaler can instantly access OCI’s GPU infrastructure across Oracle regions worldwide.

“OMNI Compute removes the barriers that traditionally kept enterprises locked into a single cloud,” said Karan Batta, SVP at Oracle Cloud Infrastructure. For Oracle, the partnership opens access to customers who may not otherwise consider OCI—but need GPU capacity now, not after months of procurement.

This dynamic reflects a broader shift in cloud competition: capacity availability and flexibility are becoming as important as services and pricing.

From Optimization to Infrastructure Orchestration

Cast AI has historically focused on continuous optimization—automating rightsizing, cost control, and performance tuning for Kubernetes workloads. OMNI Compute extends that same logic beyond a single cloud boundary.

External capacity brought in through OMNI Compute is automatically optimized using Cast AI’s existing tooling, including GPU sharing, monitoring, and rightsizing. The result is consistent behavior across environments, even as workloads span multiple clouds and regions.

For enterprises, this means scaling AI services without pinning workloads to a single provider, while still meeting compliance, regulatory, and data residency requirements.

Early Enterprise Validation

Customers already running AI in production see OMNI Compute as a practical solution to real-world constraints.

Uniphore, which operates real-time AI workloads globally, says the ability to provision GPUs across clouds without changing application code fundamentally alters how it deploys inference. “Access to reliable, affordable GPU capacity exactly where and when you need it is mission-critical,” said Erik Johnson, VP of Product Management at Uniphore.

Samsung Electronics also sees broader implications. “OMNI Compute’s unified control plane has the potential to change how enterprises like Samsung run AI infrastructure globally,” said Kyotack Tylor Kim, Head of Next Gen Cloud Group at Samsung Electronics.

The $1B Valuation Signal

The strategic investment from Pacific Alliance Ventures, backed by Shinsegae Group, adds more than capital. Shinsegae operates across retail, consumer, and digital platforms—industries increasingly dependent on AI-driven applications at scale.

PAV’s backing follows Cast AI’s recent Series C round led by G2 Venture Partners and SoftBank Vision Fund 2, with participation from Aglaé Ventures and others. Together, the funding validates Cast AI’s thesis that automation—not manual cloud management—will define the next phase of infrastructure operations.

“We see strong global demand for Cast AI’s platform,” said Hyuk Jin Chung, Managing Partner at PAV, pointing to expansion opportunities across Asia.

Global Growth and Market Momentum

Cast AI’s customer roster already includes Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and more—spanning industries from telecom to automotive to AI-native companies.

Following its Series C, the company has expanded aggressively, opening offices in Bangalore, London, New York, and Tel Aviv, and establishing subsidiaries across Europe, Asia, and North America. That footprint reflects the global nature of the problem Cast AI is addressing: infrastructure scarcity doesn’t respect regional boundaries.

The Bigger Picture

As AI workloads proliferate, enterprises are discovering that cloud-native doesn’t automatically mean cloud-flexible. GPU shortages, regional constraints, and vendor lock-in are becoming strategic risks—not just operational headaches.

OMNI Compute positions Cast AI at the intersection of AI infrastructure, Kubernetes automation, and multi-cloud strategy. By abstracting compute availability from provider boundaries, the company is effectively arguing that the future of AI infrastructure is adaptive, automated, and provider-agnostic.

For marketing and digital leaders watching the AI stack evolve, the message is clear: performance, cost, and scale will increasingly depend on how intelligently infrastructure adapts behind the scenes.

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Highspot is sharpening its pitch to revenue leaders who are tired of dashboards that explain deals after they’ve already slipped. With its Winter Product Launch 2026, the company is rolling out a new wave of agentic AI capabilities designed to do more than surface insights—they actively guide sellers on what to do next, inside live deals.

At the center of the launch is Deal Intelligence, powered by a new Deal Agent that analyzes CRM data, buyer engagement, and meeting insights in real time. The goal: help sales teams improve deal execution, increase deal velocity, and win more consistently in an era where buying journeys are longer, messier, and harder to predict.

From Fragmented Signals to a Single Deal Truth

Most GTM teams are drowning in signals—CRM updates, email engagement, meeting notes, content views—but still struggle to answer basic questions: Is this deal healthy? Why is it stalling? What should the seller do next?

Highspot’s Deal Intelligence aims to consolidate those fragmented inputs into a single, unified view of deal health. Buyer activity, CRM changes, and meeting intelligence are brought together so sellers and managers can see what’s actually happening across every active opportunity.

This is where Deal Agent comes in. Rather than stopping at analysis, the agent recommends data-backed next steps tailored to each deal. That might mean flagging risk, suggesting a deal-specific AI Role Play, or prompting the seller to launch a Digital Sales Room (DSR) to re-engage stakeholders.

Crucially, Deal Agent is built on Highspot Nexus, the company’s unified AI and analytics engine, and connects directly to the organization’s CRM. That tight integration is meant to ensure recommendations reflect how the business actually sells—not a generic AI playbook.

Agentic AI That Lives Inside the Deal

Highspot is positioning this release as a shift away from AI that advises from the sidelines. Many sales AI tools today still function as copilots that generate summaries or suggestions sellers must interpret and act on manually.

By contrast, Highspot’s approach embeds agentic guidance directly into the flow of work. Deal Agent doesn’t just highlight risk; it nudges sellers toward concrete actions that can move the opportunity forward while it’s still alive.

That distinction matters as revenue leaders look for AI investments with measurable ROI. Insight without execution hasn’t proven enough.

Digital Sales Rooms Get More Structured

The Winter Launch also expands Digital Sales Rooms, Highspot’s secure, branded spaces where sellers and buyers collaborate throughout the buying journey.

New mutual action plans, embedded directly in DSRs, allow both sides to align on roles, milestones, and timelines. For sellers, this reduces back-and-forth and late-stage confusion. For buyers, it provides clarity and structure from first meeting through close—an increasingly important factor as buying groups grow larger and consensus harder to reach.

In practice, Highspot is betting that clearer collaboration translates to shorter sales cycles and fewer deals that quietly die from inertia.

Preparing Sellers When It Actually Matters

Execution depends on preparation, and Highspot is extending its AI-powered coaching to connect directly with live deals. AI Role Play is now available inside Deal Agent, allowing sellers to practice real scenarios using the actual context of an active opportunity.

Sellers can rehearse tough conversations or stakeholder objections anytime—on web or mobile—without waiting for scheduled coaching sessions. It’s a practical move in distributed sales environments where managers can’t always provide hands-on training at the right moment.

The Winter Launch also introduces broader enablement enhancements:

• Highspot Skills, a GTM-tested framework for defining and measuring critical seller capabilities

• AI-powered skill assessments to scale coaching and training programs

• Automated AI Feedback for Training, which evaluates submissions automatically to reduce review bottlenecks and speed up seller readiness

Together, these features point to a more systematized approach to sales readiness—less reliant on ad hoc manager feedback and more grounded in consistent measurement.

Why This Matters Now

Deals are getting more complex. Buying groups are larger. Budgets are scrutinized. And sellers are under pressure to execute flawlessly with fewer resources.

Highspot’s Winter Launch reflects a broader market shift toward AI that doesn’t just analyze performance but actively guides execution. Instead of adding another tool or layer of complexity, the company is embedding recommendations directly into daily workflows.

That aligns with what analysts are seeing across the revenue tech landscape. According to the Gartner Magic Quadrant for Revenue Enablement Platforms 2025, the market is moving away from episodic, generic tools toward connected, insight-driven solutions, with AI now viewed as business-critical rather than experimental.

Highspot is clearly positioning itself on the “prove it” side of that shift.

Leadership’s Take

“Your go-to-market strategy lives or dies with the deal,” said Robert Wahbe, CEO of Highspot. “Our Winter Launch turns insight into action inside live deals, giving sellers the agentic platform they need to win more consistently.”

It’s a concise summary of the company’s bet: that execution, not analysis, is where AI can make the biggest difference for revenue teams

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