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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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

Get in touch with our MarTech Experts.

As the ad industry wrestles with shrinking signals, tougher privacy rules, and rising pressure to prove ROI, madSense and Symitri are betting that deeper AI—not looser data—will restore addressability on the open internet.

The two companies have announced a strategic partnership that natively integrates Symitri’s PRISM AI audience discovery technology directly into madSense’s OLM-powered madBuy DSP. The result: advertisers can identify, activate, and optimize high-value audiences across cookieless browsers, mobile, CTV, and programmatic channels—without bolting on new tools or cutting separate contracts.

In an ecosystem where “privacy-first” often means “less reach,” the partnership is positioning itself as a way to get more of both.

Why This Matters Now

Third-party cookies are effectively gone, regulators are watching AI more closely, and advertisers are tired of trading performance for compliance. Walled gardens still offer scale and precision—but at the cost of transparency and control. The open web, meanwhile, has struggled to keep up.

madSense and Symitri argue that the problem isn’t lack of inventory; it’s lack of intelligence. By embedding real-time, multimodal AI directly into the DSP layer, the companies aim to make addressability continuous, adaptive, and privacy-safe—rather than modeled after the fact.

Jeff White, Co-CEO of madSense, summed it up bluntly: this isn’t theoretical lift or probabilistic reach. It’s real-time discovery and activation operating as a single system.

What the Native Integration Delivers

Unlike many data partnerships that live outside the buying workflow, PRISM AI is baked directly into madBuy. Advertisers can turn it on without additional integrations or operational friction.

Key capabilities include:

• Real-time addressability expansion across cookieless and cross-device environments

• Dynamic cohort decisioning that adapts instantly to user behavior

• Omnichannel activation spanning display, video, social, mobile, CTV, and gaming

• Privacy-by-design execution, aligned with GDPR, CCPA, and emerging AI regulations

Because discovery and activation happen in the same system, campaign insights flow back into optimization loops automatically—closing a gap that has long slowed performance on the open web.

Early Results: Doubling the Addressable Footprint

The companies aren’t launching empty-handed. Pilot programs run in late 2025 showed meaningful gains, including a deployment with a top-tier pharmaceutical advertiser.

According to the companies, the integrated platform delivered:

• Nearly 2x addressable reach, expanding footprint by close to 200% through privacy-safe cohort resolution

• Closed-loop optimization, with real-time feedback driving incremental reach and efficiency improvements mid-campaign

Bill Wheaton, CEO and Chairman of Symitri, framed the outcome as something the industry has been steadily losing: true addressability outside of walled gardens—without sacrificing transparency or compliance.

Competitive Implications

This move puts pressure on both DSPs and data providers that still treat audience discovery as an external, pre-campaign exercise. As AI-driven buying becomes more autonomous, the line between planning, activation, and optimization is blurring fast.

By collapsing those stages into one system, madSense and Symitri are aligning with a broader industry shift toward always-on, AI-orchestrated media buying—where performance improves continuously, not in post-campaign reports.

It also signals a challenge to clean rooms and static cohorts that can’t adapt in real time. If this model scales, “addressability” may increasingly mean AI-driven inference rather than deterministic identity.

Availability and What’s Next

Following successful pilots in Q4 2025, the integrated madSense–Symitri solution will roll out to select enterprise brands and agency partners starting in Q1 2026.

For advertisers frustrated by shrinking signals and rising complexity, this partnership offers a clear pitch: walled-garden-level performance on the open web, without giving up privacy, scale, or control.

Whether it becomes a new standard—or just a strong alternative—will depend on how quickly the rest of the ecosystem follows.

Get in touch with our MarTech Experts.

Google Cloud and Authentic Brands Group (Authentic) pulled back the curtain on a partnership that shows what AI looks like when it’s embedded across a global retail organization—not bolted on as an experiment.

The two companies announced a strategic collaboration centered on Gemini and Google Cloud’s Vertex AI, powering Authentic’s proprietary platform, Authentic Intelligence. The goal: accelerate collaboration, creativity, and decision-making across a portfolio of more than 50 global brands, from Reebok and Champion to Sports Illustrated and Juicy Couture.

For an organization driving $32 billion in annual retail sales across 150 countries, the message is clear: AI isn’t a pilot—it’s infrastructure.

From AI Experiments to an Operating System

Authentic’s AI journey goes well beyond chatbots or isolated marketing tools. Built on Vertex AI, Authentic Intelligence functions as a shared AI layer across departments—marketing, creative, licensing, legal, finance, and business development.

According to the company, 80% of employees now use the platform weekly, a rare adoption figure that underscores how deeply AI is woven into day-to-day workflows.

Adam Kronengold, Authentic’s Chief Digital Officer, describes the platform as a force multiplier—scaling asset creation, reporting, and workflows while preserving consistency across dozens of distinct brand identities.

That balance—speed without dilution—has long been one of the hardest problems in brand licensing. Authentic believes AI finally makes it manageable.

Real Results: Higher ROAS, Faster Creative

This isn’t just about internal efficiency. Early performance data suggests tangible upside on the revenue side as well.

In initial testing, Reebok ad creative enhanced by Authentic Intelligence delivered up to 60% higher return on ad spend (ROAS) compared to traditional product imagery. That’s a notable signal for marketers still debating whether generative AI improves outcomes—or just content volume.

Google Cloud’s Gemini models play a central role here, turning text prompts into brand-specific creative, including video via Google Veo, while maintaining each brand’s tone, visual language, and heritage.

Inside the AI Agents Powering Authentic

Authentic Intelligence currently runs on 15+ specialized AI agents, each tuned to a core business function:

• Business development agents build deep profiles of potential partners, accelerating licensing deal discovery and evaluation.

• Brand and creative agents enforce brand voice and visual standards across channels while generating campaign-ready assets from simple prompts.

• Licensing and legal agents assist with contract review and analysis, reducing turnaround times without sacrificing accuracy.

Rather than replacing teams, the system is designed to remove friction—compressing weeks of work into hours and freeing staff to focus on strategy and relationships.

Why Google Cloud—and Why Retail Now

For Google Cloud, Authentic represents a showcase customer for enterprise-grade, agent-based AI in retail and consumer goods. Jose Gomes, VP of Retail & Consumer Goods at Google Cloud, framed the partnership as proof that inspiration no longer needs weeks to become execution.

The timing matters. Retailers are under pressure to move faster across more channels, personalize at scale, and keep brand storytelling consistent—while managing leaner teams. AI agents, especially when grounded in trusted data and governed workflows, are emerging as the answer.

This approach also contrasts with point solutions flooding the martech stack. Instead of adding another tool, Authentic built a centralized intelligence layer—an increasingly common pattern among AI-mature enterprises.

Implications for the Retail and Licensing Industry

Authentic’s model hints at where large brand portfolios may be headed:

• AI as brand guardian, not just content generator

• Agent-driven workflows replacing manual handoffs

• Unified creative and commercial intelligence across global markets

For licensing-driven businesses, the ability to scale creativity while enforcing brand DNA could become a competitive necessity—not a differentiator.

And for retailers watching from the NRF show floor, the takeaway is hard to miss: AI delivers the biggest gains when it’s embedded into how the business runs, not just how campaigns are made.

Get in touch with our MarTech Experts.

The Home Depot and Google Cloud made it clear that the future of home improvement retail isn’t just digital—it’s agentic.

The companies announced an expanded strategic partnership that introduces a new wave of AI-powered tools designed to deliver real-time, expert-level assistance to both DIY homeowners and professional customers such as contractors, remodelers, and renovators. Built on Google Cloud’s Gemini models and Gemini Enterprise for Customer Experience (CX), the initiative extends Home Depot’s iconic “Orange Apron” expertise far beyond the store floor—into phones, job sites, and even Google Search.

This isn’t about smarter recommendations. It’s about AI that understands intent, context, and complexity—and acts on it instantly.

From Store Associate to Always-On Digital Expert

Home Depot’s vision is an AI-first retail experience that feels native, personalized, and available wherever customers are. By deploying agentic AI across its ecosystem, the retailer is effectively turning decades of home improvement knowledge into a network of digital experts that can reason, guide, and solve problems in real time.

Jordan Broggi, EVP of Customer Experience and President of Online at The Home Depot, summed it up succinctly: the goal is to put Orange Apron expertise in every customer’s pocket—whether they’re standing in a kitchen, on a jobsite, or in an aisle.

Looking ahead, these capabilities won’t be limited to Home Depot-owned channels. The company plans to participate in agentic shopping experiences across Google Search’s AI Mode and the Gemini app, signaling a broader shift toward AI-mediated commerce.

Magic Apron Evolves Into a Conversational Project Partner

The most visible upgrade is the expansion of Magic Apron, Home Depot’s AI assistant. Once limited to product-page support, Magic Apron is now becoming a conversational, project-aware digital companion for both DIYers and pros.

Customers can describe projects in plain language—anything from fixing a leaky faucet to planning a full kitchen remodel—and receive expert guidance, personalized recommendations, and step-by-step advice. Powered by Gemini Enterprise for CX, Magic Apron is also gaining multimodal capabilities, including image uploads and visual guidance to help customers navigate complex projects.

One standout innovation: a localized, in-store Magic Apron experience. Currently piloting in select locations, the AI integrates real-time store inventory and aisle-level product locations. Ask which grout works best for glass tiles, and Magic Apron won’t just explain the difference between unsanded and epoxy—it will direct customers to the exact bay where those products live and suggest complementary items. A national rollout is planned in the coming months.

Faster Quotes and Planning for Pros

For professional customers, Home Depot is rolling out AI-powered materials list builders on its pro digital platform. Contractors can describe a project via voice or text—or upload an existing list—and the agent interprets intent to generate a comprehensive, grouped materials list.

The system even flags commonly missed essentials, helping pros create more accurate estimates in minutes instead of hours. Launched in beta in November 2025, the feature is now scaling nationally, aiming to compress planning cycles and free up time for revenue-generating work.

Smarter Last-Mile Delivery With AI Route Intelligence

The partnership also tackles one of retail’s hardest problems: last-mile delivery. Home Depot is deploying AI-powered route intelligence using Gemini and Google Maps Platform to predict and prevent delivery failures.

By combining customer-specific details—like delivery windows and site constraints—with external factors such as weather, road conditions, and access limitations, the system can identify risks before trucks roll. Multimodal AI analyzes map data to flag narrow roads, gated entrances, or unloading challenges, and will soon recommend the right equipment and crew size for complex deliveries.

The result is a more reliable, consistent experience—especially for large or professional-grade orders.

Conversational AI Replaces Rigid Customer Service

Home Depot is also overhauling customer support across chat, SMS, and voice, replacing menu-driven automation with conversational AI that understands intent and resolves issues in real time. Live today, the platform allows customers to speak naturally instead of navigating endless prompts.

Early results show higher engagement and resolution rates, pushing conversational AI beyond simple issue deflection. The company is now testing next-generation AI voice agents in select stores, freeing associates to focus on high-complexity interactions.

Empowering Associates With Gemini Enterprise

Behind the scenes, Home Depot is equipping thousands of Store Support Center associates with Gemini Enterprise, Google Cloud’s agentic platform for automating end-to-end workflows.

From predicting project bottlenecks to drafting marketing copy and auditing digital designs, specialized agents handle routine execution in seconds. The intent isn’t replacement—it’s leverage. By offloading repetitive tasks, teams can focus on strategy, creativity, and long-term growth.

Why This Expansion Matters

Retail has spent years experimenting with AI at the edges. Home Depot’s expanded partnership with Google Cloud shows what happens when AI becomes core infrastructure—connecting customer experience, operations, logistics, and employee productivity through a single intelligence layer.

As Jose Gomes, VP of Retail & Consumer Packaged Goods at Google Cloud, put it: this is retail moving from product suggestions to problem-solving.

For an industry under pressure to deliver speed, expertise, and personalization at scale, Home Depot’s approach may well set the bar for agentic commerce in the years ahead.

Get in touch with our MarTech Experts.

CRM modernizations don’t usually make headlines—but when one delivers immediate revenue impact and reins in runaway costs, it’s worth a closer look. Optrua, a Microsoft Dynamics 365 and Power Platform consulting firm, has completed a CRM transformation for Advantage Design Group (ADG) that reshaped how the organization sells, forecasts, and scales.

The project centered on stabilizing and optimizing ADG’s Microsoft Dynamics 365 environment after an earlier migration from a legacy CRM exposed unexpected challenges. While the move promised a modern, web-based system, the lack of specialized Dynamics expertise quickly became a bottleneck.

Sales processes were inconsistent, pipeline visibility was limited, and database storage costs were climbing fast—an all-too-familiar story for organizations that modernize platforms before modernizing processes.

When Modern CRM Creates New Problems

ADG’s leadership saw the warning signs early. Sales teams weren’t aligned around a shared workflow, opportunities were slipping through the cracks, and executives had little clarity on deal status. At the same time, alerts around database growth raised concerns about long-term infrastructure costs.

According to Catherine Swingle, Chief Operations Officer at Advantage Design Group, the issues were operational as much as technical. Without clear process alignment, the CRM was acting more like a digital filing cabinet than a revenue engine.

That’s where Optrua stepped in—not with a sweeping reimplementation, but with a business-first diagnostic approach.

Fixing the System Without Breaking the Business

Rather than proposing a disruptive overhaul, Optrua began with a discovery-driven consultation focused on how ADG actually operates today—and where it wants to go next. That understanding shaped a phased improvement roadmap delivered through the Optrua Care Plan, the firm’s continuous improvement engagement model.

This incremental approach mattered. It allowed ADG to address critical issues—like bloated storage usage and fragmented sales workflows—without exceeding budget or overwhelming internal teams.

Swingle pointed to CEO Ryan Redmond’s hands-on involvement as a key differentiator. The engagement wasn’t about configuring features for their own sake; it was about aligning Dynamics 365 to real-world selling behavior.

From Cost Control to Revenue Growth

The results were tangible and fast.

On the infrastructure side, Optrua helped ADG reduce unnecessary database storage, easing cost pressures and stabilizing the Dynamics environment. On the sales side, automated workflows guided sellers through a consistent, repeatable process—bringing much-needed structure to lead management and opportunity tracking.

Sales leadership gained clearer visibility into pipeline activity, making forecasting more reliable and performance easier to manage. Perhaps most importantly, ADG’s internal applications administrator was equipped with the tools and knowledge needed to sustain improvements long after the initial engagement.

Then came the revenue impact.

ADG reports an 80% increase in lead capture, a dramatic lift that underscored how process clarity and system discipline can translate directly into growth. For Swingle, the outcome validated the decision to optimize rather than replace.

A Case Study in Smarter CRM Investment

The partnership also highlights a broader trend in CRM and MarTech strategy: organizations are increasingly favoring targeted expertise over permanent headcount. By outsourcing highly specialized Dynamics work to Optrua, ADG avoided overstaffing while still accessing deep platform knowledge when it mattered most.

That flexibility is becoming critical as CRM systems grow more complex and intertwined with revenue operations, analytics, and automation. Not every organization needs a large internal CRM team—but most need the right expertise at the right time.

An Ongoing, Not One-Time, Transformation

Both companies describe the engagement as an ongoing partnership rather than a closed project. For ADG, the confidence comes from knowing its CRM can evolve alongside the business without recurring disruption.

For Optrua, the project reflects a philosophy that’s gaining traction across B2B tech: CRM success isn’t about software alone. It’s about intentional implementation, continuous improvement, and alignment with how teams actually work.

In an era where CRM platforms promise intelligence and automation out of the box, this case serves as a reminder—real gains still depend on execution.

Get in touch with our MarTech Experts.