Ecommerce platforms generate massive amounts of data—from user behavior analytics to technical performance monitoring—but that data often lives in separate tools. The result is what many digital teams call the “fragmentation tax”: disconnected insights that slow decision-making and obscure revenue opportunities.

Now Noibu wants to solve that problem by combining technical monitoring and behavioral analytics into a single platform.

The company announced it is evolving from an error-monitoring tool into a full Ecommerce Analytics & Monitoring Platform, designed to help retailers identify technical issues, understand customer behavior, and uncover revenue opportunities in one unified environment.

The repositioning reflects a broader shift in ecommerce operations, where site performance, customer experience, and revenue optimization are becoming tightly intertwined.

From Bug Detection to Revenue Intelligence

Noibu initially gained traction by helping ecommerce teams detect site errors—particularly bugs that disrupt checkout flows and prevent transactions.

That capability remains core to the platform. But as modern ecommerce stacks have become more complex, many retailers found that fixing bugs alone wasn’t enough to drive growth.

According to Kailin Noivo, President and Co-founder of Noibu, the company’s customers pushed it to expand beyond reactive debugging.

“We watched our most successful users stop treating Noibu as a reactive debugging tool and start treating it as their daily ecommerce command center,” Noivo said.

Instead of simply identifying technical issues, the platform now connects technical insights with business outcomes—helping teams understand how site problems affect revenue and conversion rates.

Linking Technical Issues to Revenue Impact

One of the platform’s defining features is its ability to assign dollar-value impact to technical issues.

Rather than presenting developers with generic error alerts, Noibu highlights which problems are actively blocking purchases or affecting high-value customers.

By combining technical monitoring with real-user session context, teams can see not just what went wrong but also how it affected a shopper’s journey.

This approach allows companies to prioritize fixes based on potential revenue recovery rather than raw error volume.

For ecommerce businesses managing thousands of daily transactions, that prioritization can significantly accelerate issue resolution and reduce lost sales.

The Platform’s Three Core Pillars

Noibu has organized its expanded platform around three strategic capabilities aimed at cross-functional teams.

Protect Revenue and Reduce Risk

The platform continuously monitors site errors, server issues, and deployment failures. Problems are automatically prioritized according to the revenue exposure they create, allowing engineering teams to focus on the highest-impact fixes first.

Unlock Conversion Growth

Beyond technical monitoring, Noibu now provides tools designed to surface experience-driven growth opportunities. These include AI-powered session search, page-level diagnostics, and performance monitoring that highlight UI and UX elements affecting conversion rates.

Align Teams With a Single Console

Ecommerce operations typically involve multiple teams—engineering, product, UX, marketing, and customer support—each using different analytics tools.

Noibu’s platform acts as a single “pane of glass” where these teams can access shared insights about site performance and customer behavior.

The goal is to reduce organizational silos and help teams align around the most impactful improvements.

Why Ecommerce Teams Need Unified Insights

The expansion reflects a common challenge across modern ecommerce organizations.

Most retailers rely on separate tools for monitoring site performance, tracking customer behavior, and analyzing revenue metrics. While each tool provides valuable data, the lack of integration often makes it difficult to connect technical issues with business outcomes.

This fragmentation becomes more problematic as ecommerce stacks grow increasingly complex, incorporating microservices architectures, headless commerce frameworks, and multiple third-party integrations.

In that environment, a small bug in a checkout integration or payment gateway can quietly cost thousands of dollars in lost revenue.

Platforms that combine technical monitoring with experience analytics aim to provide a clearer view of those risks.

Early Adoption Among Enterprise Retailers

Retailers using the platform say the unified approach helps cut through operational noise.

Alexandria Sims, VP of Transformation at Sleep Country, says the platform makes it easier for teams to align around the most impactful work.

“When it comes to managing a mature digital business, the hardest part isn’t getting more data—it’s getting teams aligned on what to do next,” Sims said.

Similarly, ecommerce leaders like Philip Krynsky of Rvinyl say the platform has evolved from a debugging tool into a broader optimization resource.

Originally used to validate technical issues, the platform is now being applied to understand the customer journey and identify where shoppers drop off during the buying process.

Integrations With Modern Commerce Platforms

To support deployment in modern ecommerce stacks, Noibu has built native integrations with major commerce platforms including Shopify, commercetools, and BigCommerce.

These integrations allow retailers to plug Noibu directly into their commerce infrastructure without extensive engineering work.

The ability to integrate quickly is increasingly important as retailers adopt headless architectures and composable commerce frameworks that rely on multiple connected services.

By acting as a central monitoring and analytics layer, Noibu aims to simplify visibility across those complex ecosystems.

What’s Next for the Platform

Looking ahead, Noibu plans to expand the platform with several new capabilities in 2026.

Upcoming features include:

• Mobile Monitoring to track performance across mobile shopping environments

• Journey Analytics to map the full customer path across sessions and devices

• Explorations for deeper behavioral analysis

• Customizable dashboards that allow teams to tailor insights to specific roles

These additions are designed to extend the platform’s visibility beyond web performance into the broader customer experience lifecycle.

The Bigger Picture

The shift by Noibu reflects a broader transformation in ecommerce technology.

As digital storefronts become the primary revenue engine for many retailers, site performance and user experience are no longer just technical concerns—they’re core business metrics.

Retailers increasingly want platforms that connect engineering insights, customer behavior, and revenue impact into a single operational view.

By repositioning itself as an ecommerce analytics and monitoring platform, Noibu is betting that the future of digital commerce management will revolve around unified intelligence rather than isolated tools.

If that vision holds, the companies that best understand the relationship between site health and revenue growth could gain a powerful advantage in the increasingly competitive ecommerce landscape.

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Artificial intelligence is reshaping programmatic advertising, but not all AI systems deliver measurable results beyond improved targeting claims. Now KNOREX Ltd. says its latest AI upgrade is producing performance gains that go well beyond incremental optimization.

The company announced the full-scale commercial deployment of major enhancements to its KAIROS AI decisioning engine, following more than six months of controlled deployments across select campaigns. The upgraded models now operate within the company’s KNOREX XPO™ platform, where they power predictive bidding, traffic quality analysis, and conversion-focused optimization.

Early results from live automotive advertising campaigns suggest significant improvements in efficiency and outcomes, including a 45% reduction in cost per acquisition (CPA) and click-to-conversion rates exceeding 10%—far above typical industry benchmarks.

The rollout marks a key step in Knorex’s strategy to position its platform around outcome-driven advertising rather than traditional click-based metrics.

Moving Beyond Click-Based Advertising Metrics

For years, digital advertising performance has often been measured by surface-level metrics like impressions and clicks. But marketers increasingly want proof that campaigns generate real business outcomes—conversions, leads, and revenue.

That shift has created demand for AI-driven optimization systems capable of analyzing deeper behavioral signals and conversion data.

Knorex’s KAIROS engine was designed specifically for that purpose. The platform analyzes real-time engagement patterns, user behavior signals, and downstream conversion data to determine which users are most likely to complete a desired action.

Instead of simply maximizing clicks, the AI focuses on predicting and prioritizing high-intent users while suppressing low-quality traffic.

The result, according to the company, is a measurable improvement in conversion efficiency.

Performance Gains in Automotive Campaigns

The enhanced KAIROS models have been tested primarily in automotive advertising campaigns, where dealerships and service providers rely heavily on digital marketing to generate customer leads.

According to Knorex, the upgraded models produced several notable results:

• 47% improvement in click-to-conversion quality

• 45% average reduction in cost per acquisition

• Click-to-conversion rates above 10%, compared with roughly 2% industry averages

• Campaign performance gains ranging from 25% to 100%

These improvements directly affect advertiser unit economics—lowering acquisition costs while increasing return on ad spend (ROAS).

For industries like automotive services, where customer acquisition costs can significantly impact profitability, these efficiency gains can quickly translate into larger advertising budgets and more scalable campaigns.

Inside the Upgraded KAIROS Models

At the core of the update are two enhanced AI components: the KAIROS Bid Model and the KAIROS CPA Model.

The upgraded bid model focuses on traffic quality filtering. It analyzes engagement signals and behavioral data before ads are served, reducing the likelihood that low-quality or non-converting users reach advertiser landing pages.

This predictive filtering approach helps prevent wasted ad spend and improves the overall quality of incoming traffic.

Meanwhile, the enhanced CPA model optimizes campaigns around cost efficiency and outcome prediction. By analyzing which traffic sources and audience segments consistently generate conversions, the system dynamically adjusts bids to prioritize high-performing opportunities.

Together, the models create a feedback loop that continuously improves campaign performance as more data becomes available.

AI as the Backbone of Programmatic Advertising

Programmatic advertising has relied on automated bidding systems for years, but the sophistication of those systems has increased dramatically with the rise of machine learning and real-time data analysis.

Modern AI platforms can process thousands of signals—from browsing behavior to contextual content—to determine the best moment and price for placing an ad.

Knorex’s KAIROS engine integrates these capabilities directly into the company’s XPO platform, which manages campaign execution across programmatic channels.

By combining predictive bidding, conversion modeling, and traffic quality filtering within one system, the platform aims to deliver a more comprehensive approach to performance optimization.

Shifting Toward Outcome-Based Monetization

The company says the latest KAIROS enhancements represent a broader shift toward outcome-driven monetization.

Instead of optimizing campaigns purely around engagement metrics, the platform is designed to prioritize measurable business results—such as leads, bookings, or purchases.

That approach aligns with a growing trend across digital advertising.

As privacy regulations limit access to third-party data and advertisers demand greater accountability, platforms are increasingly focusing on AI systems capable of linking ad exposure to real-world outcomes.

For advertisers, the promise is simple: spend less time analyzing click metrics and more time measuring business impact.

Improving Advertiser Economics and Platform Scalability

Better performance doesn’t just benefit advertisers—it also strengthens the economics of advertising platforms themselves.

When campaigns consistently generate strong results, advertisers tend to increase budgets and expand campaigns to new markets or product lines.

Knorex believes the improved performance from its upgraded AI models will help drive higher advertiser retention and increased campaign spending on the platform.

At the same time, the automation provided by KAIROS allows the system to scale efficiently, creating operational leverage as more campaigns are added.

Competition in the AI Programmatic Market

The programmatic advertising market has become increasingly competitive as major platforms invest heavily in artificial intelligence.

Large technology companies such as Google and Amazon have built sophisticated AI-driven ad ecosystems capable of optimizing campaigns across massive data networks.

Independent adtech providers like Knorex differentiate themselves by focusing on proprietary AI models and specialized performance optimization strategies.

By emphasizing outcome-based optimization rather than simple traffic generation, Knorex aims to carve out a niche in performance-driven advertising environments.

What Comes Next for KAIROS

Following the successful deployment in automotive campaigns, Knorex plans to extend the upgraded KAIROS models to additional industry verticals.

The goal is to replicate the same improvements in conversion quality, CPA efficiency, and campaign performance across sectors such as retail, finance, and consumer services.

If the results hold across these industries, the company could strengthen its competitive position in the rapidly evolving AI-driven advertising landscape.

The Bigger Picture

The latest KAIROS upgrade reflects a broader transformation underway in digital advertising.

As marketers demand clearer connections between advertising spend and business results, platforms are shifting from click-based optimization to outcome-driven decisioning systems powered by AI.

For advertisers, the appeal is obvious: better targeting, stronger conversions, and more efficient budgets.

For adtech providers like Knorex, the challenge is proving that their AI can deliver those outcomes consistently at scale.

With its latest AI model rollout, Knorex is betting that smarter decisioning—not just faster bidding—will define the next era of programmatic advertising.

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Celebrity entrepreneur and global brand builder Kathy Ireland is doubling down on artificial intelligence—this time as an investor.

Ireland, Chair, CEO, and Chief Designer of kathy ireland® Brands, announced a personal investment in Capacity, an enterprise CX automation platform founded by CEO David Karandish. The move follows a year-long deployment of Capacity’s technology within Ireland’s own business operations and signals growing confidence in AI-driven orchestration tools designed to streamline contact center and customer experience workflows.

The investment also highlights a broader shift across enterprise technology: organizations are increasingly seeking unified AI platforms that replace fragmented point solutions with integrated systems capable of managing customer interactions across channels.

From Technology User to Investor

Ireland’s decision to invest stems from firsthand experience.

Over the past year, Capacity’s platform has been implemented within the operational ecosystem supporting her global brand portfolio. According to Ireland, the technology helped streamline internal processes and simplify operational complexity.

“We pride ourselves in partnering with entities that reflect our core values of providing solutions and empowering our customers,” Ireland said in the announcement. “Our experience with Capacity over the past year has transformed our operational complexities into a streamlined process.”

The entrepreneur is known for a selective investment strategy, backing ventures that align with her business philosophy and demonstrate measurable return on investment.

That approach has helped Ireland build one of the most successful brand licensing empires in retail. Products associated with the kathy ireland® brand span numerous categories—from home furnishings to apparel—and generate billions in global retail sales.

The Fragmented Tech Problem in Customer Experience

Ireland’s investment focuses on a challenge that has become increasingly common for enterprises: managing an expanding ecosystem of AI tools.

Many contact centers today rely on multiple vendors for different functions—chatbots, agent assist tools, analytics platforms, and outbound engagement software. While each system may deliver value individually, the combination often creates complexity.

The result is what industry leaders often call “AI stack fragmentation.”

Companies must maintain multiple integrations, manage separate knowledge bases, and ensure consistent responses across communication channels. That overhead can quietly inflate operational costs and create inconsistent customer experiences.

Capacity’s pitch is straightforward: consolidate those capabilities into a single orchestration platform.

A Unified Approach to CX Automation

Capacity positions its technology as a centralized automation layer designed to unify virtual agents, human agent assistance tools, and outbound engagement systems.

Instead of managing multiple vendors, enterprises can operate customer interactions from a single platform that supports chat, voice, email, and SMS communication.

The company says this unified architecture helps eliminate the hidden operational costs associated with managing multiple point solutions—costs that can reach hundreds of thousands of dollars annually for large contact centers.

The platform also aims to improve consistency across channels by ensuring all interactions draw from the same knowledge source.

The “Train Once, Use Everywhere” Model

At the core of Capacity’s system is its proprietary AI Knowledge Orchestration Layer, which enables businesses to connect internal data sources once and deploy that knowledge across all communication channels.

This architecture addresses a common issue in AI-powered customer service systems: knowledge drift.

When different tools use separate datasets or training methods, responses can vary between channels. A chatbot might provide one answer while a voice assistant offers another.

By centralizing knowledge management, Capacity aims to ensure that both AI agents and human agents draw from the same verified information.

For enterprises handling large volumes of customer inquiries, that consistency can be critical to maintaining trust and reducing support escalations.

Beyond Cost Reduction: AI as a Revenue Engine

While many CX automation platforms emphasize efficiency and cost reduction, Capacity is also positioning AI as a tool for revenue generation.

The platform includes outbound engagement capabilities that use AI-driven campaigns to follow up with leads and re-engage customers before opportunities go cold.

Additionally, Capacity integrates conversational intelligence tools that analyze interactions between customers and agents.

Those insights feed into a continuous learning loop designed to improve both virtual and human agent performance over time.

The goal is not just automation, but ongoing optimization of the entire customer engagement process.

A Growing Market for AI CX Platforms

Capacity operates in a rapidly expanding segment of the enterprise AI market.

As companies invest heavily in customer experience technologies, many are searching for platforms capable of unifying communication channels, automation tools, and analytics systems.

Large technology providers such as Salesforce, Zendesk, and Genesys are all racing to embed generative AI into customer service platforms.

At the same time, newer AI-native startups are emerging with orchestration-focused architectures that promise deeper automation and easier deployment.

Capacity is positioning itself within that second category—AI-first platforms designed to streamline enterprise workflows rather than simply enhance existing systems.

Strategic Value of High-Profile Investors

For Capacity, Ireland’s investment carries both financial and strategic significance.

High-profile investors can help technology companies expand their market visibility while validating the practical value of their products.

Ireland’s global business network and reputation as a disciplined entrepreneur add credibility to the company’s growth narrative.

CEO David Karandish emphasized the importance of the partnership.

“Bringing a globally trusted leader like Kathy Ireland into our private investor group is a meaningful milestone,” Karandish said. “Her decision to invest underscores the strength of our technology and enriches our capability to innovate.”

The Bigger Picture

The partnership also reflects a broader convergence between consumer brands and enterprise technology.

Business leaders who operate large global brands are increasingly investing in the technologies that power their operations—from AI-driven analytics to automation platforms.

In many cases, those investments follow firsthand experience using the tools within their own organizations.

Ireland’s backing of Capacity fits that pattern: a technology adoption story evolving into a strategic investment.

As enterprises continue searching for ways to simplify AI adoption and eliminate fragmented technology stacks, unified platforms like Capacity may gain traction.

For Ireland, the bet appears straightforward: if AI can streamline operations and improve customer engagement at scale, the companies building that infrastructure could become some of the most valuable players in the next wave of enterprise software.

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As small and mid-size businesses increasingly turn to fractional leadership to scale revenue operations, Sales Xceleration is expanding its bench of senior sales executives.

The company announced the addition of six new Outsourced VPs of Sales, collectively bringing more than a century of leadership experience to its fractional sales advisory network. The move strengthens Sales Xceleration’s ability to support SMBs seeking strategic sales leadership without hiring full-time executives.

The new leaders join a growing ecosystem of fractional executives helping organizations build scalable sales processes, refine go-to-market strategies, and improve revenue predictability.

Fractional Sales Leadership Continues to Grow

Over the past decade, fractional leadership has moved from a niche consulting model into a mainstream strategy for smaller companies that need executive expertise but lack the resources for full-time C-suite hires.

Sales Xceleration has been one of the early champions of the approach, focusing specifically on outsourced sales leadership roles. Its model embeds experienced sales executives within SMBs on a part-time or project basis to build sales strategy, coach teams, and establish repeatable revenue processes.

According to Tom Gardner, the company’s Chief Community Officer, the firm remains selective about who joins the network.

“After 13 years of leading the way in fractional leadership, we’re extremely selective about the sales leaders we bring into our organization,” Gardner said in the announcement.

That selectivity reflects the growing demand for experienced operators who can step into complex sales environments and deliver results quickly.

Meet the New Outsourced VPs of Sales

The newly appointed executives bring experience spanning industries including technology, healthcare, manufacturing, and professional services.

Brian Dolan – Greater Philadelphia, Pennsylvania

With more than 25 years of sales leadership experience, Dolan focuses on helping organizations develop sustainable revenue growth strategies. His work centers on building scalable sales infrastructures, optimizing processes, and coaching high-performing teams to deliver measurable outcomes.

Brian Hadley – Charlotte, North Carolina

Hadley brings over two decades of leadership experience guiding sales teams ranging from small startups to organizations with hundreds of sellers. He emphasizes practical, repeatable sales processes that enable teams to consistently achieve performance goals.

Matthew Lang – Nashville, Tennessee

Lang, who has nearly three decades of experience leading sales organizations, specializes in building metrics-driven sales engines. His hands-on leadership style focuses on transforming sales operations into scalable systems that support predictable and profitable growth.

Dan McCoy – Great Lakes Region, Ohio

McCoy brings more than 20 years of experience in sales transformation across both large enterprises and scaling companies. His expertise centers on aligning people, processes, and performance frameworks to drive sustainable commercial growth.

Mark Miracle – Greater Denver, Colorado

Miracle has spent over 25 years leading revenue growth across multiple industries, including complex technology, SaaS, and professional services organizations. His background includes driving expansion in both startup and enterprise environments.

Alex Sagatov – Edmonton, Alberta

Sagatov brings more than two decades of experience transforming sales organizations in healthcare, industrial, and technology sectors. His expertise includes sales strategy, CRM optimization, team mentoring, and building accountability-driven sales cultures.

Why SMBs Are Turning to Fractional Executives

For many SMBs, sales leadership gaps can slow growth. Companies may have strong products and market demand but lack the strategic structure needed to scale revenue.

That’s where fractional executives come in.

Instead of hiring a full-time VP of Sales—often a six-figure investment—companies can access senior expertise on a flexible basis. These leaders typically focus on:

• Defining go-to-market strategies

• Implementing sales processes and forecasting systems

• Building high-performance sales teams

• Aligning marketing and sales operations

• Driving predictable revenue growth

This model is particularly attractive for companies transitioning from founder-led sales to more structured revenue operations.

A Broader Shift in the Sales Leadership Market

The rise of fractional leadership mirrors broader changes across the executive talent landscape.

Companies are increasingly embracing flexible leadership models that allow them to access specialized expertise without long-term hiring commitments. This trend has accelerated with the growth of remote work, digital collaboration tools, and project-based consulting frameworks.

Sales leadership is one of the most common roles in the fractional executive category, alongside fractional CFOs, CMOs, and CTOs.

For firms like Sales Xceleration, expanding the roster of experienced operators is essential to meeting demand from companies seeking strategic guidance in increasingly competitive markets.

Building Sustainable Sales Engines

Ultimately, the goal of the Outsourced VP of Sales model is to help companies move beyond ad hoc selling toward repeatable revenue systems.

That includes building clear sales processes, implementing CRM-driven forecasting, and creating accountability structures that enable teams to perform consistently.

With the addition of six new leaders, Sales Xceleration aims to extend that approach to more organizations looking to scale.

For SMBs navigating growth challenges, the fractional sales executive may be the bridge between early traction and long-term revenue stability.

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Commercial real estate (CRE) firms have spent years juggling fragmented software stacks—CRMs, marketing platforms, proposal tools, spreadsheets, and transaction systems that rarely talk to each other. The result: duplicated data, manual handoffs, and a lot of operational friction.

Now Buildout Inc. says it has a solution.

The company announced the launch of Buildout CRM, the latest addition to its Buildout Suite platform. The new product aims to unify the entire commercial real estate deal lifecycle in a single system, completing Buildout’s push to create an end-to-end, AI-powered operating platform for brokerages.

The move signals a strategic evolution for the company—from offering individual CRE software tools to positioning itself as a full workflow engine that manages deals from prospecting to commission.

A Unified Platform for the CRE Deal Lifecycle

Commercial real estate workflows are notoriously fragmented.

A typical brokerage may use one system to manage contacts, another to create marketing materials, a separate platform for proposals, and yet another for transaction management. Much of the work connecting those systems happens manually—often by brokers or administrative staff.

That fragmentation creates operational inefficiencies that can slow deals and squeeze brokerage margins.

Buildout’s approach attempts to eliminate those silos.

With the addition of CRM functionality inside Buildout Suite, brokerages can now manage prospecting, marketing, deal execution, and transaction management within a single data layer. Information entered once flows through the entire lifecycle, automatically updating across the platform.

The idea is simple but powerful: instead of stitching together multiple tools, brokerages operate from one shared system.

AI as the Workflow Engine

While many software vendors promote AI-powered features, Buildout is emphasizing something slightly different: automation embedded directly into the workflow.

Rather than layering AI tools on top of disconnected systems, the company says its platform integrates AI into the operational backbone of the brokerage.

That means AI can automatically handle repeatable tasks—data entry, document generation, workflow routing, and status updates—reducing manual work and improving accuracy.

The goal is to allow brokers to spend less time managing software and more time focusing on relationships and deal-making.

Solving the “Tech Stack Glue” Problem

According to CEO Helen Calvin, the company’s customers consistently identified the same pain point: too many overlapping tools.

“For years, brokerages have been stitching together tools and calling it a tech stack,” Calvin said in the announcement.

“What we kept hearing from customers is that the real pain isn’t a lack of software—it’s overlap and disconnect. Brokers and admins are acting as the glue between systems.”

Buildout designed its CRM specifically to eliminate that glue role.

Instead of forcing teams to move data between systems, the platform ensures that everyone—from brokers to marketers to finance teams—works from a single source of truth.

Built for the Property-Centric Nature of CRE

Unlike general-purpose CRMs designed for sales teams, Buildout’s platform is tailored to the unique structure of commercial real estate deals.

CRE transactions typically involve multiple stakeholders, long sales cycles, and complex documentation. Deals are also property-centric rather than purely contact-centric.

Buildout Suite reflects that structure by organizing data around properties, listings, and transactions rather than just leads and contacts.

Each stakeholder in the brokerage—from brokers to marketing teams to financial leadership—can access the information relevant to their role while remaining connected to the broader deal workflow.

The result is intended to reduce operational complexity while improving collaboration across teams.

The Growing Importance of AI in Real Estate

Artificial intelligence is quickly becoming a strategic priority across the real estate industry.

Brokerages are exploring AI for tasks like market analysis, marketing automation, lead generation, and transaction management. But many firms face a familiar problem: AI tools layered onto fragmented software stacks can actually increase complexity.

Buildout’s strategy is to address the underlying infrastructure problem first.

By consolidating the entire deal lifecycle into one platform, the company argues that AI can operate more effectively—drawing from a unified data source and automating tasks across the entire workflow.

That architecture could become increasingly important as AI adoption accelerates in the CRE sector.

A Shift Toward Operational Platforms

The launch of Buildout CRM also reflects a broader trend across enterprise software.

Companies are moving away from standalone tools toward integrated platforms that manage entire operational processes.

Instead of assembling multiple specialized products, organizations increasingly prefer unified systems that reduce integration overhead and streamline workflows.

For commercial real estate brokerages—where margins are often tight and deal timelines matter—operational efficiency can translate directly into revenue gains.

What It Means for CRE Tech

The CRE technology market has grown rapidly in recent years, with startups targeting everything from leasing analytics to digital transaction management.

But many brokerages still struggle with fragmented systems and inconsistent data.

Buildout’s unified platform strategy attempts to address that challenge directly.

With CRM now integrated into Buildout Suite, the company says brokerages can manage the entire deal lifecycle—from the first prospecting call to final commission—inside a single platform.

If the approach gains traction, it could signal a broader shift in CRE technology toward unified operating systems rather than loosely connected tech stacks.

For brokers, that could mean fewer spreadsheets, fewer manual handoffs—and faster deals.

Get in touch with our MarTech Experts.

Enterprise AI vendors are entering a new phase—one defined less by experimentation and more by real-world deployments. Messaging platform Quiq is leaning into that shift with a high-profile marketing hire.

The company announced that veteran enterprise software executive Jen Grant will join as Chief Marketing Officer, a move aimed at strengthening Quiq’s position in the rapidly evolving market for AI-powered customer engagement. The appointment reflects a broader industry pivot as enterprises move beyond proof-of-concept AI pilots toward operational AI agents embedded in customer-facing workflows.

Grant’s mandate is clear: help enterprises understand how to evaluate, deploy, and scale AI agents responsibly as the technology becomes a core part of customer experience infrastructure.

From AI Experiments to Production Deployments

For many enterprises, the past two years have been dominated by generative AI experimentation. Teams tested chatbots, knowledge assistants, and automated support agents—often in limited pilots or internal tools.

Now, those experiments are increasingly moving into production.

Quiq says its AI agents are already running at scale for several global brands, including Spirit Airlines, Roku, and Panasonic. These deployments handle high volumes of customer interactions across industries like travel, retail, and consumer electronics.

“Quiq has moved past experimentation and into real, scaled AI agent deployments, and that shift requires a different kind of leadership,” said CEO Mike Myer in the announcement.

The implication is significant: the AI conversation in customer service is shifting from Can it work? to Can it work reliably at scale?

Why the CMO Role Matters in the AI Era

Grant’s appointment reflects a strategic challenge many AI vendors face today: explaining what actually works in production.

While dozens of startups and enterprise vendors offer AI-powered customer engagement tools, the differences between them are often difficult for buyers to evaluate. Many companies showcase impressive demos but lack proven deployments in high-stakes environments.

Grant says the market is entering a new stage of maturity.

“Most companies are no longer asking whether AI agents work,” she said. “They’re asking which platforms they can trust in front of customers.”

That shift places marketing leaders at the center of the conversation. The job is no longer just generating demand—it’s clarifying technical capabilities, risk controls, and operational outcomes for enterprise buyers.

In other words, the modern CMO increasingly acts as a translator between complex AI systems and business decision-makers.

A Veteran Enterprise Software Operator

Grant brings a résumé that spans both marketing leadership and operational roles across major enterprise software companies.

Her past positions include senior leadership roles at Google, Box, Elastic, Dialpad, and Looker. She has also served as CEO, COO, and CMO at multiple technology firms, giving her a rare mix of product, operational, and marketing experience.

That kind of cross-functional leadership is increasingly valuable in the AI era. AI platforms don’t just introduce new software—they change how organizations structure workflows, manage risk, and interact with customers.

Grant’s role at Quiq will focus on guiding the company’s go-to-market strategy during this transition.

The Growing Market for AI Customer Agents

The rise of AI agents is reshaping the customer experience landscape.

Traditional chatbots relied on rule-based systems and scripted workflows. Modern AI agents, powered by large language models and integrated knowledge systems, can interpret complex questions, access enterprise data, and respond conversationally.

Companies see major benefits:

• Reduced support costs

• Faster response times

• Scalable customer service

• Higher satisfaction and loyalty

But those advantages come with risks.

Enterprises worry about hallucinations, inaccurate responses, and brand damage if AI systems deliver incorrect information to customers.

That’s why reliability and governance features are becoming critical differentiators among AI platforms.

Managing AI Risk in Customer Conversations

A key selling point for Quiq’s platform is its emphasis on verification and control mechanisms designed to reduce AI hallucinations and ensure responses are grounded in trusted data.

The platform includes built-in tools for validating outputs, managing knowledge sources, and maintaining brand governance—features particularly important in regulated industries or brand-sensitive environments.

For companies deploying AI agents in customer-facing roles, these safeguards can mean the difference between automation success and reputational risk.

This challenge isn’t unique to Quiq. Across the enterprise AI ecosystem—from CRM vendors to support platforms—companies are racing to build guardrails around generative AI.

The result is a new category emerging at the intersection of conversational AI, automation, and enterprise governance.

Customer Proof Points

Quiq’s customer roster suggests the technology is already being tested in real-world conditions.

At Panasonic, for example, customer service leaders say the platform helps deliver more responsive customer experiences while improving efficiency.

Roku’s product management team reports evaluating more than 30 vendors before selecting Quiq’s solution—highlighting how crowded the AI customer engagement space has become.

That level of vendor competition reflects a broader surge in AI spending. Enterprises across industries are investing heavily in AI tools that promise measurable operational improvements.

Customer service, with its high interaction volume and structured workflows, has become one of the most immediate and practical use cases.

The Next Phase of the AI Platform Market

Grant’s arrival signals that Quiq sees the market entering a new competitive stage.

In the early AI boom, vendors focused on showcasing capabilities—what the technology could theoretically do. Now the conversation is shifting toward operational outcomes: accuracy, compliance, scalability, and customer trust.

For enterprise buyers, those factors matter far more than flashy demos.

The next wave of AI platform winners will likely be determined not just by model performance, but by how well vendors integrate governance, reliability, and enterprise workflow support.

Marketing leaders like Grant will play a key role in shaping that narrative—separating hype from real deployments.

The Bigger Picture

The hiring also highlights a broader trend across enterprise software: as AI categories mature, companies often bring in experienced operators to sharpen messaging and execution.

Grant has seen this pattern before across several technology transitions, from cloud infrastructure to data analytics platforms.

AI agents may now be approaching that same inflection point.

If the industry’s trajectory holds, the next two years will likely see enterprises move from isolated AI pilots toward fully integrated AI-powered customer experience systems.

For vendors like Quiq, the challenge isn’t just building the technology—it’s proving that the technology works reliably when customers are on the line.

Get in touch with our MarTech Experts.

The communications industry has spent the past two years racing to keep up with generative AI, shifting audience behaviors, and a broader cultural rethink around leadership and trust. Now, one of the sector’s largest independent agencies wants to turn that disruption into a conversation.

Global communications and integrated marketing firm Ruder Finn has launched a new podcast, What’s Next: The Ruder Finn Podcast, hosted by CEO Dr. Kathy Bloomgarden. The show aims to unpack how artificial intelligence, innovation, and modern leadership are reshaping marketing and communications at a moment when both industries are being rewritten in real time.

The first three episodes are already live across major platforms including Apple Podcasts, Spotify, and YouTube, positioning the podcast as a thought-leadership hub for executives navigating rapid technological change.

Rather than focusing on campaign tactics or platform updates—the bread and butter of many marketing podcasts—What’s Next zooms out. Each episode explores how communications leaders help organizations interpret technological disruption, guide cultural change, and build stronger relationships with customers, employees, and stakeholders.

A Podcast About the Future of Leadership in the AI Era

In many ways, the show arrives at a pivotal moment for the communications industry.

AI tools are transforming everything from media monitoring and audience analysis to content generation and strategic decision-making. Agencies and brands alike are grappling with a fundamental question: if machines can generate messaging, what becomes of human creativity and leadership?

Bloomgarden says the answer lies in clearer thinking and more thoughtful leadership.

“As AI and automation accelerate change, communications leaders have a responsibility to help organizations move forward with clarity and optimism,” Bloomgarden said in the announcement. “This podcast is about asking better questions and shaping what comes next together.”

That framing reflects a broader shift across the marketing world. Increasingly, communications teams are expected to play a strategic role—not just amplifying messages but helping companies navigate technological disruption, public expectations, and evolving stakeholder relationships.

Launch Episodes Focus on AI, Healthcare Communication, and Curiosity

The podcast’s debut lineup offers a cross-section of voices from technology, healthcare, and venture innovation.

One episode features AI advisor Zack Kass, who discusses how artificial intelligence is influencing leadership, decision-making, and human creativity. Kass previously served as Head of Go-To-Market at OpenAI and has become a frequent commentator on how organizations can adopt AI responsibly while preserving human judgment.

Another episode explores the changing relationship between healthcare organizations and patients. Bloomgarden speaks with Ed Harnaga, former chief communications officer at Pfizer, about how strategic communication can create more personal, trust-driven patient engagement—an issue that gained urgency during the COVID-era health communications surge.

The third launch episode highlights the value of intellectual curiosity in an evolving tech landscape. Emmy Award-winning engineer and venture partner Yvette Kanouff joins the show to discuss innovation, technology investment, and why leaders must remain adaptable in a rapidly changing digital environment.

Taken together, the early episodes reveal the podcast’s core premise: communications leaders are no longer just storytellers—they’re interpreters of change.

Thought Leadership as a Strategic Platform

Agency podcasts are hardly new, but the format is increasingly being used as a long-term thought leadership engine rather than just marketing content.

Consultancies, technology firms, and agencies—from Deloitte to Accenture—have expanded podcast strategies in recent years to reach executives who consume industry insights during commutes, workouts, or remote workdays.

For communications agencies, podcasts offer another advantage: they position the firm at the center of conversations shaping the industry’s future.

In this sense, What’s Next fits into a larger strategy for Ruder Finn, which has been investing heavily in AI-driven communications tools and advisory services. By hosting conversations with AI experts, healthcare leaders, and technologists, the agency can both spotlight emerging trends and reinforce its role as a strategic advisor to global brands.

It’s a move that aligns with a broader shift across marketing services firms. As automation handles more operational tasks—content production, analytics, audience targeting—agencies increasingly differentiate themselves through strategy, insight, and leadership.

Why AI Is Changing the Role of Communications Teams

At the center of the podcast—and much of the industry’s current debate—is the impact of artificial intelligence.

AI-driven tools are already rewriting how communications teams operate. Natural language models can draft press releases, summarize research, and generate campaign copy in seconds. Data platforms can analyze audience sentiment across millions of posts and news stories in real time.

But those capabilities raise new questions about trust, governance, and human oversight.

For communications leaders, the challenge isn’t simply adopting AI—it’s helping organizations explain and contextualize it. That includes addressing employee concerns about automation, ensuring responsible data usage, and maintaining authentic brand voices in an increasingly machine-assisted content landscape.

In other words, the communications department may become the organization’s translator between technology and humanity.

The Expanding Role of the CCO

Another theme running through the podcast’s early episodes is the expanding influence of communications leadership.

Historically, the chief communications officer (CCO) role focused primarily on media relations, reputation management, and corporate messaging. Today, many CCOs are deeply involved in corporate strategy, ESG initiatives, and digital transformation.

That shift has accelerated as companies face a steady stream of public scrutiny—from AI ethics debates to geopolitical tensions and misinformation challenges.

Leaders like Harnaga, who helped guide communications at Pfizer during one of the most scrutinized healthcare moments in modern history, represent a new generation of strategic communicators operating at the intersection of science, technology, and public trust.

A Monthly Look at the Industry’s Next Phase

Ruder Finn plans to release new podcast episodes monthly, each tackling a different aspect of the evolving communications landscape.

Future topics are expected to include:

• How AI is reshaping brand communication strategies

• Innovation-driven growth in marketing and communications

• New approaches to audience engagement in an AI-powered world

• The changing relationship between technology, culture, and corporate leadership

If the debut episodes are any indication, the show will lean heavily into cross-industry perspectives—pulling voices not just from marketing but from venture capital, healthcare, technology, and academia.

That interdisciplinary approach reflects a simple reality: communications doesn’t exist in a vacuum anymore.

As AI systems influence everything from product development to customer service, the communicators responsible for shaping narratives around those technologies must understand the systems themselves.

The Bigger Picture

The launch of What’s Next highlights an important shift in the marketing and communications ecosystem.

In an era defined by generative AI, misinformation, and rapid digital transformation, the value of communications professionals is evolving from message distribution to strategic interpretation.

Organizations need leaders who can explain emerging technologies, translate complex ideas into human terms, and guide stakeholders through uncertainty.

Podcasts like this one are emerging as a natural forum for those conversations.

Whether What’s Next becomes a must-listen for communications leaders remains to be seen. But its central premise—that the future of communications lies in understanding technology as much as storytelling—captures the direction the industry is clearly heading.

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