LANC Marketing is rebranding as Spry PR, reflecting how communications agencies are evolving to meet the demands of AI-driven media ecosystems, fragmented digital channels, and increasingly performance-focused brand strategies. The agency, founded in 2023 by Stephanie Pryor, says the new identity is designed to better align with the changing nature of public relations, social media, and content marketing services.

The move comes as communications firms across the B2B marketing landscape reposition themselves beyond traditional media relations toward integrated visibility strategies spanning earned media, digital storytelling, search visibility, executive branding, and AI-era reputation management.

The rebranding of LANC Marketing into Spry PR highlights a broader transformation underway in the communications and public relations industry. As businesses navigate increasingly crowded digital environments shaped by generative AI, algorithmic discovery systems, and rapidly shifting audience behavior, agencies are under pressure to evolve beyond conventional PR execution models.

Spry PR says its new identity reflects that transition.

“This rebrand is a statement that honors what we've built and communicates where we're headed,” said Stephanie Pryor, founder of Spry PR. “The PR and comms industries don't look the same way they did even three years ago.”

That observation reflects wider changes affecting the communications sector. Traditional PR campaigns centered primarily on media outreach and press coverage are being replaced by multi-channel visibility strategies that integrate content marketing, executive thought leadership, SEO, social engagement, and digital authority building.

For agencies serving B2B technology, manufacturing, AI, and e-commerce companies, the shift has become particularly pronounced. Buyers increasingly discover brands through AI-generated search summaries, social algorithms, industry publications, podcasts, newsletters, and recommendation engines rather than through traditional advertising alone.

Spry PR’s rebrand appears positioned around that reality.

The agency says the new brand identity emphasizes agility, creativity, and adaptability — characteristics increasingly valued in communications environments where news cycles move rapidly and audience attention spans continue to shrink.

The company also signaled a move away from rigid public relations structures with the positioning statement: “Not Your Mother’s PR Agency.”

That language reflects a growing industry trend where boutique communications firms are differentiating themselves from legacy PR models by emphasizing integrated digital visibility rather than standalone media relations.

Over the past several years, communications agencies have increasingly expanded into areas traditionally associated with digital marketing and martech consulting. Firms now routinely manage brand storytelling across social platforms, search ecosystems, influencer networks, AI search visibility, and executive reputation channels simultaneously.

Major enterprise technology vendors including Adobe, Salesforce, and HubSpot have accelerated that convergence by integrating content management, analytics, social listening, and AI-powered customer engagement tools into unified marketing ecosystems.

As a result, communications strategy is increasingly becoming part of broader enterprise visibility infrastructure rather than a standalone business function.

Spry PR says it works with organizations across sectors including construction, manufacturing, AI, e-commerce, and technology. Those industries are experiencing growing pressure to improve digital authority and audience trust as competition intensifies across online channels.

According to Gartner, buyers now spend a significant portion of their purchasing journey independently researching brands across digital sources before engaging directly with vendors. That trend has elevated the importance of earned credibility, authoritative content, and consistent messaging across every customer touchpoint.

The emergence of generative AI platforms such as OpenAI ChatGPT, Google Gemini, and Microsoft Copilot is also reshaping how organizations think about visibility. Instead of optimizing solely for search rankings, brands are increasingly focused on Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), where consistent messaging and authoritative brand signals influence AI-generated recommendations and summaries.

That shift is creating new opportunities for communications agencies capable of combining storytelling, strategic messaging, and digital discoverability.

Spry PR’s emphasis on adaptability also mirrors changes in audience behavior. Consumers and B2B buyers alike are increasingly skeptical of overly polished corporate messaging and are placing greater value on authenticity, transparency, and expertise-driven communication.

The agency says its core operating principles — compassion, integrity, honesty, and transparency — will remain central despite the rebrand.

Industry analysts say trust-based communications strategies are becoming more important as misinformation, AI-generated content saturation, and declining organic reach complicate digital engagement efforts.

Research from Forrester has shown that brands with strong credibility and consistent communication frameworks tend to outperform competitors in long-term customer trust and engagement metrics.

The rebrand also reflects the agency’s geographic and operational evolution. Originally founded in Lancaster, Pennsylvania, the company has since established operations in Richmond, Virginia, prompting leadership to reconsider how the business identity aligned with future expansion plans.

“The LANC Marketing brand honored our heritage,” Pryor said. “Now, with our new home in Richmond, Va., our brand is moving forward, too.”

While rebrands are common across the agency sector, they often signal deeper strategic repositioning efforts tied to service expansion, market differentiation, or shifts in customer demand.

In this case, Spry PR appears focused on positioning itself as a modern communications partner capable of supporting organizations navigating increasingly complex digital ecosystems where media visibility, brand authority, AI discoverability, and audience trust are becoming tightly interconnected.

For businesses operating in competitive B2B markets, the evolution of firms like Spry PR underscores how communications strategy is moving closer to the center of enterprise growth and digital transformation initiatives.

Market Landscape

The communications and PR industry is undergoing significant transformation as AI-powered search, social media fragmentation, and digital trust dynamics reshape brand visibility strategies. Agencies are increasingly expanding beyond traditional media outreach into integrated content, SEO, executive branding, and reputation management services.

Enterprise platforms including Google, LinkedIn, and Meta continue influencing how brands distribute and optimize communications across digital channels. At the same time, generative AI platforms are changing how audiences discover and evaluate companies online.

According to Statista, global digital advertising and content marketing spending continues to increase as organizations invest more heavily in audience engagement, reputation management, and thought leadership initiatives.

For communications agencies, adaptability and strategic integration are becoming critical differentiators in an increasingly AI-shaped information ecosystem.

Top Insights

• LANC Marketing rebranded as Spry PR to reflect broader changes in communications strategy driven by AI-powered discovery, fragmented media ecosystems, and evolving audience expectations.
• The agency is positioning itself beyond traditional public relations by integrating content strategy, digital visibility, audience engagement, and modern brand communication frameworks.
• Growing adoption of generative AI platforms is reshaping how brands approach PR, with increased focus on AEO, GEO, digital authority, and consistent messaging across channels.
• Communications firms are increasingly competing as strategic visibility partners rather than standalone media relations providers, particularly in B2B technology and digital-first industries.
• Spry PR’s emphasis on agility, transparency, and adaptability reflects wider industry demand for authentic and measurable communications strategies in competitive online environments.

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Mitchell Zong Marketing is expanding its strategic marketing services for small and mid-sized businesses as companies increasingly shift away from short-term campaign execution toward long-term brand positioning and measurable growth strategies. The Anchorage-based firm announced broader support across audience analysis, messaging development, performance evaluation, and strategic planning — areas becoming increasingly critical as businesses compete in fragmented digital environments shaped by AI-driven discovery and changing consumer expectations.

The expansion highlights a broader trend across the marketing industry: organizations are prioritizing operational clarity and sustainable communication systems over isolated marketing tactics. As digital competition intensifies across platforms including Google, Meta, and LinkedIn, smaller businesses are under pressure to improve message consistency, audience targeting, and marketing efficiency without significantly increasing operational overhead.

Mitchell Zong Marketing’s expanded services arrive at a time when many small and mid-sized businesses are reassessing how marketing contributes to long-term growth. Rising advertising costs, algorithm volatility, and growing pressure to demonstrate measurable ROI have exposed weaknesses in fragmented marketing strategies that prioritize visibility over strategic alignment.

Founded by marketing strategist Mitchell Zong, the firm says its updated approach is centered on building structured marketing systems designed to improve communication consistency, audience understanding, and operational clarity across digital channels.

One of the primary issues the company aims to address is inconsistent brand messaging. As businesses expand across social media, search, email marketing, content platforms, and paid advertising ecosystems, communication often becomes disconnected from broader business objectives.

That challenge has become increasingly significant in the AI-driven discovery era, where platforms such as Google Search, generative AI assistants, and recommendation algorithms evaluate not only keyword relevance but also brand consistency and topical authority across digital properties.

“Organizations often move into execution too quickly without defining positioning,” Mitchell Zong said in the announcement. “Our focus is helping build sustainable systems aligned with long-term objectives.”

The company’s expanded service structure focuses heavily on strategic planning, audience analysis, communication refinement, and performance measurement. Rather than treating campaigns as isolated initiatives, the firm positions marketing as an integrated operational system where messaging, content, audience targeting, and analytics work together toward measurable business outcomes.

That systems-oriented approach reflects broader shifts taking place across the B2B marketing industry. According to Gartner, CMOs continue to face growing pressure to improve efficiency while demonstrating measurable business value from marketing investments. At the same time, research from Forrester has shown that buyers increasingly expect consistent brand experiences across every digital touchpoint, from websites and search engines to social platforms and customer support channels.

Mitchell Zong Marketing says its methodology combines audience research, communication audits, channel planning, and performance analysis to help businesses improve clarity and alignment. The firm also emphasizes continuous refinement rather than fixed campaign structures, allowing organizations to adjust strategy as market conditions evolve.

That iterative approach is becoming increasingly important as businesses navigate rapidly changing digital ecosystems influenced by AI-generated search summaries, predictive content recommendations, and algorithmic ranking systems. Many organizations are discovering that reactive marketing tactics alone are no longer sufficient to sustain long-term visibility or engagement.

Instead, firms are investing more heavily in structured messaging frameworks, content consistency, and audience-focused positioning strategies that support both traditional SEO and newer forms of AI-driven content discovery.

Mitchell Zong Marketing’s emphasis on clarity also aligns with growing industry focus on Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). As AI systems including Microsoft Copilot, OpenAI ChatGPT, and Google Gemini increasingly surface synthesized answers instead of traditional search results, businesses are under pressure to communicate value propositions more clearly and consistently.

The firm argues that unclear messaging — rather than lack of visibility — remains one of the largest barriers to sustainable growth. When audiences cannot quickly understand a company’s value, engagement rates decline and conversion opportunities weaken.

The expansion also reflects broader demand for strategic support among small and mid-sized businesses that lack large internal marketing teams. Many SMBs struggle to coordinate brand messaging, content production, channel strategy, and analytics simultaneously while managing operational constraints.

Mitchell Zong Marketing says its services are designed to simplify those processes through structured planning and clearer communication systems. The company’s approach includes evaluating how audiences interpret messaging, how campaigns align with business goals, and whether communication frameworks remain effective across multiple channels.

The focus on operational discipline stands out in a marketing industry often dominated by rapid experimentation and trend-driven execution. Instead of emphasizing constant reinvention, the company advocates for repeatable systems that improve over time through measurement and refinement.

“Strong brands are built through clarity and repetition over time,” Mitchell Zong said. “Consistency matters more than constant change.”

That philosophy mirrors broader enterprise marketing trends where operational consistency is increasingly viewed as a competitive advantage. Analysts at McKinsey & Company have noted that organizations with clearly aligned communication systems often outperform competitors in customer trust, retention, and long-term brand perception.

The company’s latest expansion also underscores how strategic marketing consulting is evolving alongside changes in the broader digital economy. Businesses are no longer competing solely for short-term attention. They are competing for sustained relevance across search engines, AI assistants, content platforms, and increasingly fragmented audience ecosystems.

For SMB leaders, the shift means marketing strategy is becoming less about isolated campaigns and more about building durable systems that connect messaging, audience insights, and operational execution into a unified growth framework.

Market Landscape

The strategic marketing services sector is evolving rapidly as businesses adapt to AI-driven search behavior, rising digital advertising costs, and growing pressure for measurable ROI. Marketing firms are increasingly expanding beyond campaign management into operational consulting, brand systems development, and audience intelligence.

Major enterprise platforms including Salesforce, Adobe, and HubSpot continue integrating AI-powered analytics, customer journey mapping, and automation capabilities into their ecosystems. However, many small and mid-sized businesses still require strategic guidance to align those tools with clear messaging and business objectives.

According to Statista, global digital advertising and marketing spending continues to rise steadily, while businesses increasingly prioritize customer retention, audience personalization, and measurable engagement performance over vanity metrics alone.

As AI-generated search and content discovery reshape online visibility, strategic clarity and messaging consistency are emerging as critical competitive differentiators.

Top Insights

• Mitchell Zong Marketing expanded its strategic marketing services to help SMBs improve audience alignment, communication consistency, and long-term brand positioning across fragmented digital channels.
• The firm’s research-driven approach emphasizes structured marketing systems, performance analysis, and continuous refinement instead of short-term campaign execution or trend-based marketing tactics.
• Growing adoption of AI-powered search and content discovery platforms is increasing demand for clear messaging frameworks optimized for SEO, AEO, and generative AI visibility.
• Small and mid-sized businesses are seeking operationally efficient marketing strategies as rising advertising costs and algorithm changes pressure organizations to improve measurable ROI.
• The expansion reflects broader industry movement toward integrated marketing infrastructure where audience intelligence, brand clarity, analytics, and content strategy operate as unified business systems.

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Automation Anywhere is expanding its push into enterprise AI automation with a new set of platform upgrades aimed at helping organizations operationalize AI across business-critical workflows. Announced at the company’s Imagine conference, the 2026 enhancements to its Agentic Process Automation (APA) platform focus on orchestration, governance, contextual intelligence, and low-code AI application development — areas increasingly viewed as foundational for scaling enterprise AI beyond isolated pilots.

The announcement reflects a broader shift underway in the enterprise software market. Organizations are no longer experimenting with standalone AI copilots alone. Instead, they are attempting to integrate AI into operational processes spanning finance, HR, customer service, IT operations, and supply chain management. That transition has exposed a key enterprise challenge: coordinating AI systems reliably across fragmented business infrastructure.

Automation Anywhere’s latest platform updates position the company alongside enterprise AI orchestration vendors attempting to define what “autonomous enterprise” infrastructure will look like over the next several years. The company says its expanded APA platform is designed to coordinate workflows that move across applications such as Salesforce, ServiceNow, SAP, and custom enterprise systems.

Unlike earlier robotic process automation (RPA) deployments that focused primarily on repetitive task automation, the new platform enhancements emphasize orchestration between AI agents, APIs, enterprise applications, and human approvals. That distinction matters as enterprises attempt to operationalize generative AI inside regulated and high-volume business environments.

“The Autonomous Enterprise depends on more than individual AI agents,” said Mihir Shukla, CEO and Chairman of Automation Anywhere, during the announcement. “It requires a system that can coordinate how work runs within departments and across the organization.”

At the center of the release is the company’s universal orchestration framework, which manages how AI-driven work moves through enterprise systems. The orchestration layer sequences tasks, routes decisions, manages handoffs, and governs execution across employees, automation bots, APIs, and AI agents within a unified workflow environment.

The announcement arrives as enterprises increasingly confront what analysts call “AI fragmentation” — a growing problem where disconnected copilots and automation tools create operational silos instead of end-to-end automation. According to Gartner, more than 80% of enterprises are expected to deploy generative AI applications by 2027, but many organizations still lack governance and orchestration layers capable of supporting production-scale AI operations.

Automation Anywhere is also introducing Automation Anywhere Code, or AAI Code, a low-code development environment designed to accelerate AI-powered workflow deployment. The platform allows teams to create enterprise applications using natural language prompts or existing operational materials such as SOPs, diagrams, screenshots, and documentation.

The company is positioning AAI Code differently from emerging “vibe coding” platforms that primarily generate application code through prompts. Instead, Automation Anywhere says the system emphasizes process planning, governance, and enterprise controls before deployment — a notable distinction for regulated industries handling sensitive operational data.

That enterprise-first approach reflects a broader trend in the AI software market. Vendors including Microsoft, Google, and Adobe are increasingly integrating governance frameworks into generative AI systems as enterprise buyers prioritize auditability, compliance, and workflow reliability over experimentation alone.

Another major addition is Context Intelligence Graph, a new capability embedded inside Automation Anywhere’s Process Reasoning Engine. The technology is designed to deliver task-specific context dynamically during workflow execution.

Enterprise AI systems often struggle with contextual overload. Many generative AI deployments expose broad enterprise datasets to models, which can introduce inaccuracies, latency, security risks, and unnecessary compute costs. Automation Anywhere says Context Intelligence Graph addresses that challenge by retrieving only the most relevant contextual information for a given process step or decision.

The system connects with enterprise knowledge bases, operational systems, policy repositories, historical execution data, and documents to generate associations and metadata automatically. According to the company, the technology was informed by insights from more than 400 million automation executions across its platform ecosystem.

In internal testing, Automation Anywhere says agents using Context Intelligence Graph and its Process Reasoning Engine demonstrated more than 30% higher accuracy compared to systems operating without contextual optimization.

The emphasis on contextual AI reflects a growing enterprise focus on retrieval-augmented generation (RAG), process-aware AI systems, and domain-specific reasoning models. Analysts at McKinsey & Company have previously noted that enterprises moving generative AI into production environments are increasingly prioritizing accuracy, explainability, and workflow grounding over general-purpose AI outputs.

Governance also features prominently in the release. Automation Anywhere introduced AI Evaluations, which allows enterprises to test and monitor agent behavior during both design-time and runtime operations. Organizations can evaluate whether AI agents follow approved execution paths, use appropriate tools, and deliver expected outcomes.

The company also announced Process Simulation, Optimization & Testing, a controlled testing environment that enables enterprises to simulate edge cases, workflow failures, and operational exceptions before deployment.

The move mirrors practices already common in DevOps and enterprise software engineering, where simulation and pre-production testing are considered critical for reducing operational risk. As AI systems increasingly automate sensitive workflows in healthcare, finance, and HR operations, governance tooling is becoming a competitive differentiator across the enterprise AI software market.

One early enterprise deployment highlighted by Automation Anywhere comes from University Hospitals of Leicester NHS Trust, part of the UK’s National Health Service. The hospital group is using APA technology to redesign administrative operations with a target of automating between 50% and 70% of administrative workloads.

According to the organization, anticipated operational outcomes include reducing recruitment timelines by 22 days and lowering temporary staffing costs by approximately £1 million annually.

The timing of the announcement is notable. The enterprise automation market is rapidly converging with generative AI infrastructure, creating new competition among automation vendors, cloud providers, enterprise SaaS platforms, and AI orchestration startups. Traditional RPA vendors are now repositioning themselves as enterprise AI execution platforms rather than simple workflow automation providers.

For enterprise marketing teams, customer operations leaders, and digital transformation executives, the shift could reshape how organizations deploy AI across large-scale operational processes. Rather than using AI as an isolated productivity layer, vendors are increasingly building systems designed to coordinate AI-driven decisions across entire enterprise workflows.

Market Landscape

The enterprise automation sector is entering a new phase where AI orchestration, governance, and contextual reasoning are becoming core infrastructure categories. Vendors including UiPath, IBM, Microsoft, and Salesforce are expanding enterprise AI automation capabilities across workflow management, copilots, and intelligent agents.

According to IDC, global spending on AI-enabled enterprise applications is expected to surpass $300 billion by 2027, driven by demand for operational efficiency, workflow automation, and AI-assisted decision-making. At the same time, enterprises are increasingly prioritizing governance frameworks as regulators and compliance teams scrutinize AI deployment practices.

Automation Anywhere’s strategy suggests the company is positioning APA as a broader enterprise operating layer for AI-driven business processes rather than a standalone automation platform.

Top Insights

• Automation Anywhere expanded its Agentic Process Automation platform with orchestration, governance, and contextual AI capabilities designed to support enterprise-wide autonomous operations across finance, HR, and customer service workflows.
• The new Context Intelligence Graph uses enterprise metadata, execution history, and knowledge systems to improve AI workflow accuracy while reducing irrelevant data exposure and operational inefficiencies.
• AAI Code introduces low-code enterprise AI application development using natural language workflow creation, targeting organizations seeking faster deployment of governed AI automation systems.
• New AI Evaluations and Process Simulation capabilities reflect growing enterprise demand for testing, monitoring, and governance infrastructure before deploying AI-driven workflows into production environments.
• Enterprise adoption examples from healthcare organizations highlight how AI process orchestration is moving beyond pilots into measurable operational transformation initiatives with cost and efficiency targets.

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New research from Ahrefs suggests that Google’s AI Overviews are accelerating the shift toward zero-click search, significantly reducing traffic flowing to external websites. According to the study, top-ranking organic pages now receive 58% fewer clicks when AI Overviews appear in search results, raising new concerns for publishers, SaaS companies, marketers, and digital businesses increasingly dependent on organic visibility.

Google’s AI-driven transformation of search is beginning to produce measurable consequences for the broader web economy.

A new study released by Ahrefs found that Google’s AI Overviews are now reducing clickthrough rates for top-ranking organic search results by an average of 58%, up sharply from the 34.5% decline observed less than a year earlier.

The findings suggest that AI-generated summaries are increasingly absorbing user attention directly inside search results pages, reducing the need for users to visit external websites altogether.

The study analyzed 300,000 keywords using aggregated Google Search Console data, comparing performance between December 2023 — before the rollout of AI Overviews — and December 2025.

According to Ahrefs, the impact extends well beyond the top organic listing.

Pages ranking in position two reportedly lost roughly half of their clicks, while even pages positioned near the bottom of the first search page experienced measurable declines approaching 20%.

The research reinforces growing concerns across the publishing, SaaS, e-commerce, and digital marketing industries that generative AI may fundamentally alter the economics of search-driven traffic acquisition.

“Search is becoming zero-click,” said Ryan Law. He noted that users increasingly receive answers directly within Google’s interface rather than navigating to publishers, brands, or informational websites.

The concept of zero-click search is not entirely new.

Featured snippets, knowledge panels, People Also Ask modules, and map packs have already reduced outbound traffic for many query types over the past decade. But AI Overviews appear to represent a more aggressive evolution because they synthesize information directly into conversational summaries capable of answering complex informational queries without requiring additional exploration.

That shift is particularly significant because informational search has historically served as one of the largest traffic drivers for publishers and content-heavy businesses.

The implications could reshape how marketers measure search visibility itself.

Historically, SEO strategies focused heavily on ranking position, organic traffic, and clickthrough rates. But as AI-generated search interfaces become more dominant, visibility inside AI-generated summaries, brand recognition, and citation presence may become equally important performance metrics.

This transition is already forcing marketers to adapt toward Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) strategies designed to improve visibility inside AI-generated search responses.

The findings also highlight Google’s increasingly complex position within the digital publishing ecosystem.

For years, Google’s search engine operated as the primary traffic distributor across the web, sending billions of visits to publishers, businesses, and creators. AI Overviews potentially shift more value capture directly onto Google’s own interface by keeping users inside search results longer.

That dynamic is creating growing tension between platform optimization and publisher sustainability.

Google has previously argued that AI-generated search experiences can drive higher-quality traffic and improve user engagement. But studies like Ahrefs’ suggest the net volume of outbound clicks may continue declining as AI summaries become more comprehensive.

The broader competitive landscape is also evolving quickly.

AI-native search platforms such as Perplexity AI and conversational systems from OpenAI are training users to expect direct answers rather than lists of links.

At the same time, Google is under pressure to defend its search dominance as generative AI changes how users interact with information online.

Research from Gartner has projected that traditional search engine volume could decline significantly over the next several years as users increasingly shift toward AI-driven conversational interfaces.

Meanwhile, Forrester has warned that brands dependent on organic discovery may face mounting acquisition challenges as AI systems intermediate more of the customer journey.

The impact is likely to vary across industries.

Publishers, affiliate websites, informational SaaS blogs, and comparison platforms appear especially vulnerable because their business models depend heavily on high-volume informational search traffic.

Brands with strong direct relationships, subscription ecosystems, or recognizable authority may prove more resilient.

The rise of AI Overviews is also accelerating a broader strategic shift in SEO itself.

Modern search optimization increasingly involves building entity authority, strengthening brand recognition, improving multi-platform discoverability, and creating content structured for AI citation rather than simply targeting keyword rankings.

Ahrefs says it is continuing to track the performance impact of various search engine results page features, including discussions, videos, forums, and AI-generated experiences.

The larger challenge for marketers is that search visibility no longer guarantees traffic.

In the AI era, ranking first may no longer mean owning the click.

Market Landscape

The search industry is undergoing one of its most significant structural changes since the rise of mobile search and social media platforms.

Generative AI is rapidly transforming search engines from link-based discovery systems into answer-generation interfaces that increasingly satisfy user intent directly within platform environments.

This shift is accelerating the rise of zero-click search behavior, where users consume information without visiting external websites.

AI-generated search summaries, conversational interfaces, and answer engines are fundamentally changing how publishers, SaaS companies, e-commerce brands, and marketers approach content visibility and customer acquisition.

As a result, SEO strategies are evolving toward Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), entity authority building, and multi-platform discoverability.

At the same time, publishers and content-driven businesses face growing uncertainty around traffic sustainability as search platforms capture a larger share of user engagement directly within AI-enhanced search experiences.

Top Insights

• Ahrefs research found Google AI Overviews now reduce clickthrough rates for top-ranking organic pages by an average of 58%.
• The study suggests AI-generated search summaries are accelerating the shift toward zero-click search behavior across informational queries.
• Traffic declines extend across the entire first page of search results, not just the top-ranking organic position.
• SEO strategies are evolving toward AI visibility, entity authority, and citation optimization rather than rankings alone.
• Publishers, SaaS companies, and content-heavy businesses face growing pressure as generative AI changes search traffic economics.

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Splio is expanding its operations in Southern Europe as the company pushes deeper into the growing market for AI-driven customer relationship management platforms. The CRM vendor announced that Barcelona will serve as a second headquarters for the company, reflecting broader industry momentum around AI-powered customer engagement, retail personalization, and the emergence of agentic commerce across European markets.

The European CRM market is entering a period of rapid transition as artificial intelligence reshapes how brands interact with customers across digital and physical channels.

From retail loyalty programs to travel marketing and omnichannel personalization, businesses are increasingly searching for platforms capable of combining customer data, predictive analytics, and AI-driven automation into unified engagement systems.

Splio is positioning itself to capitalize on that shift.

The company announced an expanded investment in Southern Europe, strengthening its operations in Barcelona while positioning the city as a strategic second headquarters for its next phase of regional growth.

The move comes only months after Splio introduced its AI-first CRM platform, which the company says is designed to help businesses adapt to emerging AI-driven consumer behaviors and the rise of what it describes as “agentic commerce.”

The expansion includes changes to executive leadership structure, increased regional responsibilities, and deeper investment in customer support, partnerships, and business development across Spain, Portugal, and Italy.

Antoine Parizot will relocate to Barcelona as part of the initiative, while Donald Pontabry will oversee Southern European development alongside his operational leadership responsibilities.

The company currently maintains a regional team of roughly 30 employees supporting approximately 100 clients across sectors including retail, travel, and consumer commerce.

Those clients include brands such as QVC, Conforama, and Piazza Italia.

The expansion reflects a broader trend unfolding across the CRM and martech industries.

As generative AI tools become integrated into consumer search, shopping, and discovery behaviors, companies are under increasing pressure to modernize customer engagement infrastructure capable of supporting AI-native interactions.

Traditional CRM systems were largely designed around email campaigns, customer databases, and workflow automation. AI-first CRM platforms, by contrast, are evolving toward real-time personalization, predictive engagement, conversational commerce, and autonomous marketing orchestration.

That evolution is being accelerated by major enterprise software vendors including Salesforce, Adobe, and Microsoft, all of which have aggressively expanded AI capabilities across customer engagement ecosystems.

Research from Gartner suggests AI-enhanced CRM systems are becoming central to enterprise digital transformation strategies as businesses seek more intelligent customer acquisition, retention, and loyalty workflows.

Meanwhile, McKinsey & Company has projected that AI-driven personalization technologies could significantly improve customer lifetime value and marketing efficiency across retail and service industries.

Splio’s positioning around “agentic commerce” reflects another emerging industry trend.

As consumers increasingly use AI assistants and conversational systems for product discovery, search, and decision-making, marketers are preparing for an environment where AI agents may influence or mediate portions of the customer journey.

That transition is forcing CRM vendors to rethink how customer data, recommendation systems, and engagement logic are structured.

“We see Southern Europe as much more than a region where we have a long-standing presence,” said Antoine Parizot, noting that AI adoption and digital behavior are evolving rapidly across the region.

The company views Barcelona as strategically important because of its growing role as a European technology and digital commerce hub.

Barcelona has increasingly attracted SaaS firms, AI startups, and digital commerce companies seeking access to Southern European markets while benefiting from the city’s expanding technology ecosystem.

Splio’s leadership argues that businesses across the region face a dual challenge: adapting to AI-driven digital interactions while maintaining engagement strategies connected to physical retail and real-world commerce experiences.

That balance is particularly relevant in Southern European markets, where brick-and-mortar retail remains culturally and economically significant even as digital transformation accelerates.

According to Donald Pontabry, organizations need CRM systems capable of bridging AI-driven customer interactions with operational realities that remain deeply tied to physical commerce environments.

The competitive CRM landscape itself is becoming increasingly fragmented as vendors race to integrate generative AI, predictive analytics, and automation into customer engagement stacks.

Companies are no longer competing solely on campaign management features or customer segmentation tools. Increasingly, the market is shifting toward platforms capable of orchestrating personalized interactions across multiple channels while adapting dynamically to AI-mediated consumer behavior.

For mid-market and enterprise brands, that shift raises new questions around data governance, personalization ethics, omnichannel consistency, and the role of AI in customer relationship management.

Splio’s regional expansion suggests that CRM vendors increasingly see geographic proximity and local operational support as important differentiators in a market increasingly dominated by global SaaS platforms.

The larger story, however, is how AI is redefining the infrastructure behind customer relationships themselves.

As commerce becomes more conversational, predictive, and automated, CRM systems are evolving from passive databases into active decision-making engines designed to shape customer experiences in real time.

Market Landscape

The CRM industry is undergoing rapid transformation as artificial intelligence reshapes digital commerce, customer engagement, and marketing automation.

Traditional CRM platforms focused primarily on customer data storage, campaign management, and workflow automation. AI-first CRM systems are now evolving toward predictive personalization, conversational engagement, and autonomous customer journey orchestration.

This shift is being accelerated by the rise of generative AI, AI-powered search experiences, and emerging “agentic commerce” environments where AI assistants increasingly influence consumer decision-making.

At the same time, businesses are seeking platforms capable of unifying digital engagement with physical retail and real-world customer interactions.

European markets are becoming strategically important for CRM vendors as organizations across retail, travel, and consumer commerce sectors accelerate digital transformation initiatives while balancing local operational requirements and privacy regulations.

The market is also seeing intensified competition between global enterprise software providers and specialized CRM vendors focused on AI-native customer engagement experiences.

Top Insights

• Splio expanded its Southern European operations and positioned Barcelona as a second headquarters to support growing demand for AI-first CRM platforms.
• The company’s strategy reflects broader industry movement toward AI-driven customer engagement, predictive personalization, and agentic commerce ecosystems.
• CRM vendors increasingly compete on AI orchestration, omnichannel personalization, and real-time engagement capabilities rather than traditional campaign management features.
• Southern Europe is emerging as a key growth region for SaaS, AI, and digital commerce platforms adapting to evolving consumer behavior.
• Businesses are seeking CRM systems capable of connecting AI-driven digital experiences with physical retail and real-world customer interactions.

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The global connected TV advertising market is expected to nearly double over the next five years, reaching $81 billion by 2030, according to new research from Omdia. The report projects that Google, Amazon, and Netflix will collectively control half of global connected TV advertising revenue by the end of the decade, signaling a major shift in power across the television and advertising industries.

The television industry’s balance of power is changing rapidly.

For decades, traditional broadcasters controlled the economics of television advertising through linear distribution networks and scheduled programming. But the rise of connected TV (CTV), streaming platforms, smart TV operating systems, and programmatic advertising is fundamentally restructuring how audiences consume content — and how advertisers reach them.

New research from Omdia suggests the transformation is accelerating faster than many legacy media companies anticipated.

According to the firm, global CTV advertising revenue is projected to grow from $44 billion in 2025 to $81 billion by 2030. Omdia also expects connected TV advertising to surpass traditional linear TV advertising during the 2030s, marking one of the largest structural changes in media economics since the rise of digital advertising.

At the center of that transition are three companies already dominant in adjacent digital ecosystems: Google, Amazon, and Netflix.

Omdia forecasts that by 2030, Google will command approximately 26% of global CTV advertising revenue, followed by Amazon at 13% and Netflix at 9%. Combined, the three companies are expected to capture half of the entire connected TV advertising market worldwide.

The findings reinforce how streaming video is evolving into a broader digital commerce and advertising infrastructure layer rather than simply an entertainment distribution channel.

“The battle for the living room is no longer only about streaming content,” said Maria Rua Aguete. She argued that platform ownership, advertising infrastructure, operating systems, and consumer data are becoming the defining strategic assets in modern television ecosystems.

That assessment reflects broader trends reshaping the advertising and media industries.

CTV advertising has emerged as one of the fastest-growing segments within digital marketing because it combines television-scale audience reach with the targeting, measurement, and programmatic capabilities traditionally associated with digital advertising platforms.

Unlike conventional broadcast TV, connected TV environments allow advertisers to leverage behavioral data, audience segmentation, retail purchase signals, and real-time optimization.

This convergence is particularly advantageous for companies already operating large-scale advertising and commerce ecosystems.

Google continues to dominate through YouTube and Android TV, both of which give the company extensive reach across connected households and advertising infrastructure. Amazon, meanwhile, is integrating Prime Video with its rapidly expanding retail media business, creating closed-loop advertising environments tied directly to e-commerce purchasing behavior.

Netflix represents a different strategic evolution.

Historically resistant to advertising, the streaming giant has aggressively expanded its ad-supported subscription tier in response to slowing subscriber growth and broader industry monetization pressures. The company’s growing advertising ambitions position it as both a premium content platform and an increasingly important participant in the global ad-tech ecosystem.

Research from Gartner suggests retail media and streaming video advertising are among the fastest-growing digital advertising categories globally, particularly as advertisers search for alternatives to cookie-dependent web targeting.

At the same time, McKinsey & Company has identified connected TV as a critical battleground for future advertising budgets because it blends brand advertising scale with digital-style performance measurement.

The implications for traditional broadcasters and television manufacturers are significant.

Omdia’s report suggests the future of television competition may increasingly revolve around operating systems, advertising layers, and commerce integration rather than content libraries alone.

That shift is already visible in Europe’s smart TV ecosystem.

The firm reports that VIDAA is emerging as Europe’s third-largest television operating system behind Android TV and Samsung Electronics’s Tizen platform.

Smart TV operating systems are becoming strategically valuable because they control content discovery, advertising placement, user data collection, and increasingly, commerce integration directly from the television interface.

“CTV companies are at risk of losing incredibly valued ground to these tech giants,” said David Tett. He warned that hardware-focused television companies may struggle to compete as device margins shrink and advertising ecosystems become more profitable than hardware sales themselves.

This reflects a broader platformization trend already visible across digital markets.

Technology companies are increasingly competing not just for audiences, but for ownership of the interfaces through which audiences discover, purchase, and interact with content and products.

Television is becoming another gateway into that ecosystem.

The convergence of retail media, streaming platforms, smart TV operating systems, and programmatic advertising suggests the future TV experience may look far more like an integrated commerce platform than a traditional broadcast environment.

For marketers, that evolution creates new opportunities around audience targeting, attribution, and interactive advertising formats.

For media companies, it raises more difficult questions about platform dependency and revenue ownership.

And for consumers, it signals that the battle for the living room is increasingly becoming a battle for data, advertising influence, and digital commerce control.

Market Landscape

Connected TV advertising is rapidly becoming one of the most strategically important segments in the global digital advertising market.

As streaming adoption accelerates and traditional linear television audiences decline, advertisers are shifting budgets toward platforms capable of delivering both television-scale reach and digital-style targeting capabilities.

The market is also seeing increased convergence between retail media, streaming services, ad-tech infrastructure, and smart TV operating systems. Companies with integrated ecosystems — including content platforms, commerce networks, advertising technology, and user identity systems — are gaining structural advantages.

At the same time, smart TV operating systems are evolving into critical control points for advertising distribution, audience data, and consumer engagement.

This is creating intensified competition among streaming platforms, TV manufacturers, operating system providers, and digital advertising companies seeking ownership of the connected household experience.

As the CTV market matures, control over the television interface itself may become as strategically important as ownership of premium streaming content.

Top Insights

• Omdia forecasts the global connected TV advertising market will grow from $44 billion in 2025 to $81 billion by 2030 as streaming and digital advertising converge.
• Google, Amazon, and Netflix are projected to control half of global CTV advertising revenue by the end of the decade through integrated platform ecosystems.
• Retail media, programmatic advertising, and smart TV operating systems are increasingly shaping the economics of modern television advertising markets.
• Smart TV operating systems are evolving into strategic commerce and advertising gateways that control audience discovery, targeting, and consumer data access.
• Traditional broadcasters and TV manufacturers face mounting pressure as advertising infrastructure becomes more valuable than hardware and content distribution alone.

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iProov has introduced Verified Meetings, a new biometric authentication capability designed to detect deepfakes and synthetic identities during enterprise video calls. The platform integrates directly into video conferencing environments and analyzes live video streams in real time to verify whether participants are authentic humans using physical cameras rather than AI-generated or manipulated video feeds.

Enterprise video conferencing has become one of the most trusted communication channels in modern business. It is also rapidly becoming one of the most vulnerable.

As remote work, digital onboarding, and virtual collaboration continue expanding, attackers are increasingly using generative AI-powered deepfakes to impersonate employees, candidates, customers, and executives during live video interactions. That shift is creating a new category of cybersecurity and identity verification challenges for enterprises relying on video-based workflows.

iProov is the latest identity security vendor attempting to address that growing threat landscape.

The company announced the launch of iProov Verified Meetings, a deepfake detection and biometric verification solution designed to authenticate video call participants in real time without disrupting meeting workflows.

The platform is part of the company’s Workforce Solutions Suite and focuses specifically on the “pre-join” stage of enterprise video interactions, where identity verification increasingly determines whether organizations approve hires, authorize financial transactions, or grant access to sensitive systems.

The launch reflects a broader industry concern surrounding the rapid advancement of generative AI tools capable of producing highly convincing synthetic video identities.

Recent incidents have highlighted how serious the risk has become. Engineering firm Arup reportedly lost $25 million following a deepfake-enabled video call scam, while cybersecurity researchers and government agencies have warned that North Korea-linked operators have used synthetic identities during remote hiring processes to infiltrate organizations.

The accessibility of generative AI tooling is accelerating those risks.

Platforms capable of creating photorealistic avatars, voice cloning, and real-time video manipulation are becoming increasingly inexpensive and widely available. Combined with virtual camera environments, those systems can allow attackers to bypass traditional visual trust signals that organizations once relied on during remote interactions.

“Organizations still largely assume that seeing a person on screen means they’re real,” said Andrew Bud. He noted that deepfakes are now both scalable and difficult for humans to detect during live interactions.

Verified Meetings is designed to counter that problem through continuous background analysis integrated directly into video conferencing platforms.

Rather than requiring users to complete separate verification workflows, the platform silently analyzes live video streams across two primary dimensions: imagery analysis and hardware integrity validation.

The imagery analysis component attempts to identify deepfakes, presentation attacks, and manipulated visual artifacts associated with synthetic media generation. At the same time, the system verifies whether the incoming feed originates from a physical camera rather than a virtualized or injected video environment.

That dual-layer approach reflects a growing realization within the identity security industry that AI-generated fraud detection increasingly requires both biometric and device-level validation.

The system provides hosts with a simplified Red, Amber, or Green status indicator designed to support immediate decision-making during live meetings. Importantly, participants are not alerted when checks occur, a design choice intended to reduce attacker awareness while maintaining accessibility and workflow continuity.

The technology also operates alongside iProov’s Security Operations Center, or iSOC, where biometric scientists, threat researchers, and red-team specialists continuously monitor emerging synthetic identity attack techniques.

That adaptive defense model is becoming increasingly common across cybersecurity markets as AI-generated attacks evolve faster than static detection systems can respond.

Research from Gartner suggests generative AI-driven fraud will become one of the defining enterprise cybersecurity challenges of the decade, particularly as deepfake quality improves and attack automation expands.

Meanwhile, McKinsey & Company has identified digital identity verification as a critical infrastructure category for enterprises adopting hybrid work, digital onboarding, and remote operational workflows.

The rise of deepfake fraud is also reshaping how enterprises think about trust itself.

Historically, video calls served as a high-confidence authentication layer for remote communication. Seeing a face on screen was generally treated as reliable proof of identity. That assumption is eroding rapidly as synthetic media systems become more sophisticated.

The implications extend beyond cybersecurity.

Financial institutions increasingly use video for account recovery and transaction approvals. HR departments conduct remote hiring interviews entirely online. Customer support teams rely on video identity checks for fraud prevention and onboarding.

As those workflows scale, enterprises may need continuous identity assurance systems embedded directly into collaboration platforms.

The competitive landscape is already evolving accordingly.

Major technology companies including Microsoft, Google, and Zoom Communications are investing heavily in AI-driven meeting intelligence, security controls, and enterprise collaboration infrastructure.

But identity verification inside live video environments remains an emerging category with relatively few mature enterprise-grade solutions.

iProov’s launch signals that biometric verification vendors increasingly view real-time meeting authentication as a major growth area within enterprise security markets.

The broader challenge for organizations is that deepfake threats are evolving faster than human intuition can adapt.

In the AI era, “seeing is believing” is no longer a reliable security policy.

Market Landscape

The rapid adoption of generative AI is transforming enterprise cybersecurity and digital identity verification markets.

Deepfake technologies capable of generating realistic synthetic video, voice, and facial impersonations are creating new attack vectors across remote work, financial services, customer onboarding, and enterprise collaboration environments.

Video conferencing platforms, once considered trusted communication channels, are increasingly becoming targets for fraud, social engineering, and infiltration attacks.

This shift is driving demand for real-time identity verification systems that combine biometric analysis, device integrity validation, and adaptive threat intelligence.

At the same time, enterprises are moving toward continuous authentication models where identity checks occur dynamically within workflows rather than through isolated login events.

As remote operations continue scaling globally, deepfake detection and synthetic identity prevention are likely to become core components of enterprise collaboration and cybersecurity infrastructure.

Top Insights

• iProov launched Verified Meetings, a biometric verification solution designed to detect deepfakes and synthetic identities during enterprise video calls in real time.
• The platform combines imagery analysis and hardware integrity checks to determine whether participants are authentic users operating physical cameras.
• Enterprises increasingly view video conferencing platforms as high-risk fraud surfaces due to the rapid advancement of generative AI and deepfake technologies.
• Real-time identity assurance is becoming critical across remote hiring, financial approvals, onboarding, and enterprise collaboration workflows.
• Adaptive threat intelligence and continuous authentication are emerging as foundational requirements for AI-era enterprise security systems.

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TeamCentral has launched Central AI, a new enterprise AI agent platform designed to connect business systems, unify operational data, and enable AI agents to execute governed actions across enterprise environments. The platform introduces CORBI™, TeamCentral’s orchestration layer for AI agents, positioning the company within a rapidly expanding market focused on operational AI automation rather than standalone conversational assistants.

Enterprise AI is entering a new phase.

For the past two years, much of the market conversation has centered on AI copilots capable of generating summaries, answering questions, and assisting knowledge workers through conversational interfaces. But enterprises are increasingly discovering that insight alone does not create operational value if AI systems cannot interact securely with the fragmented infrastructure that actually runs the business.

That challenge is driving demand for a new category of enterprise software focused on AI orchestration, system integration, and governed execution.

TeamCentral is the latest company attempting to address that gap.

The company announced the launch of Central AI, a patent-pending enterprise AI platform designed to unify enterprise data across ERP, CRM, finance, supply chain, and operational systems while enabling AI agents to execute business actions within governed security frameworks.

At the center of the release is CORBI™ — short for “Cortex of Your Business” — an orchestration layer intended to coordinate enterprise AI agents, workflows, and business logic across connected systems.

TeamCentral says CORBI™ is compatible with Model Context Protocol (MCP) connectivity standards and can operate alongside AI platforms including Microsoft Copilot, OpenAI’s ChatGPT, and Anthropic Claude.

The launch reflects a broader industry shift toward operational AI systems capable not only of generating insights, but also of taking action inside enterprise environments.

According to Gartner, enterprises are increasingly prioritizing AI orchestration and governance infrastructure as organizations move from experimentation into production-scale AI deployments. Research firms have also identified AI agent coordination and workflow execution as emerging priorities across enterprise automation markets.

The central problem many enterprises face is not necessarily AI model performance.

Instead, organizations struggle with disconnected systems, inconsistent data governance, fragmented permissions, and operational silos that prevent AI systems from interacting reliably with enterprise infrastructure.

“Most AI initiatives are not blocked by model quality; they are blocked by disconnected systems, inconsistent data, and fragmented security,” said Marc Johnson.

Central AI is designed around solving those integration and governance challenges.

The platform builds on TeamCentral’s existing no-code integration infrastructure, which already connects cloud and on-premises applications while automating workflows and synchronizing business data across systems.

Central AI extends that foundation into AI execution environments by adding shared business context, unified role-based security controls, and orchestration logic designed for AI agents.

The platform standardizes data using a common business data model, allowing AI systems to access consistent operational information across ERP, CRM, finance, and supply chain applications.

That architecture aligns with a growing movement toward semantic enterprise layers and operational context engines that provide AI agents with structured business understanding rather than isolated datasets.

The role-based governance component may prove particularly important for enterprise adoption.

One of the largest concerns surrounding enterprise AI deployment remains access control. Businesses increasingly need AI systems capable of interacting with operational workflows without exposing sensitive financial, customer, or operational information beyond authorized boundaries.

TeamCentral says its unified security layer applies consistent permissions across connected systems, ensuring both employees and AI agents can only access approved workflows and datasets.

That governance-first approach mirrors broader enterprise AI strategies emerging across platforms from Salesforce, Microsoft, and Google, all of which have accelerated investment in secure AI infrastructure, enterprise identity management, and workflow orchestration.

The launch also highlights the growing importance of MCP connectivity standards.

Model Context Protocol is emerging as an increasingly discussed framework for enabling AI agents to interact with enterprise systems, tools, and workflows in structured ways. Rather than operating as isolated chatbots, MCP-compatible agents can exchange contextual information with applications and trigger governed actions across business environments.

TeamCentral positions CORBI™ as an orchestration layer for precisely that type of operational AI ecosystem.

Potential use cases outlined by the company include supply chain exception management, inventory workflows, finance reconciliation, operational alerting, and customer or vendor data synchronization.

Those are areas where enterprises have historically depended on manual intervention, rule-based automation, or fragmented workflow software.

Research from McKinsey & Company suggests organizations implementing AI-enabled operational workflows could achieve meaningful efficiency improvements in back-office processes, supply chain coordination, and enterprise decision support over the next decade.

The competitive landscape is becoming increasingly crowded as AI vendors move beyond assistant-style interfaces into execution-focused enterprise systems.

Startups and established enterprise software providers alike are racing to build AI agent infrastructures capable of securely interacting with operational environments while maintaining governance, auditability, and compliance controls.

For TeamCentral, the differentiator appears to be its attempt to combine no-code integration, data normalization, AI orchestration, and enterprise governance into a unified operating layer.

The company is initially targeting mid-market organizations and operationally complex industries including manufacturing, distribution, finance, and supply chain management.

The broader significance of the launch lies in what it says about the evolution of enterprise AI itself.

The market is shifting from conversational productivity tools toward AI systems expected to participate directly in operational execution. In that environment, the challenge is no longer simply generating intelligent answers — it is ensuring AI can act safely, securely, and contextually inside the systems where enterprise work actually happens.

Market Landscape

Enterprise AI is rapidly evolving from chatbot-style productivity tools into operational execution platforms capable of interacting directly with business systems and workflows.

Organizations increasingly require AI agents that can access enterprise data securely, understand operational context, and execute governed actions across ERP, CRM, finance, and supply chain environments.

This shift is driving demand for orchestration layers, semantic business models, and AI governance infrastructure capable of coordinating autonomous systems while maintaining security and compliance controls.

At the same time, enterprises are struggling with fragmented infrastructure, inconsistent permissions, and disconnected data ecosystems that limit large-scale AI adoption.

As a result, vendors are increasingly focusing on AI-ready operating layers that unify data, automate integrations, and standardize business context for AI agents.

The rise of MCP-compatible connectivity standards further signals movement toward interoperable enterprise AI ecosystems where agents can securely interact across multiple applications and operational systems.

Top Insights

• TeamCentral launched Central AI and CORBI™, an enterprise AI orchestration platform focused on connecting business systems and enabling governed AI-driven operational execution.
• The platform combines no-code integration, role-based security, semantic business context, and MCP-compatible connectivity to help AI agents operate securely across enterprise environments.
• Enterprises increasingly view fragmented infrastructure and inconsistent governance — not model quality — as primary barriers to AI adoption at operational scale.
• AI agents are evolving beyond conversational assistants into workflow execution systems capable of automating supply chain, finance, and operational business processes.
• Governance, interoperability, and secure orchestration are emerging as critical competitive factors in the enterprise AI infrastructure market.

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