As enterprise attention shifts rapidly from traditional search engines to AI-driven discovery platforms, Brandi AI is strengthening its leadership team to capitalize on the emerging discipline of Generative Engine Optimization (GEO). The company announced the appointment of Liam Darmody as vice president of customer success and go-to-market operations, a move aimed at scaling execution across customer experience, revenue operations, and AI visibility strategy.

The hire underscores how quickly the GEO category is evolving from an experimental concept into a structured enterprise marketing function.

Brandi AI, which positions itself as an intelligence-driven platform for enterprise AI visibility and Generative Engine Optimization, is doubling down on operational scale as organizations rethink how brands are discovered in an AI-first ecosystem.

In his new role, Liam Darmody will oversee customer success strategy, revenue operations, and go-to-market execution. The appointment signals Brandi AI’s intent to align customer-facing operations with its broader mission of helping brands improve visibility in generative engines and answer-based systems.

The company is positioning GEO as the next evolution of search optimization—moving beyond traditional SEO rankings toward ensuring brand presence within AI-generated responses across platforms such as large language models and AI-powered search interfaces.

Brandi AI has already gained early recognition in this emerging space. The company has been named a G2 High Performer in the Answer Engine Optimization (AEO) category and was recognized by Intellyx as a 2025 Digital Innovator for its work defining Generative Engine Optimization as a discipline.

Leah Nurik, co-founder and CEO of Brandi AI, emphasized that the company’s growth phase requires operational alignment with its category leadership.

“Liam brings a powerful combination of customer-first thinking and operational rigor at exactly the right time for Brandi AI,” Nurik said. “As companies navigate the shift to AI-driven discovery and answer engines, his experience will help us translate our category leadership into measurable outcomes for customers.”

Darmody brings nearly two decades of experience across customer success, revenue operations, and go-to-market leadership roles in high-growth technology companies. His background spans multiple sectors, including online commerce, real estate technology, enterprise data, and technology consulting.

His previous roles include senior leadership positions at several notable technology firms. At Homesnap, later acquired by CoStar Group, he helped drive significant year-over-year revenue expansion. At WillowTree, later acquired by TELUS International, he built recruitment marketing systems from the ground up. At AddThis, later acquired by Oracle, he scaled enterprise accounts from zero to hundreds while maintaining strong retention performance. Earlier in his career, he joined LivingSocial as one of its early employees and contributed to its expansion prior to acquisition by Groupon.

Across these roles, Darmody has focused on scaling customer success systems and aligning operational infrastructure with rapid growth trajectories—experience Brandi AI is now looking to leverage as it expands its GEO footprint.

In his new position, Darmody will focus on aligning customer experience with go-to-market strategy, ensuring that enterprises using Brandi AI can effectively navigate the transition from keyword-based search optimization to AI-driven visibility optimization.

“AI is fundamentally reshaping how people discover and engage with brands,” Darmody said. “Brandi AI is at the forefront of defining how companies succeed in this new environment.”

The statement reflects a broader shift occurring across digital marketing: visibility is no longer solely determined by search engine ranking algorithms but increasingly by how AI systems retrieve, summarize, and present brand information in conversational interfaces.

This shift is driving the emergence of GEO and related frameworks like Answer Engine Optimization (AEO), which focus on ensuring that brands are accurately represented within AI-generated responses.

Brandi AI’s strategy places it at the intersection of marketing technology and generative AI infrastructure, where brand visibility is increasingly mediated by large language models rather than traditional search engine result pages.

As enterprises adjust to this new paradigm, demand is growing for platforms that can measure, optimize, and influence AI-driven brand representation. Brandi AI is positioning itself as one of the early movers defining how that measurement layer will function.

Market Landscape

The rise of Generative Engine Optimization (GEO) reflects a structural transformation in digital discovery.

Three major trends are shaping this shift:

First, users are increasingly relying on AI assistants and generative search tools instead of traditional search engines, reducing the dominance of keyword-based SEO.

Second, large language models are becoming intermediaries for brand discovery, summarization, and recommendation.

Third, enterprises are seeking new measurement frameworks to understand visibility within AI-generated outputs.

Brandi AI operates in an emerging category alongside early GEO and AEO-focused platforms that aim to track and influence AI-generated brand mentions.

This space overlaps with traditional SEO platforms like Semrush and Ahrefs but extends into AI observability, content optimization for LLMs, and conversational visibility tracking.

As the category matures, it is expected to converge with broader martech and AI analytics ecosystems, where brand visibility is measured not just in clicks and impressions but in AI citation frequency and contextual presence.

Top Insights

• Brandi AI appoints Liam Darmody as VP of customer success and go-to-market operations to scale its Generative Engine Optimization strategy.
• GEO is emerging as a successor framework to traditional SEO, focusing on visibility in AI-generated responses rather than search rankings.
• The company has been recognized by G2 as a High Performer in Answer Engine Optimization and by Intellyx as a 2025 Digital Innovator.
• Gartner predicts that by 2026, a significant share of search interactions will be mediated by generative AI systems rather than traditional search engines.
• Brandi AI is positioning itself at the intersection of martech and AI infrastructure, where brand visibility is defined by LLM-based discovery systems.

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Storyteq, part of ITG, is redefining the role of Content Marketing Platforms (CMPs) by positioning its system as an AI-native, agent-driven content operating system that connects creative production, marketing workflows, and digital asset management (DAM) into a unified infrastructure layer. The company says its platform enables brands to fully operationalize artificial intelligence across the entire content lifecycle—from planning and creation to optimization and deployment.

The announcement comes alongside renewed industry recognition, including leadership positioning in Gartner’s Magic Quadrant for Content Marketing Platforms, reinforcing Storyteq’s emphasis on enterprise-scale content orchestration and AI-enabled automation.

Storyteq is advancing its CMP beyond traditional content workflow tooling into what it describes as a fully integrated, object-oriented content ecosystem. The platform is designed to connect DAM systems, creative automation tools, and marketing workflows into a unified infrastructure that enables AI-driven content production and optimization at scale.

According to Andrew Swinand, CEO of ITG powered by Storyteq, the platform’s key differentiator lies in its ability to embed AI directly into content infrastructure rather than layering it on top as a disconnected feature.

“What Storyteq unserer Meinung nach von anderen unterscheidet, ist, dass es Marken endlich ermöglicht, das volle Potenzial der KI auszuschöpfen,” Swinand said. He emphasized that the platform uses AI to predict content performance, coordinate creation workflows, and continuously optimize assets for growth outcomes.

This approach reflects a broader shift in enterprise marketing technology, where AI is no longer viewed as a standalone capability but as an embedded operational layer within content supply chains.

Storyteq positions itself as a system that not only automates content production but also provides predictive intelligence about which assets are likely to perform before they are created. This predictive layer is powered by its proprietary Halo Intelligence® system, which analyzes customer data, brand intent signals, and historical campaign performance to guide content decisions.

A key component of the platform is Agent Console™, which acts as a centralized environment for building, deploying, and managing marketing AI agents. These agents are designed to automate and coordinate tasks across content creation, distribution, and optimization workflows, effectively transforming CMP operations into an agent-driven system.

John Kirk, Chief Strategy Officer at ITG powered by Storyteq, described the platform as a shift from conventional CMP architectures toward a unified AI orchestration layer for content operations.

“Storyteq macht das CMP zu einem intelligenten, agentengesteuerten Betriebssystem,” Kirk said. He noted that many organizations are currently integrating AI into fragmented marketing systems, which can increase complexity rather than reduce it.

In contrast, Storyteq aims to provide a centralized AI backbone for content marketing—one that ensures data, AI agents, and execution workflows operate within a consistent infrastructure layer.

This vision reflects a growing industry narrative: that AI delivers maximum value only when integrated into unified data and workflow systems rather than deployed as isolated tools. Without such infrastructure, Kirk warned, AI adoption can lead to duplication, inefficiency, and operational fragmentation.

Storyteq’s positioning is further reinforced by its recognition in Gartner’s Magic Quadrant for Content Marketing Platforms, marking its fourth consecutive appearance as a Leader. The company has also been recognized in the DAM (Digital Asset Management) quadrant, where it was noted for its completeness of vision.

These recognitions place Storyteq within the upper tier of enterprise CMP and DAM vendors, competing in a market that includes platforms such as Adobe Experience Manager, Sitecore, and other enterprise content orchestration systems.

The broader significance of Storyteq’s approach lies in its attempt to unify three traditionally separate domains: content creation, asset management, and marketing execution. By embedding AI agents and predictive intelligence into this stack, the company is effectively repositioning CMPs as operating systems for enterprise content rather than workflow tools.

This shift aligns with broader trends in marketing technology, where organizations are increasingly seeking to reduce tool fragmentation and consolidate operations under AI-driven orchestration layers.

Market Landscape

The Content Marketing Platform (CMP) and Digital Asset Management (DAM) markets are undergoing rapid transformation as AI becomes embedded across the content lifecycle.

Three major forces are driving this evolution:

First, the increasing complexity of multi-channel content production, which requires coordinated workflows across design, marketing, and distribution teams.

Second, the rise of AI-driven content generation and optimization, which is shifting CMPs from production tools to predictive systems.

Third, the demand for unified content infrastructure that connects DAM, CMP, and automation systems into a single operational layer.

Storyteq’s approach reflects this convergence by integrating predictive intelligence, workflow automation, and AI agent orchestration into a unified platform.

In the competitive landscape, enterprise vendors such as Adobe, Sitecore, and Bynder continue to dominate core DAM and CMP capabilities. However, newer AI-native entrants are pushing toward agent-based architectures and predictive content systems.

Storyteq’s differentiation lies in its emphasis on “AI as infrastructure” rather than “AI as feature,” positioning it within the emerging category of intelligent content operating systems.

Top Insights

• Storyteq positions its CMP as an AI-native content operating system integrating DAM, creative automation, and marketing workflows.
• The platform uses Halo Intelligence® to predict content performance based on customer data and historical campaign signals.
• Agent Console™ enables organizations to build and manage AI agents across content creation and marketing workflows.
• Gartner has recognized Storyteq as a Leader in the Magic Quadrant for Content Marketing Platforms for the fourth consecutive time.
• Industry analysts increasingly view CMP evolution as a shift from workflow tools to AI-driven content orchestration systems.

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Reshift Media, a leading digital marketing firm specializing in franchise systems, has unveiled Franify, a new AI-driven platform designed to centralize and optimize digital marketing across multi-location franchise networks. The platform was officially launched during a livestream event on April 8, 2026, marking a significant push toward unified, automated franchise marketing orchestration.

Franify aims to solve one of the most persistent challenges in franchise operations: maintaining brand consistency while enabling local marketing autonomy at scale.

Reshift Media’s Franify platform is purpose-built for franchise ecosystems, where marketing complexity increases exponentially with each additional location. By combining centralized campaign management with localized execution capabilities, the platform seeks to streamline how franchisors and franchisees coordinate digital marketing efforts across multiple channels.

The platform integrates social media management, paid advertising, analytics, and engagement tools into a single unified system. It supports major advertising ecosystems including Meta platforms such as Facebook and Instagram, as well as Google Ads, enabling franchise brands to manage campaigns across multiple digital channels without fragmenting workflows.

Steve Buors, CEO of Reshift Media, said the platform reflects more than a decade of experience working directly with franchise organizations.

“Having worked with franchise companies for more than 13 years, our team has a unique understanding of how franchise systems actually operate, and Franify reflects that,” Buors said. “With this software, franchise businesses can optimize their growth among consumers as well as prospective owners, all while striking the right balance between brand consistency and local flexibility.”

At its core, Franify addresses a structural challenge in franchise marketing: ensuring that brand messaging remains consistent while still allowing individual franchise locations to adapt campaigns to local markets.

To solve this, the platform introduces AI-powered automation and programmatic localization, enabling campaigns to be dynamically adapted for different geographic regions, customer behaviors, and market conditions. This allows franchisors to maintain governance over brand standards while empowering franchisees with localized marketing execution tools.

Key platform capabilities include unified campaign deployment across franchise networks, real-time analytics dashboards, and centralized control of social media activity, advertising performance, and customer engagement. Franchise operators can manage posts, respond to messages, monitor reviews, and track performance metrics from a single interface.

Buors emphasized that operational complexity has long been a barrier to effective franchise marketing execution.

“Managing marketing across numerous territories has always been a challenge,” he said. “Franify helps manage that complexity, giving franchise brands a more efficient way to scale.”

A key differentiator of Franify is its dual-layer design: it provides franchisors with a centralized command layer for brand governance and performance tracking, while offering franchisees a simplified, mobile-first experience designed for day-to-day usability.

This structure is particularly important in franchise environments, where local operators often juggle marketing responsibilities alongside operations, staffing, and customer service. Franify’s interface is designed to reduce cognitive load, enabling franchisees to launch and monitor campaigns without requiring deep marketing expertise.

Reshift Media’s positioning in the franchise marketing space adds further weight to the launch. The company has partnered with more than 200 franchise brands across 22 countries and has been recognized multiple times as Best Franchise Marketing Firm by the Global Franchise Awards.

With Franify, Reshift Media is extending its services model into a fully productized SaaS platform, aligning with a broader industry shift toward automation, AI-driven campaign optimization, and centralized marketing orchestration for distributed business models.

The inclusion of AI-powered automation and programmatic localization suggests that Franify is designed not only as a campaign management tool but also as a decision-support system that can optimize content and targeting based on regional performance signals.

This reflects a growing trend in marketing technology where AI is increasingly used to reduce manual campaign configuration while improving performance consistency across multi-location brands.

Market Landscape

The franchise marketing technology space is undergoing rapid transformation as multi-location brands adopt centralized digital infrastructure.

Three key trends are driving this shift:

First, increasing digital advertising complexity across platforms like Meta and Google requires unified campaign orchestration systems.

Second, franchise networks are seeking stronger brand governance while maintaining flexibility for local marketing execution.

Third, AI-driven automation is enabling programmatic localization, reducing manual effort while improving campaign relevance at scale.

Franify enters a competitive landscape that includes franchise marketing platforms, multi-location marketing SaaS tools, and broader enterprise marketing automation systems.

However, its differentiation lies in its franchise-specific architecture, combining centralized oversight with localized execution workflows tailored to franchise operations.

This positions Franify at the intersection of martech, automation, and multi-location commerce enablement.

Top Insights

• Reshift Media launches Franify, an AI-powered platform for centralized franchise marketing management and localized execution.
• The platform unifies paid advertising, social media management, analytics, and engagement tools across franchise networks.
• AI-powered automation enables programmatic localization of campaigns across different markets and regions.
• Franify reflects a broader shift toward centralized marketing orchestration in multi-location business models.
• Reshift Media has supported over 200 franchise brands across 22 countries and is a multi-time Global Franchise Award winner.

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Canva is accelerating its transformation from a visual design tool into a full-scale AI-powered work platform with the acquisition of Simtheory, an AI collaboration and agent management platform, and Ortto, a customer data and marketing automation company. The deals signal Canva’s intent to unify creativity, automation, and marketing execution into a single system spanning the entire content and campaign lifecycle.

The announcement comes as the company continues to position itself as an end-to-end operating layer for modern knowledge work, powered by AI and deeply integrated workflow orchestration.

Canva, best known as a global leader in visual communication tools, is expanding its strategic footprint in artificial intelligence and marketing infrastructure through the acquisition of two complementary platforms: Simtheory and Ortto.

The acquisitions reflect Canva’s broader ambition to evolve beyond design software into what it describes as a system where teams can ideate, create, automate, and measure work within a unified environment.

Simtheory brings AI collaboration and agent orchestration capabilities, enabling teams to build and deploy AI assistants that can operate across tools, tasks, and workflows. Ortto contributes a customer data platform (CDP) and marketing automation engine designed to orchestrate personalized customer journeys across multiple channels, including email, SMS, push notifications, and in-app messaging.

Cliff Obrecht, Canva’s Co-Founder and Chief Operations Officer, framed the acquisitions as a foundational step toward an end-to-end work platform.

“We’re excited to welcome Simtheory and Ortto to Canva,” Obrecht said. “This acquisition marks an important step toward evolving Canva from a design tool into the system where work happens end-to-end, whether it’s a quick idea or a full campaign.”

The company’s strategy reflects a broader shift in enterprise software toward unified AI-native platforms that reduce fragmentation across creative, marketing, and operational workflows. Rather than relying on disconnected tools for design, automation, customer data, and campaign execution, Canva is aiming to consolidate these functions into a single ecosystem.

Simtheory’s technology is centered on agentic AI systems that allow organizations to build intelligent workflows capable of executing tasks, collaborating across applications, and adapting to enterprise requirements. This positions Canva to extend its capabilities beyond content creation into AI-driven execution systems.

Ortto, meanwhile, strengthens Canva’s position in marketing lifecycle management. Its platform combines customer data infrastructure with automation tools that enable marketers to design and manage cross-channel campaigns from a unified interface. The system’s event-driven architecture and no-code integrations are designed to simplify real-time personalization at scale.

Together, the acquisitions support Canva’s growing “Canva Grow” initiative, which focuses on powering the full marketing and content lifecycle—from ideation and creation through distribution, optimization, and measurement.

Mike Sharkey, co-founder of Ortto and Simtheory, highlighted the alignment between the companies’ missions.

“From day one, we’ve been working to make complex things simple with both Simtheory and Ortto,” Sharkey said. “We’re excited to bring that capability to Canva’s global user base.”

The leadership continuity of Simtheory and Ortto founders within Canva also signals an emphasis on integrating AI-native talent into the company’s broader platform development strategy.

Canva noted that Simtheory’s capabilities will be showcased at Canva Create on April 16, where the company is expected to reveal its next major platform evolution. This upcoming announcement is being positioned as a significant milestone in Canva’s transition toward an AI-first productivity and content operating system.

The acquisitions also build on Canva’s recent expansion activity, which includes MagicBrief, MangoAI, and Doohly—each contributing different components of marketing intelligence, content creation, and distribution capabilities.

With more than 265 million monthly users, Canva is leveraging its massive user base to extend beyond individual content creation into enterprise-scale workflow orchestration. The addition of Ortto’s 11,000+ customers further strengthens its footprint in data-driven marketing automation.

The broader implication of this strategy is that Canva is converging three historically separate categories: design tools, AI agent systems, and marketing automation platforms. This convergence reflects an industry-wide shift toward integrated “work operating systems” where AI functions not as a feature layer but as the connective tissue across all business processes.

If successful, Canva’s evolution could reposition it from a creative productivity tool into a central hub for enterprise content creation, campaign execution, and AI-driven workflow orchestration.

Market Landscape

The enterprise software landscape is undergoing rapid consolidation around AI-native, end-to-end platforms that unify creative, marketing, and operational workflows.

Three major forces are shaping this transition:

First, the rise of generative AI is reducing barriers to content creation while increasing demand for orchestration and governance layers.

Second, marketing teams are moving toward unified systems that combine customer data platforms (CDPs), automation engines, and creative tools into a single workflow environment.

Third, AI agents are emerging as execution layers that bridge planning, creation, and deployment across enterprise systems.

Canva’s acquisitions place it at the intersection of these trends, competing not only with design platforms like Adobe but also with marketing automation ecosystems and emerging AI workflow platforms.

By integrating Simtheory’s agentic AI capabilities and Ortto’s marketing automation infrastructure, Canva is moving into a competitive space that spans Adobe Experience Cloud, HubSpot, Salesforce, and newer AI-native workflow platforms.

The key differentiator in Canva’s approach is its emphasis on unifying creative production and marketing execution within a single AI-driven interface.

Top Insights

• Canva acquires Simtheory and Ortto to expand into AI-powered workflow orchestration and marketing automation.
• Simtheory introduces agentic AI systems that enable teams to build and manage AI-driven workflows across tools and applications.
• Ortto strengthens Canva’s customer data and marketing automation capabilities across multi-channel campaigns.
• Canva is positioning itself as an end-to-end work platform spanning ideation, creation, execution, and optimization.
• The move reflects a broader industry shift toward AI-native “work operating systems” that unify fragmented enterprise tools.

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Apollo.io says its AI-driven go-to-market platform is outperforming industry benchmarks for cold email outreach and sales prospecting, according to a new independent evaluation conducted by The Tolly Group. The analysis examined how Apollo’s unified sales intelligence and automation platform performs in real-world outbound campaigns, highlighting measurable improvements in email deliverability, engagement, and meeting conversion rates compared with industry averages.

Artificial intelligence is rapidly reshaping how B2B sales and marketing teams build pipelines. In recent years, companies have invested heavily in automation tools designed to streamline prospecting, personalize outreach, and unify fragmented marketing technology stacks. Apollo.io’s latest benchmark report suggests that integrating AI with go-to-market infrastructure can deliver measurable improvements in outbound campaign performance.

The 2026 Go-To-Market Effectiveness and Data Quality Report, released by Apollo.io, evaluates the company’s platform across multiple criteria, including contact data accuracy, outbound email performance, and workflow efficiency. The assessment was conducted by The Tolly Group, an independent technology testing organization known for evaluating enterprise platforms.

To measure real-world effectiveness, analysts executed a live outbound campaign using Apollo’s platform. The test targeted 384 prospective users across 205 companies and ran over a month. During the campaign, three outreach sequences were completed for 169 recipients, allowing the researchers to evaluate multiple aspects of the platform—including contact quality, automation capabilities, and the user interface.

The results showed that the campaign achieved a 2.37% cold-to-meeting conversion rate, significantly higher than the typical industry range of 0.5% to 1.5% for cold outreach. Email engagement metrics also exceeded common benchmarks, with the campaign recording a 45% open rate, compared with an industry average of roughly 27% to 40%.

The evaluation is notable because the campaign promoted a completely new product from a vendor that most recipients had never interacted with before. The outreach also skipped the typical four-to-six-week email warming period often used to establish sender reputation. Under those conditions, response rates usually decline, yet the results still exceeded industry averages.

For enterprise sales organizations, those metrics highlight a persistent challenge in modern go-to-market operations: data quality and workflow fragmentation. Many teams still rely on multiple disconnected tools for contact discovery, outreach automation, analytics, and CRM integration. Apollo positions its platform as an alternative to this fragmented approach by combining sales intelligence, prospecting data, and outreach automation into a single system.

The Tolly Group report also noted operational efficiency benefits during the campaign. Testers reported that the platform eliminated the need to toggle between multiple applications typically used for prospecting and outreach. By consolidating those workflows, the platform attempts to simplify outbound campaign execution while maintaining data consistency across teams.

That unified approach reflects a broader trend in the marketing technology ecosystem. Platforms increasingly aim to combine data management, automation, and analytics into integrated systems that support the full sales funnel. Major enterprise ecosystems from companies like Salesforce, Adobe, Microsoft, and Google have also been expanding their AI-driven marketing and CRM capabilities to compete in this space.

Another dimension of the evaluation involved cost and feature comparisons with competing platforms. According to the report, Apollo delivered the most cost-effective model among the vendors examined, while offering a broader set of full-stack capabilities within its standard pricing tier.

That pricing model is significant in a market where sales intelligence platforms frequently charge separately for prospecting data, email automation, and analytics features. Consolidating those capabilities into a single platform could appeal to smaller sales teams or high-growth startups that want enterprise-grade tools without maintaining multiple software subscriptions.

Industry analysts say platforms that combine AI-driven prospecting, contact data enrichment, and automated outreach are becoming central to modern revenue operations. According to research from Gartner, by 2027 more than 70% of B2B organizations will rely on AI-assisted sales engagement tools to improve prospecting efficiency and conversion rates. Meanwhile, McKinsey & Company estimates that advanced analytics and AI could increase sales productivity by up to 20% in many enterprise environments.

Apollo has been positioning itself as an AI-native alternative to traditional sales intelligence tools. The platform integrates prospecting databases, automated outreach workflows, and analytics dashboards designed to help sales teams identify potential buyers, personalize messaging, and track campaign performance from a single interface.

The company has also gained industry recognition for its approach. Apollo was recently listed on multiple categories in the G2 Best Software Awards 2026, including Best Sales Software and Best AI Software Products. According to the rankings, it was the only sales intelligence platform included in the AI software category.

For enterprise marketing and revenue operations teams, the broader implication is clear: AI-powered sales engagement platforms are evolving from niche productivity tools into foundational components of the modern martech stack. As organizations increasingly rely on data-driven outreach strategies, platforms that combine accurate contact data with automation and analytics could play a growing role in shaping pipeline generation strategies.

If the results in Apollo’s benchmark report hold true across larger deployments, they could signal a shift toward unified AI-native go-to-market systems that reduce complexity while improving outbound performance.

Market Landscape

The global sales engagement platform market is expanding rapidly as enterprises seek more efficient ways to manage prospecting and outbound campaigns. According to research from IDC, organizations are increasing investments in AI-driven revenue operations tools as part of broader digital transformation strategies.

At the same time, the marketing technology ecosystem continues to consolidate. Large enterprise vendors—including Salesforce, Adobe, and Microsoft—are integrating AI copilots and predictive analytics into CRM and marketing platforms. Meanwhile, specialized platforms such as Apollo are targeting mid-market and growth-stage companies looking for unified outbound infrastructure.

As competition intensifies, the differentiators increasingly revolve around data quality, AI-driven automation, pricing models, and platform consolidation.

Top Insights

• Apollo’s independent benchmark campaign achieved a 2.37% cold-to-meeting conversion rate, significantly outperforming standard B2B outbound benchmarks and highlighting the potential of AI-driven prospecting platforms.

• The campaign recorded a 45% email open rate, surpassing industry averages and suggesting improved deliverability and targeting accuracy from Apollo’s contact database and outreach automation tools.

• Researchers ran the campaign without the typical email warm-up period and promoted a new product, yet engagement still exceeded benchmarks—an indicator of strong data quality and targeting precision.

• The evaluation also highlighted operational efficiency benefits, with Apollo’s unified platform eliminating the need for multiple sales tools across prospecting, outreach automation, and analytics workflows.

• As AI becomes central to sales engagement, platforms combining contact data, automation, and analytics may increasingly replace fragmented martech stacks used by enterprise marketing and revenue teams.

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BrightLocal has introduced AI Insights, a new feature designed to help businesses interpret complex local search performance data and translate it into clear optimization actions. The announcement comes as the local search ecosystem grows more fragmented, with businesses now needing to manage visibility across multiple platforms, review sites, and search interfaces.

Local search has become one of the most complicated areas of digital marketing. Businesses must maintain accurate listings, manage customer reviews across multiple platforms, monitor search rankings, and continuously update content to stay visible in local results.

To address that growing complexity, BrightLocal has launched AI Insights, a feature designed to transform raw local search data into prioritized recommendations for businesses and agencies.

The feature represents a shift in how local SEO platforms operate. Traditional analytics tools typically present dashboards showing rankings, reviews, and listings performance. AI Insights aims to go further by interpreting those signals and identifying the actions most likely to improve visibility.

According to BrightLocal CEO Myles Anderson, the new capability reflects a broader transition in marketing software toward decision intelligence.

“This isn't a UI refresh or a simple chatbot wrapper,” Anderson said in the announcement. “AI Insights is the first step in our evolution—from a platform that shows what happened to one that tells users exactly what to do next.”

Growing Complexity in Local Search

The launch is tied to broader shifts in how consumers discover and evaluate local businesses online. BrightLocal’s research shows that consumers now check an average of six different review sites before selecting a business, increasing the pressure on brands to maintain consistent visibility across platforms.

Those platforms often include ecosystems from major technology companies such as Google, Microsoft, and Amazon, along with specialized review and discovery services. Maintaining accurate listings and positive reputation signals across these channels has become a core component of modern digital marketing strategies.

At the same time, the volume of local search data available to marketers has expanded dramatically. Businesses can now track rankings, review sentiment, listing accuracy, citation signals, and content performance across dozens of directories. While this visibility offers opportunities, it also creates a major operational burden for marketing teams.

Many small businesses and agencies still spend hours manually reviewing dashboards and compiling reports before determining which issues require attention. AI Insights is designed to reduce that workload by automatically analyzing performance signals and identifying high-priority actions.

Turning Local SEO Data Into Recommendations

The core capability of AI Insights is a prioritization engine that evaluates local search signals and ranks optimization tasks based on their potential impact and difficulty.

Instead of offering generic suggestions, the system analyzes metrics such as:

• search rankings
• Google Business Profile health
• citation consistency
• review performance
• on-page content signals

From that analysis, the platform produces a list of recommended actions designed to improve visibility in local search results.

BrightLocal says the system is powered by 16 years of local SEO expertise, using specialized optimization frameworks rather than relying solely on generic large language model outputs.

The tool can also detect technical issues that traditional audits often miss. One example is category dilution, a problem where conflicting business categories across directories weaken ranking signals in local search.

Supporting Agencies and Small Businesses

BrightLocal built AI Insights with two primary audiences in mind: small business owners managing their own local presence, and agencies responsible for optimizing multiple client accounts.

For small businesses, the feature simplifies technical SEO analysis into practical next steps. Instead of reviewing complex reports, owners receive clear guidance on what to update and where to focus their time.

For agencies, the system accelerates local SEO audits and standardizes recommendations across clients. That scalability is increasingly important as marketing consultants manage larger portfolios of local businesses.

Early users say the tool can help identify optimization opportunities quickly. Jeremy Raymond, founder of East Texas Title & Loan, said implementing AI Insights recommendations helped improve his search rankings after updating metadata.

Meanwhile, Travis Staut, founder of Scrappy Marketing, said the feature strengthens his agency’s consulting process by validating findings and generating detailed recommendations for clients.

The Rise of AI-Driven Marketing Intelligence

The introduction of AI Insights reflects a broader trend across marketing technology platforms. Software vendors are increasingly embedding AI systems that analyze data and recommend actions rather than simply reporting metrics.

Major platforms such as Salesforce and Adobe have introduced AI copilots designed to guide marketing decisions and automate campaign optimization.

The same shift is now reaching the local search and reputation management sector.

Research from Gartner indicates that by 2027, more than 60% of marketing analytics platforms will include AI-powered recommendation engines that automatically identify optimization opportunities. Meanwhile, a report from Forrester suggests organizations using AI-driven marketing insights tools see measurable gains in campaign performance and operational efficiency.

BrightLocal’s long-term vision aligns with that trend. The company says AI Insights represents the first step toward a future where AI systems not only recommend improvements but also execute them automatically.

If that vision becomes reality, local SEO platforms could evolve into autonomous optimization engines capable of managing listings, reputation, and search performance with minimal human intervention.

For businesses competing in crowded local markets, that level of automation could become a critical advantage.

Market Landscape

Local search has become a cornerstone of digital marketing strategies for small and medium-sized businesses. According to research from Statista, more than 80% of consumers use search engines to find local businesses, making visibility in local results a key driver of customer acquisition.

At the same time, the marketing technology ecosystem is increasingly integrating AI into analytics and automation tools. Vendors are moving beyond dashboards toward AI-driven recommendation systems that interpret data and guide decision-making.

Platforms focused on reputation management, listings management, and location-based marketing are now competing to deliver automated local growth intelligence—a capability that could reshape how businesses manage their digital presence.

Top Insights

• BrightLocal launched AI Insights, an AI-powered feature that analyzes local search performance data and provides prioritized recommendations to help businesses improve visibility across search platforms and review sites.

• The system evaluates key local SEO signals—including rankings, citations, reviews, and listing health—to identify optimization opportunities and reduce the manual workload required for audits.

• BrightLocal research shows consumers now check six review platforms on average before choosing a business, increasing the need for consistent visibility across local search ecosystems.

• Agencies and consultants can use the feature to automate client audits, generate data-backed recommendations, and scale local SEO services across multiple accounts more efficiently.

• The launch reflects a broader industry shift toward AI-powered marketing intelligence platforms that move beyond analytics dashboards to deliver automated optimization guidance.

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Optimizely says enterprise marketing teams are rapidly embracing hands-on AI training, as early participants in its Opal University program built hundreds of AI agents designed to automate marketing workflows. The company reported that more than 1,100 marketing and digital leaders have already enrolled in the initiative, signaling growing enterprise demand for practical AI implementation across the marketing lifecycle.

Enterprise marketers are increasingly experimenting with artificial intelligence tools, but many organizations still struggle to move beyond isolated pilots. A new training initiative from Optimizely suggests the next phase of adoption may depend less on new technology—and more on practical education.

Optimizely recently shared results from Opal University, a hands-on training program designed to teach marketing leaders how to build AI agents within its Optimizely Opal platform. The program focuses on helping marketing and digital teams automate everyday workflows using AI-driven agents embedded within the company’s digital experience platform.

The results from the first cohorts highlight the speed at which marketers are beginning to operationalize AI. According to the company, more than 375 AI agents were built by participants in just five days across two cohorts, demonstrating how quickly teams can deploy automation when given direct access to tools and training.

A Rapid Rise in Enterprise AI Training

Demand for the program has grown rapidly. Optimizely reported more than 1,500 registrations and a waitlist exceeding 1,500 additional applicants, reflecting widespread interest among marketing leaders in learning how to integrate AI into everyday processes.

Participants represent a cross-section of global enterprises, including organizations such as LinkedIn, Zoom, DocuSign, KPMG, and Deloitte.

During each five-day program, participants build three AI agents tailored to their organization’s needs. Those agents can automate marketing tasks such as content creation workflows, experimentation analysis, search optimization, research, and campaign management.

According to Optimizely, the program is designed specifically for senior marketing and digital leaders, emphasizing practical applications rather than theoretical AI concepts.

AI Agents for Marketing Operations

The early results illustrate how marketing teams are beginning to use AI agents to automate complex workflows.

Across the initial cohorts, participants created agents covering a wide range of marketing and digital operations. These included agents focused on search optimization, content operations, conversion rate optimization, competitive research, customer success support, and compliance checks.

Some of the reported productivity improvements are significant. For example, a conversion rate optimization (CRO) prioritization workflow that previously required several hours was reduced to about 30 minutes. Performance benchmarking tasks that once took six hours were completed in less than 20 minutes.

Content migration timelines also improved substantially, shrinking from a typical seven-to-ten-day process to roughly two days when automated agents were involved. Prospect research and landing page generation tasks that previously consumed hours per week could be completed in minutes.

Those improvements highlight how AI agents are becoming an operational layer across marketing teams rather than simply a content generation tool.

Moving Beyond AI Experiments

Optimizely executives say the training initiative was created in response to a recurring pattern in enterprise AI adoption. While many marketing organizations are experimenting with generative AI tools, few have established repeatable frameworks that integrate those tools into day-to-day workflows.

Allison Skidmore said successful AI adoption often depends on empowering marketers to build tools that directly support their own processes.

When teams experience immediate productivity benefits, she said, adoption tends to spread more quickly across the organization.

That dynamic reflects a broader shift in enterprise software: the rise of agentic AI systems designed to perform tasks autonomously or semi-autonomously.

Platforms from companies like Microsoft, Google, Salesforce, and Adobe are also increasingly embedding AI agents and copilots into their enterprise software ecosystems.

AI Orchestration in the Marketing Stack

Optimizely’s Opal platform is positioned as an AI orchestration layer within the company’s digital experience platform. The system connects marketing functions such as content creation, experimentation, personalization, and campaign management.

Shafqat Islam said many marketing teams struggle with fragmented technology stacks that make it difficult to deploy AI consistently across campaigns.

Opal attempts to solve that challenge by managing governance, brand guidelines, and workflow orchestration so that AI agents can operate within defined marketing processes.

The approach appears to be gaining traction. According to Optimizely, organizations using its platform have reported improvements in marketing velocity and execution speed. Internal benchmarks indicate a 79% increase in experimentation velocity, an 85% increase in campaigns delivered, and significantly faster time to market.

The Future of AI-Native Marketing Teams

The strong response to Opal University highlights a larger transformation underway in enterprise marketing operations.

Industry analysts increasingly view AI not as a standalone productivity tool but as an infrastructure layer embedded across the marketing technology stack.

Research from Gartner suggests that by 2028, more than 40% of marketing teams will rely on AI-driven automation agents to manage campaign workflows and data analysis. Meanwhile, McKinsey & Company estimates that AI-powered marketing automation could increase marketing productivity by 20–30% in many organizations.

Optimizely’s strategy reflects that shift. The company is expanding its ecosystem of prebuilt AI agents, with more than 15 new out-of-the-box agents introduced in 2026 alone.

As those tools evolve, platforms may begin handling entire marketing processes—from campaign planning to optimization—through coordinated networks of AI agents.

For enterprise marketing teams, the next challenge will not simply be adopting AI tools but learning how to manage AI-native workflows that operate across content, experimentation, and personalization systems.

Programs like Opal University suggest that the future of marketing may depend as much on AI literacy and operational training as on the technology itself.

Market Landscape

The enterprise marketing technology market is rapidly integrating AI capabilities into core platforms. Digital experience platforms, marketing automation suites, and customer data platforms are embedding AI agents that assist with content creation, campaign analysis, and personalization.

According to IDC, global spending on AI-enabled marketing technology is expected to grow significantly over the next five years as organizations prioritize automation and data-driven decision-making.

Training initiatives such as Opal University highlight a critical aspect of this transition: organizations must develop internal AI capabilities alongside adopting new platforms. Without practical training and governance frameworks, many companies struggle to scale AI adoption across marketing teams.

Top Insights

• Optimizely’s Opal University program shows strong demand for hands-on AI training, with over 1,100 marketing leaders enrolling and hundreds of AI agents built during the first cohorts.

• Participants created 375 AI agents in five days, automating workflows across SEO, content operations, experimentation, research, and campaign management.

• Early adopters reported dramatic productivity gains, with marketing tasks such as benchmarking, CRO prioritization, and content migration completed significantly faster using AI agents.

• The program reflects a broader enterprise shift toward agentic AI systems that automate marketing processes across the full campaign lifecycle.

• As AI adoption accelerates, organizations are prioritizing training programs that help marketing teams move beyond experimentation toward operational AI deployment.

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AppTweak has introduced AI Visibility for Apps, a new analytics capability designed to help developers track how their apps appear in AI-generated recommendations. The tool aims to measure and optimize app discovery within AI-powered search systems such as OpenAI’s ChatGPT, which are increasingly influencing how users research and select mobile applications.

Artificial intelligence is reshaping how consumers search for products, services, and digital tools. While traditional discovery for mobile applications has long been dominated by app stores, AI-driven search platforms are beginning to influence the earliest stages of that journey.

AppTweak’s latest product launch reflects that shift. The company announced AI Visibility for Apps, a platform designed to help app developers and marketers understand how their apps appear within AI-generated recommendations.

The new capability aims to address a growing challenge for mobile growth teams: visibility in AI-powered search environments. Platforms such as ChatGPT are increasingly used by consumers to ask questions about productivity tools, fitness apps, finance applications, and other digital services before visiting app marketplaces.

For developers, that change introduces a new discovery layer—one that operates outside traditional app store optimization (ASO) strategies.

A New Layer of App Discovery

Historically, mobile app discovery has largely been shaped by search rankings and editorial recommendations inside app marketplaces such as Apple’s App Store and Google’s Google Play.

However, the rise of AI assistants and conversational search interfaces is changing how users explore digital products. Instead of browsing categories or entering keywords into app stores, users increasingly ask AI systems to recommend tools for specific tasks.

This shift effectively moves part of the app discovery process “upstream” from app marketplaces to AI-driven information platforms.

Olivier Verdin said the new tool was created to help marketers understand this emerging channel.

According to Verdin, while AI search traffic remains relatively small today, it tends to represent high-intent users who are already researching solutions and may be close to downloading an app.

Measuring AI Recommendations

AI Visibility for Apps is designed to monitor how frequently an application appears in AI-generated recommendations and identify the user queries driving those mentions.

The platform analyzes prompts and responses generated by AI search tools and maps them to mobile applications. From there, marketers can track visibility metrics, competitive positioning, and emerging search patterns.

Among the key capabilities of the system:

• tracking whether an app appears in AI-generated recommendations
• identifying the user intents and prompts driving discovery
• analyzing which competitors are being recommended instead
• monitoring changes in recommendation patterns over time

The platform currently focuses on ChatGPT usage in the United States, with plans to expand to additional AI search environments as the ecosystem develops.

From SEO to AI Discovery Optimization

The launch reflects a broader transformation in digital marketing strategy. For years, marketers have focused on optimizing visibility through search engine optimization (SEO) and app store optimization.

Now, a new discipline is emerging: AI discovery optimization, sometimes referred to as Generative Engine Optimization (GEO).

In this model, marketers analyze how AI systems interpret brands, products, and content in order to influence recommendation outcomes.

Major technology companies are already investing heavily in this area. Platforms from Microsoft, Amazon, and Google are integrating generative AI into search experiences, while marketing technology vendors are developing analytics tools designed to track AI-driven traffic and visibility.

Unlike traditional SEO tools built around websites and domains, AppTweak’s solution focuses specifically on mobile applications. The company says its platform uses proprietary datasets and algorithms built from years of analyzing the global app marketplace.

That specialization allows the system to map AI recommendations directly to apps rather than simply referencing brand websites or landing pages.

A Competitive Advantage for Early Movers

As AI-powered search grows, developers may need to adapt their marketing strategies to ensure their apps are recognized and recommended by AI systems.

Industry analysts say the opportunity lies in influencing how AI models interpret product features, use cases, and category relevance.

According to research from Statista, global mobile app downloads exceeded 250 billion annually, underscoring the scale of competition for visibility in the app economy. Meanwhile, Gartner predicts that generative AI will influence a significant share of consumer search interactions by the end of the decade, reshaping how digital products are discovered.

If that prediction holds true, developers who understand AI-driven discovery early could gain a significant advantage.

Tools like AppTweak’s AI Visibility platform aim to provide that early insight—helping marketers identify gaps in AI recommendations, track emerging search patterns, and adjust content strategies accordingly.

For the mobile app ecosystem, the shift could mark the beginning of a new optimization frontier—one where success depends not only on app store rankings but also on how AI systems interpret and recommend digital products.

Market Landscape

The global app marketing and analytics industry is evolving rapidly as discovery channels expand beyond traditional app stores.

Historically dominated by app store optimization platforms, the sector is now beginning to incorporate AI visibility analytics as generative search platforms reshape digital discovery.

Research from IDC suggests that AI-driven search interfaces will increasingly influence product research and digital discovery across industries, including mobile applications.

As a result, marketing technology vendors are developing new tools designed to measure how brands, products, and apps appear within AI-generated responses—an emerging category that blends SEO, AI analytics, and app intelligence.

Top Insights

• AppTweak introduced AI Visibility for Apps, a new analytics capability that tracks how mobile applications appear in AI-generated recommendations from platforms like ChatGPT.

• The platform helps developers measure how often their apps are recommended, identify user intents behind prompts, and compare visibility against competing applications.

• AI-powered search platforms are becoming a new discovery channel for mobile apps, influencing users before they visit traditional marketplaces like the App Store or Google Play.

• Unlike traditional SEO tools designed for websites, AppTweak’s solution maps AI recommendations directly to mobile applications, providing insights tailored to app marketers.

• As AI search adoption grows, early adopters of AI discovery optimization strategies could gain a competitive advantage in the increasingly crowded app economy.

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