The global RAN market continued its gradual recovery in the second quarter of 2026, according to new research from telecommunications market intelligence firm Dell'Oro Group.

Worldwide RAN revenue rose modestly year over year during 2Q 2026, slightly outperforming expectations and marking the third straight quarter of growth. The development comes after more than two years of contraction in the radio access network market, which has been pressured by uneven 5G investment cycles, inventory adjustments and differences in operator spending across regions.

Stefan Pongratz, vice president at Dell'Oro Group, said the latest results reinforce the view that the market's “coverage-to-capacity correction” is largely behind it.

The improvement is not yet a full-scale recovery. Dell'Oro continues to forecast broadly flat worldwide RAN revenue for 2026, indicating that the industry remains in a stabilization phase rather than entering a new high-growth cycle.

For telecom operators and network equipment vendors, that distinction matters. A stable RAN market provides a more predictable investment environment, but it does not necessarily signal a return to the rapid infrastructure spending associated with earlier phases of the global 5G rollout.

Three Quarters of Growth Change the Market Narrative

The most important signal from the second-quarter results may be the duration of the improvement.

One quarter of growth can be explained by temporary factors, large customer contracts or regional fluctuations. Three consecutive quarters provide a stronger indication that the market is finding a floor.

The underlying correction has been particularly visible as operators shifted from broad network coverage expansion toward capacity upgrades and targeted investments. Once large-scale 5G coverage requirements began moderating, equipment demand became increasingly tied to traffic growth, spectrum availability and individual operator investment priorities.

Dell'Oro's latest data suggests that this adjustment is becoming less severe.

However, regional performance remains uneven. Some markets continue to invest in network modernization, while others remain constrained by operator capital expenditure plans and broader economic conditions.

That unevenness is likely to remain a defining characteristic of the RAN market through the rest of 2026.

Huawei Leads, While Vendor Performance Diverges

The competitive landscape changed relatively little during the first half of 2026.

Huawei, Ericsson, Nokia, ZTE and Samsung remained the five largest RAN suppliers by worldwide revenue in 1H26. Together, the five companies accounted for approximately 96% of the global RAN market, underscoring the high concentration of the telecommunications infrastructure industry.

Huawei recorded a strong second quarter, according to Dell'Oro, while Ericsson's performance was softer than expected.

Despite the differing quarterly results, supplier rankings remained unchanged and market-share movements were described as modest.

The stability illustrates the difficulty of disrupting the global RAN supplier market. Network operators tend to maintain long-term relationships with equipment vendors because changing suppliers can involve significant technical, operational and financial complexity.

The competitive environment is also shaped by geopolitical factors. Restrictions affecting Chinese telecommunications equipment in some markets continue to influence vendor opportunities, while European and Asian suppliers operate within increasingly fragmented regional procurement environments.

What the Stabilization Means for 5G Infrastructure

The RAN market sits at the center of mobile connectivity infrastructure. It includes the radio equipment that connects smartphones, connected devices and other endpoints to cellular networks.

As 5G networks mature, the industry's growth model is changing.

The initial 5G cycle was driven heavily by coverage expansion and network modernization. Future spending is more likely to depend on capacity requirements, spectrum utilization, enterprise connectivity, private networks, fixed wireless access and new applications that generate additional network traffic.

That creates both opportunities and challenges for vendors.

Network operators need to justify infrastructure spending through measurable returns, while equipment suppliers must demonstrate why additional investment is necessary in an environment where existing 5G networks can often accommodate substantial traffic.

Open RAN is another factor influencing the long-term competitive landscape. The technology aims to create more interoperable network architectures and potentially broaden the supplier ecosystem. However, traditional RAN vendors continue to dominate commercial deployments, and the Dell'Oro rankings show that the incumbent market structure remains largely intact.

Flat Growth Still Represents Progress

Dell'Oro's unchanged 2026 outlook provides an important reality check.

Despite second-quarter revenue coming in slightly ahead of expectations, the research firm still expects global RAN revenue to remain broadly flat for the year.

For vendors, flat revenue may sound underwhelming, but following more than two years of contraction it can represent meaningful stabilization. It also gives suppliers greater visibility into demand and creates a more predictable base from which future network investment cycles can develop.

Supply-chain conditions and regional demand will remain important variables during the second half of the year.

Operators could accelerate spending if traffic growth, spectrum availability or new applications require additional capacity. Conversely, economic pressure or cautious capital expenditure plans could limit the pace of recovery.

Market Landscape

The global RAN industry remains one of the most concentrated segments of telecommunications infrastructure. Huawei, Ericsson, Nokia, ZTE and Samsung collectively captured 96% of worldwide RAN revenue during the first half of 2026, according to Dell'Oro Group.

The market is simultaneously undergoing a technological transition. Operators are moving from initial 5G deployment toward optimization, capacity expansion and increasingly specialized network architectures.

Cloud-native networking, Open RAN, network automation and AI-assisted network operations could influence the next investment cycle. Hyperscalers such as Amazon, Microsoft and Google are also becoming more relevant to telecom infrastructure as operators explore cloud-based network functions and AI-driven network management.

For established RAN vendors, the challenge is to capture new growth opportunities without undermining the economics of their existing infrastructure businesses.

Strategic Outlook

The second-quarter RAN results suggest the telecom equipment market may finally be emerging from its prolonged correction, but the recovery remains fragile.

Three consecutive quarters of growth provide a stronger foundation than the industry had at the beginning of the downturn. Yet Dell'Oro's flat 2026 forecast shows that operators and suppliers are still operating in a cautious investment environment.

The next phase of RAN growth will likely be driven less by simple network coverage expansion and more by capacity requirements, network automation, enterprise connectivity and new 5G use cases.

For vendors, maintaining market share may be as important as pursuing revenue growth. For operators, the priority will be extracting more value from existing networks while selectively investing where additional capacity or new capabilities can produce measurable returns.

Top Insights

• Global RAN revenue grew for a third consecutive quarter, suggesting the prolonged telecom infrastructure correction is moving toward a more stable phase.
• Huawei, Ericsson, Nokia, ZTE and Samsung retained their leading positions, collectively controlling 96% of worldwide RAN revenue in 1H26.
• Dell'Oro expects worldwide RAN revenue to remain broadly flat in 2026 despite stronger-than-expected second-quarter performance and improving market conditions.
• Regional demand remains uneven, making operator investment cycles, supply chains and geopolitical conditions important variables for RAN vendors.
• Future RAN growth could increasingly depend on network capacity, automation, enterprise connectivity and advanced 5G applications rather than coverage expansion.

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Finding qualified prospects can still involve a surprising amount of manual work. Sales teams often spend hours searching business directories, checking locations, verifying company details and moving information into spreadsheets or CRM systems. Outscraper's Google Maps Scraper is designed to automate that process by turning publicly available Google Maps business information into structured datasets for sales prospecting, market research and competitive analysis.

The product reflects a broader shift in B2B sales technology: as CRM platforms, marketing automation and AI tools become more sophisticated, the quality and structure of the underlying business data increasingly determine how useful those systems can be.

For sales organizations, the challenge is rarely finding any businesses. The harder task is finding the right businesses, in the right locations, with enough information to determine whether they are worth contacting.

That is where location-based business data has become increasingly useful.

Outscraper's Google Maps Scraper is designed to automate the collection of publicly available business information from Google Maps, allowing companies to search for businesses using locations, categories and keywords and export the resulting information for analysis or integration into existing workflows.

The basic proposition is straightforward: replace repetitive manual research with structured data collection.

A salesperson researching several hundred businesses individually might search Google Maps, open each listing, record company details and then organize the information in a spreadsheet. For larger prospecting projects, that process becomes difficult to maintain.

A data-collection platform can instead produce a structured dataset that sales teams can filter, qualify and enrich according to their own requirements.

That makes Google Maps data for lead generation particularly relevant to organizations that sell locally or operate across multiple geographic markets.

A business selling commercial equipment, for example, could search for companies within specific industries and locations. A marketing agency could identify local businesses that fit its ideal customer profile. A franchise development team could analyze the concentration of competitors or potential locations in a target market.

The value comes from turning location information into a prospecting signal.

Google Maps listings can contain business names, categories, locations, ratings and other publicly displayed information. When that information is organized into a structured dataset, sales and marketing teams can apply additional filters to prioritize potential customers.

That is different from relying exclusively on generic B2B contact databases.

A traditional prospect database may provide company and contact information, but location-based searches can reveal businesses according to characteristics such as geographic area, business category and local presence. For companies whose ideal customers are strongly tied to physical locations, that additional context can be useful.

The rise of AI and sales automation makes the underlying data problem even more important.

CRM systems, marketing automation platforms and AI sales assistants can automate lead scoring, segmentation, outreach and reporting. But automation is only as useful as the data feeding those workflows.

Poorly structured or outdated prospect information can result in wasted outreach, inaccurate segmentation and inefficient sales prioritization.

That creates an emerging relationship between business data extraction and AI-powered sales automation.

A structured dataset collected from public sources can be exported, analyzed and potentially connected to downstream systems. Outscraper says its platform supports flexible exports and API access, enabling organizations to integrate collected information into existing workflows, CRM environments and analytics systems.

For sales teams, the immediate application is prospect list building.

Instead of starting with a broad geographic market and manually researching every business, representatives can create lists based on categories, locations or keywords and then apply their own qualification criteria.

That can help sales development teams focus more time on account research and personalization rather than basic data entry.

Marketing agencies represent another potential use case.

Local marketing providers need to identify businesses within specific markets, understand the competitive environment and determine which companies might benefit from services such as SEO, Google Ads or reputation management. Structured location data can support that research while giving agencies a more systematic way to identify potential accounts.

Market researchers and consultants can use the same information differently.

A company entering a new city might examine the number and distribution of businesses in a particular category. A franchise organization could analyze local competition before considering expansion. A consultant could use geographic business data as an input for market-sizing or competitive research.

In each case, the data is not the final answer. It is an input into a broader analytical process.

That distinction is important because scraping business listings does not automatically create qualified leads. Sales teams still need to validate prospects, identify decision-makers, assess purchasing intent and comply with applicable privacy, platform and outreach requirements.

The quality of the resulting dataset also matters.

Duplicate listings, incomplete information, closed businesses and changes in company details can reduce the usefulness of location-based data. Businesses using automated collection therefore need processes for validation, enrichment and ongoing data maintenance.

That is where APIs and workflow integrations become increasingly relevant.

Rather than treating scraped information as a static spreadsheet, companies can potentially incorporate structured business data into broader sales and analytics workflows. This fits the direction of modern RevOps, where sales, marketing and customer data are increasingly connected through centralized systems.

The competitive landscape is also evolving.

Traditional lead-generation platforms have increasingly added intent data, firmographic information, AI enrichment and automated prospecting. Location-based business data serves a somewhat different purpose, particularly for companies whose ideal customers have a physical presence.

The opportunity for platforms such as Outscraper is therefore not simply to provide more records. It is to make geographic business intelligence easier to collect and operationalize.

That could become increasingly valuable as companies expand into new markets and seek more efficient ways to build territory-specific sales pipelines.

For smaller organizations, automation can reduce the amount of time spent on repetitive research. For larger companies, structured business data can support more scalable market intelligence and territory planning.

Ultimately, the usefulness of a Google Maps scraper depends on what happens after the data is collected.

When combined with CRM systems, sales intelligence, analytics and responsible outreach practices, location-based business information can become a practical component of modern prospecting infrastructure. Without those downstream processes, however, even a large dataset can quickly become another unmanageable spreadsheet.

Market Landscape

The B2B data market is moving toward increasingly automated prospect discovery.

Traditional lead databases remain important, but sales organizations are adding intent signals, firmographic data, AI enrichment and location intelligence to improve account selection. CRM platforms from Salesforce and Microsoft, along with sales-intelligence providers, increasingly use automation to help teams prioritize prospects and personalize engagement.

Location-based business data occupies an important niche within that ecosystem.

For local service providers, agencies, franchise organizations and businesses with geographically defined customer profiles, the physical presence of a company can be as important as its industry classification.

Google Maps is particularly useful because businesses are organized around real-world locations and categories. The challenge is converting that information into structured, searchable datasets that can feed downstream sales and research processes.

Outscraper's positioning centers on that data-collection layer.

The competitive advantage will ultimately depend on data accuracy, update frequency, extraction capabilities, integration options and how effectively customers can turn raw listings into qualified business intelligence.

Strategic Outlook

The next generation of sales prospecting will likely combine multiple data sources rather than relying on a single directory or database.

Location information can provide the initial discovery layer, while CRM records, firmographic data, intent signals and AI enrichment can help determine which businesses are actually worth pursuing.

For sales and marketing teams, the strategic goal should therefore be better qualification rather than simply more leads.

Automation can reduce repetitive research, but human judgment remains essential for validating accounts, understanding buying needs and creating relevant outreach. As AI becomes more deeply embedded in sales workflows, structured and reliable business data will become an increasingly important foundation.

Top Insights

• Outscraper's Google Maps Scraper automates location-based business research, helping sales teams build targeted prospect lists without manually reviewing individual listings.
• Structured Google Maps data can support local lead generation, competitive analysis and market research for agencies, sales teams and expansion-focused businesses.
• API and export capabilities allow organizations to connect business datasets with CRM, analytics and sales automation workflows for broader operational use.
• AI-powered sales systems increase the importance of reliable underlying business data because automated scoring, segmentation and outreach depend on accurate inputs.
• Location intelligence is particularly valuable for businesses whose ideal customers are defined by geography, industry category or physical business presence.

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dynaCERT is preparing for an aggressive second half of 2026 as the Canadian cleantech company expands its commercial outreach across Europe, Asia and the Americas.

The company, which develops technologies aimed at reducing fuel consumption and carbon emissions, has outlined a series of industry engagements targeting three markets where diesel and other fossil-fuel-intensive operations remain significant: heavy-duty transportation, ports and terminals, and stationary power generation.

Rather than treating the events primarily as conventional trade-show appearances, dynaCERT says it will use them for customer meetings, lead qualification, distributor development and relationship building. The strategy reflects a broader reality in industrial cleantech: proving that a technology works is only one part of the adoption equation. Suppliers also need to demonstrate an economic case to fleet operators, infrastructure companies and power producers.

At the center of dynaCERT's offering is HydraGEN™ Technology, which the company positions as a hydrogen-based system designed to improve combustion efficiency while reducing fuel consumption and emissions.

The company has not provided independent verification of the technology's performance claims in this announcement, making commercial validation, customer deployments and independently measured results important factors for prospective buyers.

Trucking Becomes a Key Test Market

Heavy-duty trucking will be one of dynaCERT's most visible areas of focus.

The company plans to participate in IAA TRANSPORTATION 2026 in Hannover, Germany, from September 15–20, followed by the 24-Hour Camions event in Le Mans, France, on September 26–27.

The events give dynaCERT access to fleet operators, transportation companies, equipment manufacturers and other participants in the commercial-vehicle ecosystem.

For the trucking industry, technologies that can reduce fuel use have an unusually direct business case. Fuel represents a substantial operating expense for long-haul fleets, while operators are simultaneously under pressure to reduce emissions and comply with increasingly stringent environmental regulations.

That creates an opening for retrofit technologies, provided they can demonstrate measurable economic returns without disrupting fleet operations.

dynaCERT's presence alongside NRS Racing, the Dakar Team and French distributor IPMD at the Le Mans event also gives the company an opportunity to demonstrate HydraGEN in a demanding commercial-vehicle environment.

The larger challenge will be moving from industry awareness to repeatable fleet adoption.

Ports Offer Another Commercial Opportunity

Ports and container terminals are another important target because heavy equipment, trucks, cranes and other machinery can consume large quantities of fuel during continuous operations.

dynaCERT plans to engage the sector through TOC Americas in Cartagena, Colombia, from October 20–22, as well as Breakbulk Americas in Houston and Breakbulk Asia in Singapore.

The geographic spread is notable. Rather than concentrating its sales effort in one market, the company is attempting to develop relationships across logistics hubs in North America, Latin America and Asia.

Port operators are under growing pressure to reduce emissions while maintaining high equipment utilization. Electrification is gaining momentum across portions of the port ecosystem, but the transition is not equally straightforward for every heavy-duty vehicle, machine or application.

That leaves room for fuel-efficiency technologies that can be deployed alongside existing equipment, although adoption will depend on lifecycle economics, maintenance requirements, measurable emissions reductions and compatibility with operational environments.

Power Generation Expands the Addressable Market

The company's third target market is stationary power generation.

dynaCERT plans to participate in POWERGEN International 2027 in Salt Lake City from January 18–21, positioning the event as the transition point between its second-half 2026 commercial program and its 2027 business-development activities.

The target audience includes utilities, independent power producers, engineering, procurement and construction companies, original equipment manufacturers and other power-generation stakeholders.

This segment potentially broadens dynaCERT's opportunity beyond transportation. Stationary generators and other combustion-based power systems remain important in locations where grid access is limited, backup generation is required or distributed power is economically attractive.

However, the competitive environment is also changing rapidly. Battery storage, renewable generation, hybrid systems and alternative fuels are all competing for investment alongside combustion-efficiency technologies.

For dynaCERT, demonstrating where HydraGEN provides an economic advantage over those alternatives will be central to establishing a durable position.

Cleantech Adoption Is Becoming an ROI Question

The company's event strategy illustrates an important shift in industrial cleantech. The market is increasingly moving away from broad sustainability messaging toward measurable operating economics.

Fleet owners want lower fuel bills. Port operators want higher equipment utilization and compliance with emissions requirements. Power producers want reliable output and manageable operating costs.

Technologies that can combine emissions reductions with financial savings therefore have a potentially stronger commercial proposition than solutions whose value depends primarily on environmental benefits.

But buyers also face a growing number of alternatives. Electrification, renewable energy, battery systems, biofuels, hydrogen fuel cells and efficiency technologies are all competing for capital.

That means dynaCERT's commercial expansion will ultimately be judged less by the number of industry events it attends and more by the number of qualified customers it converts, the repeatability of deployments and independently measured performance.

Market Landscape

Decarbonizing heavy transportation and industrial operations remains one of the more difficult challenges in the energy transition. The International Energy Agency has identified road transport, shipping, aviation and other hard-to-abate sectors as areas where multiple technology pathways will be required rather than a single universal solution.

For commercial fleets and industrial operators, the transition is likely to involve a mixture of electrification, alternative fuels, efficiency improvements, operational optimization and equipment upgrades.

That creates a fragmented but potentially large market for technologies that can improve the performance of existing assets.

The competitive landscape includes hydrogen technologies, battery-electric systems, renewable fuels, hybrid powertrains and digital fleet-management platforms. Companies such as Cummins, Volvo and other industrial technology providers are pursuing multiple pathways toward lower-emission commercial transportation and power systems.

dynaCERT's strategy is differentiated by its focus on hydrogen-assisted combustion and retrofit applications. Its ability to establish measurable fuel and emissions benefits across multiple equipment categories will determine how strongly that proposition resonates with fleet and industrial buyers.

Strategic Outlook

dynaCERT's second-half 2026 program represents an attempt to turn international industry exposure into a more structured commercial-development pipeline.

The emphasis on direct meetings, distributors and qualified leads is strategically more important than simply expanding brand visibility. In industrial technology, sales cycles can stretch across months or years, particularly when equipment modifications require operational testing and financial approval.

The company is also using its 2026 engagement program to build a bridge into 2027, with POWERGEN serving as an early opportunity to carry relationships and market intelligence into the next commercial cycle.

The key question for investors and potential customers will be whether those activities translate into measurable deployments and recurring revenue.

dynaCERT also disclosed that it granted 6.675 million stock options to employees, consultants, directors and officers on August 17, 2026. The options carry an exercise price of $0.20 per share and expire August 17, 2031.

Top Insights

• dynaCERT is targeting heavy-duty trucking, ports and power generation, three industrial segments where fuel costs and emissions pressures create demand for efficiency technologies.
• HydraGEN's commercial opportunity depends on proving measurable fuel and emissions benefits across existing combustion-powered equipment without disrupting fleet operations.
• The company's event strategy prioritizes customer meetings and lead qualification, signaling a shift from cleantech awareness campaigns toward structured commercial development.
• Ports and stationary power could broaden dynaCERT's addressable market beyond trucking as industrial operators seek alternatives to costly equipment replacement and electrification.
• Competition from electrification, alternative fuels and hydrogen technologies means commercial ROI and independently measured performance will remain critical adoption factors.

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Okara has launched AI CMO v2, an AI marketing agent designed to automate multiple growth functions for startups and small businesses, including SEO, generative search visibility, content production, social engagement and influencer marketing.

Rather than positioning the product as another marketing analytics dashboard, Okara says its AI CMO uses specialized agents that can identify problems and execute marketing tasks directly. The approach reflects a broader shift in MarTech from software that tells marketers what to do toward AI systems capable of carrying out parts of the marketing workflow.

For early-stage companies, building a product is often no longer the slowest part of going to market. The harder problem is attracting attention after the product launches.

SEO requires continuous optimization. Content needs regular production. Social communities move quickly. Influencer campaigns involve outreach and coordination. And the rise of AI-generated search means marketers now have another visibility layer to monitor.

Okara is attempting to combine those responsibilities into a single AI-driven marketing system.

The company has launched AI CMO v2, describing it as an AI marketing agent that operates specialized sub-agents for different growth functions. Instead of simply generating reports or recommendations, the platform is designed to execute marketing work across SEO, generative engine optimization, content, social engagement and influencer marketing.

That distinction is becoming increasingly important in the MarTech market.

For years, marketing platforms have largely operated as systems of record and analysis. SEO tools identify technical problems. Analytics platforms report traffic. Social listening tools surface conversations. Influencer platforms manage creator relationships.

The marketer remains responsible for turning those insights into action.

Okara's model attempts to change that workflow by assigning different tasks to specialized AI agents that operate in parallel.

Before those agents begin, the platform analyzes a company's website and creates what Okara calls a Design Guide and Content Strategy. These documents establish the business's positioning, keyword opportunities and potential traffic sources, providing a shared context for the downstream agents.

The first major component is the SEO Agent.

Okara says it continuously audits websites, identifies ranking problems and attempts to resolve those issues rather than simply reporting them. That is a significant distinction for businesses that lack dedicated SEO personnel, although the quality of automated changes will ultimately determine whether such an approach can safely operate without extensive human oversight.

The platform also includes a GEO Agent, reflecting the rapid emergence of generative engine optimization as a new category of search marketing.

The agent monitors how AI systems including ChatGPT, Perplexity, Gemini and Claude describe a brand. Users can configure custom prompts to monitor specific questions, while the system attempts to improve the brand's visibility in AI-generated answers.

This is becoming an increasingly relevant marketing problem.

Traditional SEO measures visibility through rankings and organic clicks. AI search can produce an answer without sending the user to a website, meaning a company may receive brand exposure without generating a conventional search impression or click.

For marketers, that creates a new measurement challenge: visibility now includes whether an AI system mentions a company, how it describes the company and which sources it uses to construct the answer.

Okara's AI Writer addresses the content side of that equation. The company says the system identifies keyword gaps where competitors are already performing well and generates SEO articles, landing pages and product content designed around those opportunities.

It also produces cover images intended to improve click-through performance across search and social channels.

The platform extends beyond owned media with its Reddit, LinkedIn and X Agent. According to Okara, the agent identifies relevant live conversations and drafts responses intended to provide useful contributions while directing interested users toward the company's product.

That capability is particularly sensitive from a brand-safety perspective.

Community platforms are fundamentally different from search engines. Automated responses that feel promotional, repetitive or disconnected from the conversation can damage credibility rather than generate growth. Okara's claim that its system is designed to contribute genuinely will therefore need to be evaluated through real-world usage rather than automation volume alone.

The final component is the Influencer Agent, which turns the platform into something closer to an AI-powered influencer marketing system.

Okara says brands can use the agent to identify creators, negotiate rates, manage deliverables and automatically pay creators after work is confirmed. The workflow is designed to run through a conversational interface without separate spreadsheets or agency coordination.

This reflects another important MarTech trend: the convergence of campaign management and AI agents.

Instead of using one platform for influencer discovery, another for SEO and another for content, Okara is betting that startups will prefer an integrated agent capable of coordinating multiple marketing functions.

The economic argument is central to that positioning.

Okara estimates that a full-time marketing hire can cost at least $5,000 per month, while combining freelance SEO, content and social support can push early-stage marketing costs toward $8,000 to $13,000 per month.

Those figures are company estimates and will vary significantly by market, experience level and scope. Still, they illustrate the market Okara is targeting: companies that need consistent marketing execution but cannot yet justify building a large specialized team.

The bigger question is whether an AI CMO can deliver the strategic quality of a human marketing organization while operating at software economics.

Marketing is unusually dependent on context. Positioning decisions, creative judgment, customer research, brand voice and community relationships are difficult to reduce to repetitive workflows. An agent can execute tasks faster, but speed does not necessarily produce a better strategy.

That means Okara's strongest potential may be as an execution layer rather than a complete replacement for marketing leadership.

A founder or marketing lead could establish the positioning, approve strategic direction and set boundaries while AI agents handle recurring research, optimization, content production and campaign operations.

The approach mirrors the broader evolution of enterprise AI. Salesforce, Microsoft, Adobe and other major technology companies are building agentic capabilities into existing business software, moving AI from a conversational assistant toward a system capable of completing multi-step workflows.

Okara is applying the same concept specifically to startup growth.

If the system can maintain brand consistency, avoid low-quality automated engagement and demonstrate measurable improvements in traffic, AI visibility, leads and customer acquisition, an AI CMO could become a meaningful new category within MarTech.

The launch ultimately reflects a larger change in how marketing software is being designed.

The next generation of platforms may not simply tell teams what happened or what they should do next. They will increasingly be expected to execute the work, learn from results and continue operating without waiting for the next campaign cycle.

Market Landscape

AI-powered marketing is moving from content generation toward agentic execution.

Earlier generations of AI marketing tools largely helped teams write copy, generate images, summarize analytics or suggest campaign ideas. Newer systems are attempting to connect those capabilities into autonomous workflows that can research, create, publish, monitor and optimize.

Okara's AI CMO sits within this transition, but its focus is particularly aimed at startups and lean marketing teams.

The competitive landscape includes broader enterprise ecosystems from Salesforce, Adobe, Microsoft and HubSpot, as well as specialized platforms for SEO, influencer marketing, social management and content production.

Okara's differentiator is orchestration. Instead of asking users to operate several specialized tools, it attempts to coordinate multiple marketing agents around a common understanding of the business.

The major challenge will be reliability. AI-generated content can be produced cheaply and quickly, but automated SEO changes, community engagement and influencer negotiations carry different levels of business and reputational risk.

Strategic Outlook

The AI CMO concept points toward a marketing stack where humans increasingly define strategy while agents handle execution.

The most valuable systems will likely combine multiple capabilities with strong approval controls, transparent reporting and measurable attribution. Human oversight will remain especially important for public-facing community activity, brand messaging, paid media decisions and creator relationships.

For startups, the appeal is clear: one platform capable of continuously handling multiple marketing workflows could reduce operational fragmentation. For established enterprises, the same architecture could eventually become an execution layer sitting alongside existing CRM, CDP, analytics and advertising systems.

Top Insights

• Okara AI CMO v2 combines SEO, GEO, content, social and influencer agents, targeting startups that need continuous marketing execution without large teams.
• Its GEO Agent monitors brand visibility across ChatGPT, Perplexity, Gemini and Claude, reflecting the growing importance of AI-generated search discovery.
• The Influencer Agent automates creator discovery, rates, deliverables and payments, bringing influencer campaign management into an AI-driven conversational workflow.
• Okara's approach reflects a wider MarTech shift from analytics dashboards toward agentic software capable of executing multi-step marketing tasks.
• The platform's biggest challenge will be balancing automation with brand safety, human judgment and measurable business outcomes across different marketing channels.

Get in touch with our MarTech Experts

Customer experience programs have become good at telling hospitality and gaming operators what guests and employees think. The harder problem is turning that information into operational change. ComOps and ROI Inc are partnering to address that gap, combining customer and employee experience intelligence with hands-on operational, marketing and workforce expertise for hotels, casinos, resorts and other service-driven organizations.

The partnership reflects a broader shift in enterprise customer experience management: from collecting sentiment and producing dashboards toward connecting insights with leadership behavior, employee performance, service delivery and measurable financial outcomes.

Hospitality and gaming organizations have access to more customer feedback, employee sentiment and operational data than ever before. Yet having more information does not necessarily make it easier to improve the guest experience.

ComOps, a strategic consulting and specialized support organization serving the hospitality, casino, travel and tourism sectors, has partnered with ROI Inc, a Tribal enterprise-focused organization specializing in operations, marketing and talent management. The companies say their combined offering is designed to help organizations move from identifying experience problems to implementing and measuring solutions.

The distinction matters because customer experience analytics often stops at diagnosis.

A hotel may discover that guests are dissatisfied with employee interactions. A casino may identify declining loyalty among a particular customer segment. An employee survey might reveal leadership or training issues. Those findings can establish where the problem exists, but they do not necessarily explain how an organization should change its operating model.

ComOps is focused on that first layer of intelligence. The company works with hotels, casinos, resorts and other service organizations to analyze customer and employee sentiment, identify experience strengths and weaknesses, and translate those findings into strategic priorities.

ROI Inc is intended to provide the execution layer.

Its professionals have experience in casino and resort marketing, human resources and operations, including leadership, guest-service training, employee performance and enterprise strategy. The company can provide organizational development, training and onsite support when the recommended changes require deeper intervention.

That combination creates a model closer to experience intelligence plus operational execution than a conventional customer experience consulting engagement.

Consider a resort where customer research identifies employee interactions as one of the strongest predictors of guest satisfaction. The answer may not be another customer-service survey. The underlying problem could involve leadership behavior, recognition programs, training, internal communication, service standards or performance management.

Likewise, customer analytics could uncover an opportunity among a valuable guest segment that requires a change in marketing investment or offers rather than frontline service training.

The partnership is designed to account for those differences rather than prescribe the same solution to every organization.

That is increasingly important as customer experience management becomes more closely connected to broader enterprise data infrastructure.

Modern hospitality operators collect information from property management systems, loyalty programs, customer relationship management platforms, surveys, digital channels and operational systems. Marketing teams can use that data to understand behavior, while human resources teams can examine employee engagement and workforce performance.

The challenge is connecting those datasets to decisions that frontline teams can actually execute.

The ComOps and ROI Inc partnership is positioned around that connection.

The companies say engagements can cover customer experience strategy, leadership development, guest-service programs, employee engagement, casino and resort marketing, onsite activation and continuous measurement.

For enterprise marketing teams, the marketing component is particularly relevant. Customer behavior can reveal opportunities to adjust segmentation, offers, reinvestment and communications. But those decisions need to fit the operational reality of the property.

A campaign designed around a specific guest segment, for example, may fail to deliver its intended value if employees are not prepared to support the experience or if the operational processes behind the offer are inconsistent.

This is where the partnership's broader proposition comes into play: customer experience is not solely a marketing problem.

It sits at the intersection of marketing, operations, human resources, leadership and technology.

The approach also aligns with a wider movement in enterprise MarTech toward connecting customer data with measurable business outcomes. Customer data platforms, marketing analytics and AI-powered customer intelligence can increasingly identify patterns, but organizations still need people and processes capable of acting on those insights.

In hospitality, that execution challenge can be particularly visible because the customer experience is delivered through human interactions as much as digital touchpoints.

A casino can optimize an email campaign, a hotel can improve its loyalty segmentation and a resort can analyze guest sentiment, but the final experience still depends heavily on what happens at the property.

That makes employee engagement another important part of the equation.

ROI Inc's focus on leadership and workforce development gives the partnership a way to address the organizational factors behind customer experience performance. Instead of treating employee experience and customer experience as separate initiatives, the combined approach connects them as parts of the same operating system.

There is also a measurement component.

The companies say they intend to evaluate customer and employee responses to initiatives and use those insights to refine strategies over time. That creates a continuous improvement cycle rather than a one-time consulting project.

The broader industry implication is that experience data is becoming less valuable when it remains isolated from execution.

For hospitality and gaming companies, the competitive advantage may not come simply from collecting more feedback. It may come from identifying which signals matter, prioritizing them correctly and having the operational capability to respond.

ComOps and ROI Inc are betting that combining those capabilities can close that gap.

The partnership does not represent a new customer data platform or marketing automation product. Instead, it highlights a different direction for experience management: using technology and customer intelligence to identify opportunities, then pairing those insights with operational expertise to create measurable change.

For hotels, casinos, resorts and other experience-driven businesses, that connection could become increasingly important as customer expectations rise and marketing, employee engagement and service delivery become more tightly interconnected.

Market Landscape

Customer experience technology has evolved from basic surveys and satisfaction measurement into a broader ecosystem involving customer data platforms, customer analytics, CRM systems, AI-powered insights and employee experience technology.

Platforms from Salesforce, Adobe and other enterprise technology providers can help organizations unify customer information, analyze behavior and personalize interactions. But technology alone does not guarantee operational improvement.

The hospitality industry presents a particularly complex case because customer outcomes are influenced by both digital interactions and physical experiences. Marketing campaigns, loyalty programs and personalized offers ultimately intersect with employees, service standards and property operations.

That creates a growing demand for systems and consulting models that connect customer intelligence with execution.

ComOps and ROI Inc are positioning their partnership within this gap. Rather than competing directly with enterprise MarTech platforms, the companies focus on interpreting experience signals and helping organizations implement the organizational changes required to act on them.

The broader competitive landscape is likely to move toward closed-loop experience management, where organizations measure sentiment, identify root causes, implement changes and continuously measure the resulting business impact.

Strategic Outlook

The next phase of customer experience management will likely focus less on collecting additional data and more on connecting existing intelligence to action.

For hospitality and gaming organizations, that means integrating customer analytics with workforce performance, leadership development, marketing strategy and operational execution.

AI could accelerate this process by identifying patterns across customer and employee data, predicting experience risks and recommending interventions. But the final value will depend on whether organizations can translate those recommendations into changes at the property level.

The ComOps and ROI Inc partnership illustrates that transition from measurement to managed execution.

Top Insights

• ComOps and ROI Inc combine customer experience intelligence with operational expertise, helping hospitality organizations move from identifying problems toward measurable business improvements.
• The partnership connects guest sentiment with employee engagement, leadership development and service delivery, recognizing that customer outcomes often depend on workforce performance.
• ROI Inc brings casino and resort operating experience, giving organizations access to practical expertise beyond traditional customer experience analytics and consulting.
• Marketing teams can use customer insights to refine segmentation, offers and reinvestment while ensuring operational teams can deliver the resulting guest experience.
• The model reflects a broader MarTech shift toward closed-loop experience management, where data informs action and subsequent outcomes improve future decision-making.

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Music promotion is moving deeper into the creator economy, with short-form video increasingly acting as a discovery engine for songs and artists. Sound.me says its creator network has now reached 3 million registered creators across 61 countries, highlighting the growing role of paid user-generated content (UGC) campaigns in music marketing.

The platform says creators have produced more than 1.5 million completed campaign videos across more than 16,000 campaigns for over 4,100 artists, record labels and brands. Its model replaces some traditional upfront influencer deals with campaigns where advertisers pay for approved creator posts.

The traditional music marketing playbook has long relied on radio promotion, playlist pitching, paid advertising, press coverage and influencer partnerships. Short-form video has complicated that model by turning ordinary creators into potential distribution channels for new music.

Sound.me is building its business around that shift.

The UGC marketing platform says its creator network has reached 3 million registered creators across 61 countries, with more than 1.5 million campaign videos completed across over 16,000 campaigns for more than 4,100 artists, labels and brands.

The platform operates as a two-sided marketplace. Artists, record labels and brands establish campaigns with budgets and instructions, while creators can apply to campaigns and produce short-form videos using the promoted music. Sound.me supports TikTok, Instagram and YouTube Shorts, and advertisers review creator submissions before approving them.

That structure is different from a conventional influencer marketing campaign.

Instead of negotiating individually with creators, a marketer can define the campaign, budget and target audience and allow creators in the network to participate. Sound.me says advertisers pay for approved posts, while creators receive compensation based on the performance and other characteristics of their content.

The company describes this as a pay-per-approved-post model.

The approach is particularly suited to music because the product being marketed is itself a piece of content that can be incorporated directly into short-form videos. A song does not necessarily need a traditional product demonstration. A creator can build a dance, transition, lifestyle clip, meme or other concept around the sound.

That makes the creative process less dependent on a central advertising team.

Sound.me's current advertiser materials say campaigns can be targeted by geography, age, gender and follower count, while creators can receive campaign instructions ranging from open creative concepts to lip-sync and transition formats.

The company's creator platform also says there is no minimum follower requirement to join. Creators can apply to campaigns, publish videos using the specified sound and receive payment through PayPal. Sound.me says its system considers factors including views, follower metrics, engagement, posting frequency and content originality when determining creator earnings.

The economics of that model are becoming more relevant as music discovery shifts toward short-form video.

A 2025 Music Impact Report from TikTok and Luminate found that 84% of songs entering the Billboard Global 200 in 2024 had first gone viral on TikTok. The report also found that U.S. TikTok users were 74% more likely to discover and share new music on social and short-form video platforms than the average short-form video user.

Those findings come from research commissioned by TikTok, so they should not be treated as neutral evidence of the entire music ecosystem. But they demonstrate why labels and independent artists are investing heavily in creator-driven discovery.

Sound.me is effectively trying to turn that discovery behavior into a repeatable marketing workflow.

Its proposition is not simply to hire influencers. The platform creates a marketplace where large numbers of creators can participate in the same music campaign, potentially generating many different interpretations of a song.

That could be valuable for labels testing a new release, but it may be even more significant for independent artists with smaller marketing budgets.

A major label can afford large creator partnerships and media campaigns. An independent artist may not. A marketplace model gives smaller advertisers the option of setting a budget and distributing it across multiple creators rather than committing most of the spend to a single influencer.

The company also emphasizes campaign approval. Every submitted video is reviewed before it counts toward an advertiser's campaign budget, according to its current platform documentation.

That creates an important distinction from open influencer marketplaces, where brands may have less control over the volume and quality of content produced.

There are trade-offs, however.

Scale does not automatically translate into cultural impact. Thousands of creator videos can generate reach without producing the one memorable moment that turns a song into a breakout hit. Advertisers also need to consider audience overlap, creative quality, disclosure requirements and whether views translate into meaningful downstream activity such as streams, follows or purchases.

Sound.me's own terms also make clear that creator payment is contingent on advertiser approval, while its system uses AI to estimate earnings for creator submissions.

That points to another important part of the platform's model: automation.

At this scale, manually negotiating with millions of creators would not be practical. A marketplace needs automated matching, campaign targeting, performance measurement and payment infrastructure to function efficiently.

Sound.me therefore sits at the intersection of creator marketing, music technology, influencer platforms and AdTech.

Its growth also illustrates a broader change in digital advertising. Brands increasingly want measurable access to creator audiences without having to build individual relationships with every creator. Platforms that can aggregate creators, automate campaign management and provide performance data are attempting to become the infrastructure layer between advertisers and the creator economy.

For the music industry, the implications extend beyond promotion.

If short-form video increasingly determines which songs receive attention, the ability to seed creative experimentation at scale could become part of the standard release strategy. Labels could use creator campaigns to test sounds and concepts before increasing advertising or promotional investment. Independent artists could use smaller campaigns to generate initial content and identify which creative angles resonate.

The long-term question is whether creator marketplaces can consistently turn distributed content production into measurable music discovery.

Sound.me's 3-million-creator milestone suggests the supply side is becoming substantial. The next test will be proving that scale can translate into repeatable outcomes for artists, labels and brands—and that creator-driven music marketing can complement, rather than simply replace, the industry's established promotional channels.

Market Landscape

Music marketing is increasingly intertwined with the creator economy. TikTok, Instagram Reels and YouTube Shorts have transformed short-form video from a social format into an important discovery mechanism for music.

TikTok and Luminate's 2025 research provides a strong indicator of the trend, reporting that 84% of songs entering the Billboard Global 200 in 2024 had gone viral on TikTok first.

That environment has created demand for platforms that can connect artists and labels with creators at scale.

Sound.me competes indirectly with influencer marketing agencies, creator marketplaces and traditional paid social advertising. Its differentiator is the focus on music promotion and a workflow built around creator videos using specific sounds.

The model also differs from playlist-pitching services. Instead of attempting to place a song inside an existing playlist ecosystem, creator campaigns aim to generate new video content around the track and encourage algorithmic discovery on short-form platforms.

The bigger competitive question is measurement. As music marketing becomes increasingly creator-led, labels will want to understand not only views but downstream streaming, audience growth, engagement and conversion.

Strategic Outlook

The creator economy is moving toward infrastructure rather than individual sponsorships.

Platforms such as Sound.me are attempting to standardize campaign creation, creator discovery, approval, payment and measurement. For music marketers, that could turn creator campaigns from an opportunistic tactic into a repeatable part of a release strategy.

The next evolution is likely to involve more sophisticated AI-based creator matching, predictive campaign optimization and attribution between creator activity and streaming outcomes.

For labels and independent artists, the opportunity is significant, but scale should remain a means rather than the objective. The most valuable campaigns will likely be those that combine creator volume with strong creative direction, audience relevance and measurable downstream results.

Top Insights

• Sound.me's 3-million-creator network signals growing demand for scalable UGC campaigns as short-form video becomes a major music discovery channel.
• Its pay-per-approved-post model gives artists and labels greater control over creator content while reducing reliance on individually negotiated influencer partnerships.
• TikTok and Luminate reported that 84% of Billboard Global 200 entries in 2024 had gone viral on TikTok, underscoring creator-led discovery.
• Sound.me's marketplace model could be particularly useful for independent artists seeking affordable ways to generate large volumes of promotional content.
• The next competitive challenge will be attribution, connecting creator-generated views and engagement with streams, audience growth and commercial outcomes.

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Public transportation operators are being asked to improve reliability, control costs and deliver more equitable mobility while managing increasingly complex operations. The problem is often not a lack of technology. It is that the technology does not necessarily work together.

Transit Technologies says its new TransitTechOS is designed to tackle that problem by connecting service, fleet, workforce and safety operations through a common data model and real-time operating layer.

The company introduced the platform as an AI-first operating suite capable of supporting different modes of transit from one platform. Rather than requiring agencies to abandon existing systems, TransitTechOS is designed to connect technology already in use and place an AI intelligence layer across those operational environments.

That approach is significant because transit organizations often run specialized software for fixed-route transportation, paratransit, fleet maintenance, scheduling and workforce management. Consolidating those data streams can provide operational teams with a more complete picture of what is happening across a network.

Transit Technologies says TransitTechOS brings together capabilities associated with its Ecolane, TripShot, TripMaster, Vestige, Passio, ByteCurve, busHive and FASTER platforms. The company describes the result as a shared operational infrastructure rather than another standalone transit application.

From Data Silos to a Shared Operating Picture

The core proposition behind TransitTechOS is relatively straightforward: transportation operators should not have to switch between multiple systems to understand why a service disruption is occurring.

The platform is designed to provide shared visibility into routes, trips, vehicles, drivers and incidents, while connecting scheduling, dispatch, fleet and safety information.

That becomes particularly useful when an operational problem crosses departmental boundaries.

A late vehicle, for example, may be caused by a maintenance issue, driver availability, scheduling constraints or a disruption on a particular route. In a fragmented environment, each team may see only part of the problem. A connected operating model can potentially expose those relationships earlier.

Transit Technologies says early design partners have already used a unified operational view to identify issues sooner, including maintenance problems associated with specific routes.

The company says its technology powers more than 4,000 clients worldwide and has supported more than 54 million rides. It also claims its technology drives a 30% to 44% increase in rides per hour. Those figures are company-provided and were not independently verified.

AI Moves From Dashboard to Operational Assistant

The more interesting element of TransitTechOS is how the company intends to use AI once those systems are connected.

Transit Technologies says employees can ask questions about operations using conversational AI, while autonomous background agents continuously identify inefficiencies and potential risks.

That represents a move away from conventional business intelligence dashboards, which typically require employees to find and interpret the relevant information themselves.

An AI-enabled operating layer could instead identify a developing issue and surface it to the appropriate team.

For transit agencies, potential use cases include identifying declining on-time performance, recognizing fleet maintenance patterns, highlighting workforce constraints and detecting safety issues across different service modes.

Srithal Bellary, chief technology, data and AI officer at Transit Technologies, said the company's goal is to turn operational visibility into action by combining natural-language queries with autonomous agents.

The practical value will depend on how accurately those agents interpret operational data and how much authority agencies are willing to give automated systems. In transportation, where decisions can affect passenger safety and service accessibility, human oversight remains critical.

Two Markets, One Technology Layer

TransitTechOS is structured around two primary markets: Connected Campus and Connected Agency.

Connected Campus targets universities, corporate and medical campuses and airports. Connected Agency covers municipal transit, paratransit and ADA services, K-12 transportation, microtransit, on-demand services and rural and regional transit.

That segmentation reflects the different operational requirements across transportation environments while maintaining the same underlying infrastructure.

The approach also places TransitTechOS within a growing category of vertical software platforms that seek to combine industry-specific workflows with AI. Instead of building a generic AI assistant and asking customers to integrate it into existing processes, vertical platforms can use domain-specific operational data to make AI more useful within a particular industry.

Competition Is Shifting Toward Connected Infrastructure

Transit Technologies is entering a market that includes specialized transportation management, fleet management, scheduling, dispatch, mobility and intelligent transportation systems providers.

The competitive question is therefore broader than whether TransitTechOS has better AI features than another transit application. It is whether an agency can connect its existing technology without undertaking a costly system replacement.

That integration-first strategy could be particularly important for large transportation organizations with years of investment in specialized software.

The company is effectively positioning TransitTechOS as an intelligence layer across its portfolio rather than simply another replacement system. If that architecture works as intended, agencies could modernize their operations incrementally while retaining specialized applications.

The model resembles a broader enterprise technology trend visible across industries: AI is increasingly being positioned as an orchestration layer that sits above fragmented operational systems.

Market Landscape

The transportation technology market is moving toward integrated platforms as agencies seek better utilization of vehicles and workers, improved service reliability and greater visibility into operational performance.

AI adds another dimension to that transformation. For transit operators, useful AI is less about generating generic content and more about interpreting real-time operational data, forecasting disruptions and supporting decisions involving vehicles, drivers, schedules and passenger service.

The same architecture is emerging in other enterprise sectors, where AI increasingly sits on top of CRM, ERP, workforce and operational systems. Platforms from Microsoft, Amazon and Google are providing much of the underlying cloud and AI infrastructure, while vertical software providers are embedding specialized intelligence into industry workflows.

TransitTechOS reflects that verticalization trend by combining domain-specific transportation software with an AI layer designed around transit operations.

Strategic Outlook

TransitTechOS arrives at a time when transportation organizations are under pressure to modernize without creating another layer of technological complexity.

The platform's biggest proposition is therefore not simply AI. It is the combination of a shared data model, connected operational systems and AI-driven decision support.

If Transit Technologies can make those components work reliably across fixed-route transit, paratransit, campus transportation, microtransit and other specialized services, the platform could help agencies move from reactive operations toward more predictive management.

The larger industry question is how far autonomous AI agents should be allowed to go. Identifying a problem is one thing; changing schedules, reallocating vehicles or modifying workforce assignments introduces significantly greater operational and governance requirements.

For transit agencies, the eventual value of AI will likely be measured less by how sophisticated the interface looks and more by whether it improves service reliability, safety, asset utilization and passenger outcomes.

Top Insights

• TransitTechOS connects transit service, fleet, workforce and safety data, addressing fragmentation that can prevent agencies from seeing operational problems early.
• Conversational AI and autonomous agents could shift transit software from passive dashboards toward proactive operational intelligence and predictive decision support.
• Transit Technologies' integration-first approach lets agencies connect existing platforms instead of immediately replacing specialized transportation management systems and workflows.
• The platform targets campuses and public agencies, reflecting demand for shared technology infrastructure across increasingly diverse transit operating models.
• AI's impact on transit will depend on reliable operational data, human oversight and measurable improvements in safety, utilization and service reliability.

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South African digital marketing agency Top Click has launched a redesigned website that puts SEO, Google Ads and Answer Engine Optimisation at the center of its service offering. The new site is positioned less as a conventional agency brochure and more as a diagnostic resource, explaining common marketing problems, how they occur and how the agency approaches solving them.

The launch comes as search marketing itself is changing. Google continues to expand AI-generated search experiences, while ChatGPT, Gemini, Perplexity and Microsoft Copilot are creating new discovery channels that are forcing agencies to reconsider how brands measure visibility.

Top Click, a South African digital marketing agency, has launched a redesigned website outlining its approach to search engine optimization, paid search and Answer Engine Optimisation (AEO).

The new website is organized around four core sections—About, SEO, Google Ads and AEO—with a single contact route for prospective customers. Rather than leading with a conventional list of marketing services, the agency says the site is structured around the problems business owners commonly encounter when they inherit underperforming search campaigns or SEO programs.

That approach reflects a wider change in digital marketing. Business owners are increasingly expected to navigate fragmented search, advertising, analytics and AI discovery systems, while the distinction between traditional search optimization and AI visibility is becoming less clear.

Top Click is attempting to address that complexity by keeping its service scope relatively narrow.

The agency has held Google Premier Partner status for eight consecutive years and has dedicated a substantial section of the new site to Google Ads. It outlines several issues it says commonly affect South African paid-search accounts, including excessive spending on branded keywords, keyword structures that fail to reflect search intent and conversion tracking that counts low-value actions as genuine sales.

The agency also takes aim at Google's automated recommendations, arguing that advertisers should evaluate recommendations against business objectives rather than automatically applying them.

Its stated process begins with an account audit, followed by keyword strategy approval, campaign restructuring, ad-copy testing and ongoing optimization. Case studies from e-commerce, financial services and retail are included alongside the explanation of its methodology.

The SEO section follows a similar model.

Top Click says its team has spent 18 years auditing South African and international websites and has identified recurring problems in accounts transferred from other agencies. These include incomplete on-page optimization, technical SEO issues, poorly targeted keyword research and backlink activity that clients were unable to independently verify.

Instead of simply identifying those problems, the agency's new website provides readers with ways to check some of the issues themselves.

That is an interesting positioning choice in an industry where agencies have traditionally controlled much of the information about the work being performed on a client's behalf. Top Click says clients receive direct access to its paid tools so they can independently verify backlinks, rankings and technical changes.

The most notable addition is the agency's dedicated Answer Engine Optimisation service.

The rise of generative search has created a new visibility problem for businesses. A company can rank well for a conventional Google query while failing to appear when a prospective customer asks ChatGPT, Gemini, Perplexity, Copilot or Google's AI search systems the same question.

Top Click's AEO offering is designed around that distinction. The agency says its process includes AI visibility audits, competitor benchmarking, prompt and share-of-voice tracking, entity optimization, answer-focused content, FAQs and structured data.

It also includes technical AI-access work and citation development, with the agency saying it monitors AI referrals through Google Analytics 4 and reports on citations, visibility and brand sentiment.

The approach is increasingly relevant as search evolves from a list of links into a combination of traditional results and generated answers.

Google's own guidance has repeatedly emphasized that established SEO fundamentals remain relevant to AI search experiences. That makes Top Click's decision to position AEO as an extension of SEO, rather than a replacement for it, consistent with the broader direction of search optimization.

Still, the agency is relatively explicit about the limitations of AEO.

Its website states that marketers cannot control which sources an AI system chooses to cite and that some aspects of AI visibility remain difficult to measure. It also sets an expected timeframe of roughly 60 to 120 days for early signals.

That caveat is important in a market where "AI visibility" has quickly become a commercial selling point.

Unlike traditional search rankings, generative AI responses can vary according to prompts, context, geography, model behavior and the sources available to the system. A brand may appear in one answer and disappear from another without a straightforward ranking position explaining why.

Top Click's emphasis on measurement, citations and share of voice suggests an attempt to create a more structured framework around that uncertainty.

The agency is also taking an unusually explicit approach to positioning its business model.

A section titled "We're not for everyone" states that Top Click is not a full-service agency, does not manage social media or email marketing, does not operate on commission or affiliate models and does not position itself as the cheapest provider.

It also says the company operates with a senior team and does not place junior employees on client accounts. Contracts are month to month rather than long-term commitments.

That narrower positioning could help the agency compete in a crowded digital marketing market by making its specialization clearer.

The larger story, however, is the expansion of search marketing itself. SEO agencies are increasingly being asked to account for visibility beyond conventional blue-link rankings, while paid-search specialists face growing pressure to prove that advertising spend contributes to meaningful business outcomes.

Top Click's new website reflects both trends. It presents SEO and Google Ads as established disciplines while treating AEO as an emerging layer built on top of a strong search foundation.

For businesses trying to navigate the transition from traditional search to AI-mediated discovery, that distinction may become increasingly important.

Market Landscape

The digital marketing agency market is shifting as Google, OpenAI, Microsoft and other technology companies introduce AI-powered discovery experiences.

Traditional SEO remains important, but marketers increasingly need to understand how their brands are represented in AI-generated answers, which sources are cited and whether AI platforms are sending measurable referral traffic.

This creates an emerging market for AEO and generative engine optimization services. The challenge is measurement: AI responses are dynamic, platform-specific and less transparent than conventional search rankings.

Top Click's strategy is to combine established SEO disciplines—including technical SEO, content, backlinks and entity optimization—with dedicated AI visibility monitoring. That positions AEO as an extension of search marketing rather than a completely separate discipline.

The paid-search side of the market is undergoing its own transformation as Google's automation increases. Advertisers have more automated campaign management and bidding capabilities, but that also increases the importance of accurate conversion data, strategic oversight and independent performance analysis.

Strategic Outlook

Top Click's website launch reflects a broader industry shift from "being ranked" to being found wherever customers search for answers.

As AI search expands, brands will need to monitor traditional organic rankings alongside AI citations, mentions, referral traffic and brand representation. The agencies best positioned for this transition will likely be those that can connect technical SEO fundamentals with measurable AI visibility rather than treating AEO as a standalone shortcut.

For enterprise marketing teams, the practical takeaway is straightforward: AI search does not eliminate SEO. It adds another discovery layer that requires new measurement methods, content strategies and entity-level optimization.

Top Insights

• Top Click's new website combines SEO, Google Ads and AEO, reflecting how search marketing is expanding beyond traditional rankings into AI-generated discovery.
• The agency positions Answer Engine Optimisation as an extension of SEO, emphasizing technical foundations, entity optimization, citations and AI visibility measurement.
• Its Google Ads offering focuses on reducing wasted spend through account audits, search-intent analysis, conversion tracking and ongoing campaign optimization.
• Top Click emphasizes transparency by giving clients access to paid marketing tools, allowing businesses to independently verify rankings, backlinks and technical work.
• The agency's explicit AEO limitations acknowledge that AI citations remain unpredictable, highlighting the measurement challenges facing the emerging AI search industry.

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