1 Introduction
The agentic web is an emerging layer of the internet where AI‑powered agents (virtual “concierges”) perform tasks on behalf of users. Unlike the PC web (where humans click links) or the mobile web (dominated by tapping on curated apps and feeds), the agentic era delegates goal‑oriented workflows to autonomous softwareikangai.com. Users tell an assistant “plan my trip” or “donate $50,” and the agent executes a chain of steps across multiple services without the user ever loading a web pageschemaapp.com. This shift is already underway; leading AI assistants (OpenAI’s ChatGPT, Anthropic’s Claude, Perplexity, etc.) are rapidly adding action capabilities, and specialized agentic browsers are emerging.
For brands and organizations, the agentic web introduces a new battle for visibility. Traditional web assets (landing pages, navigation, call‑to‑action buttons) are designed for human consumption, but AI agents do not click or scrollschemaapp.com. Agents evaluate only machine‑readable tasks they can perform and default to the first callable optionschemaapp.com. Without explicit definitions of what services are available and how to invoke them, a business becomes invisible in agentic channels. As a result, structured data—particularly Schema.org’s Action vocabulary—is emerging as a critical bridge between the human‑web and the machine‑webschemaapp.com.
Why Schema.org Action matters
Schema.org provides a standardized vocabulary for describing entities (products, events, organizations) and actions (read, subscribe, buy, donate, book, etc.). Search engines already use structured data for rich results and knowledge graphs; Google’s documentation notes that adding explicit structured data helps Google understand page contentschemaapp.com. AI agents and agentic browsers extend this need: they require action semantics—clear definitions of what can be done, required inputs, and invocation rules. The Agentic Entry Point concept defined by Schema App makes this concrete: it is a machine‑facing equivalent of a homepage that exposes tasks (e.g., BookDemo, RequestQuote) through potentialAction markup rather than UI flowsschemaapp.com. The Schema App guide recommends implementing these entry points using Schema.org Action types (e.g., BookAction, ScheduleAction, QuoteAction) and publishing them in JSON‑LD so agents can discover and call themschemaapp.com. Early adopters gain compounding visibility because agents default to trusted, invokable optionsschemaapp.com.
This report examines the market impact of agentic web adoption, assesses how major industry verticals stand to benefit from Schema.org Action integration, and analyzes how competition within those verticals is likely to change.
2 Market Impact & Growth of Vertical AI
2.1 The rise of vertical AI agents
While general‑purpose copilots (Microsoft Copilot, Google AI) have become widespread, vertical AI agents—systems tuned to a specific industry—deliver more tangible value. McKinsey notes that nearly 70 % of Fortune 500 companies use horizontal copilots, yet these provide diffuse productivity gains and often do not move top‑ or bottom‑line resultsmckinsey.com. By contrast, vertical use cases remain rare but have higher economic impact when they automate entire business processesmckinsey.com. According to Turing’s 2025 white paper, vertical AI companies founded since 2019 are achieving 80 % of traditional SaaS contract values while growing 400 % year‑over‑yearturing.com. Gartner forecasts that 80 % of enterprises will adopt vertical AI agents by 2026turing.com. Bessemer Venture Partners observes that vertical AI companies are approaching SaaS parity and seeing explosive growthturing.com.
The financial opportunity is enormous. The AI industry media outlet AIM Media House estimates that the vertical AI market was worth US$5.1 billion in 2024 and will grow to US$47.1 billion by 2030, potentially exceeding US$100 billion by 2032aimmediahouse.com. Venture capitalists consider vertical AI the “next step for the AI industry,” predicting that specialized agents will supplant general solutionsaimmediahouse.com. NEA (New Enterprise Associates) suggests that the shift from systems of record to systems of action will unlock a share of the US$11 trillion U.S. labor market—dwarfing the US$450 billion enterprise software marketnea.com. In short, vertical AI is not a niche; it is a multi‑billion‑dollar opportunity, and Schema.org action markup is one of the lowest‑friction steps toward unlocking it.
2.2 From SEO to GEO and agentic channels
Digital discoverability is also changing. SEO specialists once optimized for clicks and backlinks; now, Generative Engine Optimization (GEO) aims to make content usable by LLM‑powered answer engines. VC Cafe notes that AI overviews cause publishers to lose nearly 48 % of their clicksvccafe.com and predicts a future where most web traffic is non‑humanvccafe.com. To succeed, sites must provide semantically rich, authoritative content and structured data that LLMs can ingestvccafe.com. Schema.org markup fulfills this requirement: it gives AI models explicit context so they can categorize and reuse contentvccafe.com and helps build knowledge graphs that improve LLM accuracyschemaapp.com.
2.3 Emerging agentic protocols
Beyond schema markup, new protocols are forming an agentic stack. The Model Context Protocol (MCP) provides a standardized “USB‑C port” for AI to call external servicesforumone.com. The Agent‑to‑Agent (A2A) protocol enables agents to discover and delegate tasks to each otherforumone.com. The Agent Payments Protocol (AP2) ensures secure transactions when agents handle money or sensitive dataforumone.com. While these protocols are still emerging, simple measures such as publishing llms.txt (a machine‑readable list of prioritized URLs) and Schema.org actions can make services discoverable to agents todayforumone.com. They also future‑proof brands for more advanced capabilities.
3 Vertical Benefits of Schema.org Action
The impact of agentic interactions varies by industry. Below, we examine key verticals, illustrating how Schema.org Action can enable agentic workflows and how those workflows change competitive dynamics.
3.1 E‑Commerce & Retail
Agentic commerce
Agents are becoming shopping assistants: Perplexity already lets users purchase products directly from chatvccafe.com and OpenAI is integrating checkout into ChatGPTvccafe.com. To support autonomous purchasing, merchants need structured data that tells agents how to search for products, add items to a cart, and finalize payment. VC Cafe notes that headless commerce APIs must include endpoints such as /catalog, /cart, /checkout, and /post‑ordervccafe.com, and that current checkout APIs often redirect to GUI pages, breaking the agent’s flowvccafe.com. Schema.org offers action types like AddAction, BuyAction, OrderAction, and PayAction. By exposing these actions via JSON‑LD, a retailer can make its catalog callable, enable one‑click checkout, and communicate shipping or returns policies. Future protocols like MCP will standardize payment flows, but Schema.org actions provide immediate value.
Competition and brand control
Agentic commerce creates both opportunities and risks. Structured data and headless APIs offer access to massive user bases on platforms like ChatGPT, but they also compress brand identity. VC Cafe warns that merchants risk having their brands flattened into a single sentence in a chatbot interfacevccafe.com. Agents choose the most trustworthy and efficient provider; once they complete a purchase with a competitor, your brand may become invisibleschemaapp.com. This dynamic could accelerate winner‑takes‑most outcomes, with early adopters capturing the majority of agentic traffic. Retailers must therefore invest in both schema markup and brand‑aware agentic experiences (e.g., providing descriptive product data, personalization, and loyalty incentives) to differentiate.
3.2 Travel & Hospitality
AI travel agents are a canonical example of the agentic web: telling an assistant to “plan my trip to Tokyo” prompts it to search flights, hotels, and attractions, compare options, and book reservations without human navigationikangai.com. Schema.org includes Trip, Flight, Hotel, TouristDestination, and action types like ReserveAction and BookAction. Travel businesses that publish this data can become first‑class options for agentic planners. Without structured action definitions, an agent may default to a single booking platform or aggregator, squeezing out smaller providers.
Competitive impact. Traditional travel aggregators (Expedia, Booking.com) have long competed on user experience and breadth of inventory. In the agentic era, the battle shifts to API quality and structured semantics. Agents will evaluate pricing, availability, cancellation rules, and reputation via data rather than design. Providers who offer clear ReserveAction definitions and open booking APIs could bypass aggregators and interact directly with agentic assistants, capturing higher margins. Conversely, those who rely solely on human‑centric websites risk being commoditized.
3.3 Healthcare
Turing’s analysis highlights how vertical healthcare agents integrate with electronic health records (EHRs) to automate appointment scheduling, patient data management, clinical documentation, and diagnostic supportturing.com. A healthcare provider can expose actions like ScheduleAction (booking appointments), AssessAction (screening for risk factors), ReadAction (sharing patient information under consent), and PayAction (processing copays). Schema.org’s healthcare extension (e.g., MedicalAppointment, Hospital, Drug) provides domain vocabulary. By making services machine‑callable, providers improve patient experience and reduce administrative burdens.
Competition. Healthcare is heavily regulated. Agents must enforce privacy (HIPAA, GDPR) and follow compliance rules—vertical AI agents incorporate these requirementsturing.com. Providers that adopt Schema.org actions and invest in governance will be able to offer frictionless scheduling and triage. Those that do not may see patients routed by general‑purpose health agents to better‑structured competitors. There is also potential for new entrants (e.g., telehealth startups) to leapfrog incumbents by being agent‑ready.
3.4 Financial Services
Vertical AI agents in finance automate risk management, transaction monitoring, and regulatory reportingturing.com. Examples like Salient process over US$561 million in transactions while reducing handle times by 60 % through agents that manage loan servicing across voice, text, email, and web chataimmediahouse.com. Financial institutions can use Schema.org’s AuthorizeAction for payments, LoanOrCredit for loans, and SubscribeAction for recurring services. Agents evaluate interest rates, fees, and compliance; thus, clear semantics and machine‑readable credit offerings are essential.
Competition. Agentic finance reduces friction, enabling users to open accounts or refinance loans via a single voice command. As agents compare rates and terms, differentiation shifts from marketing to data transparency, trust signals, and regulatory compliance. Fintechs that publish detailed, machine‑readable product attributes and actions can rapidly capture users. Traditional banks risk losing brand affinity if they remain locked behind human‑centric forms.
3.5 Recruitment & HR
Vertical AI is transforming hiring processes: Apriora’s agent conducts real‑time interviews, reviews résumés, and generates predictive hiring signalsturing.com; AI interviewers operate continuously without human schedule constraintsaimmediahouse.com. Recruiters can expose ApplyAction, InterviewAction, or HireAction to allow agents to submit applications or schedule interviews. Job boards can publish JobPosting with ApplyAction endpoints. This reduces administrative workload and improves candidate experiences.
Competition. HR firms that adopt agentic interactions can offer faster, more equitable hiring. Companies that rely on manual applicant tracking may fall behind as high‑quality candidates route their applications through agentic services that optimize for speed and fairness.
3.6 Education & Learning
Although less discussed, vertical AI agents have potential in education: they can handle course registration, scheduling, tutoring, and credentialing. Schema.org offers Course, Event, RegisterAction, and JoinAction. By publishing these actions, universities and training providers allow agents to enroll students or join webinars automatically. Competitive advantage lies in being integrated into agentic learning platforms; late adopters may be invisible to AI‑driven course recommendations.
3.7 Nonprofit & Government
Forum One argues that mission‑driven organizations must prepare for an agentic future: a supporter might simply say “Donate $50 to the most effective climate change organization,” and an AI agent will find and execute the donationforumone.com. The roadmap recommends implementing Schema.org markup (Organization, Person, Article, Event) as well as actions like DonateAction, RegisterAction, or ApplyActionforumone.com. Additional standards (llms.txt, MCP, A2A, AP2) will help AI agents find the right servicesforumone.com. Organizations that publish these capabilities become callable in philanthropic or civic agent marketplaces; those that ignore them risk exclusion.
3.8 Media & Content Platforms
AI overviews, answer engines, and agentic browsers have significantly reduced traffic to publishers, with studies showing 47.55 % decline when AI Overviews appearvccafe.com. Schema.org actions like ReadAction, WatchAction, ListenAction, and SubscribeAction let content providers advertise machine‑readable entry points to their articles, videos, podcasts, or newsletters. For instance, a publisher can add a ReadAction with a target pointing to the canonical URL and a SubscribeAction linking to a subscription endpoint. This allows an agent to read or summarize content and subscribe a user without navigating a site. Media companies that adopt these actions can remain present in agentic channels; those that do not risk being bypassed by LLM summaries or aggregated news feeds.
4 Changing Competitive Dynamics
4.1 First‑mover advantages and compounding effects
Schema App notes that in the agentic era, agents default to trusted, invokable options; brands that publish tasks early become callable first and enjoy a compounding advantageschemaapp.com. Conversely, brands that delay may be excluded entirely: once an agent learns to complete a task through a competitor, it may never seek alternativesschemaapp.com. This dynamic applies across verticals. E‑commerce merchants, travel providers, healthcare systems, banks, and nonprofits that implement Schema.org actions will capture early agentic traffic and shape user preferences. Late entrants may struggle to displace incumbents because agents rely on previous interactions and trust signals.
4.2 Shifts in control points and value capture
NEA observes that software control points are moving from systems of record (e.g., CRM, ERP) to agentic layersnea.com. Traditional advantages (high switching costs, integrated workflows) are eroded when AI agents process data outside these systemsnea.com. Vertical AI providers become the new control points by orchestrating workflows and capturing user intent. This could unseat incumbents in industries like CRM (Salesforce), healthcare IT (Epic), or travel booking (Expedia) as agentic intermediaries deliver more value.
4.3 New entrants, marketplaces and standards
The agentic era incentivizes the creation of AI agent marketplaces—app stores where businesses buy, sell, and deploy specialized agents. Media analyses note that major tech giants (AWS, Google, Microsoft, Salesforce) are already building agent marketplaces, allowing enterprises to access pre‑built agents for tasks such as coding, customer service, or analyticsmedium.com. As these marketplaces mature, vertical AI startups will compete for placement and reviews, similar to mobile app stores. Standardized schema markup will be a prerequisite for listing.
New protocols also level the playing field: llms.txt provides a low‑barrier method to signal important URLs to AIforumone.com; MCP and A2A enable interoperability and discovery across agentsforumone.com. Participating in these standards early can position organizations as ecosystem stewards and shape the rules to their advantageforumone.com.
4.4 Risks and mitigation
- Commoditization & loss of brand – Agentic interfaces compress brands into functional descriptions. Without differentiation through structured metadata (e.g., quality attributes, social proof, sustainability credentials), products risk becoming interchangeable. Companies should enrich schema markup with attributes that convey unique value.
- Disintermediation & margin pressure – Agents may bypass distribution partners or aggregators, reducing referral traffic and affiliate revenue. Businesses need direct agentic entry points and loyalty programmes to retain customers.
- Compliance & governance – Incorrect or misleading structured data could lead to misrepresentation or legal issues. Organizations must define governance boundaries and ensure that published actions align with actual capabilitiesschemaapp.com. Healthcare, finance, and public sector organizations must embed privacy, consent, and regulatory rules into the schema definitionsturing.com.
- Technical debt & fragmentation – Rapid adoption of new protocols may lead to fragmented implementations. The Forum One roadmap recommends incremental maturity: start with server‑rendered content and basic schema markup, then add llms.txt and inline instructions, and finally adopt MCP/A2A/AP2【84756195807472†L115-L167】forumone.com.
5 Implementation Guidance
5.1 Add Schema.org actions today
Implement JSON‑LD in your site’s <head> or body. Use the potentialAction property to define tasks as @type “Action.” For example, an article page may specify a ReadAction target with the canonical URL, while a newsletter signup page may declare a SubscribeAction with a POST endpoint (e.g., https://example.com/newsletter?email={email}) schemaapp.com. E‑commerce sites can add AddAction or BuyAction to product pages; booking sites can add BookAction or ReserveAction; nonprofits can add DonateAction. Validate markup using Google’s Rich Results tool or equivalent. Ensure the actions reflect actual user interfaces to avoid agent failures.
5.2 Structure entity data and build knowledge graphs
Accurate entity definitions (Organization, Person, Product, Service) and relationships underpin agentic actions. Schema App stresses that structured content enables entity resolution, contextual understanding, and reuse across search, LLMs, and agentsschemaapp.com. Building a content knowledge graph reduces hallucinations and improves AI accuracy; a Data World benchmark showed that LLMs grounded in knowledge graphs deliver 300 % higher accuracyschemaapp.com. SEOs and content teams should think like data architects, modeling relationships rather than just pagesschemaapp.com.
5.3 Publish llms.txt and adopt emerging protocols
Serve a llms.txt file at your domain’s root to highlight important URLs for AI consumptionforumone.com. Use inline instructions (e.g., <script type="text/llms.txt">…</script>) to guide agents on dynamic pages. Explore the Model Context Protocol for exposing functions and actions via a standardized interfaceforumone.com. Consider the Agent‑to‑Agent protocol for collaboration and the Agent Payments Protocol for transactionsforumone.com.
5.4 Identify high‑value tasks and governance boundaries
Product leaders should select 3–5 repeatable, high‑value tasks where agentic completion generates revenue or reduces friction (e.g., BookConsultation, RequestQuote, Donate, CheckEligibility)schemaapp.com. These tasks should be rewritten as explicit Schema.org actions with defined inputs, authentication requirements, and success/failure semanticsschemaapp.com. Establish who approves new intents and maintains markup to ensure consistency and compliance.
6 Conclusion
The transition to an agentic web will likely be as transformative as the advent of the smartphone. AI agents are shifting attention from human‑driven clicks to machine‑mediated tasks, and vertical AI agents are poised to unlock billions of dollars in economic value. Structured data—particularly Schema.org Action markup—is the simplest, most immediate way to make websites agent‑friendly. It enables AI agents to discover, understand, and execute tasks; it future‑proofs content for generative search and agentic browsers; and it positions organizations to compete in emerging agent marketplaces. Vertical industries that embrace this shift—by building dual‑mode web experiences for humans and machines—stand to gain an outsized share of the agentic economy.
Early adopters enjoy compounding advantages because agents default to the first reliable optionschemaapp.com. The cost of waiting is not merely lower visibility; it is exclusion from AI‑mediated interactionsschemaapp.com. Therefore, businesses across e‑commerce, travel, healthcare, finance, HR, education, nonprofit, and media should begin publishing Schema.org actions, structuring their data, and experimenting with agentic protocols. Doing so is not just an SEO tactic; it is a strategic imperative to remain visible and competitive in the next era of the web.
