Originally published on LinkedIn in January 2026
The ground is shifting beneath transport operators, and most haven’t noticed.
On one front, AI agents are already accessing your services through channels you don’t control. Every major AI platform is pulling transport information through web scraping, public API queries, and third-party aggregators. Your schedules, routes, and real-time data are being represented to millions of users by systems you’ve never integrated with, can’t monitor, and have no way to correct when they get it wrong. This isn’t a future threat—it’s today’s reality.
On another front, hundreds of new companies are being born with agentic commerce as their founding premise. They’re not building better apps or prettier interfaces. They’re building AI-native platforms designed from day one for a world where agents intermediate every transaction, where AGI capabilities reshape what software can do, and where AI tools become embedded in every digital service. These startups understand something incumbents often miss: in an agentic economy, whoever controls the intelligence layer captures the value.
These two forces are converging into a single strategic reality for transport operators.
The company that orchestrates the journey owns the customer relationship. The operator that merely provides the vehicle becomes a commodity—interchangeable, price-compressed, invisible. Think about what happened to electricity. Essential infrastructure, but when was the last time you chose your utility provider based on anything other than price? That’s the future waiting for transport operators who cede the intelligence layer to others. You become the power grid: necessary, regulated, commoditized. Someone else builds the applications, owns the customer, captures the margin.
The agentic startups entering your market don’t need to own buses or trains. They just need to intermediate more efficiently than you can serve customers directly. And with AI agents, they will—unless you act first.
The question isn’t whether your operations will be accessible through AI agents. It’s whether you’ll control how that happens, and whether you’ll capture the value it creates.
The Gen AI Paradox Hits Transport
Here’s an uncomfortable truth from recent McKinsey research: 78% of companies are now using generative AI. Yet more than 80% report no material impact on earnings.
Transport operators face the same paradox. You’ve deployed chatbots, piloted copilots, experimented with AI-assisted scheduling. The results? Modest gains, hard to measure, difficult to scale. And most operators are asking the wrong question—focused on how to “ChatGPT” their existing direct-to-consumer channels, adding conversational interfaces to apps and websites they already own.
But while you’re optimizing your app, your customers are abandoning apps altogether. They’re asking AI assistants to plan their journeys, expecting agents to handle the complexity. The DTC channel you’re busy enhancing may not be where passengers interact with you at all in three years.
This isn’t a technology problem. It’s a deployment problem.
The companies stuck in the paradox made a common mistake: they deployed AI to automate existing tasks rather than reinvent underlying processes. They added intelligence to broken workflows instead of building new ones around what intelligence makes possible.
Consider how this plays out in passenger service during disruptions. McKinsey’s research on agentic AI identifies three levels of transformation—originally illustrated through call center operations, but directly applicable to transport. The pattern translates clearly:
Level 1 - AI-Assisted: Human agents use AI to find alternatives faster. Result: 5-10% improvement in resolution time. The process is identical; tools are better.
Level 2 - AI-Optimized: AI autonomously handles routine rebooking within defined parameters, escalating edge cases to humans. Result: 20-40% improvement. The process is streamlined; humans handle exceptions.
Level 3 - AI-Reinvented: AI proactively detects disruptions, initiates rebooking before passengers know there’s a problem, communicates alternatives, and coordinates across operators automatically. Result: 60-90% improvement in resolution time. 80% of incidents are resolved without human intervention.
Most operators are stuck at Level 1. Some are pushing toward Level 2. Almost none have reached Level 3—yet that’s where the transformative value lives.
The difference isn’t the AI. It’s the willingness to redesign the process around what AI agents can actually do.
Three Futures for Transport Operators
Every transport operator will end up in one of three scenarios. The decisions you make in the next 18-24 months will determine which one.
Scenario A: The Disintermediated Operator
In this future, AI agents access your services through whatever means available—scraping websites, abusing public APIs, pulling from third-party aggregators. You have no control over how your services are represented. When an agent tells a passenger the 8:15 bus is on time and it isn’t, you bear the reputational damage. When agents route passengers away from your services because they lack accurate real-time data, you lose ridership without knowing why.
Revenue flows to platforms and aggregators. Your operational data—the patterns, the preferences, the demand signals—feeds someone else’s optimization engine. You become infrastructure: necessary but invisible, squeezed on margins, unable to differentiate.
This isn’t hypothetical. It’s the default trajectory for operators who wait.
Scenario B: The Disoriented Modern Operator
Here, you’ve recognized that AI matters—but you’ve misread the game.
You’ve established basic API connections to the agentic ecosystem. AI agents can access your schedules and book tickets through authorized channels. You’ve checked the box on “AI integration.” But you’ve also convinced yourself that you need to become a tech company. You’re building your own chatbot—essentially trying to replicate ChatGPT or Claude, but closed, proprietary, and focused solely on selling your tickets. You’re hiring AI teams to create consumer experiences that compete with platforms backed by billions in R&D.
The market has already moved past what you’re building. While you spend 18 months developing a conversational interface, OpenAI and Anthropic and Google ship three generations of improvements. Your chatbot launches feeling dated. Passengers try it once, find it inferior to asking their general-purpose AI assistant, and never return.
Meanwhile, your underlying operations remain unchanged. Your dispatch still runs on fixed schedules with human oversight. Route changes still require supervisor approval. Disruption response still takes 10-15 minutes to coordinate. You’ve built a shiny front door to a house that hasn’t been renovated.
The result: you’ve spent your innovation budget competing where you can’t win (consumer AI interfaces) while underinvesting where you actually could (operational transformation and ecosystem integration). You’ve connected to the future without transforming for it, and built products the market didn’t need.
Many operators will land here and call it digital transformation. They’ll point to their AI initiatives, their chatbot launch, their “innovation lab.” But they’ll still be operating the same processes, serving the same commoditized role, wondering why the investment didn’t pay off.
Scenario C: The Future Operator
This is what strategic clarity looks like.
You’ve understood a fundamental truth: you’re not going to out-build OpenAI on conversational AI. And you don’t need to. Instead, you’ve focused on what only you can provide—operational excellence made accessible to the agentic ecosystem.
Your processes have been redesigned around agent autonomy. Disruptions trigger automatic rerouting and passenger rebooking before anyone reaches a delayed stop. Predictive maintenance schedules repairs during low-impact periods. Capacity optimization runs continuously in real-time.
You’ve made yourself indispensable to the ecosystem rather than trying to replace it. When a passenger asks their AI assistant to plan a journey, your services surface first—not because you built a competing chatbot, but because your data is accurate, your APIs are robust, and your MCP integration provides the rich context agents need.
The result: same infrastructure, same vehicles—but 30% higher capacity utilization, 45% reduction in service disruptions, new revenue streams from ecosystem participation. You didn’t waste capital building consumer AI products; you invested in becoming the operator that every AI product wants to work with.
The gap between Scenario B and Scenario C isn’t budget. It’s strategic focus: whether you’re trying to become something you’re not, or becoming the best version of what you actually are.
The Economics of Early Action
Let’s be specific about what’s at stake.
Why Horizontal AI Fails and Vertical AI Wins
According to McKinsey’s research on the agentic AI advantage, the gen AI paradox persists because most deployments are “horizontal”—general-purpose copilots and chatbots that assist across many functions without transforming any of them. These tools scale quickly but deliver diffuse gains that are hard to measure and harder to compound.
Vertical AI is different. It targets specific processes within specific domains, embedding intelligence into workflows where it can be measured, optimized, and scaled. Research shows the travel and transport sector is already seeing this play out:
Housekeeping optimization in hotels: 10-30% reduction in labor hours through intelligent task routing
Dynamic bundling for airlines: 20-30% revenue uplift by personalizing ancillary offers in real-time
Load factor optimization: 3-4% improvement through AI-driven demand forecasting vs. 1-2% with traditional analytics
Loyalty personalization: 15-25% revenue uplift through individualized reward offers
These aren’t pilot results. They’re operational outcomes from organizations that moved past experimentation into industrial deployment.
Transport operators have equivalent opportunities sitting untapped:
Predictive maintenance: 20-30% reduction in vehicle out-of-service time by catching issues before they cause breakdowns
Real-time capacity management: Dynamic service adjustments that match supply to actual demand, not historical averages
Disruption recovery: Coordinated response across modes and operators that turns 45-minute recovery windows into 5-minute ones
Accessibility optimization: Real-time routing that accounts for elevator status, accessible vehicle availability, and companion coordination
Personalized journey offerings: Tailored service bundles, fare options, and route suggestions based on individual travel patterns, preferences, and loyalty status—turning anonymous riders into known customers with higher lifetime value
The common thread: these aren’t general AI applications. They’re process-specific implementations that require domain expertise, operational data, and integration with existing systems. They require domain expertise, operational data, and deep integration with existing systems, exactly what generic AI deployments miss.
The Compounding Advantage
Early movers don’t just capture first-mover benefits. They establish compounding advantages that late entrants can’t easily replicate.
Consider how this works: You integrate your booking and fare systems with an MCP-enabled platform. AI agents start purchasing tickets and recommending your services. You gain data on how agents evaluate options, which fare combinations they prefer, what personalized bundles convert best. You optimize your offers based on actual agent behavior and passenger responses. Your services become the preferred choice for agent-mediated journeys. Agents recommend you more often. More transactions, better personalization, stronger position.
Meanwhile, the operator who waited is still figuring out their API strategy while you’re on your third iteration of agent-optimized operations.
This is the network effect in action. The value of integration compounds over time—not just for you, but across the ecosystem of operators, agents, and passengers who interact through the platform. Getting in early means participating in that value creation. Getting in late means paying to access value others created.
The Cost of Delay
Every month of delay has a measurable cost:
Data accumulation: AI systems improve through learning. Agents interacting with your services generate insights about passenger behavior, demand patterns, and service quality. That data has value—value that accrues to whoever captures it. Delay means that learning happens without you, through scraped data you don’t control and can’t monetize.
Integration complexity: The agentic ecosystem is evolving rapidly. Standards are being established. Patterns are being set. Operators who engage now help shape how transport integrates with AI agents. Operators who wait inherit decisions made without their input.
Competitive positioning: In fragmented markets, the operators who establish agent-trusted status first will be harder to displace. Once a passenger’s AI assistant learns that your services are reliable, well-integrated, and accurate, switching costs emerge. The inverse is also true: if agents learn your data is unreliable, that reputation persists.
Talent and capability: Organizations that start now build internal expertise—staff who understand how AI agents work, what they need, how to optimize for them. That institutional knowledge doesn’t appear instantly when you decide to catch up.
The executives waiting for the technology to “mature” are actually waiting until their competitors have insurmountable leads.
The Evolution Timeline
The transformation from today’s fragmented systems to tomorrow’s intelligent networks follows a clear progression—one that’s already underway.
Short-term (2026-2030): The Foundation and Operational Phase
This is where the ground rules get established and the first movers pull ahead.
AI agents become the primary interface for journey planning. Passengers stop comparing apps and start asking assistants. The operators with clean data feeds, robust APIs, and MCP integration become the default recommendations. Those without become invisible—not because agents deliberately exclude them, but because they simply can’t be discovered or trusted.
By the end of this window, agents move beyond recommending journeys to orchestrating them—directly interfacing with booking systems, dynamically adjusting capacity, coordinating across operators during disruptions. Demand-responsive transport shifts from exception to norm. Fixed schedules give way to dynamic optimization.
During this phase, the winners focus on four priorities: ensuring their real-time data is accurate and accessible, establishing controlled integration channels that give them visibility into how agents use their services, building the internal capabilities to learn from agent interactions, and—critically—organizing the proprietary data that isn’t publicly available. Your operational patterns, capacity utilization, passenger flow dynamics, pricing elasticity, maintenance histories—this is the data AI platforms can’t scrape. It’s the data that will power personalization, predictive services, and premium offerings in the agentic economy. Operators who structure and prepare this data now will have assets to monetize later. Those who don’t will watch competitors build intelligence layers on information they never thought to capture.
The operators who treat this as a “wait and see” period will find themselves playing catch-up for years. The foundation you build now determines your options later.
Medium-term (2030+): The Integration Phase
The boundaries between transport modes dissolve.
A passenger’s AI assistant doesn’t distinguish between your bus, a competitor’s metro, an autonomous shuttle, or a shared bike. It assembles the optimal journey from whatever components serve the passenger best. Operators who’ve established themselves as reliable, well-integrated options get included. Those who haven’t get bypassed.
Physical infrastructure begins adapting to AI recommendations—dynamic bus lanes, reconfigurable stations, demand-responsive routing that changes by the hour. The intelligence layer and the physical layer become inseparable.
This future rewards operators who started building relationships with the agentic ecosystem years earlier. The trust, the data flows, the operational patterns—these compound over time. Late entrants face an ecosystem that’s already optimized around the early movers.
A Day in the Life: 2030
María is getting ready in her apartment in northern Madrid when her home sound system plays a notification: “Your route to the coworking space has changed. Line 10 maintenance means I’ve switched you to express bus C1 and adjusted your first meeting by 10 minutes. You’ll arrive 5 minutes early and save €2 with your monthly pass.”
She never checked a screen. Never opened an app. Never compared options.
During her commute, her smartring vibrates gently at transfer points while her audio glasses provide quiet guidance—”Bus arriving in 90 seconds, platform 3”—keeping her oriented without ever pulling out a phone. When unexpected traffic hits Paseo de la Castellana, her assistant coordinates with the Metro system to hold her connecting train at Nuevos Ministerios for two minutes. She doesn’t even know there was a problem.
At midday, her 2pm meeting gets rescheduled to a location near Retiro Park. Her assistant spots a 40-minute window and books her a meditation class at a studio two blocks away—something she’d been meaning to try. It adjusts her afternoon schedule and coordinates with her colleague’s assistant to confirm the new meeting time.
Here’s what María doesn’t see:
Behind that seamless experience, an operator made choices. They connected their systems to an MCP-enabled platform. They established data governance policies. They invested in real-time feeds and in quality structured proprietary data. They chose controlled integration over uncontrolled scraping.
That operator is now the one María’s assistant trusts—not the one it works around.
The technology enabling María’s morning isn’t remarkable. What’s remarkable is how unremarkable it feels to her. The complexity has disappeared into ambient experience—no screens, no decisions, no friction. That only happens when infrastructure anticipates needs rather than reacting to requests.
The operators who built that infrastructure didn’t just survive the agentic transition. They defined it.
The CEO Mandate
McKinsey’s research on the agentic AI advantage concludes with a pointed observation: “The time has come to bring the gen AI experimentation phase to an end—a pivot only the CEO can make.”
This isn’t a technology decision that can be delegated to IT. It’s not an innovation initiative that belongs in a lab. It’s a strategic transformation that requires executive ownership.
Three Actions for Transport Leaders
1. End experimentation. Start executing.
Audit your current AI initiatives. How many are pilots that never scaled? How many delivered measurable business impact? How many are still “exploring potential”?
The honest answer for most organizations: too many experiments, too few results.
Retire the initiatives that won’t scale. Consolidate resources around the ones that will. Stop treating AI as a series of disconnected projects and start treating it as a transformation program with clear objectives, timelines, and accountability.
2. Reframe the investment question.
The wrong question: “How much should we spend on AI?”
The right question: “What’s the cost of being excluded from the agentic ecosystem?”
Every month without controlled integration, AI agents access your services through channels you don’t control, represent you in ways you can’t correct, and generate data you can’t capture. The investment isn’t optional; the only choice is whether you make it proactively or reactively.
Build the business case around risk mitigation, not just efficiency gains. The operators who get this right will capture disproportionate value. The ones who don’t will watch that value flow to competitors and intermediaries.
3. Launch a lighthouse project while building the foundation.
You don’t need to transform everything at once. Start with a bounded initiative that demonstrates value and builds organizational capability.
Information services are the natural starting point—ensuring AI agents can access accurate, real-time data about your operations through controlled channels. This can be operational in weeks, not years. It establishes the integration infrastructure, builds internal expertise, and generates data about how agents interact with your services.
From that foundation, expand into ticketing and booking integration, then operational coordination, then full ecosystem participation. Each phase builds on the last. Each generates learning that informs the next.
The lighthouse project isn’t the transformation. It’s the proof point that makes the transformation possible.
The Choice
The agentic economy isn’t arriving. It’s here.
AI agents are already accessing transport services—through channels operators don’t control, generating value operators don’t capture, building patterns operators can’t influence. Every day of delay widens the gap between the operators shaping this transition and those being shaped by it.
The parallel to what happened in other industries is exact. Hotels that dismissed online travel agencies as a niche channel watched those agencies capture the customer relationship. Retailers that viewed e-commerce as separate from “real” retail found themselves competing with Amazon. Media companies that treated digital as a side project discovered it was the main event.
Transport operators face the same inflection point. The companies that control the intelligence layer will capture the value. The companies that merely provide the underlying service will be commoditized.
But there’s a crucial difference from those earlier disruptions: this time, operators can see it coming. The technology is visible. The trajectory is clear. The playbook is emerging.
The operators who act now—who establish controlled integration, who redesign processes around agent capabilities, who build the organizational muscle to operate in an agentic ecosystem—will define how AI agents interact with transport. They’ll capture the data, the relationships, and the margins that come from being essential to the ecosystem rather than interchangeable within it.
The operators who wait will inherit an ecosystem designed without their input, optimized for someone else’s benefit, operating on someone else’s terms.
The question isn’t whether transportation will integrate with AI agents. It’s whether you’ll control how that integration happens. And whether you’ll capture the value it creates. Or whether someone else will.