The Next Chapter in Connected Vehicle Experience
From Intelligence to Agency:
The conversation around connected vehicles has changed.
Not long ago, the dominant question was infrastructure: how to connect vehicles, ingest telemetry at scale, and get data flowing reliably into cloud systems. That problem, for most enterprise operators, is largely solved. IoT has become a given. The focus has shifted decisively to what you do with it.¹
What’s emerging in its place is a more demanding and more consequential question: can the systems built on top of that infrastructure actually think, and act, on behalf of the people who depend on them? The answer, increasingly, is yes. But most organizations are nowhere near ready to take advantage of it.
The Plateau After Connectivity
The last several years produced extraordinary results in vehicle connectivity. Fleet operators can now:
Track assets in real time with granularity that would have seemed exotic a decade ago
Monitor driver behavior and receive predictive maintenance alerts
Review route histories and exception logs across entire fleets from a single screen
And yet, operational clarity has not kept pace with operational data. The dashboards are full. The insights are not.
What survey data from the transportation industry is now confirming is something practitioners have known for some time: 95% of companies consider AI important or very important to their operations. Only 19% are currently using agentic AI systems.² The gap between intention and execution is an architecture problem, and, more specifically, an experience problem. Organizations have invested in the data layer without investing equally in the layer that makes data actionable.
The Emergence of Agentic Operations
The industry is converging on a term for what comes next: agentic AI. It is worth being precise about what this means, because the distinction matters operationally.
Generative AI assists. It drafts reports, summarizes data, surfaces recommendations, and requires a human to prompt it and act on what it produces. Agentic AI operates differently. Agentic systems plan, call tools, take actions, and self-correct.³ They are not waiting to be asked. They are monitoring, interpreting, deciding, and executing, within defined parameters, but continuously and without the latency of human-in-the-loop workflows.
For fleet and mobility operations, this plays out in concrete ways:
Routing: An agentic system can continuously monitor and adjust routes based on real-time traffic conditions, weather changes, and unexpected road closures, making instant decisions without manual intervention.⁴
Maintenance: Predictive alerts transition from a dashboard notification that someone may or may not act on into a coordinated workflow that schedules service, checks parts availability, and minimizes downtime—automatically.
Dispatch: The dispatcher is no longer sifting through exceptions. The system handles routine exceptions and surfaces only those that genuinely require human judgment.
The most significant shift is conceptual: copilots suggest, agents act.³
Why the Experience Layer Becomes Even More Critical
The rise of agentic systems elevates the importance of the experience layer. If anything, the stakes are higher when AI is taking action rather than merely presenting information.
For agentic systems to operate responsibly, they need to work from a coherent, contextual model of vehicle behavior. Not raw telemetry, but interpreted meaning. Not a stream of sensor values, but a persistent understanding of what is normal for this vehicle, this operator, this route, this time of day. Without that foundation, agentic systems act on noise rather than signal, and the consequences of acting on noise scale with the autonomy of the system.
The systems that will perform best are not those with the most data, but those with the richest interpretive context. In an agentic world, these are not nice-to-haves:
Vehicle identity and behavioral history
Operational patterns across routes, operators, and time
Environmental and scheduling context
Persistent, evolving representations of vehicle health and usage
They are prerequisites.
Software-Defined Vehicles Raise the Stakes, and the Opportunity
At the same time, the vehicles themselves are evolving in ways that create both new complexity and new possibility. The shift toward software-defined vehicles is built on two critical pillars: hardware virtualization and edge AI.⁵ Vehicles are increasingly capable of processing intelligence locally, interpreting driving conditions, operator behavior, and environmental context directly within the vehicle, without adding network latency.
For enterprise operators, this creates a new architectural challenge. OEM-embedded intelligence and fleet-level intelligence need to work together, not in parallel. OEMs have struggled to scale in-house solutions. Building standalone apps for vehicle services has proven inefficient due to fragmented markets and regulatory hurdles.⁶ The result is a growing recognition that the value chain requires platform partners; systems that can aggregate, interpret, and act across heterogeneous vehicle populations without depending on any single OEM’s ecosystem.
This is precisely the moment where a vehicle-agnostic experience layer becomes strategically essential.
The Consolidation Imperative
One of the clearest signals from enterprise operators over the past year is a strong preference for consolidation. Fleets are consolidating operations onto AI-powered platforms, fostering greater cohesion and efficiency across teams.⁷ The era of best-of-breed point solutions for every operational function is giving way to integrated platforms that share a common data model and present a unified operational picture.
This consolidation pressure is healthy, but it comes with a risk. Platforms that consolidate around the data layer alone reproduce the original problem at larger scale. More data, better organized, is still not insight. What consolidation needs to deliver is not just data unification but interpretive coherence:
A shared understanding of what the fleet is doing and how it is performing
A consistent operational vocabulary across teams—dispatch, maintenance, compliance, finance
A single model of vehicle identity that persists across ownership, systems, and time
The winners in the next generation of fleet intelligence will treat experience design as a first-class engineering concern, not a layer applied after the fact.
The Human Remains the Measure
Despite the rapid advancement of agentic capability, the best practitioners in this space are emphatic on one point: the goal is not to remove humans from operations. AI agents aren’t about replacing dispatchers, safety managers, or maintenance teams, they’re about amplifying human judgment by eliminating data wrangling, pattern detection, and routine decision fatigue.³
This distinction has real architectural implications. Systems designed to amplify human judgment must surface information differently than systems designed purely for automation. Context, explanation, and appropriate escalation are not UX considerations, they are trust infrastructure. An operator who doesn’t understand why a system is recommending a course of action won’t act on that recommendation consistently or confidently. An operator who trusts the system’s interpretation will use it as a genuine extension of their own judgment.
Building that trust is not a sales challenge. It is a product design challenge, and it requires sustained investment in the experience layer over time.
What This Means for Enterprise Strategy
For enterprise operators evaluating connected vehicle platforms for 2026 and beyond, a few things are becoming clear:
Infrastructure is commoditizing. Connectivity, device management, OTA updates, and telemetry pipelines are table stakes. Differentiation will not come from those layers.
Experience is the new battleground. The quality of interpretation, the coherence of vehicle identity over time, and the capability of agentic workflows are where competitive advantage will be built and defended.
The data investment is not enough. Organizations still treating connected vehicles primarily as a data infrastructure problem are already a cycle behind. The question is no longer how much data can be collected—it is how well that data can be understood and how confidently it can be acted upon.
The experience layer is no longer an aspiration. It is the terrain on which the next generation of connected vehicle competition will be decided.
Ride Intelligence™ exists to close the gap between what vehicles know and what people can do with that knowledge; turning operational complexity into clarity that actually moves organizations forward.
Notes
IoT Analytics, State of Enterprise IoT: From IoT to Autonomous Connected Operations, February 2026. https://iot-analytics.com/state-of-enterprise-iot-from-iot-autonomous-connected-operations/
Fleet Maintenance / Fleet Equipment Magazine, Is Agentic AI Profitable for Heavy-Duty Fleets?, 2025. https://www.fleetmaintenance.com/shop-operations/ai-and-software/article/55292730/is-agentic-ai-profitable-for-heavy-duty-fleets
FleetRabbit, AI Agents Will Reshape Fleet Operations in 2026, December 2025. https://fleetrabbit.com/blogs/post/ai-agents-fleet-operations-2026
FleetOwner, Unlocking Fleet Efficiency: How Agentic AI Transforms Transportation Management for Heavy-Duty Truck Fleets, 2025. https://www.fleetowner.com/perspectives/ideaxchange/blog/55293160/unlocking-fleet-efficiency-how-agentic-ai-transforms-transportation-management-for-heavy-duty-truck-fleets
IoT Analytics, Software-Defined Vehicles Adoption: 4 Dimensions & Leading OEMs, January 2026. https://iot-analytics.com/software-defined-vehicles-adoption-4-dimensions-leading-oems/
IoT World Today, 2025: The Year of the Connected Vehicle Paradigm Shift, February 2025. https://www.iotworldtoday.com/automotive-connected-vehicles/2025-the-year-of-the-connected-vehicle-paradigm-shift
GoMotive, AI in Fleet Management: A Comprehensive Guide, March 2025. https://gomotive.com/ai-fleet-management-guide/