Fleet telematics is entering a new phase. As connectivity becomes embedded in vehicles at the factory, the industry is moving beyond the question of whether vehicles should be connected to how that connectivity should be deployed, governed and turned into measurable business value. For fleet operators, leasing companies and mobility providers, the challenge is no longer collecting data but deciding which data matters, how it should be integrated and which technology stack best supports increasingly complex operational requirements.
The transition raises strategic questions across the connected mobility ecosystem. Original equipment manufacturer-native connectivity promises lower deployment costs and broader standardization. However, aftermarket hardware continues to deliver capabilities that remain difficult to replicate through embedded systems alone. At the same time, the rapid growth of artificial intelligence, software-defined vehicles (SDVs) and electrification is reshaping expectations of telematics platforms, placing greater emphasis on interoperability, cybersecurity, predictive analytics and the ability to convert information into operational decisions rather than dashboards.
Commercial considerations are evolving just as quickly. Fleet customers are weighing the benefits of industry consolidation against the risks of vendor lock-in, while regulators, OEMs and technology providers continue to redefine the rules governing data access, privacy and platform control. Against this backdrop, the companies that succeed are likely to be those that combine technical integration with operational expertise, helping customers navigate an increasingly fragmented landscape without adding unnecessary complexity.
To explore these issues — from hybrid connectivity models and AI-enabled fleet management to interoperability, cybersecurity and the future role of independent telematics platforms — we spoke to Carlo Stefanelli, Chief Technology Officer of Targa Telematics.
Carlo Stefanelli
[Source: Targa Telematics]
The following is an edited transcript of the conversation.
Mobility Global: As large leasing, rental and corporate fleets move from aftermarket devices to OEM-native connectivity, where does the business case break down? Specifically, what key use cases still require aftermarket hardware, higher-frequency data or direct vehicle access — and how would you advise a fleet to choose between OEM data, aftermarket devices or a hybrid model?
Carlo Stefanelli: The discussion around OEM connectivity versus aftermarket devices should be framed as a question of which data source is best suited to enable specific business use cases. The key point is not whether OEM data is better than aftermarket hardware, but whether a given use case can be effectively supported by the available data source.
There is no universal answer, as the optimal technology mix depends on several factors, such as the use cases the customer wants to enable. While basic fleet visibility or electric vehicle monitoring can be supported through OEM connectivity, more advanced services may still require aftermarket devices.
The fleet profile matters, too. For instance, a leasing company has different operational requirements from a corporate fleet. The same connectivity strategy is unlikely to fit every fleet.
Third, deployment constraints play a critical role. If a leasing company wants to activate connected services on vehicles that have already been delivered to customers, OEM connectivity may be the only practical option.
Overall, the most effective approach remains a hybrid model that combines OEM data, aftermarket devices and other internet of things technologies.
Fleet platforms increasingly generate vast volumes of data, but not all signals change operational outcomes. If you were auditing a major fleet’s telematics program, which data streams would you challenge as low-value or misleading, and which underused signals would you prioritize because they have a direct impact on cost, risk, utilization, or residual value?
Not all data generated by fleet platforms translates into operational impact. The most relevant data streams are those supporting fleet optimization, predictive and proactive maintenance, safety and risk management, and vehicle residual value preservation and optimization.
Rather than volume, the real differentiator is the ability to transform data into actionable insights, identifying the right data for the high-value use cases based on domain expertise. Simple data can deliver significant value, while complex data can have low impact.
Targa Telematics operates in a multi-OEM environment where customers expect a single, normalized view of their fleet. In practice, how would you define true interoperability? Is it enough to harmonize data fields, or does interoperability also require consistency in latency, diagnostic depth, consent management, uptime and commercial terms across OEMs?
Harmonized data is the requirement that customers ask for. What is often implicit is the ability to provide data availability and reliability; cybersecurity; activation anomaly management, as each car manufacturer has its own procedures for activation, error reporting, and issue resolution; supervision and continuous monitoring, so that all vehicles transmit data correctly and consistently.
As a key integrator and enabler, Targa Telematics collects, analyses and processes vast amounts of vehicle-generated data to deliver actionable insights that enhance fleet optimization and broader mobility applications. Beyond technology, we also manage the operational, contractual and governance aspects of connectivity, ensuring compliance with data privacy regulations.
Ultimately, true interoperability is about delivering a secure, reliable, scalable and fully managed operating model that allows customers to extract value from connected vehicle data regardless of vehicle brand or connectivity source.
AI is often presented as the next major leap in fleet optimization, but many operators still need explainable, operationally actionable recommendations. In which fleet use cases does AI already create measurable value today — for example, predictive maintenance, claims reduction, driver risk, EV charging optimization or utilization forecasting — and where is the industry still selling correlation as intelligence?
Maintenance is a strong example of where AI already delivers value. The real shift comes with Agentic AI and the move from assistants that answer questions to systems that prioritize and execute actions. This transforms maintenance from a reactive to a proactive process. The true value lies not in having more data, but in the ability to synthesize context and drive action, enabled by an integrated ecosystem of reliable data, processes and stakeholders.
As Gartner has highlighted, the pace of development in Agentic AI is exceptionally rapid, with multi-agent systems and orchestration platforms expected to become a major competitive battleground over the next few years.
When a large fleet begins electrification, the business case often looks compelling on paper but becomes more complex in operation. What assumptions most commonly fail in the real world - around charging access, driver behavior, route suitability, battery degradation, energy pricing, reimbursement, vehicle availability or residual values?
The success of fleet electrification depends heavily on geography, operating conditions and vehicle usage patterns. Before making electrification decisions, fleets should always conduct a detailed assessment to determine which vehicles, routes and missions are suitable for EVs, and which are not.
What often fails is the assumption that every vehicle can be electrified at the same pace. In reality, factors including charging infrastructure availability, climate conditions and local energy costs vary significantly across countries.
A data-driven electrification assessment is crucial, as it enables fleet managers to identify where electrification delivers both environmental and economic benefits.
In connected mobility, the legal answer to data ownership is often less important than who controls access, consent, portability and commercialization. In your view, where is the balance of power moving: toward OEMs, fleet operators, drivers, regulators or independent platforms? And what does that mean for companies like Targa Telematics?
In Europe, this topic is governed by the General Data Protection Regulation (GDPR), which clearly defines the roles of all parties involved in the collection and use of data.
However, these regulations are often not fully understood by organizations embarking on connected mobility projects. For this reason, we provide legal and compliance support, assisting customers in understanding their obligations, defining the governance framework, managing privacy requirements and engaging with relevant stakeholders. This ensures that telematics programs are implemented in full compliance with applicable regulations while maintaining transparency and trust among all parties involved.
SDVs could either deepen the role of telematics platforms or reduce them to data resellers and integration layers controlled by OEMs. What conditions would allow an independent telematics provider to gain strategic relevance in a SDV ecosystem, and what conditions would make OEMs dominant?
The data collected from vehicles is primarily used to improve and re-engineer operational processes, which are typically digitalized across various applications and systems that can benefit from vehicle data. Data ingestion should thus be viewed as integral to every system that leverages telematics data, rather than as a standalone component within the architecture.
Telematics and data ingestion function as a plug-in that enables individual applications to consume and use vehicle data within their specific business context.
Looking ahead, I do not expect this model to change. The systems of record that support business processes will continue to be the customer's own applications and enterprise systems. The key challenge remains the effective integration of vehicle data into these systems to generate tangible business value.
Cybersecurity in telematics is often discussed in abstract terms, but the practical risk surface is broad: vehicles, cloud platforms, APIs, mobile apps, driver identities, third-party integrations and internal processes. If you had to prioritize the top three near-term risks for a pan-European fleet telematics provider, what would they be and how would you mitigate them?
Cybersecurity is a central issue in connected mobility. Targa Telematics manages large volumes of connected vehicle and IoT data, where security and traceability are not simply IT requirements but business-critical assets.
In a pan-European telematics environment, three priorities stand out. The first is data protection and data integrity, ensuring that information remains accurate, secure and trustworthy throughout its lifecycle. The second is infrastructure resilience and service continuity while the third is advanced monitoring and incident response capabilities, including Security Operations Center (SOC) activities, SIEM technologies, threat intelligence and response procedures.
Cybersecurity is a fundamental pillar of our platform. Our solutions have been developed according to a Secure by Design approach, ensuring that security and privacy are embedded across the entire data lifecycle. Continuous monitoring, governance and incident response capabilities are combined with rigorous operational processes to achieve the highest levels of reliability and trust.
Far from a technical issue, cybersecurity is a core enabler of trust. As fleets become increasingly connected, protecting data will become a critical differentiator for telematics providers.
The fleet technology market is consolidating, but scale does not automatically create better customer outcomes. From a fleet customer’s perspective, which capabilities genuinely improve through consolidation — such as OEM access, geographic coverage, data benchmarking, product investment or compliance support — and where does consolidation risk create lock-in, slower innovation or weaker service quality?
The real question in consolidation is whether greater scale translates into greater value for customers. There are several areas where scale genuinely improves outcomes.
First, access to OEM ecosystems becomes easier for larger providers, as building and maintaining integrations across multiple manufacturers requires significant investment.
Second, consolidation enables broader geographic coverage combined with local expertise. International fleets expect a single technology partner capable of supporting operations across multiple countries while understanding local requirements.
Third, scale unlocks higher levels of investment in innovation. As our CEO Nicola De Mattia recently highlighted, AI is likely to create a gap between technology leaders and followers, making scale an important enabler of innovation.
Fourth, larger platforms can generate richer benchmarking and data intelligence, creating opportunities for improvement that smaller datasets cannot replicate.
Looking three to five years ahead, which connected mobility trend do you believe is most overestimated by the market — autonomous fleets, fully OEM-native telematics, EV transition speed, AI-led fleet automation, vehicle-to-grid, or SDV monetization — and what evidence would make you change your mind?
The real transformation will come from AI. While IoT provides the data foundation, AI is what turns data into actions and measurable business outcomes. Agentic AI will reshape customer processes, automating complex workflows, optimizing operations and enabling faster decisions at scale.
IoT and AI will exert a significant impact on customer processes. Organizations that do not adopt AIoT [AI and the Internet of Things] will be left behind.
