“With AI, decisions are made before problems happen”

In this exclusive conversation, Nitin Lahoti, Founder and Director of Mobisoft Infotech, explains that AI is fundamentally transforming fleet management from reactive fixing to predictive decision-making. Logistics and EV companies are now embedding AI into existing workflows—not as isolated pilots—to optimize routes, reduce downtime, and improve driver collaboration. Lahoti emphasizes that long-term success requires a strong data foundation and integrating AI intelligence directly into core business operations.

How are logistics companies realistically moving past AI pilot projects to implement solutions that drive measurable change in daily operations?

We are seeing logistics companies move beyond AI pilots. They are focusing on use cases directly linked to operational ROI such as route optimization, predictive maintenance, driver behavior analysis, and demand forecasting. The real progress happens when companies integrate AI into their existing workflows instead of treating it as an isolated experiment.

Organizations are also investing heavily in data readiness. They are cleaning and labeling their operational data to maintain structure. This forms the foundation for scaling AI effectively. Many are combining AI with IoT and automation systems. This enable real-time decisions that improve fleet utilization, minimize idle time, and reduce fuel expenses.

Equally important is how leadership teams are approaching measurement. They are tracking impact early, identifying specific gains in cost, time, and accuracy. Rather than pursuing large, abstract AI initiatives, companies are implementing modular solutions that deliver visible results quickly and then expanding based on those outcomes. This practical, results-oriented approach is what is turning pilot projects into sustainable operational improvements.

What is the single biggest strategic shift in fleet management (beyond cost reduction) that you attribute directly to AI adoption?

If I had to pick one, it would be predictive decision-making. Earlier, fleet management was all about reacting. Managers would fix things whenever vehicles broke down or the route got delayed. Now, with AI, decisions are made before problems happen. It’s not just about saving money; it’s about gaining foresight.

But there’s another subtle shift, data accountability. Fleet managers are no longer relying solely on intuition. They’ve started trusting data-backed insights. AI has made performance discussions more objective, more transparent. And surprisingly, that’s improving collaboration between drivers and operations teams.

It’s not perfect yet. Some still treat AI as a fancy reporting tool. But the ones using it as a real-time decision partner? They’re redefining efficiency and reliability in ways we couldn’t have imagined five years ago.

For smaller EV and logistics firms, what is the most practical first step they should take to use Generative AI to compete smarter against larger competitors?

I think the most practical first step is to use Generative AI to automate and enhance customer and operational intelligence. They should begin with tools that deliver immediate efficiency gains without requiring heavy infrastructure.

A simple example is using AI assistants to streamline daily operations. They can generate delivery summaries, respond to customer queries, draft route reports, or convert field data into actionable insights. These applications save time and reduce manual effort. Teams can focus more on customer service and business growth.

Another useful starting point is using Generative AI to analyze internal data such as driver feedback, maintenance records, or route histories to identify recurring issues or areas for improvement. With tools like ChatGPT or custom-trained models built on their own operational data, smaller firms can access decision intelligence that rivals what larger competitors develop with far greater investment.

In essence, smaller companies do not need to begin with complex AI systems. They should start by embedding Generative AI into existing workflows to automate reporting, planning, and communication. Once the data foundation and processes are well established, expanding into predictive and optimization capabilities becomes both achievable and impactful.

When investing in AI, what is the key factor that determines whether a logistics company will achieve long-term innovation versus a short-term technology fix?

I would say, its the commitment to building a strong data and process foundation. Companies that approach AI as a one-time project may see quick results. But they struggle to sustain meaningful progress. The organizations that succeed over time are those that embed AI into their core operations. For instance, linking it directly to data management and workflow optimization to improve decision-making practices.

In practical terms, this involves several steps. First, investing in clean, connected, and accessible data. This helps the AI models learn, adapt, and deliver accurate insights. Second, redesigning processes to align with AI-driven recommendations instead of placing AI on top of outdated workflows. Third, upskilling teams so that operations, IT, and drivers understand and trust the insights generated by AI. Finally, establishing clear business metrics such as delivery accuracy, uptime, and sustainability, and tying AI performance directly to these measurable outcomes.

Long-term innovation happens when AI becomes a part of how the organization operates daily rather than being treated as a technology project owned solely by the IT team. The real advantage comes from building intelligence into the business itself.

How is Mobisoft Infotech leveraging technology to optimize electric vehicle fleet operations and improve efficiency in the logistics sector?

We are focused on helping logistics and mobility companies make their electric vehicle fleets smarter and more efficient. Our goal is to make them easier to manage through data-driven and connected technology solutions.

Our approach combines AI, IoT, and cloud-based fleet platforms to provide operators with real-time visibility and control over their EV assets. These solutions optimize route planning, energy management, and charging schedules so that fleets operate longer, with higher efficiency and reduced downtime.

For example, our AI-powered route optimization engine continuously adjusts delivery routes based on battery levels, traffic patterns, and charging station availability. Predictive analytics further monitor vehicle health and battery performance, allowing operators to schedule maintenance before issues escalate, which improves reliability and reduces overall ownership costs.

Through advanced telematics and smart charging integration, we also help fleets manage energy consumption effectively, avoid peak-hour costs, and lower their carbon footprint. Our mobility platforms enable seamless management of both electric and internal combustion engine vehicles within a single, unified system.

In essence, Mobisoft Infotech is guiding the logistics sector toward intelligent fleet management, where every route, charge cycle, and operational decision is informed by real-time data, resulting in improved efficiency, performance, and sustainability.