Key Highlights

This is a statement by Dr Katarzyna Marczuk, CEO of ALEET — finalist of the 17th edition of the Sukces Pisany Szminką Awards in the Startup of the Year category.

In her conversation with the editorial team, she talks about what building a technology startup really looks like. She speaks about artificial intelligence, algorithms, and innovation, but also about responsibility for people, making decisions under uncertainty, and continuously confronting the product with the real needs of the market.

You can read the interview — focused on leadership, responsibility, and building a startup for the transport and logistics sector — at the link below: https://sukcespisanyszminka.pl/katarzyna-marczuk-startup-aleet/

Competitive advantage will be gained not by the companies that buy AI tools first, but by those that learn to use them in everyday operational decisions. Is the implementation of a fleet digital twin merely an investment in new technology, or is it also a moment of building organisational experience that, in a few years, will be just as valuable as the solution itself?

Technology evolves much faster than organisations learn to use its potential. This applies to nearly all solutions based on artificial intelligence algorithms. It is far easier to decide to purchase an AI tool than to make those responsible for its implementation aware of the competencies required and the skills that can be developed through the process.

Technology as strategy, not a project

We have become accustomed to viewing technological implementations primarily as projects. We talk about timelines, system integrations, and return on investment. We also touch on the change that affects decision‑making, operational process management, and the use of data within organisations. This means working in an environment that relies on increasingly large data streams, understanding the relationships between business and operational processes, and assessing the value of system‑generated recommendations. Solutions based on artificial intelligence are therefore not just technological projects — they become elements of business strategy. Yet we rarely consider which competencies emerge during their implementation and how they reshape organisational functioning and workplace culture. From a business perspective, this may be one of the most important values of today’s technological transformation.

In this context, it is worth noting that technologies being implemented today — such as the fleet digital twin — will become widely available in a few years. Competitive advantage will not necessarily belong to the companies that buy AI tools first, but to those that learn to use them effectively in everyday operational decisions.

This is clearly visible in the case of fleet digital twins used in road transport management and powered by AI. Their potential goes far beyond fast integration and data processing. They change how organisations are managed. They allow companies to analyse the consequences of decisions before they are made, model operational scenarios, and support processes in real time. However, technology remains only a tool — ultimate responsibility for its use and the decisions made still lies with humans. Unfortunately, this is precisely why new implementations are often perceived as an additional operational burden. In reality, they become a process of building and developing new competencies among those responsible for using this new potential.

These competencies should not be reduced to knowledge of a specific technological solution. They involve understanding business processes operating in an AI‑supported environment — for example, comparing operational scenarios, assessing the quality of system recommendations, and understanding trade‑offs between costs, fleet utilisation, and SLA (Service Level Agreement).

A broader perspective on technological implementation It is worth looking at the implementation of new technologies from a wider perspective. Those who first acquire competencies for managing AI‑supported operational processes will gain the advantage. In a few years, access to solutions entering the market today will no longer be a differentiator — but the competencies gained during their implementation will allow employees to navigate an increasingly AI‑supported environment more effectively, regardless of their role or age.

Rising driver labour costs, fuel and fleet maintenance expenses, rapidly increasing road tolls, severe SENT system penalties, and deteriorating business conditions in the EU market mean that more and more companies operate on the edge of profitability — or shut down entirely. How can transport businesses grow under such conditions, and is it possible to increase profitability?

Profitability in the transport services sector has been nearly exhausted, and the ability to maintain financial liquidity is rapidly declining. The industry is urgently seeking solutions that could improve the market situation for transport entrepreneurs. Appeals and discussions among industry organisations and transport companies show that they expect legislative changes that would improve business conditions — such as partial fuel excise refunds, maximum payment terms, and other measures related to liquidity and labour costs. What stands out is that discussions about the future of transport increasingly focus solely on regulatory issues.

In the transport services sector, financial results are not determined by a single strategic decision, but by thousands of everyday operational choices. Route planning, vehicle assignment, driver working‑time organisation, loading management, and handling downtime — each of these decisions affects profitability. In practice, even small inefficiencies repeated daily can, over the course of a month or year, determine the financial outcome of the entire company. Management teams see the final financial result, but often cannot quickly identify which processes, vehicles, contracts, or operational events are responsible for its deterioration.

The problem does not lie in data quality — what matters is how quickly data is processed and what decisions are made based on it. A medium‑sized transport company today has TMS, ERP, GPS, Business Intelligence reports, and dozens of Excel sheets. There is more data than ever before. And yet transport company owners still ask the same questions: which operational variant will be the cheapest, how much fleet do we really need, and how will a new contract or a change in work organisation affect financial results? Between data and decision still stands a human, intuition, and an attempt to analyse the future.

This is precisely why the ability to analyse future operational scenarios using AI technology is the future of the Polish road transport sector.

Digital Twins (Digital Twin Systems – DTS) allow companies to move from analysing what has already happened to simulating the consequences of decisions before they are implemented. In practice, this means the ability to quickly identify why a particular vehicle, route, or contract generates a loss — and from which moment the problem occurs. DTS not only enables analysis of results but also simulation of the effects of changes before they are introduced.

The role of digital twins is not to create yet another source of data, but to build a digital model of operations that allows companies to analyse the impact of operational decisions on financial results before they are implemented. In practice, this means shifting from management based on retrospective analysis to management based on simulation and forecasting the consequences of future actions.

This is the direction that already sets the standards for logistics and supply chain management in the most competitive markets — and long‑term competitiveness will be built on decision‑making agility.

We invite you to listen to a podcast featuring Dr Katarzyna Marczuk, CEO of ALEET, recorded by the editorial team of Logistyka.net.pl.

In the conversation with Dr Katarzyna Marczuk, the Logistyka.net.pl editors ask the ALEET CEO how the fleet digital twin supports and enhances the technological management of freight forwarding and fleet operations. How do algorithms support route management, and what is the role of humans in this process? How does the system behave when faced with different requirements across companies and countries? Does it help reduce forwarding and transport‑related costs?

The podcast is available at the following link:

https://logistyka.net.pl/podcast-o-technologii-na-glos-cykl-obok-logistyki-cyfrowy-blizniak-w-zarzadzaniu-flota-i-spedycja/

The dynamic digitalisation of the logistics and transport sector and the growing use of AI in process and fleet management make operational data the most valuable assets of organisations, and their protection an increasingly significant challenge. Experts at Aleet, creators of the fleet digital twin, emphasise that effective data protection is no longer solely the domain of IT departments. It requires a coordinated approach that spans the entire organisation, from operations to strategic management.

At Aleet, we treat operational data security as a strategic element that must be embedded in the system architecture from the very beginning. We design our fleet digital twins for transport management so that data protection, access control, and continuous threat monitoring are integral parts of the operational environment, not layers added at a later stage. As a result, organisations using our technologies build genuine resilience based not only on tools, but on a coherent and secure data management model across the entire organisation.

Who creates the risk – technology or people?

The ISACA (Information Systems Audit and Control Association) report indicates that the main vectors of cyberattacks are social engineering (44%), exploitation of vulnerabilities (37%), and malware (26%). At the same time, 43% of cybersecurity specialists consider an attack highly likely within the next year. In the logistics environment, where operational data is distributed and used across multiple points in the organisation, these threats take on a systemic nature.

A systemic problem

Operational data security today requires the ability to make decisions under uncertainty and effective collaboration between departments. It is an area where technology must be supported by organisational competencies. Building a security culture that includes all employees, not only IT specialists, is crucial. We clearly observe that organisations relying solely on IT teams are reaching the limits of their capacity. This is why cooperation with specialised technology partners is gaining importance. It not only relieves internal teams but, above all, introduces security standards already at the system design stage.

AI supports security

AI is becoming a natural component of operational systems in logistics, but its role in security depends on the quality of implementation. At ALEET, we treat AI as a tool that supports threat analysis and prediction within the operational environment. What matters most is that AI operates within a controlled architecture in which data security is a design assumption, not an add-on. Our fleet digital twins enable not only data processing but also full control over data flows, real-time anomaly detection, and threat scenario simulation. This gives organisations not only efficiency but also genuine resilience to risks.

Data security cannot be limited to a single function or the IT department. In an AI‑driven environment, a holistic approach is essential—one that encompasses all levels of activity. This means integrating security with operational processes, investing in competencies, and cooperating with partners experienced in building secure data environments.

In the logistics and transport sector, this is a prerequisite for maintaining operational continuity and competitive advantage.

For over a year, Aleet has been implementing the fleet digital twin for road freight transport in Poland – a dynamic operational‑economic model that replicates the real functioning of fleets and transport operations. It is an analytical model that processes current and historical data, learns from real operations, and evaluates thousands of operational scenarios before recommending the optimal one.

For years, the road transport sector operated under a simple assumption: whoever scales volumes, has capital, and owns an internal IT department sets the rules. In other words, global players dictate rates, deadlines, and margins, while everyone else adjusts. Today, this assumption has been challenged. Not by EU regulators or investment funds, but by technology – solutions created and available in Poland.

If we accept that in an industry with single‑digit margins (typically between 3% and 5%), every improvement in efficiency has strategic significance, then the reductions in operational costs of up to 30% and profitability increases of several dozen percent – declared and, as Aleet emphasises, confirmed in practice – have a structural impact and determine the viability of the entire business. This is not cosmetic change. It is a transformation of the economic equation.

The myth of technological advantage

Large logistics organisations have long built a narrative around their extensive IT departments as a source of competitive advantage. Hundreds of specialists, multi‑year projects, proprietary planning modules – all intended to create a competitive edge and a barrier to entry for smaller operators. In practice, many of these structures focus on maintaining existing infrastructure, integrations, and day‑to‑day operational support. Innovation is often subordinated to stability, and breakthrough projects frequently stall in internal procedures and compromises.

The Aleet fleet digital twin does not replace transactional systems; instead, it builds an analytical layer above them that reflects real fleet constraints such as driver working hours, downtime, road tolls, contract structures, and characteristic bottlenecks. The model is calibrated to match operational reality – not simplified assumptions. Based on this, AI algorithms analyse thousands of scenarios: different variants of vehicle allocation, changes in the sequence of order execution, modifications to contract structures, or entering new relationships and cargo flows. Each variant is evaluated in terms of unit costs, resource utilisation, and impact on financial results.

One model instead of a team of dispatchers and planners

According to Aleet, a single implemented fleet digital twin model can perform work equivalent to that of an entire team of planners or dispatchers. The cost of implementation corresponds to an ROI (Return on Investment) of no more than 3 to 6 months, often even shorter. Implementation time is measured in weeks, and the solution integrates with any existing systems without requiring IT infrastructure reconstruction.

In practice, this means the ability to safely test high‑risk decisions – from reorganising route networks to changing rate structures – before a company makes financial commitments. Instead of relying solely on the experience and intuition of a planner or dispatcher, the company receives simulations of thousands of variants along with their cost and margin implications.

Poland as a pioneer of change

The first European implementation of a fleet digital twin was carried out in Poland, one of the largest and most competitive road transport markets in the European Union. Today, the solution is already operating in several transport and logistics companies, covering real fleets, real contracts, and real profit‑and‑loss accounts. This is not a pilot or a technology demonstrator, but an operational tool used in daily management. The results are not assumptions – they are precisely calculated. Implemented models have delivered operational cost reductions of up to 30% and significant profitability increases at the level of individual routes, contracts, and entire business segments. In practice, this means a shift in decision‑making – from reactive management to planning based on simulations of thousands of operational scenarios and their impact on margin.

In this context, the question for the industry is no longer whether the technology can work, but how quickly it will become the new standard in transport management.

In a sector where every percentage point of margin determines survival, it is precisely such solutions that change the rules of the game.

In light of new legal obligations, rising costs, driver shortages and increasingly complex supply chains, business owners are asking themselves: how can road transport be managed efficiently today?

The start of the year brings further regulatory and operational changes to the EU road transport industry, adding to the pressure for efficiency. From mid-2026, vehicles with a GVW of 2.5 to 3.5 tonnes that carry out international transport will be required to use smart tachographs. This is a significant change, especially for express and last-mile transport. At the same time, the latest EU statistics show that road transport still accounts for around 75% of all inland freight transport in the EU.

This means the key challenge for companies is how to manage it more efficiently, under cost pressure, a shortage of drivers and growing operational complexity. In addition, as part of a pilot programme, longer heavy goods vehicle combinations of up to 25.25 m are being allowed on Polish roads. While this can improve transport efficiency, it also creates new challenges: it changes route planning, fleet utilization and operating costs. Given these changes, it is worth asking where to look for margin today, and which strategic decisions to make over the longer term.

Where can you find margin, and how should transport be managed today?

The answer seems simple: margin can be “found” in highly advanced technology. The digital twin of road transport is the newest solution, already available and being implemented in Poland, the first such deployment in Europe. It is a dynamic operational and economic model that reflects the real-life operation of a fleet, its transport processes and costs, and then lets you test decisions through simulation before they are put into practice.

The biggest change in managing transport with a digital twin is that you discuss transport with a full picture of the relationships between costs, fleet utilization and planning decisions, because the best scenario is selected from thousands of calculated ones. Importantly, the solution integrates with existing systems and with any other systems, including TMS, and its implementation time is measured in weeks.

As a result, digital transformation becomes realistic not only for the largest operators, but also for mid-sized companies and, in the future, smaller transport companies too, especially in the express delivery segment.

How does a fleet digital twin work, and how much does it cost?

Implementing a fleet digital twin does not require huge financial outlays or a revolution in IT infrastructure. Practically any medium-sized or large transport company, regardless of fleet size, can implement it. A digital twin is an investment that pays for itself quickly, which is why both road hauliers and courier companies are turning to it. The return on investment and the margin come, among other things, from reducing empty runs, better matching orders to the available fleet and drivers, and cost transparency at the level of individual routes and customers. In courier and last-mile services, the greatest value comes from the strategy for area coverage, time windows, delivery priorities, and the impact of volume changes on fleet utilization and service quality. In practice, an investment in a digital twin most often pays for itself within 3 to 6 months, and with large-scale operations or high costs of empty runs, even within 1 to 2 months.

One example is a carrier / road transport operator serving three major contracts at the same time: a retail chain (many delivery points, high deadline pressure), a manufacturer (fewer points, but long loading times) and an e-commerce company (large fluctuations in orders).

Until then, planning had been based on the dispatcher’s “experience,” and costs were settled in aggregate. The digital twin simulated three scenarios: (1) keeping the current fleet allocation, (2) dedicating part of the vehicles to the most unpredictable customer, and (3) shifting part of the volumes to other time windows and changing the loading sequence. The system showed that one of the contracts was “eating up” the margin through expensive overtime and empty runs on the return trip.

After implementing the recommended scenario, the company reduced empty kilometres, improved driver utilization within working time limits, and restored profitability at the level of individual routes and customers.

Another example is the dilemma of whether to buy or rent additional vehicles ahead of the season. For November and December, the company was considering renting extra vehicles, but feared it would not be able to carry the costs if there were not enough loads. The digital twin made it possible to safely test scenarios with volume fluctuations of +10% to +20%, with different fuel prices, driver availability and working time restrictions, as well as scenarios involving subcontractors. The simulation result showed at what point it pays off more to strengthen the fleet, and at what point it is better to change the route structure (e.g. consolidation) and cut empty runs. As a result, the decision was not made by intuition but based on transparent costs and risks, with no impact on current contracts.

The benefits are measurable. A digital twin makes it possible to safely test hundreds of operational decisions without financial risk or errors, in order to choose the most efficient solution. This means a real chance of cutting costs by up to 30% and increasing profits by several dozen percent.

(13 January 2026)

Aleet goes far beyond the traditional models of managing road transport and vehicle fleets. Carriers can now increase cost efficiency and profitability by up to 30% if they change their approach to fleet and operational management.

Aleet is revolutionising road freight transport through the use of fleet digital twins and unique AI‑driven transport analytics. This means far more efficient fleet utilisation, the elimination of empty mileage, higher profits, and greater resilience to current climate and geopolitical challenges.

It marks the end of the traditional Transport Management System (TMS) era. A digital twin instead of a traditional Transport Management System (TMS). The Aleet fleet digital twin is a dynamic operational‑economic model based on intelligent data analysis that replicates real transport processes along with their costs, constraints, and interdependencies. Operating within one coherent analytical environment, it enables the simulation of thousands of scenarios before an operational decision is made. A dispatcher or planner can automatically analyse how specific choices – related to routes, fleet engagement and individual vehicles, schedules, contract handling, or resource allocation – will affect costs and financial outcomes under different market and road conditions. Importantly, thanks to AI algorithms, data processing takes place within seconds, making the work of planners, dispatchers, and freight coordinators more efficient than ever. It allows them to select solution variants without operational or financial risk, ensuring maximum cost and time efficiency of the fleet, and thus profitability.