AI-Enabled Agile Delivery in Toll Collection Systems

Aycan Altınöz · 2026-10-22 · Updated: 2026-10-22

Short answer: Toll collection systems are systems with no tolerance for downtime, where field hardware, central software, payment infrastructure, and public-sector and operator stakeholders work together. What makes delivery most difficult in these systems is the high availability requirement, the integration of software with field hardware, and teams that are often distributed across different countries. AI-supported agile transformation makes delivery in this domain more predictable and safer through test automation, CI/CD, flow metrics, and log analysis. The measure of speed is not the number of releases; it is whether the system in the field continues to operate uninterrupted and correctly.

Why Is Delivery Difficult in Toll Collection Systems?

There is no tolerance for downtime: Tollbooth, free-flow, and parking systems operate at all hours of the day. An error affects traffic flow, revenue collection, and user experience at the same time.
Field hardware and software are intertwined: License plate recognition cameras, vehicle classification sensors, tag and card readers, tollbooth hardware, and central software must work correctly together.
Payment and reconciliation are critical processes: Correct matching of passage records, reconciliation with payment systems, and dispute processes leave no room for error.
Multi-stakeholder structure: Public institutions, operating companies, banks and payment institutions, hardware suppliers, and field teams are part of the same project.
Distributed teams and different markets: The product may operate simultaneously in different countries, under different regulatory and operational conditions; teams may also be located in different countries.
Long-lived systems: Systems in the field operate for years; existing operations must be protected while new features are added.

A Field Experience: Agile Transformation with Distributed Teams in Three Countries

In an organization developing toll collection systems, the agile maturity journey of distributed teams in Turkey, India, and the Philippines was managed. The prominent practices and outcomes in this work were:

Approximately 15% increase in predictability: Success metrics were defined together with the teams; planning and commitment processes were monitored through these metrics, increasing delivery predictability by approximately 15%.
Combining CI/CD and AI-supported SDLC with the Scrum rhythm: CI/CD and AI-supported software development processes on Azure DevOps were synchronized with Scrum ceremonies; manual testing effort decreased and release quality improved.
Making bottlenecks visible with data: Lead time and cycle time analyses were monitored through work tracking boards, enabling process bottlenecks to be identified and eliminated.
A shared rhythm across different time zones: A shared planning and review rhythm was established among distributed teams, reducing the likelihood that dependencies would turn into delays.

How Should Agile Ways of Working Be Adapted in Toll Collection Systems?

Dimension	Classic software product	Toll collection system
Release	Frequent and direct	Gradual: first a test field or a single lane/tollbooth, then general release; a rapid rollback plan is mandatory
Testing	Software tests	Additionally, hardware integration tests, load and accuracy tests with real passage data, reconciliation tests
Definition of Done	Code reviewed, tests passed	Additionally, validated in the field environment, transaction accuracy and performance thresholds met, rollback plan ready
Stakeholders	Product and business unit	Public institutions, operator, payment institutions, hardware suppliers, and field teams
Success criterion	Delivered feature	System availability, transaction accuracy, prevention of revenue loss, and delivery predictability

Where Does AI Create Value in Toll Collection Systems?

Area	AI use	Risk level	Required control
Software delivery	Code assistants, generation of test scenarios and test data, support for code review	Medium	Automated quality gates and human review
Requirements and documentation	Summarizing specification and contract requirements, identifying missing scenarios	Low	Validation by analysts and the business unit
Log and operations analysis	Finding failure patterns from field device and system logs, incident summarization	Medium	Validation by the operations team
Image processing and license plate recognition	Flagging unreadable or suspicious records, prioritizing the manual review queue	High	Accuracy thresholds, human review, dispute process
Revenue assurance	Anomaly detection in unmatched passage and payment records	Medium–high	Approval by the reconciliation team
Project management	Analysis of flow metrics, risk signals, automatic preparation of status reports	Low	Assessment by the project manager

For every AI output that charges the user or affects the penalty and dispute process, the decision must remain with a human, accuracy thresholds must be defined, and the dispute process must be clear.

Working with Distributed Teams: Different Countries, Shared Rhythm

Shared working hours: At least a few hours of shared window should be planned for teams in different time zones; critical ceremonies should be placed within this window.
Written and visible communication: Decisions, acceptance criteria, and dependencies should be kept written and up to date in the work tracking tool; AI-supported summaries enable teams in different time zones to access information quickly.
Shared definitions: Definition of Done, quality gates, and metric definitions should be the same across all teams; otherwise, comparison and integration between teams become more difficult.
Cultural awareness: Ways of giving feedback, raising issues, and making decisions vary from culture to culture; coaching should make these differences visible.
End-to-end ownership: Teams should be held responsible not only for their own components, but also for how the components work together.

Which Metrics Should Be Tracked in Toll Collection Projects?

Layer	Metrics	What does it indicate?
Delivery	Lead time, cycle time, sprint goal achievement rate, waiting caused by dependencies	How fast and predictable delivery is
Quality and stability	Defects escaping to production, change failure rate, number of rollbacks	Whether speed is being gained at the expense of stability
Operations	System availability, mean time to resolve incidents, number of field interventions	The reliability of the system in the field
Transaction accuracy	Rate of correctly matched passages, rate of records requiring manual review, dispute rate	Whether revenue and user trust are being protected
Artificial intelligence	Reduction in manual testing effort, shortened root cause analysis time through AI-supported analysis	The real contribution of AI to delivery

Roadmap for Teams Developing Toll Collection Systems

Current state and baseline metrics: Map the delivery flow, dependencies between teams, testing effort, and the source of defects found in the field.
Shared operating model: Create a common Definition of Done, quality gates, and metric definitions for all teams; establish a shared rhythm for distributed teams.
Test automation and CI/CD: Implement automation that will reduce manual testing effort, as well as gradual release and rollback mechanisms.
Gradual use of AI: Start with test design, documentation, log analysis, and reporting; preserve human approval in areas that charge users.
Measurement and improvement: Track predictability, quality, availability, and transaction accuracy against the baseline period; prioritize bottlenecks with data.

Nex4Future Approach

Nex4Future addresses the agile transformation of distributed teams, CI/CD and AI-supported software development processes, and data-driven delivery management together in toll collection and similar systems that must operate without interruption. Engagements can begin with the AI Delivery Health Check, in which delivery processes and AI opportunities are analyzed in two weeks. During the implementation phase, AI-Supported SDLC Transformation, Hands-on Scrum and Kanban, and Agile Operating Model Design and Scaling programs are designed according to the need.

For experience in other sectors: Sector Experience

Frequently Asked Questions

Can agile methods be applied in toll collection systems?

Yes. However, releases should be performed gradually, first validated in a test field or on a limited number of lanes and tollbooths, and a rapid rollback plan should always be ready. Definition of Done should include validation in the field environment, transaction accuracy, and performance thresholds.

Where can AI be used in toll collection systems?

The main areas are code assistants and test scenario generation in software delivery, requirements and specification analysis, analysis of field and system logs, anomaly detection in unmatched passage and payment records, and project reporting. Human approval should be preserved for decisions that charge users or affect the dispute process.

How is agile work managed with distributed teams in different countries?

Shared working hours should be planned, decisions and dependencies should be kept written in the work tracking tool, Definition of Done and metric definitions should be the same across all teams, and cultural differences should be made visible through coaching. In an engagement carried out with teams in Turkey, India, and the Philippines, this approach increased delivery predictability by approximately 15%.

How can testing effort be reduced in toll collection systems?

Test automation connected to the CI/CD pipeline, test scenarios based on real passage data, and AI-supported test design reduce manual testing effort. Hardware integration and field tests, together with automation, should be carried out as separate work items in the plan.

How is success measured in toll collection projects?

Delivery metrics (lead time, cycle time, sprint goal achievement), quality and stability metrics (defects escaping to production, change failure rate), operations metrics (system availability, incident resolution time), and transaction accuracy metrics (rate of correctly matched passages, dispute rate) should be tracked together and against the baseline period.