Technology, data and AI
Technology, data and AI
Connecting sourcing decisions with day-to-day execution
It is difficult to create a feedback loop when procurement, execution, market intelligence and performance data sit in separate systems. Technology can reduce manual work and help teams connect what happens before, during and after a tender.
When asked where technology, including AI and AI agents, could have the greatest impact, respondents selected comparing bids and modeling award scenarios (54%), accessing and interpreting freight market intelligence (52%), analyzing historical data and forecasting requirements (52%), and monitoring performance and identifying corrective action (40%).
Where technology and AI could have the greatest impact
Comparing bids and modeling award scenarios
Accessing and interpreting freight market intelligence
Analyzing historical data and forecasting transportation requirements
Monitoring performance and identifying when corrective action is needed
Comparing bids and modeling award scenarios
Accessing and interpreting freight market intelligence
Analyzing historical data and forecasting transportation requirements
Monitoring performance and identifying when corrective action is needed
Technology can also help when execution misses the plan. If an awarded carrier rejects a load, a connected platform can help identify qualified alternatives, benchmark rates and tender within predefined rules. Those transactions can then reveal where rates, capacity commitments or sourcing strategies need to change.
Transporeon perspective
Automate spot sourcing with clear human oversight
Autonomous Procurement helps shippers automate spot freight sourcing from carriers they know and trust, using rules and limits set by their team. You can define which situations require human review, monitor the results and adjust the approach as conditions change.
Build the data foundation for practical AI use cases
Grounded in reliable data, AI tools can help logistics teams query operational metrics, surface lane exceptions, and model market scenarios using natural-language questions. However, the effectiveness of any application still depends on robust data foundations, connected systems, and clear governance.
Poor or inconsistent data quality was the leading barrier to AI adoption, followed by security, privacy, or governance concerns, unclear ROI, and doubts about reliability or accuracy. When underlying data quality is compromised, AI loses most of its potential value—as one respondent noted, even “capturing total costs of a shipment, including linehaul, fuel, accessorials, etc., for use in downstream analysis” remains a challenge.
Useful AI requires reliable data, connected systems, clear rules for data use, and people who can assess its recommendations and remain accountable for decisions.
Start with one specific problem—such as identifying lanes with declining carrier acceptance—define the data needed to investigate it, and agree who will review the findings before action is taken.
Transporeon perspective
Talk to your data in natural language
The AI-powered Market Insights Assistant can answer questions about available rates as well as trends across specific lanes and equipment types. With guided prompts, you can generate custom reports to help you understand the implications of the market data. For additional workflow-level support, Trimble Arc Agent provides dozens of ready-to-use skills and built-in connections.
