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with new tools. Success depends on linking workforce management, time recording, and financial controls so that technology reduces friction rather than adding to it.
What are the most common integration gaps you see across logistics and wholesale organizations? These generally sit between operational systems such as warehouse, transport, and fleet platforms, and the finance, workforce, and governance systems that support them. While each system may work well alone, many organizations are still operating in silos rather than as part of a connected model.
Breaking down these silos allows operational data to flow through into finance and workforce management in near-realtime, creating a more accurate and actionable view of cost, productivity, and compliance across the organization.
How do these integration challenges directly affect decision-making, accountability, and operational visibility? Poor integration slows decision-making and reduces confidence in the data. When information is fragmented, leaders and managers spend time reconciling reports rather than acting on them, delaying responses and increasing reliance on instinct over evidence.
Our research also highlights a clear perception gap between senior leaders and managers on the ground around whether systems are genuinely enabling effective decision-making. That disconnect weakens accountability, as it becomes harder to trace outcomes back to specific decisions or teams.
The main barriers are data readiness, trust, and skills. AI depends on clean, connected data, yet many logistics operators still struggle with fragmented systems that limit AI’ s ability to operate within day-to-day workflows.
This gap between ambition and reality is pronounced: while AI is widely cited as a top investment priority, 45 percent of organizations report that it is used in less than a quarter of their everyday operations, highlighting how foundational issues are holding adoption back.
Concerns around opaque decision-making can slow adoption unless organizations have clear governance around how AI is developed and used. OneAdvanced has addressed this through a UK data-sovereign AI engine embedded within its platform, and by becoming one of the first 100 organizations globally to achieve the ISO 42001 certification, which provides independent assurance that AI is being governed responsibly, helping organizations move from experimentation into trusted, operational use.
Where do you see AI delivering the most immediate, practical value for logistics operators today? The most immediate value from AI is emerging in areas such as governance and compliance, demand and labor forecasting, spend analysis and cost anomaly detection, predictive maintenance, asset utilization, and exception management.
This focus on practical, real-world value is reinforced through OneAdvanced’ s strategic partnership with the Road Haulage Association, where we work closely with operators to ensure AI-enabled capabilities are shaped around regulatory, compliance, and day-to-day operational realities.
AI investment is growing, yet adoption remains limited. What’ s holding organizations back from using AI more widely in warehousing, transport, and fleet management?
How can organizations modernize their systems without overwhelming already stretched operational teams? Planned, incremental change is essential. Organizations should prioritize platforms that
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