From hype to impact: where is AI truly delivering value today?
Not long ago, when ChatGPT took the world by storm, the promise of AI seemed boundless. Excitement swept through every industry. And the world of logistics is no exception. Think about it: a business defined by efficiency, scaling and timing coincide closely with what AI promises to be. And yet as interest in this burgeoning technology is only increasing, actual real-world outcomes seem to be tentative.
Case in point: the latest BCG survey – in collaboration with Alpega – reveals that not only are most logistics companies in pilot or experimental phase, but that only 13% of LSPs have so far seen a measurable impact.
Read on to discover some of the highlights of this wide-ranging survey, and some of the surprising findings it reveals.
The AI reality check
The consensus is clear: at present, AI is not delivering value everywhere. According to the report, only about one in ten LSPs report measurable financial impact from AI, while most LSPs and shippers are still in exploration or planning mode. Furthermore, lack of clarity on ROI is one of the key barriers to large-scale adoption.
The current status quo reveals that most AI adoption initiatives are still:
- Pilots
- Isolated
- Not scaled
Yet classing this as failure is premature. Rather, it should be categorized as a lack of focus and practical application within day-to-day operations.
Where AI does manage to deliver value
As AI – and the people using it – find its way, it’s becoming clear that certain areas are more fruitful for business yields than others.
The BCG report reveals 3 key areas where AI is making inroads, by increasing outputs.
Transport planning & execution
Leading in terms of impact, this area of logistics is seeing returns with operational AI applications. Examples include:
- Route optimization
Reduces costs, delays, and emissions.
- Network optimization
Better capacity allocation, as well as more resilient operations and cost reductions.
- Backhaul reduction
As the study shows, predictive analytics and algorithms make this possible, with 64% adoption among LSPs, who are narrowly leading shippers in AI adoption.
Demand forecasting
These include:
- Predictive volume planning
Using real-time data and those from previous freight loads, AI can offer more accurate estimates.
- Capacity optimization
Avoiding empty loads and miles, by careful calculation and rapid responses.
Visibility & tracking
60% of shippers focus on visibility and tracking, as per the report, suggesting AI is viewed primarily as a productivity tool at present.
Examples:
- Real‑time tracking
AI cleans and enriches live location data, so teams always know exactly where shipments are and what’s changing.
- Predictive ETA
AI forecasts arrival times using traffic, patterns, and delays, giving far more accurate ETAs than basic GPS.
- Anomaly detection
AI spots unusual delays, off‑route behavior or risks instantly so teams can act before small issues become major disruptions.
Where AI is not yet delivering
As with any new technology, there comes a period of discovery followed by experimentation. Naturally this period includes much trial and error. Even small gains need to be proven and measured, before scaling up. In short: those adopting AI should learn to walk before they run. Proving that wider application of AI is still in the infant phase, the survey highlights these broader areas where AI is not yet delivering value:
- Full end-to-end automation
- Fully autonomous logistics operations
- Large-scale transformation programs
Simply put, being overly ambitious with AI inputs and outcomes seem not yet to be translating to value. Careful implementation seems to be the way to wider scaling.
That said, the 13% of providers who have embedded AI in core operations are already pulling away from the 56% still exploring or testing it.
Finding success means following a pattern
As the survey highlights, successful AI adoption shares 3 characteristics:
- Close to operations
- Embedded in workflows
- Measurable impact
And yet, in many of these results, the value measured stems from targeted use cases rather than enterprise-wide transformation. Though these cases hint at the road ahead. For now, scaled, end-to-end adoption remains rare across the industry.
What’s revealing is that earlier AI concerns revolved around cost and tech limitations, whereas AI success now has become an operating model challenge. As such, organizations that master a) integration, b) change management, and c) outcome measurement will pull ahead. Three key points to focus on.
How executives can act on this
The survey reveals an industry slowly coming to terms with AI, and gently making inroads to benefit from it. Here are some ways to do the same:
- For now, focus beats scale
Don’t try and let AI do everything. Use the learnings from 2 or 3 use cases, with clear results, and apply the techniques and systems to help achieve it.
- Never lose sight of ROI – from the start
AI is a tool, and all tools should have a purpose. Return on Investment should be the guiding North Star, rather than needlessly complicating existing processes. Logistics providers must make ROI explicit and operational, not conceptual.
- Integrate AI into systems
AI should blend with your workflows, prioritizing outcomes. This requires redesigning TMS and operational workflows around AI, not bolting it onto unchanged processes.
- Avoid innovation for innovation’s sake
Launching pilots without an eye on follow-though is a pointless endeavor. A path to scale should always be in your plan, and even better, in your reach.
Embracing possibilities: Alpega’s AI roadmap
Like almost anyone in logistics, Alpega is excited about the promise and potential of AI. Collaborating on this survey helps us to see potential ways the whole industry can benefit. Yet for this to happen, it’s not just about identifying use cases. It’s about taking the most salient learnings and applying them to real business environments.
To quote Alpega CEO, Daniel Cohen: “We’re focusing investment on the core advantage: a unified platform connecting shippers and carriers, built on a strong foundation of shared data.”
This core positioning presents R&D that’s centered on turning fragmented logistics data into real-time, actionable signals.
To achieve this, Alpega is embedding AI directly into core workflows, so decisions are automated and scaled.
All said: AI only matters if it’s used daily and removes manual work.
In conclusion
AI is already creating value in logistics — but only where companies focus on execution. This does not mean a “once size fits all” approach. It’s finding the unique solution that serves you, your clients, and operations.