Artificial intelligence (AI) has dominated conversations across the logistics and transportation sector, all largely pointing toward AI as the catalyst for a new era of efficiency and resilience. 

Read also: How Artificial Intelligence Is Reshaping Global Supply Chains

But recently, there’s been a noticeable shift. The enthusiasm that’s shaped boardroom discussions, industry conferences and transformation roadmaps for the past few years has started to cool, and more logistics leaders are questioning whether AI can deliver the transformational outcomes that were once promised.

At first glance, that may appear to be a warning sign. In reality, it could be exactly what the industry needs.

If we delve beneath the surface of the scepticism, what we’re seeing is not a rejection of AI, but an industry entering a much more mature phase of AI adoption; one that’s defined less by hype, and more by scrutiny. Companies are asking tougher questions, demanding evidence of value rather than accepting ambitious promises at face value. 

That scrutiny is increasingly justified. Research from BCG found that only 22% of organisations have progressed beyond proof-of-concept AI deployments, while just 4% are generating substantial value. 

In an industry where margins are tight and operational performance is everything, scrutiny is not slowing progress – as it’s so often assumed to – it’s helping ensure investment is directed towards technologies that can genuinely improve outcomes.

This shift is not a failure of AI, and nor should it be seen as one. It’s what happens when innovation meets operational reality. 

The end of the ‘AI will fix everything’ era

One of the main drivers behind growing scepticism is that many organisations initially approached AI as a universal solution to solve all the problems, rather than a targeted business tool.

That mindset was always likely to encounter challenges in logistics. The sector operates against a backdrop of volatile supply chains, labour shortages, regulatory complexity and geopolitical uncertainty. Businesses cannot afford to invest in ways that may not produce value months or years down the line. They need technologies that can demonstrably improve operations.

Yet many early AI initiatives have been driven more by momentum than necessity. Organisations rushed to introduce generative AI interfaces, deploy chatbots or launch broad transformation programmes without first identifying a clearly defined operational challenge.

The outcome is often a predictable one. Teams spend time adapting to new systems without seeing meaningful gains and processes become more complicated. Perhaps, most dangerously, expectations quickly outpace results.

The issue is not AI itself, the issue is deploying AI where it does not materially improve decision-making or operational efficiency. In logistics, technology succeeds when it helps people make better decisions faster. If it cannot eliminate manual effort or improve operational performance, it risks becoming another layer of complexity rather than a competitive advantage.

Where AI is delivering real value

The organisations seeing the strongest returns are taking a far more focused approach. Rather than pursuing transformation for its own sake, they’re applying AI to specific operational challenges where results can be measured clearly.

One area where AI is proving particularly valuable is predictive supply chain intelligence. By combining AI with retrieval-augmented generation (RAG) and operational data sources, dispatchers can evaluate a far greater volume of risk factors than would be possible manually. This enables teams to identify potential disruptions earlier, assess likely impacts and make faster, more informed decisions before deliveries are affected. 

AI is also helping logistics operators address growing sustainability requirements. Intelligent commercial vehicle (ICV) agents can support more accurate calculations of CO2 emissions footprints, helping organisations better understand environmental performance while improving estimates for emissions-related costs, tolls and reporting obligations.

For EV fleets, predictive maintenance platforms are improving reliability across charging infrastructure. By analysing vehicle, battery and charger data in real time, these systems can anticipate faults, improve charging session planning and increase uptime across EV charging networks, reducing operational disruption. 

Route optimisation remains one of the clearest examples of AI delivering operational value. Penske research found that 35% of logistics operators reported improvements in route optimisation, while 40% of AI users achieved gains of at least 50% in fuel usage, cost reduction or distance travelled through optimisation initiatives. 

What connects these examples is not the sophistication of the technology, but the clarity of the objective. The problem is well defined, the baseline is measurable and success is easy to evaluate. These applications fit into existing workflows and deliver benefits that operations teams can recognise quickly.

By contrast, initiatives that begin with the broad ambition of “applying AI to logistics operations” often struggle because they lack clear objectives and measurable outcomes. At that point, organisations are still experimenting rather than executing.

The hidden costs that many organisations overlook

Beyond the excitement surrounding AI’s capabilities lies a challenge many organisations have underestimated: proving that the investment is worthwhile.

During the height of the hype cycle, conversations often focused on what AI could do while paying less attention to what it would take to deploy and maintain at scale. As we move through 2026, logistics leaders are becoming far more aware of those realities.

There are direct costs associated with infrastructure, software licensing, specialist talent, cloud computing resources and ongoing model maintenance. Broader economic pressures, including rising energy demand and the increasing cost of advanced semiconductor technology, are also shaping the equation. At the same time, organisations are becoming more exposed to pricing decisions made by AI providers themselves. As model capabilities advance, several leading AI vendors have introduced higher pricing tiers and premium services, creating potential for AI operating costs to rise over time, rather than fall. 

So at the organisational level, the challenge becomes even more complex. Agentic AI systems require governance frameworks, robust data management processes, monitoring capabilities and significant integration work.

This is promoting some businesses to re-evaluate the economics of AI adoption. Recent reports suggest that organisations including Microsoft and Uber have faced rapidly growing AI-related costs, with executives questioning whether increasing AI expenditure is translating into proportionate business value. In some cases, companies are finding the cost of large-scale AI usage can be higher than initially anticipated, particularly when token consumption, compute requirements and enterprise-wide deployment costs are factored in.

As a result, initiatives that appear relatively inexpensive during pilot stages can become considerably more costly when rolled out across an organisation. That does not mean businesses should avoid AI investment, but it does mean those investments must be thoroughly and intentionally evaluated.

The question should no longer be, “What will this AI solution cost?” but rather, “What value will it create, and what capacity will it unlock?” When it’s framed in those terms, AI becomes a strategic business decision rather than a technology experiment.

Trust is the real differentiator

The organisations generating meaningful value from AI are not necessarily moving the fastest. They are the ones building trust in the technology as they scale it.

Much of the conversation around AI has centred on transformation, but the greatest gains often come from solving long-standing operational frustrations such as reducing administrative burdens on planners, improving visibility across fragmented supply chains and helping dispatchers respond more effectively to disruption. These factors may not sound revolutionary, but they can have a significant impact on performance.

What separates successful adopters is their willingness to build incrementally. Rather than attempting to automate entire functions overnight, they identify targeted opportunities, demonstrate value and expand from there.

This approach matters because trust remains the deciding factor in whether AI succeeds in a logistics environment. Operations teams need confidence that recommendations are accurate and actionable and leadership teams need confidence that promised efficiencies will translate into measurable business outcomes. Above all, organisations as a whole need assurance that new technologies will strengthen resilience rather than introduce additional risk.

Scepticism signals a more mature market

For years, AI was positioned as a cure-all capable of solving almost every operational challenge. Many industries embraced that narrative and invested heavily in projects that struggled to deliver meaningful returns.

The reality is more nuanced. AI has enormous potential to transform logistics, but only when it’s applied to clearly defined problems, supported by quality data and aligned with measurable business objectives.

What the industry is experiencing now is not a loss of confidence in AI, it’s a shift from promises to proof.

In that sense, scepticism may be one of the most important developments the logistics industry has seen. It signals a market that is maturing, asking better questions and becoming far more focused on results. At the end of the day, that’s where lasting transformation begins.

Author Bio

Volodymyr Zavadko is the Delivery Director at Intellias – a global software and engineering company – where he leads large-scale, domain-focused teams delivering advanced technology solutions for the transportation and mobility sector. With more than 20 years’ experience spanning IT and mobility, his work sits at the intersection of engineering excellence and commercial strategy, enabling the world’s leading companies to modernize operations and accelerate digital transformation. 

Volodymyr leverages deep experience in location data and connected mobility to develop  modern systems for EVs and fleets that support global trends in business process optimization, and the transition to renewable energy.