The logistics sector has embraced AI, and according to BCG’s 2026 AI in Logistics Executive Survey of 30 leading global logistics players, 97% of executives rank AI as a strategic priority, 70% have an  AI strategy, and 67% have a dedicated AI budget.

However, just 13% say that  AI is delivering measurable financial impact.

In an article, BCG offers reasons for the disconnection. They identified three problems:

Fragmented Data  AI depends on having clean information flow freely between tools, yet most logistics players must grapple with fragmented, siloed legacy systems that were never designed to talk to one another. AI itself can help solve this, but an industry-wide resolution is still a distant prospect.

Isolated Solutions  Most companies deploy AI as isolated point solutions rather than a connected set of capabilities across a workflow or domain.

The Human Gap  Companies are underinvesting time and capacity in the human side of AI deployment. Many of them haven't established effective processes to make the transition, and they lack the talent and change management practices necessary to sustain adoption. As a result, although logistics players are spending on AI and deploying it across a complex web of use cases, the effects are rarely transformational.

Some companies are succeeding in finding a financial payoff for AI and BCG found that they follow these paths. 

Start with the destination, not the technology. Most transformations open with a narrow, forward-thinking question about which technology and tools to deploy. A better approach is to first ask what kind of logistics company the organization wants to become. The answer to this question should help the company set the architecture so that the technology can serves that vision. Without this encompassing strategic understanding, even well-run pilots pile up as a portfolio instead of a system.

Build the connective tissue early. In practice, this principle works on two levels. Technically, it means building a unified data layer to enable core systems to share clean information, and setting clear rules for when AI may act versus when a person must step in. Organizationally, it means constructing a federated operating model: a lean central hub that owns standards, platforms, and AI value tracking, paired with teams in or close to the business units that deploy and run the initiatives against their own P&L. The central hub orchestrates; accountability resides with the business units. Companies that defer building this connective tissue guarantee that their pilots will remain pilots and that their value will stay local.

Sequence for compounding, not coverage. Each wave of deployment should make the next wave stronger. The unit of progress is not a single use case but a value package—a small bundle of connected use cases that share data and reinforce one another. Ten connected use cases beat 50 isolated ones. Start with one value package inside one domain, but then link those packages into a network.