The uncomfortable number in artificial intelligence in logistics industry discussions is not how many companies are interested. It is how few are organized enough to do anything durable with that interest. ABI Research found that 94% of surveyed supply chain professionals planned to use AI or generative AI for decision support within two years, while Gartner found that only 23% of supply chain organizations had a formal AI strategy.[1] That is not a small execution lag. It is a structural gap between stated intent and operating discipline.
The Gartner figure is broader than logistics, and the ABI figure measures stated intent rather than verified deployments. Even with those caveats, the contrast matters because logistics is exactly where loose AI ambition tends to collide with hard constraints: transport management systems that do not speak cleanly to warehouse systems, planners who already have too many exception queues, customer commitments that cannot wait for model experimentation, and operations teams who inherit whatever the pilot team leaves behind.
The pilot count is less impressive than the value count
The more useful benchmark comes from the BCG and Alpega survey of more than 180 logistics leaders published in January 2026. It found that about 40% of both logistics service providers and shippers are beyond pilots, yet only 13% of LSPs and 7% of shippers can point to measurable financial impact from AI. Another 56% are still exploring or piloting.[2]
That distinction is doing a lot of work. “Beyond pilots” is not the same as scaled value. A routing model used by one planning team, a forecasting assistant tested in one region, or a customer-service summarization tool attached to an inbox may all be real deployments. They may also sit awkwardly beside the actual decision process, with savings too small or too hard to isolate for finance to recognize.
For logistics leaders, the 13% figure is the one that should stay on the screen. It asks whether the tool changed the economics of the operation, not whether the company bought software, built a model, or held a steering committee. In logistics, that difference shows up quickly. If an AI system recommends a different carrier mix but procurement rules block the change, there is no value. If it predicts a service failure but no one owns the escalation path, there is no value. If it improves ETA accuracy but the customer-facing workflow still relies on manual updates, the benefit leaks out before it reaches the P&L.
The industry does not lack places to apply AI. Tender acceptance, appointment scheduling, demand sensing, slotting, dock planning, claims triage, exception management, fraud detection, and labor planning all contain repeatable decisions with messy inputs. The weak point is not imagination. It is the translation from model output into changed behavior, changed accountability, and changed financial results.
Leadership disengagement turns AI into an operations-side orphan
ORTEC’s November 2025 survey of more than 2,000 executives adds the missing management layer. Nearly one-third of logistics leaders lacked direct senior leadership engagement in AI and machine-learning projects, and the survey associated that gap with stalled deployments and lower ROI. Only 15.5% reported extensive integration.[3]
The ORTEC and BCG surveys should not be treated as one continuous dataset. They use different samples and methodologies. But they point in the same direction: logistics AI stalls when it is treated as a technical workstream instead of a change to how decisions are made.
Senior leadership engagement matters for a practical reason. AI projects in logistics usually cross boundaries that no single functional owner can fix alone. A network optimization recommendation may affect sales promises, transport procurement, warehouse labor, customer service scripts, and finance’s measurement of savings. A predictive maintenance model may require maintenance, operations, asset management, and dispatch to agree on when a vehicle is pulled from service. A planning assistant may change who is allowed to override a recommendation and how those overrides are audited.
Without leadership attention, those decisions get postponed or pushed down to the people least able to resolve them. The operations team is then asked to “adopt” a tool whose incentives, data dependencies, and exception rules were never settled. That is how AI becomes another dashboard: technically available, occasionally useful, and quietly bypassed when the day gets busy.
The barriers are connected, not independent
The top blockers reported across the BCG and ORTEC material are not the price of AI tools. They are integration complexity at 32.4%, lack of in-house expertise at 22.7%, and unclear ROI at 20.8%.[2][3] Those numbers are easy to read as three separate problems. In practice, they usually reinforce one another.
Integration complexity raises the cost of proving value. Fragmented data from TMS, WMS, ERP, telematics, order management, and carrier portals makes even a simple use case harder to operationalize. The model may work on a curated dataset, but Monday morning decisions depend on late EDI messages, missing appointment data, inconsistent accessorial codes, and master data that no one fully owns.
Lack of expertise then makes the integration problem harder to diagnose. The missing skill is not only data science. Logistics companies need people who can translate between dispatch constraints, network economics, data architecture, and change management. A model can be statistically sound and still fail because it recommends actions that planners cannot take, or because the recommendation arrives after the cutoff time that matters.
Unclear ROI is often the final symptom. If no one has defined which decision will change, who will act differently, what baseline will be used, and which cost line should move, the business case stays vague. For a more detailed treatment of the measurement problem, ChainSignal’s article on realistic AI ROI in logistics by use case separates value claims by use case rather than treating AI as one budget category.
This is why procurement-led AI adoption tends to disappoint. Buying the platform does not resolve the process ownership question. It does not clean up the integration layer. It does not decide whether planners are expected to follow recommendations, challenge them, or use them only as background context. The software may be necessary, but it is rarely the scarce ingredient.
Formal strategy is not paperwork; it is a decision filter
The 23% formal-strategy figure should not be read as a preference for thick planning documents.[1] A formal AI strategy earns its keep only if it prevents scattered effort. It should tell the organization which decisions are worth augmenting, which systems must be connected first, what risks are unacceptable, who owns adoption, and how value will be measured.
In logistics, that filter is especially important because attractive AI use cases are everywhere. A company can pilot dynamic pricing, autonomous procurement support, demand forecasting, yard visibility, customer-service copilots, and warehouse labor optimization in the same year and still not build a repeatable capability. The work fragments across functions. Data teams build proofs of concept. Vendors demonstrate narrow wins. Operations teams keep their existing workarounds because no one has retired the old process.
Strategy narrows the field. It forces a company to say, for example, that service-failure prediction matters more this year than warehouse chatbot experimentation because customer penalties and manual exception handling are measurable pain points. Or it says that shipment visibility will not be improved with another analytics layer until milestone data quality is fixed. These are not glamorous choices, but they are the kind that make AI survivable inside the operation.
The absence of that filter explains why high intent can coexist with low value. A company can be sincere about AI and still lack the governance to decide which pilots deserve integration funding, which ones should be stopped, and which process owners must be accountable after launch. If every function runs toward its own use case, the portfolio may look active while the operating model barely changes.
What separates the minority that scales
The BCG/Alpega survey shows that measurable financial value is still concentrated in a minority of logistics organizations.[2] The useful question is not whether those organizations found a magic model. It is what they were willing to change around the model.
Scaled AI usually requires a company to make several unglamorous commitments. It must connect enough systems for recommendations to arrive where work actually happens. It must assign process owners who can change rules, not just observe outputs. It must give frontline teams a clear reason to trust or challenge recommendations. It must measure value against a baseline that finance and operations both recognize. It must also stop pilots that cannot cross those thresholds.
None of those commitments is primarily about model accuracy. Accuracy matters, especially in high-consequence decisions. But the surveys point to a more ordinary bottleneck: companies are trying to insert AI into workflows that were not designed to absorb it. When the workflow is fragmented, the data is contested, and the owner of the decision is unclear, even a strong recommendation becomes optional noise.
This is also where the common “pilot purgatory” language can be misleading. Pilots are not the enemy. A disciplined pilot can test whether a decision is worth automating or augmenting. The problem is a pilot with no path to integration, no adoption owner, no financial baseline, and no leadership forum capable of removing blockers. That is not experimentation. It is deferred accountability.
The gap is widening around readiness
There is still a temptation to explain slow AI progress in logistics through technology affordability or model maturity. That explanation is becoming less convincing. The evidence available here points somewhere else: high intent, limited formal strategy, modest measurable value, uneven leadership engagement, and barriers rooted in integration, expertise, and ROI clarity.[1][2][3]
The minority that scales AI is not simply buying better software. It is making the surrounding organization fit for the tool. That means clearer priorities, senior leaders who stay involved past the announcement, systems work that reaches the operational floor, and process owners who can change how decisions are made. The companies that avoid those commitments may still have plenty of AI activity. They will just keep mistaking activity for execution.
References
- Supply Chain AI Statistics, OpenSky Group.
- AI Is Already Moving the Logistics Industry Forward, Boston Consulting Group, January 2026.
- AI Leadership Gap in Logistics, ORTEC, November 2025.
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