Gartner's 2025 survey shows 94% deployment. 40% cite value extraction as a top challenge. The separation is between platform buyers and process builders — earnings season will expose it.
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A 2025 Gartner Future of Logistics Survey found that 94% of supply chain logistics leaders either have a transportation management system or plan to deploy one within two years. The same survey, however, revealed that 40% flag value extraction from existing technology investments as a top-three challenge. This is not a story about slow adoption. It signals that a material share of the sector's IT spending is not yet translating into operating improvements, and that gap is becoming a catalyst for a rerating as the market starts separating platform buyers from process builders.
Publicly traded truckload carriers J.B. Hunt Transport Services (JBHT), Werner Enterprises (WERN), and Schneider National (SNDR), along with logistics software providers Descartes Systems Group (DSGX) and Manhattan Associates (MANH), have all invested heavily in digital platforms. The survey suggests that software spend alone is not a reliable leading indicator of margin expansion or competitive advantage. For traders, the question is no longer who is buying the tech. It is who is actually extracting the value.
The 94% deployment figure confirms that transportation and logistics companies have been aggressive in adopting supply chain management systems. The signal worth trading on sits inside the other statistic. 40% of respondents named value extraction from existing technology investments as a top challenge. That figure is large enough to indicate that a meaningful portion of the sector's IT capital is not yet showing up in operating metrics. When technology spending turns into a cost layer rather than a productivity driver, the earnings risk shifts from "not spending enough" to "spending on the wrong implementation path."
Mark Wallin, GM and SVP of Product at Phillips Connect, describes a recurring pattern. An organization invests in a new platform, runs it for a year, and sees nothing change. Leadership receives driver performance data every week but has no system for acting on it. The data is generated, the software is running, yet the operation never changes around the investment. Wallin's observation isolates the failure mechanism. The platform produces information; no process forces a decision. The result is a faster version of the same old confusion.
Autonomous trucking will eventually reshape certain route corridors, and developers like Aurora Innovation (AUR) are running commercial pilots. For most transportation operators, however, the question of when autonomy arrives at scale is less useful than whether their current operation can absorb it. Wallin draws a clear distinction: autonomy changes the role of the driver; automation removes friction from work that already exists, such as manual verification, calendar-driven maintenance, and reactive service failure detection. The operational foundation required for autonomous systems is the same foundation that good automation already demands: clean data, consistent processes, and clear decision rules. Organizations that skip that groundwork will find that removing the driver does not remove the underlying complexity. It exposes it.
Key insight: The organizations that get the most from automation start with one problem, not everything at once.
Wallin points to an example that should be a case study for every logistics analyst. A fleet that has long required manual pre-trip inspections may digitize the form without asking whether the inspection itself is still necessary. The sensor data already on the vehicle answers most of what the walkthrough was designed to confirm. Because redesigning the process felt risky, the company kept the old step and wrapped new technology around it. Resources were spent to preserve something that no longer needed to exist. This is not a technology failure. It is a decision-execution failure, and it shows up in asset utilization and maintenance cost lines that financial analysts can track.
Wallin identifies decision latency as the metric that surprises most leaders when they first track it: the time between when an alert fires and when someone actually acts on it. In transportation operations, decision latency directly affects equipment availability, dwell time, and customer service recovery. An operation where a maintenance alert sits unacted upon for hours consumes capacity that a low-latency competitor can turn into revenue. Decision latency is not a reported GAAP metric. It shows up indirectly in fleet utilization rates, repair and maintenance cost per mile, and on-time delivery percentages–all of which are reported either in earnings releases or in industry benchmarking data.
Practical rule: Look for the workarounds–personal spreadsheets kept because the shared system is not trusted, managers who learn about problems by phone instead of alert. Those gaps are a direct signal of where automation investments are failing to deliver.
External signals analogous to internal workarounds exist. They include repeated mentions of "process integration" on earnings calls without reference to specific metric improvements, capital expenditure guidance that shows rising software spend but flat or declining asset productivity, and customer satisfaction scores that lag despite technology upgrades. These are not as clean as a reported number. They often show up in the narrative before the numbers break.
Large truckload carriers have been among the most visible technology spenders. J.B. Hunt's digital freight matching investments, Werner's EDGE platform, and Schneider's digital marketplace all represent serious capital allocation toward automation. The risk for these stocks is that the market has already priced in a technology premium that assumes faster margin improvement than extracted value can deliver. If these companies do not show operating ratio compression that is distinguishable from the cycle, the valuation premium tied to their tech strategies will come under pressure. That creates a watchpoint around second-quarter earnings, when freight demand seasonality will either confirm or weaken the trend.
Descartes Systems (DSGX) and Manhattan Associates (MANH) sell the platforms that the 94% are deploying. For these companies, the Gartner survey is both a validation of demand and a caution about renewal cycles. If a material share of customers are struggling to extract value, the long-term growth rate embedded in revenue multiples may face a reset, particularly if churn rates begin to tick higher or contract sizes stall. The vendor that can demonstrate a methodology for value realization will earn a durable premium; the one that merely ships software will eventually face growth that slows faster than the market expects.
Investors will get the next tangible data points from the truckload carriers' and logistics software vendors' quarterly reports. The key items to monitor are not just whether technology spending is continuing. The question is whether any carrier quantifies the return on that spending in terms of improved utilization, lower deadhead percentage, or a reduced driver turnover rate linked to better dispatch and load-matching processes. A carrier that simultaneously announces a new digital initiative and reports a worsening operating ratio trend has not yet closed the gap, and the stock may be penalized for the mismatch. Conversely, a carrier that highlights a specific process redesign, ties it to a measurable gain in asset turns, and then delivers sequential operating ratio improvement would offer evidence that the value gap is closing.
Aurora Innovation (AUR) and its competitors will continue to announce pilot milestones and commercial launch dates. When those milestones arrive, the market will need to assess whether the carrier customers adopting those autonomous services have the operational readiness to integrate them. A carrier that cannot point to a track record of absorbing automation into its workflows may see friction rather than savings in the early stages, delaying the revenue inflection that autonomous trucking developers have been guiding toward. The companies that have built the operational foundation will be the ones that capture the value first.
stock market analysis remains essential for tracking how transportation sector valuations respond to these operational signals. The industry's technology transformation is underway; the tradeable edge sits with the investors who distinguish between technology that is merely deployed and technology that has actually changed how freight moves. best stock brokers can help position trades around the earnings calls that will start to separate the platform buyers from the process builders.
Prepared with AlphaScala research tooling and grounded in primary market data: live prices, fundamentals, SEC filings, hedge-fund holdings, and insider activity. Each story is checked against AlphaScala publishing rules before release. Educational coverage, not personalized advice.