Oshkosh says it is using AI and autonomy in selected products, services and internal operations, with improved throughput, cost reduction and operational efficiency as the clearest stated value mechanisms. But the company has not disclosed a standalone AI return-on-investment figure. Investors therefore have to assess AI as one possible contributor to broader operating and financial goals—not as the proven cause of them.
Where is Oshkosh using AI?
Oshkosh describes its work as combining AI, autonomy, electrification and connectivity in purpose-built vehicles and equipment. Its products serve markets including construction, firefighting, aviation, refuse collection, defense and delivery. The company’s 2025 Annual Report says it is “developing, integrating and using” AI and autonomy in certain products, services and internal operations. That wording describes a range of activity, not a claim that every product or facility uses AI.
The clearest public explanation of an operating use came in Oshkosh’s June 5, 2025 Investor Day release: the company said autonomous technologies that leverage AI are part of its efforts to improve throughput companywide, alongside cost-reduction initiatives and broader operational-efficiency work. The statement describes the intended mechanism; it does not disclose a measured throughput gain or isolate AI’s contribution.
What evidence would show that AI is creating value?
A useful test separates measures close to the process from company-level results. Oshkosh explicitly names throughput, cost reduction and operational efficiency. The more detailed plant measures below are analytical suggestions, not metrics the company has reported for its AI program.
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| Layer | What to measure | What Oshkosh has disclosed |
|---|---|---|
| Operational leading indicators | Throughput; cycle time; downtime; first-pass yield; labor hours per unit; cost per unit | Throughput, cost reduction and operational efficiency are named in the June 5, 2025 Investor Day release. The other measures are suggested ways to test those aims, not disclosed Oshkosh results. |
| Business outcomes | Segment margins; adjusted operating income; revenue growth; free-cash-flow conversion | Oshkosh’s 2028 company targets, announced in 2025, include the figures shown below. They are corporate targets, not AI-attributed outcomes. |
| Strategic context | Backlog and contract execution | Oshkosh reported a $14.6 billion backlog as of March 31, 2025, and said existing contracts and backlog support approximately 50% of its targeted 2028 revenue growth. |
To connect an AI-enabled process to a business result, compare the process with a predeployment baseline, then check whether the improvement persists after implementation and operating costs. If a process becomes faster but requires enough additional equipment, computing, support or labor to erase the savings, the speed gain alone is not evidence of net value. A companywide result also needs a credible connection to the specific process and evidence that the gain can be repeated across plants or product lines.
What are Oshkosh’s 2028 financial targets—and what do they prove?
Oshkosh set the following companywide targets for 2028 in 2025. They provide context for evaluating its operating strategy, but they are forward-looking goals, not realized returns from AI.
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| 2028 company target | Value |
|---|---|
| Revenue | $13 billion–$14 billion |
| Adjusted operating-income margin | 12%–14% |
| Adjusted earnings per share | $18.00–$22.00 |
| Free-cash-flow conversion | More than 90% |
The Investor Day release frames these as goals and cautions that they are not guarantees. The release does not assign a portion of any target to AI. Revenue, margins, earnings and cash flow can also be affected by backlog, contracts, pricing, product launches, segment mix, labor, supply-chain execution and capital allocation. In particular, Oshkosh said existing contracts and backlog support approximately half of its targeted 2028 revenue growth, so it would be misleading to attribute the full growth ambition to AI.
How can autonomy and AI improve manufacturing throughput?
In principle, autonomous technologies can help a production operation complete work more smoothly—for example, by coordinating or automating parts of a process where delays, handoffs or variability constrain output. Higher throughput matters financially only if the additional output can be delivered safely, meets quality requirements and creates useful value, such as lower cost per unit or more saleable production. Oshkosh’s public statement identifies throughput as an intended benefit, but does not specify a particular factory workflow, deployment, quantified gain or realized savings.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could prevent Oshkosh from realizing the benefits?
Oshkosh’s 2025 Annual Report says the benefits of AI depend on data quality, system integration, workforce adoption, computing resources, and the ongoing performance and availability of third-party technology providers. These are practical conditions, not merely technical details: unreliable or poorly integrated data can undermine outputs, while systems that workers cannot effectively adopt may fail to change the operation.
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The report also warns that AI systems can produce inaccurate, incomplete or biased outputs. Failures could lead to safety, cybersecurity, cost, reputational, legal or customer-acceptance problems. For vehicle and equipment businesses, safety and dependable performance are especially important constraints on whether an automated process can be deployed and scaled.
For an investor, the most informative future evidence would connect a defined AI-enabled process to a durable operational improvement, disclose costs and implementation conditions, and show how that improvement contributes to business outcomes without conflating it with other growth drivers. Oshkosh’s disclosures establish a strategy and intended mechanisms; they do not yet provide a standalone AI ROI measure.
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