Emerson expands NI Nigel AI as $EMR targets a 50% reduction in test development workloads

Emerson Electric Co. (NYSE: EMR) has expanded NI Nigel AI across its test and measurement software portfolio, moving the technology from an engineering adviser into a system capable of generating code and automated test sequences. The new capabilities allow engineers to use prompts to create code in NI LabVIEW and develop sequences in NI TestStand, while Nigel AI is also being extended across NI InstrumentStudio, NI FlexLogger and NI VeriStand. Emerson said internal benchmarking across representative engineering workflows showed reductions of up to 50% in the time and effort required to develop and deploy test systems. The launch matters because test engineering has become a significant bottleneck as automotive, semiconductor, electronics, aerospace and defence products grow more software-intensive and difficult to validate. The unresolved issue is whether Emerson can convert productivity claims into greater software adoption, recurring revenue and margin expansion within the National Instruments business it acquired for $8.2 billion.

How does NI Nigel AI change the way engineers build and deploy automated test systems?

NI Nigel AI initially operated primarily as an adviser inside Emerson’s test software. Engineers could use natural-language questions to locate functions, analyse existing code and receive recommendations about potential modifications. Emerson said the earlier system could work across more than 700 functions within its software environment, helping users navigate tools that otherwise require significant specialist knowledge.

The 2026 release changes the level of automation. Nigel AI can now generate LabVIEW code from prompts and create automated sequences within TestStand. Instead of only explaining how an engineer might construct a test, the software can produce an initial implementation that the engineer reviews, modifies and validates.

LabVIEW is widely used to create measurement, automation and control applications, while TestStand manages sequences through which products and components are tested. Expanding Nigel AI into these environments means artificial intelligence can potentially influence both the individual test routines and the broader order in which validation procedures are performed.

Emerson is also extending Nigel AI across InstrumentStudio for interactive measurements, FlexLogger for sensor-based data logging and VeriStand for hardware-in-the-loop testing. This gives the company an opportunity to create an AI layer covering more of the testing lifecycle, from early experimentation and data collection through system validation and deployment.

The significance is not simply that engineers can produce code more quickly. A test platform becomes more valuable when information from one stage can support decisions elsewhere. An engineer investigating a measurement in InstrumentStudio could potentially carry the context into a LabVIEW application or TestStand sequence without rebuilding the workflow manually.

Why does the shift from AI adviser to AI author carry greater commercial significance?

Software assistants that answer questions can improve convenience, but they may not change the economics of engineering substantially. Code and sequence generation address a larger portion of the workload because they reduce the time spent translating a testing objective into an operational system.

Emerson said its internal benchmarks showed productivity improvements of up to 50% across representative workflows. This figure should be treated as a company-reported benchmark rather than a universal customer outcome. The realised benefit will vary according to project complexity, engineer experience, existing code quality, hardware integration and the amount of validation required before generated content can be deployed.

Even a smaller improvement could be commercially relevant. Engineering organisations frequently face pressure to launch increasingly complicated products without proportionately increasing test teams. Semiconductor devices contain more functions, vehicles require validation across mechanical and software systems, and aerospace programmes must document large numbers of safety-critical requirements.

Test development can delay product launches when specialised engineers become a constrained resource. AI-generated starting points may allow experienced engineers to spend less time creating repetitive structures and more time reviewing difficult failure scenarios, improving coverage and investigating unexpected results.

For Emerson, the economic opportunity lies in making Nigel AI part of the customer’s normal engineering process rather than an occasional feature. If the system reduces project time while remaining reliable, customers may have a stronger reason to standardise on the NI LabVIEW+ Suite, expand licences across additional teams and retain the platform through future development programmes.

However, Emerson has not disclosed separate pricing for the new capabilities or quantified the expected revenue contribution. It remains unclear whether AI authorship will be included in existing subscriptions, used to support higher renewal prices or sold through premium packages. The difference will determine whether the launch primarily improves customer retention or creates a new source of incremental revenue.

How does Nigel AI strengthen the investment case behind Emerson’s $8.2 billion NI acquisition?

Emerson completed its acquisition of National Instruments Corporation in October 2023 at an equity value of $8.2 billion. The transaction added software-connected automated test and measurement systems to a portfolio historically associated with process automation, industrial controls and instrumentation.

The acquisition was based partly on the expectation that test and measurement would give Emerson exposure to faster-growing markets including semiconductors, electronics, transportation, aerospace and defence. NI also brought a software ecosystem that could support higher recurring revenue and closer integration between instruments, engineering applications and customer workflows.

Nigel AI provides a strategic mechanism for improving the return on that acquisition. Rather than relying only on demand for testing hardware, Emerson can use AI-enabled software to increase the productivity and perceived value of the broader NI platform.

The latest reported performance suggests that Test & Measurement has become one of Emerson’s stronger growth businesses. Second-quarter fiscal 2026 Test & Measurement sales increased 16% on a reported basis and 12% on an underlying basis to $414 million. Adjusted segment EBITA rose to $109 million from $87 million, while the adjusted margin increased to 26.3% from 24.3%.

During the six months ended March 31, 2026, Test & Measurement revenue reached $823 million, representing reported growth of 15% and underlying growth of 12%. By comparison, the larger Control Systems & Software business recorded a 1% underlying decline during the same period.

These numbers make the Nigel AI launch more important than a routine software update. Emerson is adding functionality to a segment that is already growing faster than the overall company and improving profitability. Sustained AI-enabled adoption could help justify the acquisition price by supporting revenue growth, software attachment and operating leverage.

The risk is that the 2026 growth rate may partly reflect cyclical recovery or customer investment cycles rather than a permanent acceleration. Emerson will need to show that innovation within the NI portfolio can produce continued organic growth after comparisons become more demanding.

Can AI-generated engineering code improve productivity without weakening test reliability?

The consequences of an error in engineering test software can be more serious than an incorrect response from a general-purpose chatbot. A flawed sequence may fail to detect a defective component, produce an inaccurate measurement or incorrectly indicate that a system has passed validation.

This is especially important in automotive safety systems, semiconductor manufacturing, aerospace equipment and defence electronics. The purpose of testing is to establish confidence that a product operates within defined limits. AI-generated code that saves time but introduces hidden errors could weaken the very process it is intended to improve.

Emerson said Nigel AI operates inside structured test environments and uses engineering code, modular instrumentation, configurable workflows and structured data to produce outputs aligned with real system constraints. The company has also emphasised traceability, security, governance and continued human control over validation and decision-making.

This approach is commercially sensible because enterprise and regulated customers are unlikely to accept autonomous code generation without review controls. The strongest proposition is not that Nigel AI replaces test engineers. It is that the system produces a technically informed starting point while the engineer remains accountable for verifying logic, coverage and results.

Generated content will therefore need to be inspectable and reproducible. Engineers must be able to understand what the system created, identify the source of assumptions and determine whether code changes affect the integrity of previous test results.

Emerson also linked the design of Nigel AI to regulatory expectations including the European Union Cyber Resilience Act. That does not automatically establish compliance for every customer deployment, but it indicates that the company recognises that engineering AI will be assessed through security and governance requirements as well as productivity.

The most persuasive evidence would come from customers showing shorter development cycles without higher defect leakage, test escapes or rework. Until such operational results are published, the 50% productivity figure should be viewed as an encouraging internal benchmark rather than a guaranteed outcome.

How could Emerson monetise Nigel AI across hardware, software and engineering workflows?

The NI business combines instruments, data-acquisition systems, test hardware and software. This gives Emerson several potential routes through which Nigel AI could influence financial performance.

The first is software adoption. Engineers who can access AI-generated code and sequences may be more likely to adopt the complete LabVIEW+ Suite instead of purchasing individual products. This would increase the number of applications used by each customer and deepen the relationship with the NI ecosystem.

The second is retention. Engineering systems often remain in use across multiple product generations. When test code, hardware interfaces and organisational knowledge are built around one platform, replacing it can require extensive validation and retraining. AI features that improve productivity could strengthen those switching costs.

The third is hardware attachment. Faster software development may encourage customers to deploy more automated test systems, creating demand for NI instruments, controllers and data-acquisition equipment. The AI capability could therefore support hardware revenue even when it is delivered primarily through software.

The fourth is expansion among less-specialised users. LabVIEW and automated testing require technical knowledge that can limit adoption outside experienced engineering teams. Natural-language interaction and code generation may lower that barrier, allowing more employees to create basic systems while specialists retain responsibility for final validation.

The fifth is customer data and workflow learning. Emerson can improve purpose-built engineering models when it understands the structures, common errors and workflows associated with real test applications. However, it must maintain strong controls around customer intellectual property and proprietary product information.

The most attractive financial outcome would be an increase in recurring software revenue accompanied by higher hardware utilisation and limited incremental support costs. The weaker outcome would involve customers using AI functionality without paying more while Emerson absorbs additional model, infrastructure and development expenses.

What do Emerson’s broader financial results reveal before the August earnings test?

Emerson reported fiscal second-quarter 2026 net sales of $4.56 billion, representing reported growth of 3% and underlying growth of 0.5%. Underlying orders increased 5%, while adjusted earnings per share rose 4% to $1.54. Operating cash flow declined 6% to $779 million and free cash flow fell 6% to $694 million.

Management said demand had remained resilient, particularly within Software & Systems, although sales were affected by disruption associated with the conflict in the Middle East. Emerson expects fiscal 2026 net sales growth of approximately 4.5%, underlying sales growth of around 3% and adjusted earnings per share of between $6.45 and $6.55. Full-year free cash flow is projected at $3.5 billion to $3.6 billion.

The company has also shifted its capital-allocation emphasis toward shareholder returns, with plans to distribute approximately $2.2 billion during fiscal 2026 through about $1 billion of share repurchases and $1.2 billion of dividends. This creates a useful discipline around AI investment. Emerson has the balance-sheet and cash-flow capacity to develop Nigel AI, but the programme must compete with other uses of capital and eventually support the company’s growth and margin objectives.

Emerson is scheduled to report fiscal third-quarter results after the market closes on August 4, 2026. The update should provide evidence on whether the strong Test & Measurement momentum continued and whether the company remains on track for the stronger second half embedded in its guidance.

Investors will also be looking for progress toward Emerson’s longer-term financial framework, which targets $21 billion of net sales, a 30% adjusted segment EBITA margin, adjusted earnings per share of $8 and a 20% free-cash-flow margin by fiscal 2028. The company expects to generate $12 billion of cumulative free cash flow between 2026 and 2028.

Why has Emerson stock rallied ahead of the NI Nigel AI launch and August earnings?

Emerson shares closed at $151.81 on July 28, 2026, gaining 1.68% during the session and recording a sixth consecutive trading-day increase. The stock remained about 8% below its 52-week high of $165.15 and above the 52-week low of $122.64.

Based on closing prices, Emerson shares gained approximately 9% between July 21 and July 28. The stock was also about 6.3% higher than its June 29 close of $142.82. Emerson’s market capitalisation stood at approximately $85 billion at the July 28 closing price.

The July 28 rise coincided with the Nigel AI announcement, but it also occurred during a positive broader market session and should not be attributed solely to the product release. Product announcements of this type are unlikely to change near-term group earnings materially unless management provides evidence of customer contracts or increased software revenue.

Market sentiment appears constructive but not unqualified. Recent analyst actions have included an upgrade by JPMorgan and reductions to some price targets ahead of third-quarter earnings, reflecting optimism about Emerson’s automation portfolio alongside concern about the sales ramp required to meet second-half expectations.

The stock’s recent rally raises the importance of the August 4 results. A strong Test & Measurement performance and maintained guidance could reinforce the view that Emerson’s transformed portfolio is delivering growth. A weaker result would suggest that enthusiasm around industrial AI and software innovation is running ahead of near-term operating evidence.

What will prove whether NI Nigel AI is creating durable value for Emerson shareholders?

The first proof point will be adoption. Emerson needs to demonstrate that existing LabVIEW and TestStand customers are actively enabling Nigel AI and extending its use across InstrumentStudio, FlexLogger and VeriStand.

The second will be monetisation. Management should eventually clarify whether AI functionality is supporting premium subscription tiers, stronger renewals, new customer wins or higher software revenue per account.

The third will be customer productivity. External case studies showing shorter test-development cycles, reduced engineering effort and faster product validation would give greater credibility to Emerson’s internal benchmark.

The fourth will be quality. Customers must demonstrate that development speed is not accompanied by poorer test coverage, software defects or additional validation work.

The fifth will be segment profitability. Continued revenue growth and margin expansion within Test & Measurement would suggest that Emerson is earning an acceptable return from the NI acquisition and subsequent software investment.

Nigel AI represents a credible extension of Emerson’s strategy because it applies artificial intelligence to a specialised engineering problem rather than adding a generic chatbot to an industrial product. Test environments contain structured code, instruments, measurements and repeatable workflows, giving a purpose-built system more context than a general productivity assistant.

What has improved is the breadth of the software, its ability to produce engineering content and the financial momentum within Test & Measurement. What remains unresolved is how many customers will use the new functionality, how much they will pay and whether reported productivity improvements can be achieved without weakening validation quality.

The investment thesis would strengthen if Emerson reports sustained double-digit Test & Measurement growth, improving margins and customer evidence showing that Nigel AI is reducing development cycles. It would weaken if AI capabilities generate attention but remain a bundled feature with limited effect on subscriptions, hardware demand or the return earned on the $8.2 billion NI acquisition.

What are the key takeaways from Emerson’s NI Nigel AI software expansion?

• Emerson expanded NI Nigel AI across the complete NI LabVIEW+ Suite on July 28, 2026.
• Nigel AI can now generate LabVIEW code and automated TestStand sequences from engineering prompts.
• The software is also available across InstrumentStudio, FlexLogger and VeriStand workflows.
• Emerson reported internal test-development productivity improvements of up to 50%.
• The benchmark is company-reported and may vary according to project complexity and validation requirements.
• Test & Measurement revenue increased 12% on an underlying basis during the first six months of fiscal 2026.
• Second-quarter Test & Measurement adjusted EBITA margin improved to 26.3%.
• Emerson acquired National Instruments for an equity value of $8.2 billion in October 2023.
• Emerson shares closed at $151.81 on July 28, about 8% below their 52-week high.
• Fiscal third-quarter earnings on August 4 will provide the next test of segment growth, margins and full-year guidance.