IN FOCUS6-8 min read

How artificial intelligence is improving industrial software

Manufacturers are increasingly turning to digital solutions to boost productivity. We discuss how AI has the potential to turbo-charge this trend and highlight two company case studies.

Engineer using AI in industrial setting


Dan McFetrich
Global Sector Specialist

The rise of AI in industrial software is a trend that investors cannot afford to ignore. As manufacturers increasingly turn to digital solutions to boost productivity and reduce waste, we believe that the integration of AI into these platforms will be a game-changer and provide two examples of this below. By driving innovation, improving functionality, and opening up new markets, AI-powered industrial software is set to play a critical role in shaping the future of the manufacturing industry.

Industrial software is critical for manufacturing

Manufacturing employment has largely recovered since the pandemic, but growth has started to stagnate. We also continue to see worrying skills shortages in key roles throughout the manufacturing process. Industry is struggling to fill skilled job openings, and the US Manufacturing Institute estimates that around 425,000 of these positions will remain unfilled by 2029.

"Middle skill" positions projected to be unfilled through 2029

Chart showing manufacturing PPI vs employment cost index

Manufacturing workforce demographics also continue to age. This looming demographic shift and consequent shortage of workers is one of the pillars of Schroders’ 3D Reset framework. The 3Ds (demographics, decarbonisation and deglobalisation) are shaping a new economic regime.

Given these continuing demographic and skills shortages, US manufacturing wage inflation rates are proving far stickier than general product inflation in the manufacturing industry, driving manufacturer margin pressures. The below employment cost index shows total compensation growth remains well ahead of pre Covid levels.

Manufacturing producer price inflation vs employment cost index

Chart showing number of middle skill positions projected to remain unfilled in US

This margin pressure is driving manufacturers to digitise to improve industrial productivity. Industrial software adoption has been, and continues to be, a key enabler of manufacturing productivity growth, and this need has been accelerated by COVID.

AI enhances industrial software optimisation and accelerates adoption

What will be the impact of AI-enabled industrial software? We strongly believe that the impending integration of AI into industrial software development will significantly enhance the value propositions for manufacturers. Specifically, we anticipate the following benefits from AI deployment:

  1. Increased efficiency: AI can drive coding efficiencies for software engineers, accelerating innovation timescales and streamlining bespoke tendering proposal processes.
  2. Enhanced functionality: AI can improve software platforms' reliability, interface and usability. Machine learning algorithms can analyse data inputs to identify patterns, while AI utilises this information to make real-time decisions, ultimately enhancing the software's value proposition.
  3. Market expansion: The integration of AI can lead to industrial software being adopted in new markets, as the enhanced capabilities and improved performance open up new opportunities for application.

Case study 1 – From Emerson’s recent client event “Emerson Exchange” in Germany

Emerson, the global leader in process automation systems, illustrates these trends in action. At its bi-annual client event in Dusseldorf, the company showcased its vision for "boundless automation," with software playing a central role. Emerson's DeltaV process automation system currently employs 600 employees in software development and 70 in hardware development, underlining the shift the company has made towards software-driven solutions.

At many plants, the original automation control system’s hardware control code may be 15-20 years old with limited documentation. Emerson has therefore just launched a “Revamp” product, using AI to help customers modernize their automation installed base, replacing obsolete, unsupported control systems with its latest DeltaV control system.

DeltaV revamp

Cloud based modernization of legacy DCS. PLC and safety systems to DeltaV

  • Generative AI copilot for project teams
  • Near-instant analysis of project scope
  • Automated migration of base layer of control
  • Machine learning generated rules and recommendations
  • Reduced risk and capital costs
  • Improved schedule and efficiency

Source: Emerson, April 2024

This tool analyzes the current configuration files of the installed obsolete system, and using AI, translates the configuration file into a DeltaV control system configuration, automatically generating up to 70% of the required configuration. This can save millions of dollars in labour costs to convert the configuration from one system to another and eliminates months from the modernization project schedule. It can also document the obsolete code, generating narratives that explain the code, allowing Emerson’s project teams to quickly create new code when needed.

Emerson expects AI to help test and validate these new software control systems, speed up product development cycles, and accelerate a bespoke tender proposal process for new clients. Emerson also sees these developments driving new business potential in verticals like life sciences and water.

Case study 2 – SAP is the global leader in Enterprise Application software

We also think AI could be a potential game changer for SAP. Like most software companies, SAP will benefit from increased efficiency in software development, i.e coding. Indeed, the company recently announced an organisational restructuring with the ambition to increase automation via greater usage of AI.

However, perhaps more importantly, AI could provide a significant boost to SAP’s revenue outlook. One of the most impactful use cases of AI is in back office given the scope for efficiency savings, and SAP’s systems essentially run the back office of a company (for example. finance, HR, procurement, and supply chain). So, in theory, the combination of the nature of its products, coupled with the vast amount of data captured in SAP’s systems mean that SAP can deliver significant productivity benefits to its customers and capture a share of those savings.

While still early days, as SAP is in the process of launching AI related product features, the interest in AI could help drive adoption of SAP’s new cloud enterprise resource planning (ERP) product. Owing to the stickiness of an ERP product, SAP’s existing customers have been somewhat hesitant to migrate from their current on-premises product. However, as customers can only access AI related features with SAP’s new product, AI has the potential to push customers towards the new product, as they will be cognisant that late movers could be at a significant competitive disadvantage.

It is also worth mentioning that SAP has priced its AI enabled product at a 30% premium. Overall, we believe AI is likely to provide a significant tailwind to both SAP’s top line as well as margins.

These two examples demonstrate how AI will improve industrial software product development and open up new use cases.

Securities mentioned are for illustrative purposes only and not a recommendation to buy or sell.

This article is issued by Cazenove Capital which is part of the Schroders Group and a trading name of Schroder & Co. Limited, 1 London Wall Place, London EC2Y 5AU. Authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority. 

Nothing in this document should be deemed to constitute the provision of financial, investment or other professional advice in any way. Past performance is not a guide to future performance. The value of an investment and the income from it may go down as well as up and investors may not get back the amount originally invested.

This document may include forward-looking statements that are based upon our current opinions, expectations and projections. We undertake no obligation to update or revise any forward-looking statements. Actual results could differ materially from those anticipated in the forward-looking statements.

All data contained within this document is sourced from Cazenove Capital unless otherwise stated.


Dan McFetrich
Global Sector Specialist


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