Artificial Intelligence is coming, and like other technology trends, AI follows a hype curve. Where we are in the hype curve is debatable. Most experts and analysts suggest early on, in the growth phase. Regardless, interest in the technology and its potential is high. A recent study by consultants McKinsey found that 72% of businesses are investing in AI. A new AI is created or another type of job is at risk from the incoming AI revolution almost daily.
Aerospace is no exception. But asking ChatGPT to design you a plane isn’t going to work. Instead, AI companies are beginning to emerge, presenting bespoke Industrial AI solutions. These AIs are tailored to meet industry requirements in terms of accuracy, reliability, performance, security and capabilities.
AI TAKEOVER
Enterprise software company IFS (Industrial and Financial Systems) is expanding rapidly from its ERP roots and aims to become the leading global company for Industrial AI by the end of the decade, primarily financed by private equity investment. Over the last two years the company has been quietly acquiring smaller firms working on developing Industrial AI, such as asset management company Copperleaf and worker management company Poka. IFS is also a member of the All-Party Parliamentary Group in the UK on the application and impact of AI.
“IFS will be the undisputed world leader in Industrial AI by 2029,” proclaimed Mark Moffat, CEO of IFS at the company’s customer event in Birmingham UK in May.
Recent research commissioned by IFS, “Industrial AI: the new frontier for productivity, innovation and competition,” questioned 1,700 senior decision-makers across several industries. Half of the respondents were optimistic that with the right strategy for AI, value could be realized in the next two years, and a quarter believed in the next year.
Moffat believes Industrial AI represents as much an opportunity as a threat for aerospace businesses: “It can take process chains, assess where the blockages are, find the opportunities to save money and unlock value. But you have to lean into this to understand what AI can do for your business – in every part of your business.”
People are now familiar with natural language processing (NLP) AIs such as ChatGPT. These NLPs provide a front-end for users to interact with Industrial AI algorithms. Other types of AI can then be applied to industrial applications and data – generative, predictive, and agentic AI. It is mainly these three types of AI that currently form the basis of Industrial AI applications.
Once an Industrial AI application has been developed by IFS and a client, it is placed in a bank of applications alongside others, and can be accessed via IFS Cloud, the company’s core software platform. Unlike traditional siloed business systems, this “unified” cloud platform integrates multiple enterprise functions, such as ERP, EAM, FSM, and now Industrial AI, into a single accessible ecosystem.
There are already 200 Industrial AI applications, or “capabilities” as the company terms them in the IFS Cloud. These have been added since the launch of the IFS’ Industrial AI initiative last year. IFS Cloud is updated twice annually, in April and September, with new features and AI capabilities added each time.
VERTICAL MARKETS
Headquartered in Sweden, IFS works in a range of sectors, but its background is strongest in the oil and gas sector.
“We operate primarily in capital-heavy, asset and data-rich industries,” says Vijay Hadavale, director of aerospace and defense presales at IFS. “We have a business unit dedicated to aerospace and defense that serves both the commercial and defense sectors.”
IFS splits A&D into specific sub-verticals: airlines and operators, support providers, airport service organizations, A&D manufacturing, independent MRO facilities, defense contractors, and defense forces.
“Our value proposition centers around enabling control across the entire A&D value chain – build, operate, maintain and support,” Hadavale says. “Our architecture means customers can select and deploy the specific capabilities they need.”
The flexibility extends to different operational models, from project-based to discrete manufacturing, small component fabrication to major systems assembly. The platform also provides maintenance and engineering capabilities compliant with industry regulations such as Part M, CAMO, Part 145, Part 121, and ITAR requirements across air, land, sea, and space domains.
The solutions are designed to integrate with existing systems and meet the demands of airworthiness certification. “Our aviation capabilities cover all regulatory requirements – quality assurance, inspection protocols, certification processes, and airworthiness compliance,” Hadavale says. “We’ve embedded these essential functions within our software platform to comprehensively manage the maintenance lifecycle.”
“Industrial AI goes beyond consumer chatbots to provide actionable insights” STEPHANIE POOR, MANAGING DIRECTOR OF UKI AND BENELUX, IFS
The integration of AI into established processes IFS already supplies represents a significant advance for industry, Hadavale claims. “AI is automating workflows and increasing productivity for technicians, engineers, and planners,” he says. “We’ve developed and released numerous use cases since last year, including maintenance scheduling optimization based on our Planning and Scheduling Optimization engine. These algorithms can be applied across manufacturing scheduling, maintenance and testing operations.”
This approach transforms testing from an isolated activity into a fully integrated component within workflows, allowing companies to manage test equipment, control plans, test parameters, and compliance documentation through a single platform, Hadavale explains.
SECURITY COMPLIANCE
Aerospace and defense companies face security challenges when adopting new technologies, including cloud-based AI solutions. According to Hadavale, these concerns have slowed adoption, despite the sector’s traditionally innovative nature.
“In the last decade, consumer industries have surpassed aerospace and defense in certain areas of digital transformation,” Hadavale explains. “A&D is highly protected and secure by necessity. Hesitancy toward cloud adoption stems from regulatory requirements, compliance mandates, and legitimate concerns about cybersecurity.
“In response we’re focusing on compliance, certifications, and regulations. In the US we’re working toward CMMC and FedRAMP certifications, while also addressing jurisdiction-specific requirements worldwide We already maintain ISO 27001, SOC 1, SOC 2, and GDPR compliance, with dedicated European services for data residency requirements.”
The extensive documentation requirements for aerospace testing and certification present significant opportunities for AI-driven efficiency gains. “AI brings automation to this ecosystem that increases productivity across the workflow,” Hadavale says.
AI IN TIME
With the increasingly perilous state of geopolitics, aerospace and defense firms are under pressure to scale up rapidly. Interest is growing in AI tools that can deliver the operational capacity improvements needed, while meeting compliance requirements. “Many defense contractors want to update and upgrade their technology to increase throughput. They are dealing with significant backlogs,” Hadavale says.
“They want to increase throughput – do more with the same workforce and facilities,” Hadavale emphasizes. These organizations can use AI to improve productivity by automating processes and making operations faster, more accurate, and more agile, while maintaining customer satisfaction through service level agreements.
Away from scheduling, Hadavale believes one of the most promising AI applications for aerospace is knowledge transfer. This is particularly critical for an industry facing talent challenges.
“The aging workforce and talent acquisition difficulties represent major challenges for the aerospace sector,” Hadavale says.
“With AI and co-pilot technologies, legacy knowledge becomes immediately accessible through context-aware conversational interfaces.”
This capability could dramatically transform the onboarding process for new technical personnel. “You can bring new talent up to speed much faster,” says Hadavale. “These systems can effectively capture and transfer the specialized knowledge of experienced engineers.”
The knowledge transfer mechanism will extend beyond simple documentation, into applications that use augmented reality, remote assistance, and contextualized information delivery systems to preserve critical expertise even when veteran staff depart. The approach could help aerospace companies ensure technical continuity even when specialized expertise leaves the organization.
“The systems capture the knowledge of engineers” VIJAY HADAVALE, DIRECTOR OF AEROSPACE AND DEFENSE PRESALES, IFS
HALLUCINATIONS
However, the specter rising above AI, especially for industrial applications, is hallucinations, where AIs invents false data. Industry requires analyses and assessments that must be precise. The risks and consequences of failure are higher in mission-critical sectors where lives are at stake, such as aerospace.
Moffat agrees on this critical nature: “We know you can’t get this stuff wrong,” he says. “Planes can fall out of the sky. It’s mission-critical, which is one of the reasons why we are focusing on Industrial AI as different from the generic, large language models.”
Moffat believes a careful approach is needed when deploying Industrial AI in aerospace. And while agentic AIs could be applied within aerospace workflows, he does not see them as making decisions that affect operations for a long time. For example, if an AI summarizes a critical report, he is clear on the limitations. “I think in mission-critical, high-stakes operations, it will be a while before we get a genuine, automated agent. You will still need to put a physical signature on documents,” he says.
While the need for human verification in critical systems isn’t changing anytime soon, the capability of Industrial AI is growing rapidly. Like many sectors and jobs, those who turn their backs to this new technology could risk being left behind.
WHAT IS INDUSTRIAL AI?
Industrial AI is artificial intelligence built specifically for industrial applications.
Unlike general AI, which focuses on mimicking human intelligence, industrial AI is tailored for automating and optimizing complex industrial processes. It leverages data from sensors, machines, and networks to improve decision-making, enhance productivity, and drive innovation.
Stephanie Poor, managing director of UKI and Benelux, IFS says, “Industrial AI goes beyond consumer chatbots. It provides actionable insights and harnesses data effectively to boost productivity.”
IFS is not the only industrial software company adding AI functionality to its products. Companies from design software firms like Dassault and Autodesk, to manufacturing suppliers like Rockwell and Siemens have AI-powered enhancements in their products. But so far, the only company to try and redefine its entire product portfolio using AI is IFS.
DIFFERENT TYPES OF AI
There are many different types of AI, with new types being developed at pace. For business and industry on a general level AI can be split into three different categories – predictive, generative and agentic.
Predictive AI uses historical data to forecast future events or trends to help with decision-making and strategic planning. An example could be forecasting customer demand or predicting product failure.
Generative AI creates new content, such as text, images, videos, code or designs. It can be used to replicate human creativity and innovation. Examples include generating marketing copy, creating new engineering designs or composing music.
Agentic AI are autonomous systems that can analyze, plan and act independently. The AI agents can be used to make decisions, execute tasks, and adapt to changing situations. Example include software co-pilots, robotic assistants, self-driving cars, or autonomous logistics systems.
