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.”
AI can automate product workflows, including in testing departments
“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.”
Industrial AI agents are already being used in planning logistics and for optimizing manufacturing
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.
AI can enable insights into test and inspection data that lead to process efficiency improvements
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.
As chronic wounds such as diabetic ulcers, pressure ulcers, and articular wounds continue to challenge global healthcare systems, a team of researchers from China has introduced a promising innovation: AI-integrated conductive hydrogel dressings for intelligent wound monitoring and healing.
This comprehensive review, led by researchers from China Medical University and Northeastern University, outlines how these smart dressings combine real-time physiological signal detection with artificial intelligence, offering a new paradigm in personalized wound care.
Why it matters:
Real-time monitoring: Conductive hydrogels can track key wound parameters such as temperature, pH, glucose levels, pressure, and even pain signals-providing continuous, non-invasive insights into wound status.
AI-driven analysis: Machine learning algorithms (e.g., CNN, KNN, ANN) process sensor data to predict healing stages, detect infections early, and guide treatment decisions with high accuracy (up to 96%).
Multifunctional integration: These dressings not only monitor but also actively promote healing through electroactivity, antibacterial properties, and drug release capabilities.
Key features:
Material innovation: The review discusses various conductive materials (e.g., CNTs, graphene, MXenes, conductive polymers) and their roles in enhancing biocompatibility, sensitivity, and stability.
Smart signal output: Different sensing mechanisms-such as colorimetry, resistance variation, and infrared imaging-enable multimodal monitoring tailored to wound types.
Clinical applications: The paper highlights applications in pressure ulcers, diabetic foot ulcers, and joint wounds, emphasizing the potential for home care, remote monitoring, and early intervention.
Challenges & future outlook:
Despite promising advances, issues such as material degradation, signal stability, and AI model generalizability remain. Future efforts will focus on multidimensional signal fusion, algorithm optimization, and clinical translation to bring these intelligent dressings into mainstream healthcare.
This work paves the way for next-generation wound care, where smart materials meet smart algorithms-offering hope for millions suffering from chronic wounds.
Stay tuned for more innovations at the intersection of biomaterials, AI, and personalized medicine!
As shopping period settles, students may notice a new addition to many syllabi: an artificial intelligence policy. As one of his first initiatives as associate provost for artificial intelligence, Michael Littman PhD’96 encouraged professors to implement guidelines for the use of AI.
Littman also recommended that professors “discuss (their) expectations in class” and “think about (their) stance around the use of AI,” he wrote in an Aug. 20 letter to faculty. But, professors on campus have applied this advice in different ways, reflecting the range of attitudes towards AI.
In her nonfiction classes, Associate Teaching Professor of English Kate Schapira MFA’06 prohibits AI usage entirely.
“I teach nonfiction because evidence … clarity and specificity are important to me,” she said. AI threatens these principles at a time “when they are especially culturally devalued” nationally.
She added that an overreliance on AI goes beyond the classroom. “It can get someone fired. It can screw up someone’s medication dosage. It can cause someone to believe that they have justification to harm themselves or another person,” she said.
Nancy Khalek, an associate professor of religious studies and history, said she is intentionally designing assignments that are not suitable for AI usage. Instead, she wants students “to engage in reflective assignments, for which things like ChatGPT and the like are not particularly useful or appropriate.”
Khalek said she considers herself an “AI skeptic” — while she acknowledged the tool’s potential, she expressed opposition to “the anti-human aspects of some of these technologies.”
But AI policies vary within and across departments.
Professors “are really struggling with how to create good AI policies, knowing that AI is here to stay, but also valuing some of the intermediate steps that it takes for a student to gain knowledge,” said Aisling Dugan PhD’07, associate teaching professor of biology.
In her class, BIOL 0530: “Principles of Immunology,” Dugan said she allows students to choose to use artificial intelligence for some assignments, but that she requires students to critique their own AI-generated work.
She said this reflection “is a skill that I think we’ll be using more and more of.”
Dugan added that she thinks AI can serve as a “study buddy” for students. She has been working with her teaching assistants to develop an AI chatbot for her classes, which she hopes will eventually answer student questions and supplement the study videos made by her TAs.
Despite this, Dugan still shared concerns over AI in classrooms. “It kind of misses the mark sometimes,” she said, “so it’s not as good as talking to a scientist.”
For some assignments, like primary literature readings, she has a firm no-AI policy, noting that comprehending primary literature is “a major pedagogical tool in upper-level biology courses.”
“There’s just some things that you have to do yourself,” Dugan said. “It (would be) like trying to learn how to ride a bike from AI.”
Assistant Professor of the Practice of Computer Science Eric Ewing PhD’24 is also trying to strike a balance between how AI can support and inhibit student learning.
This semester, his courses, CSCI 0410: “Foundations of AI and Machine Learning” and CSCI 1470: “Deep Learning,” heavily focus on artificial intelligence. He said assignments are no longer “measuring the same things,” since “we know students are using AI.”
While he does not allow students to use AI on homework, his classes offer projects that allow them “full rein” use of AI. This way, he said, “students are hopefully still getting exposure to these tools, but also meeting our learning objectives.”
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Ewing also added that the skills required of graduated students are shifting — the growing presence of AI in the professional world requires a different toolkit.
He believes students in upper level computer science classes should be allowed to use AI in their coding assignments. “If you don’t use AI at the moment, you’re behind everybody else who’s using it,” he said.
Ewing says that he identifies AI policy violations through code similarity — last semester, he found that 25 students had similarly structured code. Ultimately, 22 of those 25 admitted to AI usage.
Littman also provided guidance to professors on how to identify the dishonest use of AI, noting various detection tools.
“I personally don’t trust any of these tools,” Littman said. In his introductory letter, he also advised faculty not to be “overly reliant on automated detection tools.”
Although she does not use detection tools, Schapira provides specific reasons in her syllabi to not use AI in order to convince students to comply with her policy.
“If you’re in this class because you want to get better at writing — whatever “better” means to you — those tools won’t help you learn that,” her syllabus reads. “It wastes water and energy, pollutes heavily, is vulnerable to inaccuracies and amplifies bias.”
In addition to these environmental concerns, Dugan was also concerned about the ethical implications of AI technology.
Khalek also expressed her concerns “about the increasingly documented mental health effects of tools like ChatGPT and other LLM-based apps.” In her course, she discussed with students how engaging with AI can “resonate emotionally and linguistically, and thus impact our sense of self in a profound way.”
Students in Schapira’s class can also present “collective demands” if they find the structure of her course overwhelming. “The solution to the problem of too much to do is not to use an AI tool. That means you’re doing nothing. It’s to change your conditions and situations with the people around you,” she said.
“There are ways to not need (AI),” Schapira continued. “Because of the flaws that (it has) and because of the damage (it) can do, I think finding those ways is worth it.”
When investors think about artificial intelligence (AI) and the chips powering this technology, one company tends to dominate the conversation: Nvidia (NASDAQ: NVDA). It has become an undisputed barometer for AI adoption, riding the wave with its industry-leading GPUs and the sticky ecosystem of its CUDA software that keep developers in its orbit. Since the launch of ChatGPT about three years ago, Nvidia stock has surged nearly tenfold.
Here’s the twist: While Nvidia commands the spotlight today, it may be Taiwan Semiconductor Manufacturing(NYSE: TSM) that holds the real keys to growth as we look toward the next decade. Below, I’ll unpack why Taiwan Semi — or TSMC, as it’s often called — isn’t just riding the AI wave, but rather is building the foundation that brings the industry to life.
