60 Second Spotlight on Ali Parendeh, ADSP
Ahead of the Instrumentation, Analysis and Testing Exhibition taking place on 29 April at Silverstone Race Circuit we caught up with Ali Parendeh to find out more about the topic of his mini seminar presentation.
Please briefly explain your current role and involvement with AI.
I started my career as a mechanical engineer and gradually transitioned into the AI field. Since my transition, I’ve authored a book on productising generative AI and in my current role, lead an engineering team to build AI systems tailored to each business. These systems help automate workflows, reduce costs and enhance insights-driven decision making for our clients.
How would you say your industry has evolved over the past 5 years?
Over the past five years, the industry has seen a significant shift toward digitisation and the adoption of AI and machine learning. Traditional manual processes have increasingly been supplemented or replaced by AI tools, allowing for more precise predictions, faster decision-making, and improved safety. The integration of IoT devices has also enabled real-time data collection, opening up new opportunities for leveraging AI technologies in the industry.
What is the main challenge in the field of AI at the moment?
The main challenge in the AI field is maintain transparency, reliability, and ethical use of AI. Issues such as algorithm bias, the need for high-quality data, and the reproducibility of AI models remain significant adoption barriers alongside difficulties finding talent, identifying appropriate business case and collection of high-quality data. Additionally, there is a growing concern about AI’s carbon footprint and the difficulty of scaling these technologies across existing complex systems.
What developments are going on in your industry that could have an impact on the future of engineering?
Developments in predictive analytics, autonomous inspection systems, and generative AI tools in digesting ever expanding engineering documentation are shaping the future of engineering. Furthermore, advances in AI for anomaly detection, coupled with edge computing for real-time processing, are setting new standards for efficiency and safety.
Can you briefly explain what excites you most about the current state of AI?
What excites me most is AI’s ability to uncover patterns and insights in data that were previously inaccessible. In addition, today’s AI tools, particularly in natural language processing and generative AI, has created new opportunities to simplify complex engineering tasks and surfacing the right knowledge at the right time across large multi-disciplinary teams.
How do you see AI influencing the role of engineers and the skills they’ll need in the future?
AI is redefining the role of engineers by automating their routine tasks, allowing them to focus on high-level problem-solving and innovation. Engineers will need to gain skills in data analytics, programming, and AI model interpretation. Soft skills, such as adaptability and cross-disciplinary collaboration, will also become increasingly important as AI continues to integrate into engineering workflows.
What are some challenges industries might face as they adopt AI technologies for measurement and testing?
Industries may face challenges like the need for significant upfront investment in AI talent and tools, while integrating with legacy systems. Managing the change in workforce dynamics as automation takes over traditional roles is also another concern. Ensuring data accuracy and mitigating bias are other concerns when adopting AI in areas such as measurement and testing.
What trends in AI do you think engineers and businesses should keep an eye on in the next few years?
Key trends to watch for include the rise of explainable AI (XAI) tools for better transparency and the better affordability of edge computing platforms like NVIDIA Jetson devices for real-time AI applications. Language models are also more accessible, giving rise to new tools that need to crunch lots of textual data. The development of ethical AI frameworks and regulations like the AI EU act is another crucial area that businesses and engineers should monitor closely.
In your opinion, what should schools, colleges and universities do about embracing AI for engineering?
Educational institutions should integrate Python, AI and data science into engineering programmes, by focusing on both the theoretical knowledge and practical aspects. Offering interdisciplinary programs that combine engineering with computer science, ethics, and business can prepare students for AI-driven roles. Additionally, fostering partnerships with industry leaders for hands-on projects and internships can bridge the gap between education and real-world needs.