By Ts. Dr. Syahril Anuar Idris, Head of CAIRA & Head of Programme (MSc & PhD Engineering), UNIMY

When we discuss the value of an AI and robotics engineer, the conversation almost immediately defaults to technical proficiency. We look for mastery in system integration, familiarity with edge computing architectures, and the ability to orchestrate complex multi-agent frameworks. But as AI systems transition from isolated scripts to autonomous networks making real-time decisions, technical prowess is no longer the sole differentiator as it is merely the baseline.

I was recently reminded of this during the BEM National Engineering Convention (NEC) 2026: International Foresight Engagement. Conducted by world-renowned experts Prof. Alexander Sokolov, UNESCO Chair on Future Studies, and Prof. Jonathan Calof, UNESCO Chair on Anticipatory Systems for Innovation and Venture Creation, the session highlighted a fascinating reality. Foresight is often pigeonholed as a high-level policy or economic tool. However, its core methodology offers a highly structured approach that is incredibly interesting to connect and apply directly to AI development.

Here is why applying the methodology of Strategic Foresight provides a critical framework for the next generation of technological innovation.

A Methodology for Structuring the Unpredictable
In AI and robotics development, we are constantly dealing with unpredictable variables. Strategic Foresight introduces a rigorous, solidifying structure PLAN, ANALYSE, STRATEGISE that provides a clear methodology for anticipating change and managing uncertainty.

When we embed this foresight methodology into our research and development pipelines, we are no longer building systems in a vacuum. Instead, we can accurately measure technological and environmental readiness before a single line of code is written. It provides a structured way to ask not just “Can we build this AI?” but “Is the infrastructure, workforce, and ecosystem genuinely ready to sustain it?”

Justifying the AI Decision Path
As we deploy localized AI and vision-language models, the human engineer’s role is shifting. We are moving from writing every function to defining, governing, and auditing the system’s logic.

This is where the application of foresight methodology becomes deeply valuable for strengthening evidence-based decision-making. By using foresight as a framework for problem analysis, engineers can logically map out and justify every decision an automated system is designed to make. It acts as a structural guardrail, moving AI from a “black box” of predictive guessing to a justified sequence of actions. Whether deploying an automated robotic arm or a corporate governance AI, the foresight methodology ensures that the system’s planned actions lay perfectly within the resources available.

The UNIMY Edge: Engineering for Tomorrow
At the University Malaysia of Computer Science and Engineering (UNIMY), this is the broader perspective we are actively driving. As we look toward shaping the future of engineering education and embedding future-ready competencies into our curriculum, the goal of the

Center for AI, Robotics & Automation (CAIRA) is to cultivate tech professionals who look beyond the hardware and the prompt.

We train our students to understand that a beautifully engineered system only holds value if it is built on a solid foundation of problem analysis.

The most capable engineers of the next decade will not just be the ones who know how to deploy a model. They will be the ones who know how to apply globally recognized methodologies, like Strategic Foresight, to ensure their innovations are targeted, justified, and perfectly aligned with the real world’s readiness to adopt them.

About the Writer
Ts. Dr. Syahril Anuar Bin Idris is currently the Head of the Center of Artificial Intelligence, Robotics & Automation (CAIRA), as well as the Head of Programme for the Master of Science in Engineering and PhD in Engineering programs at the University Malaysia of Computer Science & Engineering (UNIMY). He specializes in engineering and technology, teaching subjects such as robotics, artificial intelligence, and system integration for undergraduate and postgraduate programs. He completed his Bachelor’s degree in Manufacturing Engineering (Robotics & Automation) with Universiti Teknikal Malaysia Melaka and went on to earn his Master of Engineering in Manufacturing Systems.

He then completed his Ph.D. in Advance & Intelligent Manufacturing from the same institution. Following this, Dr. Syahril had diverse exposure working as a technical consultant, researcher, and entrepreneur across various industries, managing projects and developing prototypes for autonomous cars and smart factories. He is currently a certified Technology Specialist with the Malaysia Board of Technologists and an accredited Train The Trainer by the Human Resource Development Corporation (HRDC), actively coaching the younger generation in emerging digital technologies.