The design process is rarely linear. Before a building takes shape, it begins with an idea — and for many creatives, few things are more daunting than a blank page, or more fittingly, a blank wall.
Generative design offers a new starting point, giving designers and developers an AI-powered boost to get ideas flowing. In this interview with Frontier Enterprise, Raji Arasu, Chief Technology Officer of Autodesk, explains how generative design can reduce time, effort, and resource demands, while opening new possibilities for the architecture, engineering, construction, and operations (AECO) industries.
How is AI changing design and engineering beyond automation?
AI and machine learning are already improving productivity by automating repetitive tasks, allowing designers, engineers, and other creative professionals to focus more on strategic activities like innovation rather than manual processes.
But AI is now evolving from automation to augmentation, helping people work more intelligently. Generative AI tools allow users to simulate, explore, and iterate on ideas more efficiently, reducing downstream risks and broadening the range of viable solutions.
This shift is supporting more creative and insightful outcomes — from new products to more sustainable buildings and smarter cities. By combining human ingenuity with AI, we are entering a new phase in how things are designed and made.
In early-stage planning, firms are using AI-powered tools to incorporate sustainability and resilience goals into conceptual design more effectively. Research into generative 3D shape creation continues to evolve, enabling more precise digital modelling for design and production.
This reflects our broader view that AI should support human creativity, not replace it.
What challenges does AI face in construction and manufacturing?
AI holds significant promise, but implementation in AECO and manufacturing must account for the complexity of these industries. Strict design requirements, regulatory standards, and the need for precision mean that new technologies must be thoroughly tested and cannot compromise accuracy or trust.
Construction faces even greater hurdles than manufacturing due to the less standardised nature of work sites. Safety is a primary concern, alongside issues like unreliable connectivity, high entry costs, limited talent, and poor data quality. For example, many AI tools rely on real-time input from sensors and actuators, yet job sites often lack stable internet infrastructure. This disrupts machines such as robots and monitoring systems that depend on reliable connections.
To address these challenges, firms must work closely with researchers and domain-specific experts to develop solutions tailored to their environments.
Despite the barriers, momentum is building. According to 2025 research, 68% of APAC respondents plan to increase AI investments, and the same proportion believe AI will improve their industries — a clear indication of its transformative potential.
How does industry convergence shape problem-solving in enterprise tech?
Industry convergence influences enterprise technology in two key ways.
First, it enables cross-industry innovation by allowing organisations to adapt solutions from one sector to another. A classic example is the adoption of digital twin technology, which began in aerospace in the 1960s and later spread to manufacturing, AECO, and healthcare.
By working across diverse industries, we can identify practices with broader potential. For instance, advancements in visualisation and content creation from media and entertainment can enhance architectural design. Likewise, generative scheduling and rapid change management can improve construction project delivery.
Second, design and production workflows are becoming more integrated. Complex considerations such as cost, material availability, and operability — once addressed during manufacturing or construction — can now be factored in much earlier through data and AI. This shift allows organisations to improve predictability and profitability by addressing key constraints during the design phase.
Convergence also fosters connected ecosystems. By integrating tools and technologies across design and production, organisations can create interoperable data environments that improve collaboration, efficiency, and innovation across industries.
What role does generative design play in bridging concept and implementation in large-scale projects?
Generative design, when combined with AI, helps close the gap between concept and real-world execution by enabling more informed, data-driven decisions. It is reshaping how large-scale projects are approached, especially in Asia-Pacific, where urban growth and evolving sustainability regulations are driving the need for smarter design and construction practices.
Instead of following a linear design process, generative tools support iterative exploration. Designers can test ideas quickly, adapt in real time, and develop solutions that are constructable, cost-effective, and environmentally conscious.
As project complexity increases, success will depend on the ability to evaluate many design permutations early in the process. By setting parameters related to materials, cost, and performance, teams can simulate outcomes, compare options, and identify optimal solutions that might not surface through traditional workflows.
This approach also supports sustainability goals by helping teams reduce material use, minimise carbon impact, and improve building performance from the outset — all critical as regulatory and environmental pressures grow across the region.
As AI becomes more integrated into creative workflows, how should enterprises balance productivity and creativity?
AI offers new opportunities for creative teams, but it should serve to enhance — not replace — human ingenuity. By automating repetitive tasks, AI can free up time for professionals to focus on ideation, problem-solving, and innovation.
Enterprises can maintain this balance by focusing on four key areas:
- Augmentation over replacement: AI can generate insights and design options, but humans remain essential for applying judgment, interpreting context, and making creative decisions.
- Upskilling and talent development: Organisations need to invest in both AI expertise and creative fluency. Training should go beyond tool usage to include how AI can be used in imaginative and effective ways.
- A culture of experimentation: Teams should be encouraged to test AI across workflows, as experimentation often uncovers unexpected value and fosters innovation.
- Responsible use: Clear guidelines are essential to ensure transparency, fairness, and data privacy. Ethical practices build trust and support sustainable AI adoption across the business.
How can enterprises adopt AI without disrupting existing systems?
Many enterprises, especially in Asia-Pacific, are cautious about adopting AI due to concerns about disrupting widely deployed systems and operations. The key to successful integration lies in a phased, practical approach that supports continuity and minimises risk.
One strategy is modular integration — introducing AI into existing workflows gradually, focusing first on low-risk features that offer clear productivity gains. This allows teams to demonstrate value early without overhauling systems.
Another is to build on existing infrastructure. Rather than replacing legacy tools, AI can often be layered onto current platforms using APIs and other connectors to generate insights and automate tasks.
AI can also help unify fragmented data by automating classification, tagging, and normalisation, making existing data more usable without requiring a full-scale overhaul.
Finally, successful adoption depends on ongoing training and support. Equipping employees with the skills to work effectively with AI, while ensuring oversight and compliance, helps organisations scale adoption responsibly. Broad access, paired with clear governance, encourages experimentation and supports long-term adoption.
