The age of computational design has finally arrived. Incorporating data and artificial intelligence (AI) at every stage of a design process transforms our design tools, and perhaps the very definition of what it means to design and engineer. We have started to tap the enormous opportunity in the data flows and computational capabilities available to us. And as access to data — and our ability to extract knowledge from it — grows, we see opportunities for organizations to rethink design processes at a fundamental level.
We see a near future where today’s quantitative change in computational capabilities powers a qualitative shift in design practice. Manipulating geometry will soon no longer be the primary activity that distinguishes design and engineering from other practices. Instead, in the vision below we outline how teams of designers and engineers will work with a range of ‘AI assistants’ to define generative product possibility spaces that simultaneously define multitudes of possible design solutions. Overall, we see the increased use of AI assistants and data-driven processes as a way to make designers exponentially more productive.
The potential of AI for design assistance has existed since the earliest days of CAD in the 1960s. It remained more theoretical than practical for decades, largely because the skills for designers to fully take advantage of computational capabilities and data were, until recently, quite rare. Most architects can’t also be expert programmers, statisticians, and data scientists, so the potential has largely remained locked up in our devices — hinted at in exotic solutions, but not part of everyday workflows.
However, as we’ve seen with the various AI-driven tools that have begun to appear in the rest of our lives — the AI that uncannily suggests the correct next word in an email, the map that finds a surprisingly efficient route around a traffic jam — AI assistants are beginning to hit their stride as tools that are accessible to people without a technical AI background.
We don’t see AI assistants monolithically. There isn’t going to be a single unified helper, or a chatbot, or a replacement junior engineer. Many AI design assistants will play different roles across the design process and will complement the skills of human designers. During early stages of a design process, AI assistants can provide quantitative research and collect customer preferences. As a design space is defined, AI assistants can help distill constraints and requirements. AI assistants can then generate many design options and help their human teammates curate and select from those options based on various simulations and evaluations. Powering this constellation of AI assistants is a digital thread that connects data from design, simulation, manufacturing, sales, and usage. Collectively, this computational design system can enable companies to develop more product variations with fewer resources.
How then can companies achieve a transition to AI assistant-driven computational design? From the production perspective, creating small-run product lines is feasible today with CNC machines and vast catalogs of off-the-shelf components that can be algorithmically reassembled. Once a design can be produced with a set of specific manufacturing machines and interchangeable parts, these are minimal barriers to making changes to a design later on.
The real challenge lies in design talent capacity. A design team’s capacity is linear: the number of designs you can produce is directly proportional to the number of designers you have. It doesn’t scale economically without some form of parametric and generative systems to support those designers in creating exponentially many more designs without hiring exponentially more designers.
