Generative Design: A Process-Driven Approach Beyond Specific Tools

Allister Lewis
Founder and CEO 
Automated Data Driven Design (ADDD)

Introduction: The Proliferation of Generative Design Tools

Generative Design (GD) tools have become increasingly accessible, empowering architects, engineers, and clients to explore innovative designs and streamline project feasibility. But with so many options, how can AEC professionals choose the right tool to meet their needs? This article dives into the reasons behind the rise of GD tools, the challenges they present, and practical steps for selecting the best option for your projects.

What is Generative Design?

GD is a technology-driven approach to optioneering designs by using algorithms to explore a vast number of possible solutions based on defined constraints and goals. By inputting parameters such as unit mix, desired building heights, spatial layout requirements, and performance criteria, GD software generates numerous design options. This allows designers to evaluate and select the most optimal solution. Instead of manually crafting each option, designers can review a wide range of solutions, filter and adapt them further, enabling new creativity, or simply getting optimal answers faster for clients.

The Expanding Landscape of Generative Design Tools

As GD becomes increasingly popular, so too has the number of available tools. Today, the ADDD Marketplace features around 35 options, with this explosion of tools both exciting and overwhelming. It is clear that GD companies have identified early stage feasibility as an area that GD can be applied to effectively. The software should help designers make decisions faster, using data to confirm project viability, and communicate options more effectively than traditional CAD/BIM authoring tools. However, this presents a challenge for professionals to understand which one is best for them, manage licence costs, and then understand how to integrate into their own tech stack effectively.

Breaking Down Barriers to Adoption

Despite the promise of GD, there are still notable barriers to adoption for many AEC professionals.

1. Time to Learn: GD tools have opened the door for many users to explore data driven design but within a proprietary format, without a steep technical learning curve of visual scripting. GD tools still require time and effort to master, which can pose challenges for busy professionals.
2. Cost: Licensing fees vary from free trials and low introduction fees, to expensive Software as a Service (SaaS) models. When added to the growing list of software required by a company, this is an additional cost that puts pressure on companies already struggling to manage cash flow. While GD tools may offer long-term savings by accelerating workflows, the recurring monthly costs can deter adoption.
3. Workflow Integration: Not all GD tools integrate smoothly with existing software ecosystems, which can create friction. Users often rely on a core suite of tools, and if a GD solution disrupts these workflows, it may slow productivity. Ensuring compatibility and ease of integration with a company’s preferred BIM authoring software is crucial for broader adoption.
4. Functionality: These tools are new and expanding their range of functionality they offer, however no single tool has managed to provide the comprehensive design tool that meets all user requirements. A careful study of requirements is needed to make sure the software does what is needed. 

Generative Design as a Tool for Clients and Developers

An emerging trend in GD is the shift from ConTech companies trying to sell to architects, and instead selling directly to developers and clients who may wish to understand site potential before hiring a design team. Tools that automate site analysis and feasibility studies allow them to receive insights into purchases at a much earlier stage. This has an impact on the architects’ traditional role particularly in the early stage design process. This shift presents both opportunities and challenges. While it may reduce architects’ involvement in early feasibility studies for some clients, it also presents an opportunity for them to use these tools for those clients that want greater data insights, but don’t want to use the tool themselves. By accessing data and insights generated by GD solutions quickly and easily, architects could provide value faster, creating more refined designs that align with clients’ initial requirements. This approach suggests architects should adapt, adopt a solution, and potentially provide new services for clients. Users could enhance their role in delivering data-driven projects with this approach.

The Risks and Challenges of Generative Design Adoption

While the potential benefits of GD are clear, there are some risks that users should consider carefully:

1. Quality vs. Quantity: The ability to generate a multitude of designs quickly can be both a blessing and a curse. With too many options, architects may face decision fatigue, or find themselves sacrificing quality for speed.
2. Cost and Learning Barriers: For many architects, the cost and time to learn required to adopt these tools remain hurdles, especially for smaller firms.
3. Dependence on Technology: Over-reliance on GD tools can sometimes overlook essential design considerations that come from experience, intuition, and human creativity. Designers who depend heavily on GD software should augment this with a hands-on design approach that allows for unique, site-specific insights.

How to Assess Generative Design Tools with the FASS Criteria

With the growing variety of GD tools, users need a consistent way to evaluate software that goes beyond features and marketing hype. This is why at ADDD, I have developed an assessment criteria, based on the Future AEC Software Specification (FASS). The aim is to enable a consistent approach to reviewing software and means that they can be compared to each other. This assessment methodology was presented at NXTDEV and has now been used to assess four Clash Detection and Issue Management software. The outcome is a quantitative framework of questions designed to bring clarity and consistency to software evaluation. When combined with a qualitative approach, where users can communicate their findings and thoughts, a report can be produced that supports the AEC industry to have a consistent way to assess software.

The assessment provides a structured, objective way to assess GD tools on critical factors such as ‘Designing in Context & Scale’, ‘User Experience’, ‘Modelling Capabilities’, and the other criteria from the FASS. By applying the FASS criteria, users can identify tools that align with their needs, budget, and project demands, allowing them to make decisions that suit their requirements. This approach not only simplifies the selection process but also ensures that chosen tools meet the demands of modern AEC workflows. I am also advocating for Best For …’ results, rather than one software being better than another, as this is too simplistic. In a landscape flooded with options, having a consistent assessment method like the  FASS can be invaluable, helping users to navigate to the appropriate GD that is ‘Best For Their Needs’.

Generative Design Report

Over the last three months I have reviewed and assessed GD tools and this has been compiled into a comprehensive and thorough document that includes:

• Assessment of GD options
• Features review
• A ‘Best For … ‘ terminology to help understand the best use case for a specific GD options
• Created a Cheat Sheet of the options and key functionality
• Discussed an present possible work flow options

This report is the first of its kind in the industry and the aim is to support AEC professionals, clients, developers and Real Estate professionals to find the best GD software for them.

Conclusion: Charting a Way Forward in the Age of Generative Design

GD tools will undoubtedly disrupt early stage design stages within the AEC industry, offering powerful capabilities that allow users to explore, create, and iterate with unprecedented speed. This will require adaptability, a consistent structure to assessing software, and a way to help strategic decision-making within organizations. As GD options continue to search for their specific niche, AEC professionals have the opportunity to explore and embrace these tools as allies in their work, with the opportunity to lead to better client outcomes. With resources like the FASS Assessment, users can assess, compare, and select the solution that aligns with their organizational goals, empowering them to succeed in a digital future that balances creativity with technology. 

How To Choose The Best Generative Design Software For You

Step 1: Define Project Goals and Requirements

• Scope: Identify the project type and sector (e.g., residential, commercial, infrastructure).
• Objectives: Determine what you need the software to accomplish (e.g., optimise layout, enhance sustainability, automate early design stages).
• Workstage Relevance: Specify the project phase (e.g., concept development, detailed design, construction planning) for which you’ll use the tool.

Step 2: Identify Target User Disciplines

• Primary Users: Decide if the tool is for architects, engineers, planners, or a multidisciplinary team.
• Interdisciplinary Compatibility: Prioritise tools with collaboration features if multiple disciplines are involved, ensuring integration with BIM or CAD platforms for smoother team workflows.

Step 3: Search for Tools on the ADDD Marketplace

• Use Filters: Search by project type, user discipline, features, or RIBA/AIA stages to narrow down your options.
• Explore Comparisons: The ADDD Marketplace may offer comparisons or descriptions that highlight each tool’s unique capabilities, helping you match software to your project’s specific needs.

Step 4: Evaluate Core Features and Capabilities

• Feature Set: List essential features like parametric modelling, AI-driven analysis, or real-time data visualisation.
• Automation and Customisation: Check if the software allows customizable automation for tailored workflows, which can enhance efficiency.
• Sustainability or Compliance: If environmental goals are key, prioritise tools offering lifecycle analysis or sustainable design options.

Step 5: Assess Ease of Use and Learning Curve

• Skill Level: Assess your team’s technical background to determine if they can quickly learn and adopt the software.
• Training and Support: Look for available training resources, community support, or dedicated help, especially if the software has a steep learning curve.

Step 6: Check Workflow Integration and Compatibility

• Platform Integration: Confirm compatibility with core platforms like Revit, Rhino, AutoCAD, or IFC for seamless BIM integration.
• Data Import/Export: Ensure the software supports the necessary file types and data exchange formats across your project.
• Collaborative Features: For team-oriented projects, consider tools with real-time collaboration or cloud integration to support smooth communication and data sharing.

Step 7: Consider Cost and Licensing Options

• Budget Alignment: Evaluate pricing models (subscription, one-time licence, or pay-per-use) and align with your project budget.
• Scalability: Check if the software offers pricing tiers or modular add-ons to accommodate future project needs.

Step 8: Apply a Quantitative Evaluation Framework (e.g., FASS)

• Quantitative Scoring: Use a framework like the Future AEC Software Specification (FASS) to assess options based on weighted criteria, such as usability, cost-effectiveness, and compatibility.
• Consistency: FASS provides an objective scoring system, ensuring consistency and clarity when comparing multiple software options.

Step 9: Test and Validate with a Trial or Pilot Project

• Trial Runs: Opt for software trials or demos, running a small pilot project to evaluate functionality.
• Team Feedback: Collect input from team members to confirm that the software meets project needs, integrates well, and enhances productivity.

Step 10: Gather Feedback and Refine Use

• Continuous Improvement: Encourage your team to provide ongoing feedback on software performance, usability, and integration with workflows.
• Evaluate Long-Term Use: Assess the software’s impact over time, adapting as needed for future projects or exploring alternative tools if requirements evolve.

Step 11: Conduct a 6/12-Month Tech Stack Review

• Scheduled Assessment: Set a recurring review of your tech stack every 6 or 12 months to evaluate software performance, relevance, and alignment with evolving project needs.
• Identify Gaps and Opportunities: During each review, assess if new tools are needed, if existing ones should be upgraded, or if certain software is no longer effective.
• Optimise Workflow: Ensure all tools remain compatible and continue to support seamless workflows, adjusting as necessary to integrate new solutions that improve efficiency and project outcomes.