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Computation in the business of design is an organizational capability, not a software purchase. Nate, the author of Proving Ground’s 2016 framework, argues that firms get lasting value by investing in people, combining specialist expertise with broadly distributed skills, and defining computation around a specific business or design problem. That advice remains useful for architecture, engineering and construction practices, provided you treat the original software references as date-bound. Current documentation describes Grasshopper as a visual programming editor included with Rhino, while Dynamo is an open-source visual programming platform installed with Revit. The practical question is therefore not simply whether a tool can automate something, but whether your firm can develop, govern and apply the capability to produce a valuable result.
This article interprets the recommendations in Nate’s “Computation in the Business of Design – 3 Things to Consider,” published by Proving Ground on April 7, 2016 and later by Building Design + Construction. The recommendations are an author’s framework, not a measured rule for every practice.
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What does computation in the business of design actually mean?
In this context, computation means using programmable logic, data and repeatable workflows to support design decisions and delivery. It can include parametric studies, automated documentation, model checking, geometry generation, analysis setup, data exchange or a small internal utility that removes repetitive work. The capability belongs to the firm even when a particular application changes.
That distinction matters in construction and design businesses. A purchased application may provide nodes, scripts or an interface, but it does not decide which tasks are worth automating, who is responsible for checking the result, or how a workflow fits the firm’s design principles and commercial model. A computational strategy must connect those decisions to the way projects are won, designed, coordinated and delivered.
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Nate’s article was written when firms were asking whether “Grasshopper or Dynamo can [do a particular task].” He recommends reframing the question as: What can we create that will help us better solve [x] design problem.
That change in wording moves the discussion from features to outcomes.
Why are people the first investment?
People determine whether computation becomes a dependable practice or remains a collection of impressive demonstrations. Nate writes that, “As with most things that drive success in service organizations, people should be the real focus of business investment for executing a successful computational design strategy.” The firm needs people who can use existing tools, understand the design intent behind them, and eventually create or adapt tools when the standard workflow does not fit.
Those skills have to work with the firm’s design principles and business ethos. A highly optimized script that conflicts with how a practice develops concepts, documents decisions or communicates with clients may create more friction than value. Conversely, a modest automation that matches established standards can become a dependable part of project delivery.
Build a practical skills ladder
- Tool users: Staff can run approved definitions, enter inputs correctly, interpret outputs and recognize when a result needs human review.
- Adaptors: Staff can change parameters, connect components, modify simple scripts and make controlled adjustments to a more complex algorithm.
- Tool builders: Staff can frame a problem computationally, design reusable workflows, document assumptions, manage data and support other users.
- Technical owners: Senior practitioners provide standards, review risk, decide what belongs in production and retire tools that no longer meet project needs.
Training should use the firm’s own design problems rather than isolated software exercises. A short workshop might begin with a repetitive task that teams already understand, then show how inputs, rules and outputs map to the design process. The learning objective is not to make every employee a programmer; it is to make the organization more capable of choosing, using and questioning computational methods.
Current product descriptions help clarify what the tools are, but not what your organization should do with them. Rhino’s official Grasshopper documentation describes Grasshopper as a visual programming editor included with Rhino for parametric and generative design. Autodesk’s Revit 2026 Dynamo documentation describes Dynamo as an open-source visual programming platform installed with Revit, with Revit-specific programming nodes. The original 2016 descriptions of Grasshopper and Dynamo should not be treated as current packaging or pricing guidance.
What should a people investment include?
- Choose accountable owners. Name a business sponsor who can remove obstacles and a technical owner who can maintain definitions, standards and documentation.
- Map current capability. Record who can run, adapt, build and review computational workflows. Include project managers, designers, engineers, BIM staff and information managers, not only a central technology group.
- Allocate learning time. Practice requires scheduled time for training, code review and documentation. If learning is permitted only after project work, the capability will remain dependent on a few volunteers.
- Teach review as well as creation. Users need to know the assumptions, valid input ranges, failure conditions and manual checks for every production tool.
- Reward reuse and knowledge sharing. A definition that another project team can safely understand and adapt is more valuable than a one-off script that only its author can operate.
Should computation be handled by specialists or distributed across teams?
The strongest answer is usually a deliberate combination. A specialist group can develop deep expertise and provide technical leadership, while distributed working knowledge lets more project teams participate and prevents the capability from becoming a bottleneck. Nate’s article warns that a specialist-only model has limits as a firm scales, but it does not provide outcome data or a universal staffing ratio.
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| Organizational approach | Strength | Typical limitation | Useful control |
|---|---|---|---|
| Dedicated specialist group | Deep algorithmic, scripting and technical expertise; consistent standards | Requests queue up, and project teams may not understand or own the result | Embed specialists early and transfer knowledge to project staff |
| Distributed capability | Broad participation, faster local decisions and stronger connection to project context | Methods can diverge; advanced problems may exceed local skills | Shared templates, review gates and a central community of practice |
| Hybrid model | Combines depth with reach and creates a path for staff development | Requires clear boundaries, funding and communication | Central standards and coaching with project-level ownership |
Use four questions when choosing the balance: How deep is the required expertise? How broadly must teams participate? Can the model scale as project volume grows? And can project leaders set the right context and constraints? These are organizational comparison axes, not a promise that one structure will always produce better project outcomes.
How should the 70/20/10 idea be used?
Nate proposes a 70/20/10 staff-skill mix: 70% of a computation-enabled team able to use prepared tools, 20% able to build simple tools and modify complex algorithms, and 10% with advanced tool-building skills. It is a heuristic offered by the author, not a survey finding, validated benchmark or required ratio. The percentages should be adjusted for the firm’s size, project types, risk profile and available support.
| Proposed share | Capability | Management implication |
|---|---|---|
| 70% | Use prepared tools reliably and interpret results | Prioritize clear interfaces, training, documentation and quality checks |
| 20% | Build simple tools and modify more complex algorithms | Provide code review, mentoring and a safe path from experiments to production |
| 10% | Advanced tool building and technical direction | Protect time for architecture, maintenance, standards and difficult problems |
For a small practice, one person may occupy all three capability levels at different times. For a large organization, the percentages may describe a department, a project network or a learning community rather than the entire company. The useful question is whether enough people can use a workflow safely and whether enough advanced practitioners can maintain it.
How do you define business value before choosing a tool?
Start with a specific design or delivery problem and a desired result. Do not assume that software has a predefined feature for every need. A useful brief states what is repetitive, error-prone, slow, difficult to coordinate or strategically differentiating; who is affected; what constraints apply; and what evidence would show improvement.
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Nate identifies two examples of value: speeding up repetitive design problems and creating a tool or service that differentiates a firm in its market. Those possibilities imply different measures. An internal automation may be judged by reduced preparation time, fewer manual transcriptions or more options explored within a deadline. A client-facing capability may be judged by whether it supports a service the firm can explain, deliver consistently and price responsibly.
A business-value brief
- Problem: Describe the task in project language, such as repeated layout studies, model data cleanup or coordination checks.
- Users: Identify who supplies inputs, who runs the workflow and who approves the output.
- Desired result: Define the decision, drawing, model data or service outcome the workflow must produce.
- Constraints: List software versions, file standards, deadlines, data permissions, design rules and required human review.
- Value signal: Choose observable indicators such as turnaround time, number of viable options, consistency, traceability or a new service capability.
- Exit condition: Decide when to stop, redesign or retire the workflow if it cannot meet the need safely.
This brief also prevents “automation theater,” in which a sophisticated definition is celebrated without changing a meaningful project outcome. A simple tool that solves a recurring bottleneck can be a better investment than an elaborate generative system that no team can maintain.
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How can a construction or design firm put the framework into practice?
A phased roadmap turns the three considerations into operating decisions. The sequence below is an implementation pattern, not a claim that every firm needs the same timetable.
- Inventory recurring problems. Ask project teams where they repeat inputs, rebuild geometry, transfer data, check models or wait for specialist help. Rank problems by frequency, consequence and clarity of the desired result.
- Select one bounded pilot. Choose a task with known inputs, a manageable risk level and a user who can provide prompt feedback. Avoid starting with the firm’s most complex project or an undefined “innovation” program.
- Assign a mixed team. Pair a specialist or advanced builder with the people who understand the project context and will use the result. Agree who owns decisions, documentation and support.
- Define checks before building. Specify acceptable inputs, expected outputs, manual verification and a rollback path. For model-changing automation, preserve the source data and make changes traceable.
- Document and teach. Provide a short guide, example files, known limitations and an escalation route. Have a second person run the workflow so the firm can identify hidden dependencies.
- Review value after delivery. Compare the result with the original brief. Capture time saved or quality gained only from project records and stated assumptions; do not convert an isolated success into a universal performance claim.
- Scale selectively. Promote workflows that solve a repeatable problem, have an accountable owner and pass review. Keep experimental definitions separate from production tools.
What commonly goes wrong, and how can you correct it?
Buying or learning a tool before naming the problem
Symptom: Teams produce demonstrations but cannot explain which project decision improves. Correction: Rewrite the request as a problem-and-result brief and reject features that do not serve it.
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Symptom: A central group receives late requests and delivers files that project teams do not understand. Correction: Involve end users at problem definition, give them ownership of inputs and outputs, and use specialists as coaches and reviewers.
Making every employee responsible for advanced scripting
Symptom: Staff feel pressured to master programming that their role does not require, while critical expertise remains shallow. Correction: Use capability levels: broad safe use, a smaller adaptation layer and protected advanced expertise.
Automating without a quality or recovery plan
Symptom: A script produces plausible but incorrect geometry, data or documents, and no one knows how to undo it. Correction: Add sample cases, input validation, review checkpoints, versioned files and a manual fallback before production use.
Treating a historical software description as current
Symptom: Procurement, training or expectations rely on the 2016 article’s packaging language. Correction: Check current vendor documentation for the software and version in your environment. Grasshopper’s current official page and Autodesk’s Revit 2026 Dynamo documentation are appropriate starting points, but neither replaces a firm-specific workflow decision.
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When is outside help useful?
External support can be appropriate when a firm needs an initial roadmap, a focused computational-design workshop or project-specific tool development that internal staff cannot yet provide. The Proving Ground article itself names roadmap consulting, workshops and project services as possible paths. Treat those as examples of support categories, not as proof that any provider is right for your practice; define the problem, deliverables, ownership and knowledge transfer before commissioning work.
Whether development is internal or external, retain control of the business brief, acceptance criteria, source files, documentation and maintenance plan. A delivered script without those assets is a dependency, not a capability.
What should leaders remember from the three considerations?
Computation succeeds when it is treated as part of the operating model for design and delivery. Invest in people whose computational skills fit the firm’s design principles; combine deep specialists with enough distributed knowledge for teams to participate; and tie every initiative to a defined problem and result. The 70/20/10 mix can stimulate a staffing conversation, but it is not an industry statistic. Finally, use current software documentation for current product facts and the 2016 article for its organizational argument. That combination lets a firm preserve the durable insight while avoiding obsolete assumptions.
Frequently asked questions
Does a firm need a dedicated computational-design department?
No. A dedicated group can be valuable for advanced work and standards, but a smaller practice may assign the responsibilities to a few trained staff with protected time. The important safeguards are clear ownership, review, documentation and a way for project teams to build working knowledge.
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Is the 70/20/10 split a recommended hiring target?
No. It is Nate’s proposed heuristic from 2016, and the source explicitly allows the mix to vary by organization. Use it to discuss capability coverage, then adjust it to your projects, scale and risk rather than presenting it as a benchmark.
Can Grasshopper or Dynamo replace design judgment?
No. They can encode rules, generate options and automate data operations, but people still define goals, constraints, assumptions and acceptable trade-offs. Every production workflow needs a human review appropriate to the consequence of an error.
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How do we know when to retire a computational tool?
Retire or rebuild it when its inputs no longer match project standards, its owner is unavailable, its software dependencies are unsupported, or its review cost exceeds its benefit. Preserve a readable record of what it did and why it was retired so teams do not recreate the same dependency.
What should a project leader ask before approving automation?
Ask what decision or deliverable improves, who verifies the output, what happens when inputs fall outside the expected range, and how the team can recover from an incorrect result. Those questions expose whether the proposal is a managed workflow or merely an attractive demonstration.
Frequently Asked Questions
Does a firm need a dedicated computational-design department?
No. A dedicated group can be valuable for advanced work and standards, but a smaller practice may assign the responsibilities to a few trained staff with protected time. The important safeguards are clear ownership, review, documentation and a way for project teams to build working knowledge.
Is the 70/20/10 split a recommended hiring target?
No. It is Nate’s proposed heuristic from 2016, and the source explicitly allows the mix to vary by organization. Use it to discuss capability coverage, then adjust it to your projects, scale and risk rather than presenting it as a benchmark.
Can Grasshopper or Dynamo replace design judgment?
No. They can encode rules, generate options and automate data operations, but people still define goals, constraints, assumptions and acceptable trade-offs. Every production workflow needs a human review appropriate to the consequence of an error.
How do we know when to retire a computational tool?
Retire or rebuild it when its inputs no longer match project standards, its owner is unavailable, its software dependencies are unsupported, or its review cost exceeds its benefit. Preserve a readable record of what it did and why it was retired so teams do not recreate the same dependency.
What should a project leader ask before approving automation?
Ask what decision or deliverable improves, who verifies the output, what happens when inputs fall outside the expected range, and how the team can recover from an incorrect result. Those questions expose whether the proposal is a managed workflow or merely an attractive demonstration.
