The New Economics of Design

For twenty years, we’ve designed the same way: we plan, then we make.

We research extensively, write requirement documents, and once things feel safe, we start making and coding.

This wasn’t arbitrary. All that planning made economic sense.

Making things was expensive. Every prototype required designers, developers, and hours of work. Testing required complicated setups, and scheduling was a nightmare. Iteration meant going back through a cycle all over again, which was already long and exhausting.

So we front-loaded everything but the making.

We had researchers make us long reports. We had PM make detailed project plans with cool charts. We had designers work from complicated briefs.

We tried to get it right before committing to the expensive part: making.

A.I. breaks that logic.

A.I. has collapsed the cost of making to nearly zero.

What used to take a designer three days now takes thirty minutes. Do we really need all that planning if a designer can prototype an idea in the time it takes to grab a coffee?

When making was expensive, planning reduced risk.

When making is cheap, planning is procrastination.

Let me show you how the economics break down for a typical project.

The Traditional Planning Approach

A company needs to redesign its core user dashboard, and they’re going to take the traditional planning approach.

Here’s the product team assigned:

12 Weeks of the Planning Approach

This is what projects looked like before AI. The team wants to make sure they “get it right the first time,” so they don’t waste those precious dev resources because making is expensive.

Here’s the weekly project breakdown:

Week 1-2: Discovery Research

• Researcher conducts 15 user interviews (80 hrs × $72 = $5,760)
• PM observes half the sessions (40 hrs × $81 = $3,240)
• Designer observes select sessions (20 hrs × $72 = $1,440)

Week 3: Synthesis

• Full team synthesis workshop (5 people × 8 hrs = 40 hrs)

  • PM: 8 hrs × $81 = $648
  • Designer: 8 hrs × $72 = $576
  • Researcher: 8 hrs × $72 = $576
  • Senior Dev: 8 hrs × $88 = $704
  • Mid Dev: 8 hrs × $75 = $600

• Researcher creates insights deck (30 hrs × $72 = $2,160)

• Designer creates journey maps and personas (20 hrs × $72 = $1,440)

Week 4: Requirements

• PM writes requirements doc (25 hrs × $81 = $2,025)

• Designer starts competitor research (15 hrs × $72 = $1,080)

• Stakeholder alignment meetings (5 people × 4 hrs = 20 hrs)

  • PM: 4 hrs × $81 = $324
  • Designer: 4 hrs × $72 = $288
  • Researcher: 4 hrs × $72 = $288
  • Senior Dev: 4 hrs × $88 = $352
  • Mid Dev: 4 hrs × $75 = $300

Week 5-6: Concepting

• Designer explores three directions (60 hrs × $72 = $4,320)

• Weekly reviews (5 people × 4 hrs × 2 weeks = 40 hrs)

  • PM: 8 hrs × $81 = $648
  • Designer: 8 hrs × $72 = $576
  • Researcher: 8 hrs × $72 = $576
  • Senior Dev: 8 hrs × $88 = $704
  • Mid Dev: 8 hrs × $75 = $600

Week 7: Stakeholder Review

• PM prepares presentation (15 hrs × $81 = $1,215)
• Designer prepares presentation (15 hrs × $72 = $1,080)
• Designer revisions based on feedback (30 hrs × $72 = $2,160)

Week 8-9: High-Fidelity Design

• Designer refines chosen direction (80 hrs × $72 = $5,760)
• Senior Dev reviews for feasibility (20 hrs × $88 = $1,760)
• Mid Dev reviews for feasibility (10 hrs × $75 = $750)

Week 10: Build Prototype

• Senior Dev builds interactive prototype (60 hrs × $88 = $5,280)
• Mid Dev builds interactive prototype (60 hrs × $75 = $4,500)
• Designer provides assets and specs (20 hrs × $72 = $1,440)

Week 11-12: User Testing & Iteration

• Researcher recruits and runs 10 usability tests (60 hrs × $72 = $4,320)
• PM observes sessions (20 hrs × $81 = $1,620)
• Designer observes sessions (20 hrs × $72 = $1,440)
• Researcher synthesizes findings (20 hrs × $72 = $1,440)
• Designer iterates based on findings (40 hrs × $72 = $2,880)

The Planning Approach by the numbers

And that timeline assumes the dashboard is what the user really needs.

What if a dashboard is just something the VP of product wants, but the real need is user notifications? 😱 The team’s mental model didn’t match the user’s mental model.

If you wait until week 12 to test that assumption, are you de-risking or are you procrastinating testing that risk?

Now you’re restarting, but you’ve already spent $64k and three months. All that “de-risking” didn’t de-risk anything. It just made the eventual pivot more expensive.

Now you have to start the twelve-week process over for notifications, or worse: you launch the dashboard anyway because you spent all that money on it. I guess you’ll have to wait five years for the v2 before you can fix things.

There has to be a better way.

The AI Experimentation Approach

Same team. Same roles. Same dashboard redesign. Different process.

This time, the team’s goal is to use AI to test first, and plan later once they have some real user data. So, the team tries a new process based on the cost-effectiveness of making with AI.

4 Weeks of the AI Experimentation Approach

Day 1: Make First Hypothesis

• Designer uses AI to generate 3 crude dashboard concepts (4 hrs × $72 = $288)
• Designer creates quick functional prototype (2 hrs × $72 = $144)

Day 2-3: First Learning Cycle

• Researcher runs quick remote tests with 5 users (10 hrs × $72 = $720)

• PM watches live, takes notes (6 hrs × $81 = $486)

• Designer watches live, takes notes (6 hrs × $72 = $432)

• Team synthesis: what did we learn? (3 people × 2 hrs = 6 hrs)

  • PM: 2 hrs × $81 = $162
  • Designer: 2 hrs × $72 = $144
  • Researcher: 2 hrs × $72 = $144

Day 4-5: Iterate Based on Reality

• Designer creates version 2 based on findings (6 hrs × $72 = $432)
• Researcher tests with 5 different users (10 hrs × $72 = $720)
• Learn that dashboard concept is wrong…users want notifications 😱

Week 2: Pivot to Notifications

• Designer prototypes notification system (8 hrs × $72 = $576)
• Researcher tests with users (10 hrs × $72 = $720)
• Designer iterates on tone and timing (6 hrs × $72 = $432)
• Researcher tests again (10 hrs × $72 = $720)

Week 3: Refine and Expand

• Designer adds complexity based on what’s working (12 hrs × $72 = $864)

• Senior Dev assesses technical feasibility (8 hrs × $88 = $704)

• Mid Dev assesses technical feasibility (8 hrs × $75 = $600)

• Researcher runs A/B test with two approaches (12 hrs × $72 = $864)

• Team synthesizes patterns (5 people × 2 hrs = 10 hrs)

  • PM: 2 hrs × $81 = $162
  • Designer: 2 hrs × $72 = $144
  • Researcher: 2 hrs × $72 = $144
  • Senior Dev: 2 hrs × $88 = $176
  • Mid Dev: 2 hrs × $75 = $150

Week 4: Validated Direction

• Designer creates detailed specs (16 hrs × $72 = $1,152)
• Senior Dev builds production-ready version (30 hrs × $88 = $2,640)
• Mid Dev builds production-ready version (30 hrs × $75 = $2,250)
• Researcher runs final validation testing (8 hrs × $72 = $576)

The AI Experimentation Approach by the numbers

The real cost savings of experimentation

The AI Experimentation Approach saved $47,724 and 8 weeks over the traditional planning-first approach.

With AI Experimentation, you can run three projects in the time it used to take you to run one project!

But the real savings are bigger than that.

In the traditional approach, testing happens at the end. So if testing reveals you need to pivot (which it often does), you’re starting over at week 12. That’s another 6-8 weeks and $30-50k to iterate to something that works.

In the experimentation approach, you pivoted on day 5. The cost? Six hours of design time. About $430.

This is the new economics of design.

When you can prototype in hours instead of weeks, when you can test continuously instead of in scheduled phases, when you can iterate based on evidence instead of defending a plan, the entire cost structure of design work changes.

You’re not just faster. You’re learning different things, at different moments, for different costs. You’re making decisions based on reality instead of predictions. And you’re dramatically reducing the risk of expensive late-stage pivots.

The old economics optimized for “getting it right the first time” because iteration was expensive. The new economics optimize for “learning if you’re wrong as quickly as possible” because iteration is cheap, we test the idea fast with something that feels real.

Most teams are still operating under the old model. They’re spending thousands of dollars and months of planning, when they could spend a fraction of that time experimenting.

Even if you’re using AI tools in the planning approach, it won’t take advantage of these new economics until you have an AI-powered process to match the new economics.

Why This Matters for Product Organizations

This isn’t just a design process debate. It’s a strategic advantage.

Speed to validated learning. The Experimentation Loop puts real user feedback in front of product decisions within days, not months. When your competitors are still in “Define” phase, you’ve already shipped and learned.

Capital efficiency. Whether you’re a startup watching burn rate or an enterprise justifying headcount, spending $16k to validate a direction instead of $64k changes what’s possible. That’s 4x the experiments for the same budget.

Reduced risk of sunk cost. Phase-based processes create psychological and political pressure to defend early decisions. When you’ve spent 8 weeks and $50k on a direction, pivoting feels like failure. When you’ve spent 3 days and $2k, pivoting feels like learning.

Better use of talent. Look at the hour distribution. In the planning approach, the designer spends 340 hours. Most of that time is spent on documentation, alignment, and high-fidelity polish before validation. In the AI Experimentation approach, they spend 62 hours, but most of it is spent on rapid prototyping and iteration based on real feedback. Which version of the job would you rather do? Which version attracts better talent?

Faster time to revenue. If you’re building something new, every week in process is a week not in market. The AI Experimentation Approach gets a validated product in front of customers in one month instead of three. That’s two extra months of learning, iterating, and generating revenue.

What It Takes to Make This Shift

This isn’t an easy transition. It’s not just about adopting new tools. It’s about rewiring how product teams think and operate.

Leadership alignment. Executives need to understand why “more research upfront” isn’t always de-risking. Sometimes it’s just expensive delay. The cost breakdown above is usually enough to start that conversation.

New team rhythms. AI Experimentation needs daily or near-daily synthesis, not weekly status meetings. Teams need to be colocated or have strong async practices. Decisions happen faster, which means decision-making authority needs to be pushed down.

Different artifacts. Instead of journey maps, personas, and strategy decks, you’re producing hypotheses, experiment cards, and learning logs. The documentation shifts from “what we think” to “what we learned.”

Tooling and infrastructure. AI-assisted design tools, rapid prototyping platforms, and continuous user feedback systems. The technology exists, but most organizations just haven’t integrated it into their workflow.

Cultural permission to be wrong. This is the hard one. Phase-based processes feel safe because they defer judgment. Experimentation requires stating a hypothesis and testing it, which means being wrong visibly and frequently. That has to be celebrated, not punished.

The Opportunity for Strategic Advantage

Most product organizations are still running a planning-first approach like the Triple Diamond or something like it. They’re spending months and six figures to reach the same insights they could get in weeks for a fraction of the cost.

That’s not because they’re bad at their jobs. It’s because their process was designed for a world where making things was expensive.

That world is gone.

The organizations that recognize this first will ship faster, learn faster, and outmaneuver competitors who are still stuck in quarterly planning while they’re already iterating on a validated product.

The tools have changed. The economics have changed.

The process needs to change, too.

Hire Jeff to perfect your AI Experimentation

Are you looking to evolve your project processes to match AI’s capabilities?
Let’s start experimenting!

*Salary sources (November 2025, U.S.): Glassdoor, Indeed, Built In, Salary.com.

*Hourly rates: include 30% overhead for benefits, taxes, and employment costs.