The Two-Speed Commerce Split: Why Your Ecommerce Team is About to Bifurcate

Code costs nothing now. Knowing what to build costs everything.

That's not just a software problem—it's the defining tension reshaping every commerce team from Shopify agencies to enterprise retail operations. While everyone debates whether AI will replace developers, a quieter revolution is splitting the entire commerce workforce into two distinct classes.

And most teams have no idea it's already happening.

The Great Commerce Bifurcation

Walk into any ecommerce agency today and you'll see the future of commerce jobs playing out in real-time. Two types of workers are emerging, and the gap between them is widening daily.

Class One: The Specification Drivers

These are the commerce professionals who've learned to translate vague merchant requests into precise instructions that humans—or increasingly, AI agents—can execute flawlessly. They don't build Shopify stores anymore; they architect them. They don't write product descriptions; they define the frameworks that generate thousands of them.

Take the Shopify Plus partners already using AI to deliver what used to require 20-person teams. One specification-savvy developer with the right agent infrastructure can now handle the technical complexity of a full enterprise migration. But here's what's crucial: their value isn't in the coding. It's in their ability to hold the entire merchant's business model, customer journey, and technical constraints in their head simultaneously.

When a merchant says "make our checkout more user-friendly," these professionals translate that into: "Implement one-click purchase for repeat customers, reduce form fields from 12 to 7, add trust signals above the payment section, and A/B test the button copy against our current 3.2% conversion rate." That's a specification. "Make it user-friendly" is just vibes.

Class Two: The Production Workers

Everyone else is executing tasks faster than before, but they're still executing the same tasks. They're using AI tools to write product copy quicker, resize images more efficiently, or debug theme code with assistance. The work feels more productive, but the fundamental economic advantage hasn't changed significantly for these professionals.

These commerce professionals are being commoditized in slow motion. Entry-level positions at ecommerce agencies are disappearing. Junior developers, once the pipeline for senior talent, find their traditional learning path blocked by AI that handles the foundational tasks they used to cut their teeth on.

The writing is on the wall: 70% of hiring managers in tech already say AI can handle the work that interns traditionally did. In commerce, that percentage is higher because so much of junior-level ecommerce work follows predictable patterns.

Revenue Per Employee Tells the Story

The numbers are stark. Traditional ecommerce agencies operate with industry-standard revenue per employee ratios. But AI-native commerce companies are rewriting the economics entirely.

While agencies still staff projects with account managers, designers, developers, QA testers, and project managers, the specification-driven shops are running with radically smaller teams and exponentially higher output. They're not doing more of the same work faster—they're doing fundamentally different work that creates more value per person.

The merchant doesn't care how many people touched their project. They care whether their conversion rate improved and their customers can find what they're looking for. When one specification-driven professional can deliver the same business outcome as a traditional five-person team, most of that team's economic value evaporates.

The Specification Bottleneck

Most ecommerce projects that fail don't fail because of bad development or poor design. They fail because nobody clearly specified what success looks like for the merchant's specific customers and business model.

"Optimize our product pages" isn't a specification—it's a hope. The entire discipline of conversion rate optimization evolved as a way to force specification out of vague merchant desires. We need mechanisms for converting "increase sales" into instructions precise enough that designers, developers, or AI can execute against them.

But here's what's changed: when building a custom Shopify solution used to take three months and cost £50,000, merchants were forced to think carefully about requirements. The cost of implementation acted as a filter on specification quality.

AI is removing that filter. Now a merchant can get a basic store built in days for under £1,000 in AI costs. But if the specification was wrong—if the checkout flow doesn't match how their customers actually buy, or the product categorization doesn't align with how their audience searches—they haven't saved three months. They've potentially launched something that actively harms their conversion rates.

The scarce resource in commerce isn't the ability to build stores, write copy, or design interfaces. It's the ability to define precisely what the store should do to serve each merchant's unique customer base.

Knowledge Work Convergence

This pattern isn't limited to the technical side of ecommerce. Customer service, marketing, merchandising, and operations are all converging on the same fundamental challenge: translating merchant intent into specifications that systems can execute.

A customer service manager who writes "improve response times" is operating at the wrong level of abstraction. The specification-literate manager writes: "Route returns questions to the automated flow, escalate refund requests above £200 to humans, and maintain under 2-minute first response times for pre-purchase questions during business hours."

A merchandising manager who says "optimize our homepage" versus one who specifies: "Feature products with >4.5 stars and >10 reviews in the hero section, rotate seasonal collections every 14 days, and ensure mobile users see the value prop within 3 seconds of page load."

The underlying cognitive task is identical across all these functions: you're translating vague business intent into precise enough instructions that human or AI systems can execute them reliably.

The Practical Path Forward

If you're leading a commerce team, the strategic question isn't whether AI will affect your people—it's which side of the bifurcation they'll land on. The companies that get 30-40% of their workforce into specification-driven roles will dominate their markets through superior unit economics.

For individual commerce professionals, the path is clearer than it appears. Start writing specifications for your current work. If you're in customer service, document the decision trees you use. If you're in marketing, define the success criteria for each campaign. If you're in merchandising, specify the rules that govern your category decisions.

Most importantly, start working with AI tools not to do your job faster, but to understand what AI can and cannot do reliably. That knowledge—where the machine excels and where human judgment remains essential—becomes the foundation of high-value specification work.

The transition is happening whether commerce teams prepare for it or not. The only variable is which side of the split they end up on. But unlike previous technology shifts, this one rewards precision over experience, clarity over seniority.

In commerce, that's actually encouraging news. The best customer experience designers have always been specification-driven. They've always started with customer needs and worked backward to solutions. They've always defined success before building anything.

The future of commerce jobs doesn't belong to those who can build things faster. It belongs to those who can specify exactly what needs building and why. The merchants' customers were never waiting for more features—they were waiting for the right ones.

Now we have the tools to build the right ones at scale. We just need the judgment to know what "right" means for each merchant's unique context. That judgment—that specification capability—remains deeply, irreplaceably human.

The question is whether your team will develop it before the market demands it.