A sales VP builds her own pipeline analytics dashboard over a weekend. A marketing director creates a campaign optimization tool between meetings. A product manager ships a working prototype before writing a single requirements document. None of them writes a line of code.
Onil Gunawardana, a product management executive who has built eight major enterprise software products at companies including Snowflake, Google, and LiveRamp, calls them “domain expert builders” — professionals who use AI to turn what they know into tools that work.
“The skill being democratized isn’t coding,” Gunawardana says. “It’s the ability to turn domain expertise into working solutions. And domain expertise has always been distributed across the entire organization, not concentrated in engineering.”
Grand View Research estimates the low-code platform market at $24.8 billion in 2023, growing at a 22.5% CAGR to surpass $100 billion by 2030. But those numbers tell an incomplete story. The “low-code” category used to mean specialist platforms like Bubble and Mendix — tools that still required a builder’s mindset and significant learning curves. In 2026, it increasingly means Replit, Cursor, Claude Code, and OpenAI Codex — AI-native tools where the interface is a conversation, not a visual editor.
For the first time in enterprise technology, the barrier between “I have an idea” and “I built it” has effectively collapsed.
What domain expert building looks like in 2026
A product manager who wanted a customer analytics tool used to face a familiar sequence: write a specification, lobby for engineering resources, wait for a sprint cycle, and hope the result matched the vision. Today, she describes what she wants to an AI agent and has a working prototype in hours. Not a mockup. A functional tool that queries real data and produces real insights.
AI agents handle the execution layer — writing code, configuring databases, designing interfaces — while the human provides strategic direction.
Gunawardana has watched product development evolve through several eras — a progression he mapped in the 5Ps of Product framework. “Each shift reduced the distance between insight and implementation,” he says. “Domain expert building eliminates that distance entirely. The person who understands the problem builds the solution directly.”
Three patterns emerging across the enterprise
Gunawardana points to three domain expert builder archetypes he has seen reshaping how enterprises operate.
The Analyst-Builder. A financial analyst who spends hours reconciling data across spreadsheets describes the reconciliation logic to an AI agent and has a working tool by the end of the day. The analyst understands the edge cases, the exception handling, the business rules — knowledge that would take weeks to transfer to an engineering team. A McKinsey analysis found that generative AI could handle 60-70% of the routine tasks that consume employees’ time — freeing them to focus on judgment, strategy, and the work that actually requires human expertise.
The Marketer-Builder. A marketing director who wants to test whether an audience segment responds differently to personalized pricing builds a test framework, connects it to campaign data, and runs the experiment — without filing a single engineering ticket. “The counterintuitive part,” Gunawardana says, “is that the marketer’s tool often outperforms what engineering would have built, because she encodes domain knowledge that no specification document fully captures.”
The PM-Builder. Instead of a slide deck arguing for a new feature, a product manager demonstrates a functional version built with AI assistance. Stakeholders interact with a working prototype, not a wireframe. Validation happens before specification, not after delivery.
Unlocking the domain expert builders you already have
The domain expert builder model doesn’t replace anyone — it amplifies everyone. A company with 500 employees no longer depends on a handful of engineers to turn ideas into reality. It has 500 potential builders.
Engineers benefit most of all, Gunawardana says. Instead of fielding an endless queue of feature requests, they focus on complex systems, platform architecture, security, and scale. They build the platforms and guardrails that enable domain expert builders to build safely.
The companies seeing the biggest returns invest in what Gunawardana calls “builder infrastructure” — the platforms, APIs, data access layers, and governance frameworks that make it safe for non-engineers to build.
“I’ve seen what happens without it,” he says. “One enterprise had 40 teams build 40 separate data extraction tools, none of which followed security standards or talked to each other. The cleanup cost more than the infrastructure would have.”
Most leaders haven’t internalized the paradox: the companies that will build fastest are the ones that invest most heavily in constraints.
The World Economic Forum’s Future of Jobs Report identifies AI and big data as the fastest-growing skills globally, but the most valuable new skill isn’t technical. It’s orchestration — clearly articulating problems, decomposing them into solvable components, and directing AI agents to build solutions.
The organizations that empower domain expert builders with both orchestration skills and builder infrastructure won’t just move faster. They’ll make “I had an idea, and I built it” the most ordinary sentence in the enterprise.
David Potter is Senior Contributing Editor at Digital Journal. He brings years of experience in tech marketing, where he’s honed the ability to make complex digital ideas easy to understand and actionable. At Digital Journal, David combines his interest in innovation and storytelling with a focus on building strong client relationships and ensuring smooth operations behind the scenes. David is a member of Digital Journal’s Insight Forum.
