No-code development in enterprise software

The growing role of low-code and no-code development in enterprise software

Most companies I’ve worked with are using some form of low-code or no-code tooling now. The pressure is real: we need to ship faster, there aren’t enough developers, and budgets are tight.

So we’re abstracting more.

These platforms shift how teams work and what actually gets built.

Why low-code/no-code is rising in the enterprise

These platforms take the stuff we build over and over—forms, workflows, CRUD operations, dashboards—and turn them into drag-and-drop components. They bundle in authentication, connectors to other systems, and hosting. You get faster iteration. Domain experts can own more of the solution without waiting on engineering for every small change.

Benefits beyond speed

• What used to take weeks now takes days for standard business apps.
• Domain experts can prototype without waiting on engineering. I’ve seen Product Owners with some technical chops build working POCs on their own.
• Security, auth, and logging come built-in (when the platform is set up correctly).
• Developers write less boilerplate. More time for actual problems.
• Prebuilt connectors to legacy systems and SaaS tools.
• Cheap enough for one-off departmental apps that wouldn’t justify custom development.
• Sometimes the UI is terrible and you end up rebuilding it anyway.

Real challenges to manage

• Security and compliance aren’t automatic. You need to enforce access controls, data residency, and audit trails.
• Lifecycle management gets messy fast—versioning, environments, rollback procedures.
• Testing is hard. How do you write end-to-end tests for a visual workflow?
• Performance problems hide in those visual models. You don’t see the N+1 queries until production.
• Vendor lock-in is real. Per-user pricing adds up, and proprietary formats make it expensive to leave.
• Someone has to own these apps. Training and support don’t happen by themselves.

How dev teams can work with these tools

• Treat the low-code platform like a product. It needs governance, SLAs, and a roadmap.
• Build reusable components: APIs, event streams, data models, design system pieces.
• Set up guardrails early—role-based access, data policies, promotion rules between environments.
• You need CI/CD for this stuff too. Version control, automated testing, policy enforcement.
• Logs, metrics, tracing. Make observability the default, not an afterthought.
• Pair domain experts with engineers from the start. Clarify who owns what.
• Have an escape hatch. When the visual model gets too complex, there should be a path to custom code.
• This is harder than it sounds. The line between “just use the platform” and “rebuild it in code” is blurry.

Patterns that seem to work

• API-first design. Stable contracts let non-engineers compose safely.
• Build reusable UI blocks and automation components. Control access to data.
• Clear domain boundaries. Each team owns their data, documents their APIs.
• A small Center of Excellence team sets standards and creates templates. They measure what’s actually happening.
• Tag every app. Track usage, cost, compliance. You can’t manage what you don’t measure.

What this means for programming

The work is changing. We’re writing less code from scratch, spending more time connecting systems and building reusable components. AI is handling more of the repetitive stuff—code generation, test writing, boilerplate.

I think professional developers will increasingly focus on the hard parts: performance, security, user experience that actually matters. Low-code handles the boring CRUD apps and workflow automation.

The boundary between “real coding” and visual development is already fuzzy. Platform engineering and policy-as-code are becoming standard practice. Where this all goes, I’m not entirely sure. But the abstractions keep getting higher.

Start small and measure

Pick two or three low-risk processes to automate. Set up a governed platform with templates and guardrails. Put together cross-functional teams.

Measure cycle time, defect rate, and adoption over 90 days. We use JIRA labels like ‘Claude’ to track which work was assisted by AI tooling.

The backlog won’t shrink on its own. Someone has to drive this.

Looking to move beyond visual automation tools? My Agentic Workflows Guide shows how to architect reliable AI-powered systems using the DOE Framework—perfect for when your automation needs outgrow low-code platforms.