The world of software development is undergoing a paradigm shift. As artificial intelligence continues to evolve, it’s reshaping how humans interact with machines—transforming code from cryptic symbols to conversations in natural language. This evolution is birthing a new era: Natural Language Programming (NLPg), where instructions are written not in code but in plain English (or any human language). At the forefront of this transformation stands our platform, a next-generation business process automation platform that’s enabling Natural Language Programming for real-world process automation.

Our team dives into what Natural Language Programming is, how it works, and how our customers leverage our platform to accelerate enterprise automation through AI-driven workflows, intelligent decision-making, and human-friendly application development.

What is Natural Language Programming?

Natural Language Programming (NLPg) allows users to write software logic using everyday language rather than traditional programming syntax. Instead of writing:

if (invoice.Amount > 1000) { Approve(); } else { FlagForReview(); }

An NLPg system could interpret:

"If the invoice amount is greater than 1000, approve it. Otherwise, flag it for review."

This abstraction enables business users, analysts, and domain experts to actively contribute to automation without needing to learn a programming language.

Why Natural Language Programming Matters

1. Bridges the gap between business and IT
   NLPg enables business stakeholders to directly define or refine logic without intermediaries.
2. Reduces development time
   Natural language instructions are faster to compose, understand, and validate.
3. Improves maintainability
   Logic written in natural language is easier to audit, review, and modify.
4. Enables AI to reason and assist
   AI models can parse and respond to natural language, enabling intelligent suggestions and automation.

The Rise of Natural Language Workflows

FlowWright is not just a BPM platform. It is a process orchestration engine, a low-code/no-code application builder, and now, an AI-augmented Natural Language Programming platform. We integrate Natural Language Processing (NLP), Large Language Models (LLMs), and AI agents into its workflow engine, enabling users to describe logic in plain language, which the engine interprets and translates into executable processes.

Here's how we do it:

1. Describing Processes Using Natural Language

Ourplatform allows users to describe workflows in a simple sentence format, such as:

“When a new purchase order is submitted, validate the total amount. If over $10,000, send it to the finance director. Otherwise, auto-approve and send to vendor.”

Using an AI agent embedded within FlowWright, this sentence is parsed and dynamically converted into a BPMN-compliant process with the appropriate steps, decision gates, and assignments.

This means users no longer have to drag-and-drop every process element—they can describe the intent, and we build the skeleton process automatically.

2. Smart Form Logic with Language-Based Rules

Our Forms module now supports rule definitions using natural language. For example:

“If the user selects 'International' as shipment type, display customs declaration field.”

The AI behind FlowWright translates this instruction into visibility rules and form behavior definitions without writing JavaScript or conditions.

3. Building Decision Tables Using Language Prompts

Instead of manually filling rows in a decision table UI, this allows users to define complex logic like:

“If the customer is gold tier and invoice is over $5000, apply 10% discount. If customer is silver and invoice is over $8000, apply 5%. Otherwise, no discount.”

The system parses this and populates a multi-row decision table with the correct conditions and actions.

4. AI-Powered Process Generation

Using our  “AI Builder”, a user can type:

“Create a workflow to onboard a new employee. Include document verification, IT provisioning, HR orientation, and manager review.”

The builder will:

• Create a workflow with steps like “Document Verification,” “Provision Laptop,” “HR Orientation Session,” and “Manager Approval.”
• Auto-assign roles to tasks
• Add SLAs and notifications
• Suggest standard forms to attach

This reduces what might have taken days of configuration into a few minutes of prompt writing.

5. Natural Language Automation Agents

We support automation agents that understand plain instructions to:

• Trigger processes
• Update workflow data
• Monitor KPIs
• Send alerts or escalate tasks

For instance:

“Send me a daily summary of workflows pending more than 2 days.”

Behind the scenes, FlowWright creates a scheduled workflow that queries pending tasks and emails the summary to the user.

6. Document-Driven Workflow Initiation

Our native integration with AI-driven document understanding allows users to upload documents (invoices, contracts, etc.), and based on the natural language content and extracted entities, the platform:

• Identifies document type
• Matches to an appropriate workflow
• Starts the process with prefilled data

For example, uploading a contract with a clause “terminate within 30 days” will trigger a review and escalation workflow.

7. Continuous Learning from User Inputs

Each time users describe logic or processes in natural language, our AI learns from the patterns, enhancing its future recommendations. Over time, it builds a semantic understanding of your organization's processes and language.

Examples of user queries it adapts to:

• “Remind me to approve all invoices every Friday.”
• “Auto-assign legal review tasks to the compliance team.”
• “Generate a dashboard for vendor onboarding KPIs.”

8. Combining NLP with ETL and AI Insights

With the introduction of our ETL engine, natural language instructions can be used to define data pipelines:

“Extract customer records from Salesforce, clean duplicates, and load into SQL.”

We translate this into an ETL process with connectors, transformers, and a scheduler—all built from the language instruction.

9. AI-Enhanced Debugging and Suggestions

When building workflows, users often encounter logic gaps or performance issues. FlowWright’s embedded AI can now:

• Highlight unreachable paths
• Suggest missing branches
• Detect conflicting rules
• Recommend improvements based on historical usage

All this via conversations like:

“Why is this step never executed?”
“Is there a faster way to complete this approval chain?”
“Can we eliminate manual data entry in this process?”

Examples of Natural Language Programming in FlowWright

Here are real-world examples where our NLPg capabilities have delivered rapid automation for our customers:

• Pharmaceutical QA: A QA team defined a drug test approval process in natural language. FlowWright auto-generated the workflow, assigned reviewers, and linked documents.
• Finance Automation: Controllers typed rules like “Flag invoices from new vendors over $20K.” FlowWright created a detection and approval flow using the ETL engine.
• IT Helpdesk: Users described incident triage rules in plain text. FlowWright converted them into decision logic with dynamic assignments and SLAs.

The ROI? Impressive

Organizations that have adopted NLPg in FlowWright have reported:

• 60% reduction in time to create new workflows
• 80% faster training for non-technical users
• 3x increase in process adoption across departments
• 50% fewer logic bugs due to clear, human-readable rules

What's next for NLP?

Natural Language Programming isn’t just a feature—it’s the next frontier of human-computer interaction. As our product team continues to integrate smarter AI models and deeper contextual understanding, it’s not far-fetched to imagine:

• Voice-driven workflow creation
• Conversational debugging
• Self-healing workflows
• Agents that act on goals, not just instructions

Ready to embrace the Natural Language Programming is democratizing automation?  Schedule a demo to explore our NLP features and discover how it can transform your organization’s ROI using workflow automation.