From Long Projects to Rapid Builds
AI is reshaping enterprise software development timelines, especially in complex industrial environments where projects traditionally dragged on for months. Two emerging categories stand out: AI industrial automation platforms and AI-native enterprise configuration tools. Both use large language models and “agentic” assistants to automate repetitive coding and configuration work, compressing delivery cycles and changing who can build software in the first place. Instead of relying solely on scarce specialist developers, organizations can now let subject-matter experts describe what they want in natural language or via simple artifacts like legacy forms. The AI then translates these requirements into production-ready applications or configurations. This shift not only boosts app development speed; it also reduces the risk of miscommunication between business and technical teams. The result is a new development model where AI handles the heavy lifting, and humans focus on validation, governance, and innovation.
Cognite Flows: AI-Native Apps for Industrial Operations
Cognite Flows exemplifies how AI industrial automation is transforming plant and factory software. Built on Cognite’s Industrial Knowledge Graph, it ties AI-driven recommendations directly to live operational data, giving frontline operators a single-screen workspace for tasks, alerts, and decision support. For developers, the platform’s agentic AI coding tools and AI-native architecture allow tailored industrial applications to be built and deployed in days instead of months, radically accelerating app development speed. Customers already report tangible benefits. One pharmaceutical user cut the time required to deliver automated AI workflows that previously demanded a large team working for several months. Idemitsu is using Flows to capture and operationalize decades of plant expertise, with an eye toward proactive AI agents that can help manage complex operations. B. Braun leveraged the platform to rapidly refine user experiences, achieving a unified, contextualized view of asset health and near-instant UX updates within just four weeks.
Dyna Software’s Platform Copilot and the Future of ServiceNow Configuration
On the enterprise side, Dyna Software’s Platform Copilot aims to remove one of the biggest bottlenecks in ServiceNow configuration: waiting for developer capacity. The agentic AI tool connects directly to a customer’s ServiceNow development instance, reads existing schemas and configurations, and lets business users describe desired changes in natural language or upload images of legacy forms. It then generates wireframes, validates them against the live environment, and builds the configuration. Dyna says Platform Copilot can handle about 80 percent of enhancement work that typically flows through ServiceNow development teams, dramatically increasing app development speed on the platform. An early project saw a business analyst upload more than 200 legacy catalog forms, review generated wireframes within minutes, and push production-ready configurations without developer intervention. For organizations facing multi-year backlogs of PDF forms to digitize, this instance-aware AI promises a step change in ServiceNow configuration efficiency and quality.
Democratizing Development and Freeing Teams for Innovation
Both Cognite Flows and Platform Copilot point to a broader shift in enterprise software development: democratization. By hiding much of the technical complexity and using natural language interfaces, these tools enable operators, business analysts, and process owners to participate directly in app creation and configuration. Instead of translating requirements through multiple layers of documentation and handoffs, domain experts can iterate with AI in real time, while developers step in for complex edge cases and governance. This redistribution of work changes the role of development teams. Rather than spending most of their time on repetitive, low-level configuration tasks, engineers can focus on strategic initiatives such as new digital services, advanced analytics, and robust integration architectures. Industrial operators and enterprises gain faster value from their platforms while reducing the risk of technical debt, as AI helpers increasingly enforce best practices and instance-specific constraints by design.