The next wave of enterprise transformation is intelligent, not just digital. With low-code and AI, organizations now build and evolve applications up to ten times faster, while embedding intelligence and automation into everyday processes. In this article, we look at how low-code and AI work together in real enterprise projects and what makes this combination so effective in practice, especially now that agentic AI is starting to take its place in enterprise applications.
Digital transformation helped enterprises modernize. But most organizations now realize that digitization alone isn’t enough. What they need are systems that learn, adapt and improve over time. That shift marks the beginning of intelligent transformation.
Low-code platforms and AI are at the core of this change. Low-code gives businesses speed, structure and governance. AI adds the ability to analyze, predict and act. Together, they turn enterprise systems from static applications into dynamic ecosystems that respond to data in real time.
We see this every day in projects across industries from pharma and manufacturing to financial services. Intelligent transformation is no longer a vision of the future; it’s the new operating model for modern enterprises.
From Digital to Intelligent Transformation
Digital transformation was about digitizing workflows, automating manual steps and improving efficiency. It delivered value, but mostly by replicating existing processes in digital form. Intelligent transformation goes further. It focuses on creating systems that learn from data, predict outcomes and support better decisions.
As AI becomes embedded in every layer of technology, the goal shifts from process automation to continuous optimization. Applications are no longer static tools, but adaptive systems that evolve with business needs. This approach allows organizations to react faster to change, discover insights earlier and make operations more resilient.
For leaders, the challenge is no longer about technology adoption, but about integration: combining data, low-code platforms and AI in one coherent strategy. When these elements work together, enterprises move from efficiency to intelligence, and that makes all the difference.
How Low-Code Accelerates Innovation
Low-code platforms have redefined how enterprise applications are created and improved. They replace long development cycles with modular design, visual workflows and automated lifecycle management. This approach removes much of the technical debt that slows innovation and makes updates easier to deliver and control.
According to OutSystems, organizations using AI-powered low-code can build and evolve applications up to ten times faster than with traditional development methods. Speed, however, still comes with structure. Modern low-code platforms combine full-stack capabilities with strong governance, scalability and security, allowing teams to move quickly without losing stability or compliance.
Low-code shortens the path from idea to production and creates space for continuous improvement. It enables teams to focus on solving business problems instead of managing complexity, and that is where real innovation happens.
The Business Value of the AI-Accelerated Enterprise
When low-code and AI work together, the result is more than faster development. It changes how value is created across the organization. Teams deliver applications up to ten times faster, but they also maintain them more easily and improve them continuously. This reduces cost, shortens release cycles and helps align technology with business priorities.
According to OutSystems and KMPG survey, 75 percent of organizations report up to a 50 percent reduction in development time through AI and automation. Real examples confirm this. Banca Generali improved operational efficiency by 50 percent and reduced development costs and effort by up to 40 percent after adopting a low-code approach. These results show how speed translates into tangible business outcomes, not just technical gains.
Automation powered by AI turns applications into systems that adapt to change. Data becomes actionable, used not only for reporting but for real-time decisions. As a result, companies see measurable gains in efficiency and customer experience.
The combination of speed, intelligence and governance allows enterprises to scale innovation safely. It enables IT and business teams to work in one environment, closing the long-standing gap between those who define processes and those who implement them.
Real-World Use Cases
The impact of AI-powered low-code is already visible across industries. Global enterprises are using it to shorten delivery times, modernize legacy systems and add intelligence to everyday operations.
As an OutSystems partner, we draw on their real-world implementations, which showcase how low-code and AI can deliver measurable results across industries. The following examples highlight the practical impact of this technology.
In Roche, low-code was used to build “Roche Chat,” an internal application that supports employees with real-time information and AI-based assistance. The solution improves productivity and connects data from multiple systems through a single interface.
Redington Gulf applied low-code and intelligent automation to streamline accounts receivable and rebate tracking across 37 markets. One process was accelerated tenfold, improving transparency and cash flow management.
At HEINEKEN, the company’s goal is to return one million hours to employees by 2025 through automation and AI-driven low-code initiatives. This approach allows local teams to develop solutions independently while staying aligned with global standards.
The market continues to validate these technologies across sectors. In banking, low-code and AI enable intelligent onboarding and anti-fraud workflows through orchestration of data and automation. In pharma, document understanding and compliance tracking built in OutSystems with GenAI integration support quality and regulatory processes. In CPG and manufacturing, AI-driven supply chain visibility and predictive maintenance dashboards improve uptime and operational planning.
Scaling Responsibly: Roadmap and Governance
Adopting AI and low-code at scale requires a structured approach. Successful organizations start with focused pilots, testing ideas in areas where risk is low and impact is measurable. This builds confidence and creates early results that help expand adoption.
A clear governance model is essential. Platforms such as OutSystems, Microsoft Power Platform, Salesforce and Tulip all provide built-in controls for access, deployment and compliance, but technology alone is not enough. Enterprises still need shared standards for data usage, model oversight and integration with their existing security frameworks.
Fusion teams that combine business experts, IT and AI specialists help bridge strategy and execution. They ensure that innovation stays aligned with governance and that every new solution strengthens, rather than fragments, the enterprise architecture.
Scaling responsibly means balancing speed with trust. Organizations that combine clear rules, shared ownership and transparent monitoring can grow their AI and low-code ecosystems with confidence.
Most enterprises follow a clear maturity path: Experiment → Scale → Intelligent Enterprise.
Each step builds on the previous one, turning individual initiatives into an integrated, data-driven operating model.
Low-Code and AI. What We’ve Learned From Enterprise Projects
We see low-code and AI as parts of one ecosystem, not separate technologies. They work best when combined — when the speed and structure of low-code meet the intelligence and adaptability of AI. Over years of implementing these solutions across industries, we’ve learned what really makes them work in enterprise environments. These are the principles we follow in every project.
- Start small, scale fast
Adoption succeeds when it begins with focused pilots that show measurable value. Once results are clear, scaling across the organization becomes easier and less risky.
- Keep governance close
Innovation needs structure. Security, compliance and documentation are part of the process from the start, not a layer added later.
- Build with the business, not for it
Co-creation between IT and business teams leads to better decisions and stronger ownership. When both sides work together early, solutions evolve naturally with the organization.
- Integrate, don’t isolate
Low-code applications bring the most value when they connect to enterprise data, AI services and existing systems. Integration turns fast delivery into long-term efficiency.
- Measure what matters
Every project should have clear outcomes, whether it’s time saved, errors reduced or user satisfaction improved. Metrics make innovation tangible and repeatable.
- Focus on capability, not dependency
Our goal is to strengthen client teams. We share knowledge and design systems that can be maintained and expanded independently, ensuring that progress lasts.
We continue to look at digital transformation through practice, not theory. Low-code has already changed how enterprises build and manage applications, and AI is now amplifying that change. Together they form a foundation for faster, smarter and more adaptive systems that evolve with the organization.
The next chapter in this evolution is agentic AI. It moves beyond assistance into autonomy, allowing systems to plan, act and learn within enterprise environments. In practice, it means applications that can trigger actions, coordinate workflows and make context-aware decisions without constant human input. For complex organizations, this kind of autonomy can reshape how operations scale and how value is delivered.
We treat this shift the same way we approach every new technology: by testing it in real projects, understanding where it brings real benefit and how it fits within governance and architecture. Intelligent transformation is still unfolding, and agentic AI is clearly becoming part of its future.
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