Software projects once moved at the pace of yearly budgets and big‑bang deployments. Today, the companies that ship quickest are drawing on two ideas that have only recently matured enough to warrant enterprise-level trust: low‑code application platforms and generative AI assistance. They are different tools, yet together they collapse delivery timelines and lower the barriers that kept smaller teams from tackling large‑scale problems.
A Quick Primer, Without the Jargon
Low‑code/no-code (LCNC) platforms hide repetitive plumbing – data connections, authentication, deployment pipelines – behind visual canvases and pre‑built components. A product owner can sketch out a workflow in the morning and see it running in a secure cloud environment before lunch.
Generative AI, on the other hand, is helpful for writing the snippets of code that are required for complex edge cases that may fall outside those visual blocks. When combined with responsible context engineering, Large Language Models can draft the initial starting point for code, so engineers spend their time refining instead of handling boilerplate, rote tasks. In the story below, we’ll dive into how we crafted a high-impact solution for one of our clients, using a balanced blend of low-code tools, along with generative-AI enabled software development.
Turning Risky Invoices into Reliable Revenue with a Power Platform
Our client, a premier provider of parking and mobility solutions, was processing roughly a billion dollars a year in invoices through a tangled web of Excel files. As their business continued to grow, they knew that the risks surrounding this process were growing as well, and something needed to change. As every month passed the risk grew: one corrupted sheet, one analyst on holiday, and cash flow would stall. Management consultants proposed an eighteen‑month ERP installation. The CFO did the math and pictured eighteen extra months of risk. Instead, he opted for a solution to be built on top of Microsoft Power Platform -the toolset already included in the firm’s Microsoft 365 agreement and asked us to make it production‑grade.
We began with Dataverse, the platform’s built‑in data layer, which absorbed dozens of spreadsheets into a single, version‑controlled schema. Power Apps replaced once‑manual data entry screens; Power Automate orchestrated approvals and notifications. Where those tools hit their ceiling – complex revenue‑share calculations, PDF generation, bespoke bank files – we pointed to a generative model at the problem.
It produced the underlying Azure Functions, stubs of documentation, and even the validation tests. Engineers reviewed the output, tuned edge cases, and committed the code to the same repository that holds the visual assets. We ensured we did not skimp on the end user experience, by layering in custom React components when needed. This extra dose of UX attention made a significant difference in end-user adoption and didn’t take much time, thanks to the generative AI implemented.
Within months, our solution allowed the client’s month-end billing cycle to run end‑to‑end without human intervention. The finance team dropped from seven specialists to three analysts, who now focus mostly on handling edge cases and forecasting. Processing times that used to span four business days now rarely exceed four hours.
Where Microsoft Power Platform Paid Off
The gain was not speed alone. Because Power Platform integrates with Azure AD/Entra, the company skipped a separate authentication service. Licensing was already covered and there was no new SaaS line item. The data never left the tenant, so compliance auditors needed fewer meetings. In short, the solution fits within the enterprise ecosystem instead of sitting beside it.
Why More Leaders Are Turning to Low-Code / No-Code Platforms
Gartner predicts that by 2029,1 enterprise low-code application platforms will support 80% of mission-critical applications worldwide, up sharply from just 15% in 2024. Increased adoption is largely attributed to three core drivers:
- The Acquisition Imperative: Manual processes are acquisition killers. Due diligence teams flag Excel-based operations as critical risks.
- The Talent Reality: “Do more with less” isn’t a suggestion anymore. The most profitable companies are finding it increasingly feasible to simultaneously improve operations, while simultaneously freeing people up from low-value, tedious processes and tasks.
- Ownership Shift: Tech-savvy employees are taking charge of digital products, eliminating traditional IT bottlenecks and speeding up time-to-value.
For anyone who might be skeptical:
If you think your processes are too complex for low-code. What is the key difference? We leverage generative AI to handle the complex logic that low-code alone couldn’t touch.
The Delivery Difference
The right mix of generative AI and LCNC platforms lets your team deliver robust, enterprise-grade solutions in a fraction of the time – no trade-off between speed and quality, just both. Here are the two key takeaways for why we find value in both:
- Generative AI-enabled software engineering provides clear acceleration, without diminishing quality. Research conducted across Microsoft, Accenture, and a Fortune 100 company involving 4,867 developers found that those using GitHub Copilot achieved a 26% increase in completed tasks, with no negative impact on code quality observed2. Additional studies show even higher gains, with GitHub reporting that developers complete tasks 55% faster using AI coding assistants, and some enterprise implementations seeing throughput improvements of 50-70%34. These productivity gains are expected to increase as AI tooling continues to improve.
- LCNC platforms make solutioning more accessible to business users. They can function as self-documenting solutions, so we don’t have to spend extra effort translating a code-based artifact into an interpretable visual for business users. They also allow for savvy business users to make refinements to the solution without involving development teams for every small ask. This reduces costly context-switching for engineers and unblocks value creation for the business.
Your tech stack shapes AI-driven engineering outcomes:
Context engineering is now a key focus in developer experience. An effective, context-optimized stack can set your team apart.
Low / No-Code Platforms and Context-Optimized Stacks
With generative AI, “context is king.” Large language models (LLMs) can only reason with the information your stack supplies. As we continue to mature our understanding of how to most effectively leverage generative AI, we’ve come up with the concept of the context-optimized stack.
The idea behind this concept is that your stack is optimized to provide high-value context to LLMs. Low-code platforms typically fit well in context-optimized stacks, given that they publish exhaustive API references and official reference patterns, which AI can leverage for high-precision results.
Choose or refine technologies that present an unambiguous, well-organized context, and teach teams to curate it. With the right stack, AI becomes a force multiplier; without it, even the best model will struggle.
Technology Isn’t the Goal
Successful digital initiatives rely on the proper coordination of people, operating models, design, data, and technology. Here are some of the common pitfalls we guard against:
- No engaged executive sponsor
- Vague or unmeasurable success criteria
- Missing standards for responsible, rapid innovation
- Poor-quality upstream data
- Misalignment between solution and operating model
- IT-driven (vs. business-driven) agendas
- UX treated as an afterthought
Our approach surfaces and resolves these issues – central or peripheral – so our clients mature alongside the solution.
Your Next Move
The convergence of low-code and generative AI isn’t coming – it’s here. Organizations that harness this combination thoughtfully will build solutions faster, operate more efficiently, and adapt more quickly to change.
We’ve demonstrated this successful formula multiple times for our clients, so if you have any questions, we’re here to help. The question isn’t whether to embrace this approach, but how quickly you can start.
Don’t let your competitors outpace you. The shift from yearly deployments to rapid delivery is happening now. Discover how low-code platforms and generative AI can collapse your delivery timelines and empower your team to tackle problems that once seemed impossible.
Sources
1 Gartner: Gartner Magic Quadrant for Enterprise Low-Code Application Platforms. https://www.gartner.com/en/documents/5844247
21InfoQ (September 2024): “Study Shows AI Coding Assistant Improves Developer Productivity”. https://www.infoq.com/news/2024/09/copilot-developer-productivity/
ZDNet: “The data suggests gen AI boosts software productivity – for these developers”. https://www.zdnet.com/article/the-data-confirms-genai-boosts-software-productivity-at-least-for-beginning-developers/
4MIT Sloan: “How generative AI affects highly skilled workers”. https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-affects-highly-skilled-workers
