Transforming software development with AI and low code
The convergence of artificial intelligence (AI) and low code platforms is redefining how organisations undertake software development. Rapid innovation once required legions of highly specialised developers⎯now, AI-assisted development and low code toolkits can help businesses design and deploy solutions at pace—without sacrificing quality or security.
Why does this matter
For starters, the demand for technology projects is outpacing the supply of skilled developers. Organisations across industries face extensive software backlogs, yet they struggle to find the right talent to build these solutions. Meanwhile, user expectations keep climbing, and the competition never waits.
AI and low code to the rescue
AI-powered tools excel at handling tedious, repetitive coding tasks—think debugging boilerplate code or performing routine code reviews—so developers can tackle more creative challenges. Meanwhile,
low code development platforms allow non-technical staff to build custom mobile apps using intuitive interfaces, now even using natural language prompts via AI assistants, drastically reducing the strain on professional developers.
A knowledge worker in the finance department might create an internal reporting dashboard; a field technician could build a simple mobile tool to log tasks. These efforts free up IT teams to focus on larger, more complex projects while maintaining the necessary oversight to ensure
security and compliance.
Some organisations think
generative AI is insecure and expensive, while others believe low code development platforms can’t meet their complex requirements. However, AI can be secure if data governance and proper guardrails are in place—such as configuring the right information security and labelling mechanisms.
Similarly, low code platforms, particularly Microsoft’s, provide extensive out-of-the-box functionality that can be customised with additional development if needed.
On the other hand, there’s a misconception that low code is always cheaper and faster overlooks the fact that success depends on how well the organisation aligns with the platform’s capabilities. Organisations need to be open to adjusting their development processes to fit the
low code framework, rather than forcing outdated or overly complex workflows into it.
It is common in companies for staff to suffer from a type of "Stockholm Syndrome" when it comes to their existing development processes and need to have their minds opened to alternate approaches.
However, adopting AI and low code offers an opportunity to rethink and optimise workflows for efficiency rather than just replicating old systems in a new environment.
Let’s look at three ways AI and low code drive results:
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Increasing developer productivity.
Developers can offload routine tasks to AI, speeding up their workflow and letting them concentrate on complex features or architectural decisions while also reducing the burden on senior staff members from being asked questions or checking work. They work faster, but they also tend to be happier—no one likes doing mundane development tasks and fixing the same bug a hundred times. More productivity means greater cost efficiencies, and therefore greater innovation, solving organisational challenges at a more rapid pace.
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Automating repetitive tasks.
AI tools can spot anomalies, clean up code, and even suggest feature improvements. Low code platforms handle setup, deployment, and integration details automatically, allowing teams to focus on delivering real value. The result? Faster turnarounds, fewer drudge tasks, and a more motivated workforce.
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Empowering non-technical staff.
With low code, business users can create apps—simple ones, like an approval workflow or a basic survey tool—using visual components or natural language prompts. These non technical people might lack formal programming backgrounds, but they intimately know the processes and pain points, making them uniquely qualified to streamline operations. When AI is part of the mix, they can also get immediate suggestions or error-spotting assistance, boosting their confidence and output.
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Security and compliance without compromise
One natural concern is whether speeding up development might jeopardise organisational safeguards.
In reality,
AI and low code platforms typically come with built-in guardrails. They standardise best practices for authentication, encryption, and data handling, ensuring new apps align with security and compliance requirements.
Data governance and security frameworks are non-negotiable. Generative AI can be secure if the right data governance is in place. You can configure guardrails around information security and labelling.
Microsoft, for example, has well-established security, governance, and compliance frameworks embedded within its low code platforms. Organisations can leverage these built-in and well established capabilities rather than reinventing the wheel. While additional security and compliance layers can always be added, many foundational safeguards are already provided within Microsoft’s ecosystem.
Organisations should also avoid tackling overly critical use cases first—starting with lower-risk projects allows for testing and refinement before scaling up. Choosing the right implementation partner is crucial, as is running pilot programs in an isolated environment before wider adoption. Continuous feedback and learning should be built into the process to ensure ongoing improvements before committing to a large-scale rollout.
Leading from the CIO’s chair
CIOs face tough decisions daily: how to reduce that backlog of apps, identifying and finding the right talent, and how to maintain security in a fast-paced environment. For many, AI and low code have become indispensable parts of the solution. But the transition needs careful thought:
It’s important to start small with a focused pilot. Organisations should pick a use case big enough to matter but not so critical that a single hiccup sets everyone back. Once you’ve ironed out security, data governance, and basic processes, you can confidently roll out more complex AI and low code projects at scale.
The road ahead
The synergy between generative AI and low code isn’t just a trend—it’s becoming necessary for organisations aiming to deliver software development at scale and speed. Automating repetitive coding tasks, enabling non-technical staff to create meaningful applications, and safeguarding essential business data with standardised governance can significantly boost productivity and morale. AI is now also instrumental at various stages of the testing lifecycle for a low code system.
AI agents are a big right now. They allow organisations to use natural language commands to automate repetitive, mundane tasks—freeing your people to focus on delivering real business value.
These
AI-driven automation tools can significantly enhance efficiency, and they’re going to become more embedded in everyday processes. Over the next few years, you’ll see even tighter integration of AI within low code platforms, helping organisations move faster and rely less on specialised skill sets or specific vendors.
However, it’s crucial not to rely solely on generative AI and low code. “You still need the right talent—people who understand architecture, governance, and advanced development practices,” cautions Gunawan. “AI and low-code really shine when they fill the gaps for experienced developers, but they can’t replace them outright.”
Ultimately, good data governance, the right security measures, and thoughtful piloting can make or break any large-scale AI and low code rollout. Get those foundations right, and you’ll be well on your way to real digital transformation.
Adopting AI and low code might be the decisive factor in bridging your talent gap, slashing development timelines, and enabling your teams to focus on projects that truly drive growth and customer satisfaction. In a business environment that refuses to slow down, could this be your catalyst for lasting, future-ready innovation?
