As agentic AI takes hold across the technology industry, development tools are rapidly integrating AI-powered features. Experts say there is a rising demand for AI-assisted low-code and no-code development tools.

“The demand is huge,” says Marc-Aurele Legoux, owner of Marcus-Aurelius Digital. “These tools allow anyone with zero to little coding knowledge to develop something that would otherwise either cost them a fortune or years of time, experience, and effort.”

Legoux says he frequently uses AI technology to create custom-coded tools that either help with user experience or to quickly set up an environment for clients to inspect or test.

Such technologies “can dramatically shorten development timelines, lower technical barriers for non-engineers, and enable rapid prototyping of niche, business-specific applications,” says Aaron Grando, vice president, creative innovation at Mod Op, a marketing and advertising agency.

Grando notes that AI-assisted coding has shifted the economics of software development. “Many problems that required significant engineering investment can now be executed by smaller teams with more focused domain knowledge, even individuals. When people who need solutions are empowered to build for themselves, they get to the core of the problem faster and solve it more holistically,” he says.

Mod Op has deployed AI coding assistants to engineers, as well as no-code agent builders for staff of all experience levels, “unlocking that speed and expertise” across the entire organization, Grando says.

Demand is surging for AI-augmented, low-code and no-code tools, says Ishan Amin, founder of WP Expert Services. He sees two reasons for the surge: “Instant application creation and powerful task automation,” he says.

“On the creation front, tools like Lovable.dev and Bolt.new now give users the ability to build entire standalone web or mobile applications without any coding knowledge,” Amin says. “A user can simply describe their needs in a chat, and the AI generates the front-end design, application logic, and the complete back end, including cloud database support.”

For businesses, “this is a game changer, as product managers, designers and developers can quickly develop full-scale apps,” Amin says.

As for task automation, platforms available on the market allow users to automate complex work tasks using simple drag-and-drop components, says Amin. “The days of manually scripting these connections are gone, as the AI now handles that scripting in the background.”

As a technology and product leader for more than 20 years, Amin is seeing “seismic, month-over-month changes.” Product managers “now have to move incredibly fast,” he says.

Another proponent of these tools is Sonu Kapoor, an independent software engineer. “These platforms are breaking down traditional developer barriers, allowing cross-functional teams to contribute directly to software creation while AI handles much of the scaffolding, validation, and logic suggestions,” he says.

Having architected AI-integrated systems for enterprises such as Citicorp, Sony Music Publishing, and Cisco, “I’ve seen firsthand how AI copilots are turning low-code platforms into intelligent development environments,” Kapoor says. “They’re no longer ‘toy tools.’ They’re becoming serious productivity engines.”

8 best practices for AI-powered low-code and no-code development

Development teams and organizations can take concrete steps to enhance the likelihood of success with AI-augmented, low-code and no-code development tools. Experts using these tools offered the following best practices for incorporating them into development workflows.

1. Create a governance strategy

“Establish governance and review pipelines early,” Kapoor says. “Even though AI copilots can enforce patterns and spot regressions, developers still need to validate scalability and maintainability.”

As part of governance, organizations need to manage their data boundaries carefully. “Many AI builders depend on user input and API calls that can inadvertently expose sensitive data,” Kapoor says, “Setting up strong data governance prevents that risk.”

Without governance, “low-code AI models become [a] liability,” says Nik Kale, principal engineer at networking and security provider Cisco Systems. “One of the first lessons we learned at Cisco was that low-code AI tools without built-in governance can quickly become unmanageable at enterprise scale.”

For Cisco’s Digital Adoption Platform (CDAP), governance is integrated directly into the development process, Kale says. “Every workflow or automation created by business teams undergoes automated checks for explainability, privacy impact, and performance before release,” he says. “This ‘governance-by-design’ approach helps prevent AI drift and ensures compliance with both internal and external standards.”

AI-assisted code generation “can accelerate prototyping, but code reviews and observability policies [overseen by humans] remain essential to maintain reliability,” says Akash Thakur, global site reliability engineering and cloud resilience architect at IT services and IT consulting firm Cognizant.

“Pair domain users with engineering mentors to ensure quality and performance,” Thakur says. “The biggest ROI comes when business intuition and technical discipline meet.”

Also see: How to start developing a balanced AI governance strategy.

2. Don’t assume AI replaces experience

Users of these tools need to have at least a basic understanding of how they work and the principles of software development.

One of the presumed benefits of low-code and no-code tools is that they are easy to use, thereby enabling people with little or no programming skills or experience to develop code. But it’s a mistake to assume that anyone can produce code quickly using these tools.

As someone who has been using vibe coding—a software development approach where a user describes needed functionality in natural language and an AI tool generates and refines code—Legoux says AI tools have their shortcomings.

“Forget the idea that you will be able to create a full-blown application within a few hours if you have zero experience of creating apps in the first place,” Legoux says. “This is probably the most common misconception I see every day. You need some sort of experience and knowledge before getting started.”

Also see: Is vibe coding the new gateway to technical debt?

3. Treat AI as a co-worker, not a replacement

Another best practice is to treat AI as a strategic planner and co-author, not a replacement for people, Grando says. “The best results come when humans with deep knowhow help the AI understand the problem completely,” he says. “AI tools don’t inherently understand product requirements, governance, or compliance. Human oversight is essential to finding a solution that checks all the boxes.”

Non-engineers and solo solution builders should start with narrowly defined problems in areas over which they have full control, such as their day-to-day routines, Grando says. “This lowers complexity and risk, builds confidence, and leads to more wins,” he says. “When a problem needs a solution that’s bigger than an individual or small team, or becomes a critical part of your process, that’s when it’s time to bring in engineers and architects.”

4. Measure outcomes tied to business value

“A successful no-code initiative isn’t measured by how many automations are built, but by what they achieve,” Kale says. “We use telemetry dashboards that correlate automation outcomes to key metrics such as case deflection, meantime to resolution, and customer satisfaction.”

By surfacing those metrics to both developers and business owners, adoption becomes self-sustaining rather than a one-time experiment, Kale says.

Across Cisco’s customer-facing platforms, its AI-augmented Digital Adoption and Support Fabric has delivered 22% faster first-touch resolutions vs. pre-launch baselines, and a 15% boost in engineer productivity through richer diagnostics and fewer repeat steps, among other benefits, Kale says.

5. Master prompting with clarity and context

Providing clear, specific prompted instructions and background context is critical, Grando says.

Users need to articulate desired outcomes, data sources, and reference materials. “Strong prompting and strategic context layered into building workflows leads to better code, fewer revisions, and more strategically aligned solutions,” Grando says.

6. Remain dedicated to the tasks at hand

Another important practice for succeeding with AI coding tools is to focus intensely on the problem at hand, as it’s very easy to get lost in the innovative, new technologies, Amin says.

“The tools available today are powerful and make it possible to build almost anything, but they won’t tell you what to build,” Amin says. “Knowing the specific problem you want to solve is critical to success.”

7. Focus on domain-specific training and feedback loops, not generic automation

“Generic low-code AI tools often fail because they lack context,” Kale says. “Within Cisco’s AI Support Fabric, we train models using domain-specific telemetry from customer support cases and security endpoints. This allows automation to understand intent—for example, diagnosing endpoint issues or predicting recurring incidents—rather than executing generic process steps.”

Organizations adopting similar domain-trained low-code approaches have reported significant reductions in escalation volume by aligning automation with their specific operational language, Kale says.

8. Understand the limitations of the tools

It is vital to know a tool’s limitations before you start, Amin says. “Users need to do thorough research to understand where they can expect to find ‘blockades,’” he says. “All platforms have them; you just have to know what they are and determine if they will be a hindrance to your specific project.”

Conclusion

AI-augmented low-code and no-code tools can help drive productivity and innovation when used correctly, based on these best practices:

1. Create a governance strategy
2. Don’t assume AI replaces experience
3. Treat AI as a co-worker, not a replacement
4. Measure outcomes tied to business value
5. Master prompting with clarity and context
6. Remain dedicated to the tasks at hand
7. Focus on domain-specific training and feedback loops, not generic automation
8. Understand the limitations of the tools