Rise of the citizen developer: GenAI and the democratisation of code

App development harnessing generative artificial intelligence (GenAI) might be democratising coding and potentially freeing up resource, but organisations must carefully manage contributors without prior coding experience.

According to Jon Puleston, vice-president of innovation in the profiles division at Kantar, even an accurate synthetic persona based on a data set of 25,000 real people containing 250 predictive demographic variables can give “very erratic” results even to simple questions, such as ‘Do you own a dog?’.

“In our experiment, this could only be predicted with 75% accuracy even using the most advanced AI and machine learning [ML] technique we applied, against a 52% chance based on our demographic data,” Puleston says. “This highlights the sheer amount of inputs required to make synthetic data models accurate enough for generalised commercial use. When making a business decision worth billions of dollars, there are risks.”

Kantar’s experiment suggests complexities count against talk of “citizen developers” when it comes to the rise of natural-language generative-AI tools in particular, with Puleston adding: “Real human insights are still the heart of good market research.”

After all, the detailed context of any given activity can often prove crucial. Kicking a football between goalposts is in principle a simple act, but it takes years of practice to reliably do well.

Leslie Kanthan, CEO and co-founder of code optimisation company TurinTech, says that traps await the unwary – even if an organisation has its own datasets ready to go. “Engaging with AI code and in-house applications is good because it gets people using this cutting edge stuff, allowing idea generation and creativity. But there’s the governance of that,” Kanthan says.

If creating an application with sensitive data sets, for instance, without a coding background, you’ll be hard-pressed to justify to regulators and compliance exactly what the application is doing and not doing.

“Just saying that you built an application and it solves this problem is not good enough for regulators,” Kanthan says. “They need to know that the data is being kept right, and so forth.”

That said, GenAI can help junior developers a year or two in sharpen their edge, and development might be expanded to wider contributions while remembering the need for robust testing, review and user adoption as tools advance.

Do not forget that cleaner code can be more sustainable, consuming less resource including energy, Kanthan adds. “Right now, think about [GenAI] realistically. It’ll be a few years still before you have robust applicational use for even your most advanced uses,” he says. “You need to know exactly what you’re doing.”

Ask what exactly you are bringing into the company and how to make use of it. Today’s GenAI is best used as an assistant to expertise, not a substitute for it – and develop appropriate checks and balances and governance in every use case. “Know what you’ve created and how you’ve been able to justify that,” Kanthan adds.

Kjell Carlsson, head of AI strategy at Domino Data Lab, notes that offerings such as Amazon Q billed as accessible to non-coders will typically be of limited use. The question is not whether you need skills to use a tool, but can you actually use that tool to good effect? A rule of thumb is conversely that you’ll need to “up your humans”.

Compare the challenge with using Visual Basic or Excel macros, for instance: those take skill or a technical background. And what about documentation?

In businesses with less resource, where less-experienced developers might adopt ChatGPT, for instance, to build apps, you cannot really have “a whole lot” of confidence in the quality of the results, Carlsson confirms.

“It takes a lot of understanding on the part of an end user to know what good looks like,” he says. “It can be a great way to get up to speed quickly and do bog-standard things. Not if it’s a task that you usually use SQL for or – heaven forbid – COBOL.”