The rise of artificial intelligence in software development is transforming the commercial landscape, delivering faster product launches and more personalized customer experiences.
To dig‘s $40 million Series A financing round in September for its AI-powered code testing platform reflects growing investor confidence in AI’s ability to streamline business operations and improve digital services.
Coding tools such as GitHub copilot And OpenAI‘S Codex are changing the way companies build and deploy software. These advanced machine learning models can suggest code snippets, complete functions, or create entire code files from prompts or existing code.
“AI coding tools greatly increase developer productivity by automating a number of repetitive tasks and code suggestions,” Dhaval Gajjarhead of technology Text dropa Software-as-a-Service (SaaS) company, to PYMNTS. “This can lead to faster development cycles and therefore a reduction in time-to-market.”
These tools “maintain code quality based on best practices and catch potential errors early in the development phase,” Gajjar said. “It reduces the extensive testing and debugging process and thus saves money a lot of time and resources.”
AI transformation
The impact goes beyond productivity gains. Amazon CEO Andy Jassy highlighted the impact of the company’s AI-powered code transformation capability, Amazon Qon social platform X.
“The average time to get a application to Java 17 plummeted what is typical 50 developer days until just a few hours,” he posted. “We estimate this has saved us the equivalent of 4,500 developer years of work…”
These efficiency gains could reduce development costs and timelines across industries, accelerating innovation and time-to-market for new features and products.
The power of AI in software development is especially pronounced in e-commerce.
“In the field of e-commerce, tools such as GitHub Copilot and Cursor prove particularly valuable for quickly implementing standard features,” Developer NagCEO of QueryPalan e-commerce solutions provider, told PYMNTS. “They excel at generating standard code for product catalog structures, basic shopping cart functionality, and user authentication flows.”
Balance between innovation and risk
AI-generated code offers personalization and customer experience benefits.
“AI-generated code can easily and quickly traverse large data sets containing customer preferences and behavior,” Gajjar said. “For example, you can easily generate a product recommendation using AI just now keeping track of a user’s previous purchases and browsing history.”
The technology also promises improved transaction security.
“AI can also be used to generate adaptive security algorithms that detect and prevent fraud real time” said Gajjar. “For example, an AI tool would provide a code for a payment gateway so that a fraction of transactions will automatically raise red flags based on established fraud patterns, similar to how PayPal or Stripe use AI for fraud detection.”
Integrating AI into software development comes with challenges.
“There have been cases where AI-generated code introduced subtle bugs into inventory management systems, leading to oversales or stockouts,” Nag said.
Denisse Damienan AI researcher, raised the alarm again.
“The rise of hyper-personalization threats is a concern,” she told PYMNTS. “Scammers could use AI to generate realistic customer service voices or emails, tricking customers into disclosing sensitive information or making fraudulent purchases. With AI-generated code creating customized digital experiences, the line between legitimate personalization and malicious exploitation can blur.”
These risks underscore the need for human oversight.
“The biggest risk companies face with AI-powered coding tools is when engineers rely on them too heavily without thoroughly reviewing the output,” says Damian. “AI can sometimes generate code that looks correct but contains bugs or security flaws. If developers fail to recognize these issues and blindly rely on AI, they could introduce serious vulnerabilities into proprietary systems.”
Gajjar outlined the risks associated with proprietary technology and cybersecurity.
“AI models trained on proprietary codebases can ultimately learn sensitive information that the model replicates, exposing the system to unauthorized access,” he said, adding that there are risks associated with relying on third-party AI technology and potential supply chain vulnerabilities.
The industry may further specialize in AI tools.
“We’ll likely see more e-commerce-specific AI coding assistants,” says Nag. “These could be trained in specialist e-commerce frameworks and best practices, making them even more valuable to the sector.”
However, he also had a warning.
“This specialization could also increase the risk of homogenization in e-commerce platforms, potentially making unique, innovative implementations more valuable than ever,” he says.
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