How AI Is Changing Website and Mobile App Development, and What It Means for IT Jobs

To understand what AI changes, it helps to picture the traditional approach that most teams followed until recently.

A project began with requirements gathering, where business analysts and project managers interviewed stakeholders and wrote long specification documents. Designers then produced wireframes and visual designs screen by screen, a process that could take weeks of revisions. Developers translated those designs into code by hand, writing the front end, the back end, the database layer and the integrations line by line, and searching documentation and forums whenever they hit a problem. QA engineers wrote test cases and tested features manually or built automated test suites over time. DevOps engineers configured servers, deployment pipelines and monitoring. And after launch, the team maintained the software, fixed bugs and added features, often spending more time understanding old code than writing new code.

Every one of these stages depended on skilled people doing a large amount of repetitive work. Much of a developer’s day was not creative problem-solving but typing out predictable patterns, looking up syntax, writing tests for obvious cases and hunting for small mistakes. That repetitive layer is exactly where AI has had the biggest impact.

AI does not change just one step. It touches the entire software development lifecycle, from the first idea to long-term maintenance.

AI tools can turn rough meeting notes into structured user stories, suggest acceptance criteria, spot gaps and contradictions in a specification and summarize research about competitors and user needs. This does not replace conversations with clients and users, but it shortens the time between a conversation and a clear, reviewable plan.

What still needs people: understanding the real business problem behind a request, balancing the priorities of different stakeholders and deciding what not to build. AI can only work with the information it is given, and the most important requirements are often the ones nobody has written down yet.

Design has changed dramatically. AI can generate layout options from a short description, create realistic content for mockups, suggest color palettes and typography, check designs for accessibility issues such as poor contrast and produce working prototypes that stakeholders can click through much earlier than before. Some tools can now turn a design file or even a hand-drawn sketch into front-end code.

What still needs people: understanding users, creating a coherent brand experience and making the hundreds of small decisions that separate a usable product from a generic one. AI-generated designs tend to look similar to each other because they are trained on what already exists. Good UI and UX design still depends on research, empathy and taste.

The first stage was smart autocomplete, where tools suggested the next line or block of code as a developer typed. The second was chat-based assistance, where developers could describe a problem in plain language and receive working code, explanations or fixes. The third, now well underway, is agentic coding, where AI agents can read an entire codebase, plan a change across many files, run commands and tests, fix their own errors and present a finished change for a human to review. Tools such as GitHub Copilot, Cursor, Claude Code and others are widely used in professional teams for this kind of work.

In practice, AI now writes a large share of first-draft code in many teams. It is especially strong at boilerplate, standard patterns, data transformations, API integrations with well-documented services, converting code between languages and frameworks and explaining unfamiliar code.

What still needs people: system architecture, decisions about trade-offs, security design, complex business logic, performance under real load and, above all, reviewing and taking responsibility for every line that goes into production. AI can write code quickly, but it can also write code that looks right and is subtly wrong, uses outdated approaches or introduces security weaknesses. The skill of a developer is increasingly in knowing the difference.

AI can generate unit tests and test cases from existing code, create realistic test data, suggest edge cases a person might miss, run automated visual checks across many screen sizes and help diagnose why a test failed. This is a significant change, because testing was often the first thing cut when deadlines were tight. With AI, teams can reach much better test coverage for the same effort.

What still needs people: deciding what quality means for a particular product, exploratory testing that looks at the product the way a real user would, and judging whether a feature actually solves the problem it was meant to solve.

AI reviewers can scan every change for common bugs, style problems, potential security vulnerabilities and missing error handling, and leave comments before a human reviewer even looks. This catches many problems earlier and frees senior developers to focus their review time on design and logic.

What still needs people: security is one of the areas where human expertise matters most. AI-generated code can include vulnerable patterns, outdated dependencies or insecure handling of user data, and AI tools themselves introduce new risks, such as developers pasting confidential code or customer data into external services. Responsible teams set clear rules on which tools can be used, with what data and with what review.

AI helps write infrastructure configuration, deployment scripts and monitoring rules, explain cloud billing, analyze logs and suggest the likely cause of an incident. Some platforms can now detect unusual behavior and recommend fixes automatically.

What still needs people: designing reliable infrastructure, planning for failure and recovery, controlling cloud costs and making the final call during an incident. Our cloud and DevOps services use automation heavily, but the architecture and accountability remain with engineers.

Maintenance has always consumed a large share of the total cost of software. AI makes it much easier to document existing code, understand a legacy system nobody fully remembers, upgrade old frameworks and refactor messy code into cleaner structures. For businesses running on aging systems, this may be one of the most valuable changes of all, because modernization projects that once seemed too risky or expensive are becoming practical.

What still needs people: deciding what to modernize and in what order, managing risk during migration and making sure nothing important is lost along the way.

For websites, AI has made the early stages dramatically faster. A marketing site that once took weeks from brief to first draft can now reach a working prototype in days, with AI helping to produce layouts, content drafts, responsive front-end code and basic SEO structure.

AI is also changing what websites do. Sites increasingly include AI-powered search that understands natural language, chat assistants that answer customer questions using the company’s own content, personalized content and product recommendations, and automated workflows behind contact and quote forms. For online stores, an AI recommendation engine can personalize every visit automatically. If you want to understand how that technology works, our guide on [what an AI recommendation engine is and how it works](https://infosparkles.com/what-is-an-ai-recommendation-engine/) explains it in plain language.

At the same time, websites now need to be found by AI as well as by people. More users ask AI assistants for recommendations instead of searching in the traditional way, so clear, well-structured, factual content and proper structured data have become more important, not less.

What has not changed is that a serious web platform, such as a large online store, a marketplace or a customer portal, still needs careful engineering. Performance, security, integrations, payments, data protection and the ability to scale are exactly the areas where AI-generated shortcuts cause the most damage. You can see how we approach these in our enterprise eCommerce development work.

Mobile development has its own challenges, including multiple platforms, many device sizes, app store rules and demanding expectations for speed and battery life. AI helps with many of them.

AI tools speed up building screens in cross-platform frameworks such as Flutter and React Native, converting designs into native components, writing the code that connects an app to its back end and generating tests across different devices and screen sizes. They also help with app store listings, release notes and responding to user reviews.

The bigger shift is in what mobile apps can do. Phones now run AI models directly on the device, which allows features such as real-time image recognition, voice commands, offline translation, smart photo editing and personalized suggestions without sending private data to a server. Users increasingly expect apps to understand natural language, anticipate their needs and automate small tasks for them.

Building those features well still takes experience. Choosing between on-device and cloud AI, protecting user privacy, keeping apps fast and battery-efficient and passing app store review all require engineering judgment. That is the focus of our mobile application development team.

One of the most talked-about trends is vibe coding, a term for building software by describing what you want to an AI in plain language and accepting what it produces, often without reading the code in detail. A growing number of AI app builders let people with no programming background create working prototypes, simple websites and internal tools in hours.

This is genuinely useful. Founders can test an idea before investing in full development. Business teams can build small internal tools without waiting for IT. Designers can turn concepts into clickable prototypes. For simple, low-risk projects, these tools can be all that is needed.

The limits appear when software needs to be relied on. AI-built apps often struggle with security, because the person building them may not know what to check. They can become difficult to change as they grow, because nobody fully understands how the code fits together. They may not handle real traffic, complex permissions, payments, data protection rules or integrations with other business systems. And when something breaks, there may be no one who can fix it.

A useful rule of thumb is that vibe coding is excellent for exploring ideas and poor for running a business. Many companies now use AI builders for the first prototype and then bring in an engineering team to build the production version properly, often reusing the prototype as a clear, working specification. That combination can save a great deal of time and money.

The honest answer is that it depends on the task, the developer and how the tools are used.

Early research was very positive. In a controlled experiment published by GitHub, developers using GitHub Copilot completed a defined coding task 55 percent faster than those without it, according to GitHub’s report on quantifying Copilot’s impact on developer productivity.

Later research added important nuance. A 2025 randomized study by the research organization METR found that experienced open-source developers working on large, familiar codebases actually took 19 percent longer to complete tasks when using AI tools, even though they believed the tools had made them faster. You can read the details in METR’s study of AI’s impact on experienced developers.

Developer surveys show the same mixed picture. The Stack Overflow Developer Survey 2025 found that a large majority of developers, around 84 percent, were using or planning to use AI tools, yet trust in the accuracy of AI output had fallen, with more developers distrusting it than trusting it.

Put together, the evidence suggests that AI delivers big speed gains on well-defined, self-contained and repetitive tasks, and smaller or even negative gains on complex work in large systems where understanding context matters most. The time saved writing code can be lost again reviewing, correcting and debugging it. Teams that get the most value are those that use AI deliberately, for the right tasks, with strong review practices, rather than applying it to everything.

This is the question behind much of the anxiety, and it deserves a straight answer.

The picture is mixed rather than catastrophic. The World Economic Forum’s Future of Jobs Report 2025 lists software and applications developers, along with AI and big data specialists, among the fastest-growing roles over the rest of the decade, as organizations invest heavily in digital products and AI.

However, the effect is not the same for everyone. A 2025 study by researchers at the Stanford Digital Economy Lab, titled Canaries in the Coal Mine, found that employment for early-career workers aged 22 to 25 in the occupations most exposed to AI, including software development, had fallen noticeably since the spread of generative AI, while employment for more experienced workers in the same fields held steady or grew.

In other words, demand for experienced people who can design systems, solve hard problems and supervise AI output remains strong, while the traditional entry point into the profession, junior roles built around straightforward coding tasks, is getting narrower.

The work most affected is routine and well defined: writing standard code from clear specifications, simple bug fixes, basic testing, converting designs into standard screens, writing documentation from scratch and building simple websites from templates. Roles that consisted mostly of these tasks are changing the fastest.

At the same time, demand is rising for work that AI cannot do alone or that AI itself creates. This includes software architecture and system design, security engineering, integrating AI features into products, data engineering, reviewing and validating AI-generated code, AI governance and compliance, product management and technical leadership, and specialist skills in areas such as cloud infrastructure, performance and mobile.

There is also a well-known pattern in economics, often called the Jevons paradox, where making something cheaper to produce increases total demand for it. As software becomes faster and cheaper to build, businesses that could never afford custom software start commissioning it, and companies that already build software start building much more of it. Many people in the industry expect the overall amount of software being created to rise sharply, which supports demand for people who can build it well.

This is not the first time technology has threatened to make programmers obsolete. High-level programming languages, visual development tools, open-source libraries, cloud platforms and no-code tools were all expected to reduce the need for developers. Each time, they made individual developers more productive, lowered the cost of building software and ultimately increased demand for software and the people who build it. AI is a much larger shift than any of these, and the transition will be uncomfortable for some people, but the long-term pattern so far points toward change rather than disappearance.

If you work in IT or are planning a career in it, the most useful response is to focus on the skills that become more valuable as AI handles more routine work.

Strong fundamentals – Understanding how software works, including data structures, networking, databases and security, is what lets you spot when AI output is wrong. People who skip the fundamentals and rely entirely on AI tend to hit a ceiling quickly.

System design and architecture – Deciding how the pieces of a system fit together, how it will scale and how it will fail is still very much human work, and it is where experienced developers add the most value.

Reviewing and debugging code –  As AI writes more first drafts, the ability to read code critically, find subtle problems and fix them becomes one of the most important skills in the profession.

Working effectively with AI – Knowing how to break a problem down, give AI the right context, check its work and decide when not to use it is now a core professional skill, much like knowing how to search effectively became one in the past.

Security awareness – Understanding common vulnerabilities, data protection and the specific risks of AI tools is increasingly expected of every developer, not just security specialists.

Business and product understanding – Developers who understand the problems their software solves, talk comfortably with clients and users and think about outcomes rather than just tasks are the hardest to replace.

Communication – Explaining trade-offs, writing clear specifications and working well in a team matter more when the typing is increasingly done by machines.

For students and junior developers, the entry path has become harder but not closed. Building real projects, contributing to open source, learning to use AI tools well while still understanding what they produce, and developing a specialization all help stand out in a more competitive market.

For businesses, AI brings real benefits, along with some new risks worth understanding before starting a project.

Faster time to market – Prototypes, early versions and routine features can be delivered faster than before, which means you can test ideas with real users sooner.

Better use of budget – When routine work takes less time, more of your budget can go toward the parts that make your product distinctive, such as user experience, unique features and integrations.

Higher quality when used well – AI-assisted testing, review and documentation can raise the overall quality of software, provided the team applies strong human review.

Smarter products – AI is not only changing how software is built but also what it can do. Personalization, intelligent search, chat assistants, document processing and workflow automation are now realistic features for businesses of almost any size. Our AI-driven automation services focus on exactly these capabilities.

Modernization becomes practical – Upgrading legacy systems is faster and less risky with AI assistance, which helps businesses that have been held back by aging software. This connects directly to the ideas in our guide on how custom software development helps businesses scale faster.

Security vulnerabilities in AI-generated code that nobody reviewed properly.

Hidden technical debt, where software is built quickly but becomes expensive and slow to change later.

Data privacy issues if confidential information or customer data is entered into AI tools without proper controls, which can conflict with laws such as GDPR, UK GDPR, CCPA and Australia’s Privacy Act.

Intellectual property questions about who owns AI-generated code and whether it reproduces licensed material.

Unrealistic expectations, such as assuming that because AI can produce a demo in an afternoon, a production-ready platform should cost almost nothing. The demo is usually the easy part.

Before hiring a development company, it is worth asking how they use AI in their process, which tools they use and what data those tools can access, how AI-generated code is reviewed and tested, how they protect your confidential information and intellectual property, whether you will receive full ownership of all code, and how AI affects their timelines and pricing. A good partner will answer these questions openly and specifically. You can also review their past work in their case studies to see what they have actually delivered.

We use AI every day, and we think about it as a tool that makes our engineers faster, not a replacement for their judgment.

Our developers use AI assistants to speed up routine coding, explore unfamiliar code, generate tests, draft documentation and investigate bugs. Our designers use AI to explore layout ideas and build early prototypes that clients can react to sooner. Across the team, AI helps us move from idea to working software faster, which means clients see progress earlier and more of their budget goes into the features that matter.

We also set clear rules. Every line of AI-assisted code is reviewed by an experienced developer before it reaches production. Client code and data are only used with tools and settings that protect confidentiality. Architecture, security design and final decisions always rest with our engineers. And clients receive full ownership of everything we build.

Beyond using AI to build software, we build AI into the software itself, from custom AI recommendation engines to intelligent automation such as our AI sales intelligence and outreach platform. And we continue to deliver the kinds of platforms where engineering depth matters most, including marketplaces, booking systems and enterprise systems and SaaS products.