Why custom software development is taking off again

For a long time, the safer path was to buy off-the-shelf software, connect a few SaaS tools, build workarounds where needed, and only consider custom software once the pain became impossible to ignore.

That still makes sense in plenty of cases. A business shouldn’t build custom software just because it can. Accounting, payroll, email marketing, simple CRM and basic project management are usually better handled by proven SaaS products.

But something has changed.

AI coding agents are helping experienced developers move faster. Businesses are realising that useful AI needs access to their own data and workflows. Startups can test product ideas sooner. And growing companies are questioning whether a generic software stack is still enough.

The result is a renewed interest in custom software, bespoke internal tools, AI-powered applications and lean product development.

The short answer

Custom software development is taking off again because businesses increasingly need software that fits the way they actually work, while AI-assisted development is making well-scoped projects faster and more achievable.

The market reflects it. Grand View Research valued the global custom software development market at US$43.16 billion in 2024 and projected it to reach US$146.18 billion by 2030, a compound annual growth rate of 22.6%. The same research puts the Australian custom software development market on a path from roughly US$967.5 million in 2024 to US$3.71 billion by 2030.

That doesn’t mean every business should start building from scratch. It means the business case for custom software is stronger when the workflow is specific, the data is valuable, or the software can create a real operational advantage.

Businesses are outgrowing generic software stacks

Most businesses don’t start with custom software. They start with what’s available. A spreadsheet here, a CRM there, a project management tool, an accounting system, a few automations, and a lot of manual effort holding it all together.

There’s nothing wrong with that in the early stages. It’s often the fastest way to test a process before investing in a permanent system.

The problem starts when the workaround becomes the operating model. That usually looks like disconnected tools, duplicate data entry, unclear ownership, slow reporting, manual approvals, customer information spread across multiple systems, or team knowledge living in someone’s head.

Spreadsheets are part of this story, but they’re not the whole story. They’re usually a symptom of a bigger issue: the business has grown faster than its systems.

This is still very visible in Australian SMEs. An OFX/Ipsos survey of more than 500 SME accountants and finance decision makers found that 80% of SMEs still use manual processes for expenses, with manual processes, security concerns and late payments identified as the biggest barriers to efficient financial management.

The opportunity for custom software isn’t “replace every spreadsheet with an app”. It’s to ask a sharper question: where is the business losing time, accuracy, visibility or scalability because the current system doesn’t fit?

Sometimes the answer is a better SaaS product. Sometimes it’s a simple automation. But sometimes the right answer is custom software designed around the way the business actually works.

AI agents have changed the economics of software development

The biggest shift in custom software development isn’t that developers now have better autocomplete.

It’s that software teams now have access to AI coding agents that can help plan work, write code, generate tests, debug issues, review changes, refactor existing systems and work across larger codebases.

That matters because custom software has always had one major barrier: cost. For many small and medium-sized businesses, the business case used to be difficult. A manual process might have been inefficient, but it worked. An off-the-shelf product might not have fit perfectly, but it was available immediately. Building something custom often felt too expensive or too risky.

AI hasn’t removed the need for experienced developers. It’s made them more important. Someone still needs to understand the business process, make architecture decisions, protect security, design the user experience, review the code, test the system and decide what should not be built.

But AI has changed how much a strong development team can get done. The numbers back this up:

• In January 2026, JetBrains surveyed over 10,000 developers and found 90% were already using AI at work. It also found 22% were already using AI coding agents, with 66% of companies planning to adopt coding agents within the next 12 months.
• Black Duck's 2026 research found AI coding assistants had reached 97% adoption among surveyed enterprise software engineers and DevOps professionals. The same study found 92% of teams reported improved productivity and release velocity, with developers reclaiming an average of eight hours per week.
• Jellyfish's 2026 AI Engineering Trends report analysed data from more than 1,000 companies, 200,000 engineers and 37 million pull requests. It reported 71% median AI adoption across companies, a 27% median AI code ratio, and 2x pull request throughput for top AI adopters.

This is why custom software is becoming more accessible. A business that previously couldn’t justify a custom internal tool may now be able to launch a lean first version sooner. A startup that once needed a larger engineering budget to validate an MVP may now get a usable product into customers’ hands faster. A company stuck between generic tools may now have a more practical path to software that actually fits.

But there’s an important caveat. AI doesn’t make software development risk-free. Black Duck also found that nearly 90% of teams encounter issues with AI-generated code, with bottlenecks appearing in manual review, security testing and code rework.

That’s why the real advantage isn’t “AI writes code”. The real advantage is experienced developers using AI well. The businesses that benefit most won’t be the ones that generate the most code. They’ll be the ones that use AI to build the right software faster, with proper architecture, testing, security and product thinking in place.

It’s the same lesson we’ve written about in how AI changed startup development in 2026: AI is a force multiplier for experienced teams, not a replacement for them.

The models are getting better at real software work

The improvement in AI-assisted development is also being driven by a relentless pace of better models.

Anthropic is a good illustration of the cadence. It followed Claude Opus 4.6 with Opus 4.7 in April 2026 and Opus 4.8 in May 2026, each one better at sustaining long, agentic coding tasks, working reliably in large codebases, and reviewing and debugging its own output. In June 2026 it released Claude Fable 5, its most capable publicly available model yet, which posts a leading 80.3% on the SWE-Bench Pro coding benchmark (though its rollout was quickly complicated by new US export-control restrictions).

OpenAI’s GPT-5.5 points in the same direction. OpenAI positions it as a model built for complex, real-world work: writing code, researching online, analysing information and moving across tools to get things done. Its published coding evaluations include 58.6% on SWE-Bench Pro and 82.7% on Terminal-Bench 2.0.

The point isn’t any single model. It’s the trajectory. Benchmarks aren’t the same as business outcomes, and a high score doesn’t guarantee a project will succeed. But the direction is unmistakable: AI is moving from simple code suggestions toward agentic software development, where models operate across files, tools, tests, repositories and workflows, and where a new frontier model seems to arrive every few weeks.

That changes what’s possible inside a development team. Developers can spend less time on repetitive implementation and more time on the higher-value parts of a project: understanding the business, designing the right system, integrating data, improving the user experience, testing edge cases and making sure the software is maintainable.

Internal software is becoming the foundation for useful AI

The second major reason custom software is taking off is that businesses are moving beyond generic AI tools.

A chatbot can be useful. It can help draft emails, summarise documents, generate ideas or answer general questions. But the real value comes when AI can work with the specific context of a business: customers, jobs, invoices, documents, policies, approvals, products, orders, support history, project data and operational workflows.

This is where custom software becomes powerful. Imagine asking:

• "Which jobs are likely to run late this week?"
• "Which customers have unresolved support issues?"
• "Which quotes are waiting for approval?"
• "Which invoices are overdue and linked to high-value accounts?"
• "Draft a customer update based on the job history, our policy and the latest project notes."

For an AI system to answer those questions properly, the business needs more than a chatbot. It needs structured data, permissions, workflows, audit trails and clear business rules. In other words, it needs good software underneath the AI.

This is why internal systems and AI strategy are becoming connected. Gartner has predicted that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.

NTT DATA explains the practical reason this matters: Retrieval-Augmented Generation gives large language models access to internal data, improving accuracy and relevance when that data is properly structured, governed and validated.

That’s the important point for SMEs and startups. AI is much more useful when it sits on top of systems that reflect how the business actually works.

• Custom software can be the operating layer: the workflows, data, permissions and actions.
• The LLM can become the intelligence layer: helping people search, summarise, decide, draft, analyse and automate.

Together, they’re far more valuable than either one on its own.

Australian SMEs are interested in AI, but many need a practical starting point

AI adoption is growing in Australia, but plenty of businesses are still working out how to turn interest into useful implementation.

The National AI Centre reported that across December 2025 to February 2026, 43% of Australian SMEs reported some level of AI adoption, with adoption reaching 44% in February 2026. The same report found that not knowing where to start, trust and relevance remain major barriers for businesses that haven’t adopted.

This is where custom software can play a practical role. For many businesses, the first useful AI project isn’t a futuristic autonomous agent. It’s something grounded in an existing workflow:

• search across internal documents
• summarise job notes or support history
• generate first-draft customer responses
• classify incoming requests
• extract data from forms or PDFs
• create internal dashboards
• assist staff with decision support
• connect systems that currently require manual handoffs

These aren’t just AI projects. They’re software projects with AI inside them, and that distinction matters. A good AI feature still needs user permissions, data access, security, fallback rules, logging, testing, human review and integration with the rest of the business. Custom software gives AI somewhere useful to live.

Startups can build useful first versions faster

Custom software is also becoming more attractive for startups.

A founder still needs to be careful. Building too much too early is one of the easiest ways to waste money. Validation still matters. Customer conversations still matter. A sharp MVP scope still matters.

But the cost and speed equation has changed. AI-assisted development, modern cloud infrastructure, mature APIs, component libraries, serverless platforms and better development workflows mean a lean first version can often be built faster than it could a few years ago. We’ve broken the current numbers down in our guide to the cost to develop a startup in 2026.

That doesn’t mean a serious SaaS product, marketplace, fintech app or AI product is suddenly cheap. Good software still takes strategy, design, engineering and iteration. But founders can often get to a useful first release sooner, and speed changes the feedback loop. The faster a startup can put a product in front of real users, the faster it learns what people value, what they ignore, what they’ll pay for and what shouldn’t be built at all.

For startups, custom software development isn’t just about building an app. It’s about building the smallest credible product that can test the business. It’s a theme we explored in vertical AI agents: the next wave of Aussie startups.

Custom software is not always the right answer

It’s worth saying clearly: custom software isn’t always the best choice.

• If an existing product solves the problem well, use it.
• If a process is still changing every week, a spreadsheet or no-code tool may be better for now.
• If the problem is small, temporary or low-value, custom development may be overkill.

The strongest custom software opportunities usually share a few traits:

• The workflow is important to the business.
• The current process creates repeated manual work.
• The data is valuable but scattered.
• The team needs better visibility or control.
• Customers or staff need a better digital experience.
• Existing SaaS tools almost fit, but not quite.
• The software could create a competitive advantage.
• The business wants to connect AI to its own data and workflows.

That’s where bespoke software development can be a smart investment. Not because custom software is trendy, but because the business has reached a point where better systems can unlock better performance.

What businesses should build first

The best custom software projects don’t start with code. They start with a clear business problem. Before building, it’s worth asking:

• What process are we trying to improve?
• Where is the current friction?
• Who uses the system?
• What data needs to be captured?
• What decisions should the system support?
• Which tasks should be automated, and which still need human approval?
• What existing tools should we integrate with?
• What is the smallest version that would create value?

This matters even more now that AI can make teams move faster. Speed is only useful when it’s pointed in the right direction.

The best first version is usually smaller than people think. It might be an internal dashboard. A customer portal. A workflow tool. A quoting system. A lightweight CRM. A reporting layer. A SaaS MVP. A marketplace prototype. An AI assistant connected to internal documents.

The goal isn’t to build everything. The goal is to build the right foundation.

So, why is custom software development taking off?

Several trends are converging at once:

• Businesses are outgrowing disconnected tools and manual workflows.
• AI coding agents are helping experienced developers build faster.
• LLMs are becoming far more useful when connected to internal systems and data.
• Startups can test product ideas with leaner first versions.
• SMEs are starting to see software not just as an operating cost, but as a way to create leverage.

The important nuance is that custom software hasn’t become effortless. It’s become more achievable. That’s a much more useful idea.

Custom software still needs good strategy, design, engineering, testing and support. But for many businesses, the old calculation has changed. If a custom system can save time, reduce manual work, improve customer experience, connect data, support AI, or create a product that customers will pay for, the case is stronger than it used to be.

The businesses that get the most value won’t be the ones that build the most software. They’ll be the ones that build the right software.