If you’ve been following the rise of AI, you’ve probably heard the hype that software development is now 10x faster, dramatically cheaper, and capable of producing more than ever before.
Though as a business owner considering a new software project, a customer portal, an internal system, or enhancements to an existing platform, you’re probably asking a much simpler question:
“Realistically, how much time and money can AI really save me on my software project?”
The honest answer is: it depends…
AI is genuinely revolutionising the way software is built. At iNNsite, we’ve embraced an AI-first approach across many areas of development and we’ve seen significant productivity gains as a result.
But we’ve also discovered that AI doesn’t magically remove work. Instead, it changes where the work happens.
Understanding this can help business owners set realistic expectations and get the most value from an AI-first development team.
Does AI Make Software Development Faster?
Yes!
For many development tasks, AI can significantly reduce the time required to complete work.
Developers can use AI tools to:
- Generate code faster
- Research technical solutions
- Create documentation
- Identify bugs
- Refactor existing code
- Build prototypes quickly
- Generate test scenarios
Tasks that may have previously taken hours can sometimes be completed in minutes. This allows development teams to explore more ideas, iterate faster and spend less time on repetitive work. For clients, that often means quicker feedback cycles and faster progress during development.
Does AI Make Software Development Cheaper?
Sometimes.
This is where many of the headlines become misleading.
While AI can reduce the effort required to complete certain tasks, it doesn’t eliminate the need for:
- Planning that aligns with business objectives
- Scaleable and robust architecture
- On-going security reviews
- Testing cycles
- Quality assurance
- User acceptance testing
- Project management to manage time, costs and deliverables
In some cases, AI can reduce project costs. In other cases, the biggest benefit isn’t lower cost – it’s the ability to deliver more value, and more powerful solutions, faster, within the same budget. And… instead of spending weeks building a single solution, teams can often evaluate multiple approaches before committing to one.
The AI Productivity Paradox
One of the most interesting things we’ve observed is that AI can shifts bottlenecks. Before AI, development was frequently the slowest part of the process, but now developers can produce significantly more work in the same amount of time. And while that’s a great thing – it also creates a new challenge. Someone still needs to review, test and validate everything being produced.
Imagine a development team that previously delivered one major feature per week. If AI allows that team to deliver three features in the same timeframe, the testing workload, and onus on the business to review their product and provide feedback also increases. Suddenly, this aspect of quality assurance can become a bottleneck if not managed effectively.
The project lags not because development is slower, development has sped up – because more work is arriving for the humans to review, to run through compliance and end user testing – at a faster pace than ever before. And so this is where some teams might find some lag.
The Hidden Costs of AI Development
Many people assume AI is almost free, because after all, a monthly AI subscription doesn’t sound expensive. However, once teams start using AI heavily, the picture changes. AI-first development often involves:
- Multiple AI subscriptions
- Usage-based AI services
- Development tools
- API costs
- Team licences
- Additional infrastructure
At iNNsite, we’ve seen some of our most productive developers, our “AI power users“, rapidly consume AI resources as they build, test and iterate at much higher speeds. They can blow their limits quickly which drives up cost. In most cases, the productivity gains far outweigh the additional cost. But it’s an important reminder that AI is not a magic button that completely removes expenses from the software development process.
What We’ve Learned at iNNsite
As our team has increasingly adopted AI-powered development tools, we’ve experienced both the advantages and the challenges.
Our developers are able to move faster than ever before:
- Research takes less time.
- Prototypes can be built quickly.
- Documentation is easier to generate and maintain.
- Many repetitive tasks can now be automated or accelerated.
But… at the same time, we’ve had to rethink other parts of our delivery process.
- Our QA team has had to adapt to a greater volume of work moving through the pipeline.
- Review processes have become even more important.
- Internal AI costs have increased for some of our heaviest users – producing significantly more work than they were previously.
And so the result is not “less work” because now we can do more! It’s a different distribution of work.
Questions Business Owners Can Ask AI-First Development Teams:
AI can help developers write code faster, but it doesn’t guarantee that code is correct. Ask how features are tested, reviewed and validated before release.
A mature development team should be able to explain:
- Their QA process
- How testing is performed
- How defects are tracked and managed
- What safeguards exist before software reaches production
If a team is producing more code than ever before, they should also be investing in processes that maintain quality.
AI-generated code still becomes part of your software. So while AI can accelerate development, experienced developers should still be reviewing architecture, security, maintainability and performance.
Questions worth asking include:
- Who reviews AI-generated code?
- How is security assessed?
- What standards are followed during development?
- How do you ensure long-term maintainability?
The speed of delivery means little if the software becomes difficult to support or introduces unnecessary risk.
Not all AI providers operate in the same way. Different models and platforms have different approaches to:
- Data storage
- Data retention
- Security controls
- Compliance certifications
- Geographic hosting locations
- Privacy policies
If your project involves customer information, financial records, intellectual property or commercially sensitive data, it’s important to understand how AI tools are being used throughout the development process.
Questions worth asking include:
- Which AI models do you use during development?
- Is project data sent to third-party AI providers?
- How is sensitive information protected?
- Can AI usage be restricted for projects with specific compliance requirements?
- How do your AI practices align with our business policies and obligations?
For many organisations, AI adoption isn’t just a productivity decision. It’s also a governance and risk-management decision.
A trustworthy development partner should be transparent about both the benefits and the safeguards.
Despite the hype, successful software projects still rely on human judgement.
Requirements gathering, solution design, stakeholder communication, testing, change management and strategic decision-making all remain critical.
A good development partner should be clear about where AI adds value and where experienced people remain essential.
Not necessarily.
AI can reduce effort in some areas, but software projects are rarely limited by coding alone. Planning, testing, integrations, approvals, user feedback and deployment activities still require time and expertise.
Rather than focusing solely on cost reduction, consider the broader benefits AI can provide:
- Faster iterations
- Faster prototyping
- More opportunities to test ideas
- Better documentation
- Reduced time spent on repetitive tasks
In many cases, the biggest advantage is the ability to deliver more value, more quickly, without compromising quality.
So, How Much Faster and Cheaper Is AI Development Really?
There isn’t a single percentage that applies to every project. Some tasks may be completed several times faster than before, but there may be more features being delivered in that same window of time, meaning more value delivered.
Some project costs may reduce. Some timelines may shorten. But successful software projects are still built on clear requirements, good communication, effective testing and experienced people making informed decisions.
AI is changing software development in a big way. The businesses that benefit most won’t necessarily be the ones using the most AI. They’ll be the ones working with teams that understand how to combine AI, process and human expertise effectively.
So in the end, AI hasn’t yet eliminated the need for good development practices. If anything, it makes them more important than ever!
To learn more about AI first development, or to scope out your next project – get in touch with our team @iNNsite.