Find the right AI use cases before investing in development.
Connect AI to your existing systems and workflows.
Build autonomous AI agents that automate complex tasks.
Design and develop AI solutions tailored to your business.
Build secure, scalable AI systems for enterprise environments.
Capabilities
Want to start a software project with us?
Book a time with our technology experts →
Make informed technology decisions before investing in development.
Validate your product idea with user and business insights.
Define the right scope to launch faster and reduce risk.
Confirm your idea is technically viable before you build.
Legacy software modernization →
Upgrade outdated systems for better performance, security, and scalability.
DE*updated August 2026
According to research published in the PM World Journal, only 31% of IT projects finish on time, on budget, and on scope.
Another 50% are “challenged,” meaning they slip on at least one of those three, and 19% fail outright.
The truth is that giving an accurate project timeline is a very hard task. There’s just so much variability and uncertainty that it’s becoming a near-impossible feat.
Fortunately, there are ways around this. This article will discuss software development timelines and the tactics you can use to estimate them better.
Key takeaways
Software development time estimation is the process of forecasting how long a project will take to build, based on scope, complexity, and team skill.
The five main software estimation methods are: analogous, bottom-up, parametric, three-point (PERT), and story points/velocity estimation. Each fit a different project profile, and picking the wrong one can significantly skew your estimates. Combining two methods is smarter than relying on just one.
Design complexity, software complexity, integration requirements, and data migration are the four factors most likely to blow a well-scoped estimate. Integrating with legacy systems that have no modern API is the least predictable of the four, since you often have to build the connective layer from scratch.
The productivity gains from AI-assisted development are real: agentic engineering can cut delivery time in half on the right kind of work. But, that only happens when senior engineers are actually reviewing what the AI produces.
A software development time estimate is a forecast of the time you’ll need to complete a software project.
The estimate is based on a number of factors, like:
It’s a crucial step for project planning, budgeting, and resource allocation.
Need an estimate for your project? Let’s talk →
You’ll be talking with our technology experts.
An accurate time estimation will help you set realistic expectations for your projects and improve project outcomes.
Accurately estimating development time is key for successful project delivery – but, why is that the case?
For starters, with an accurate time estimate and detailed timeline, you’ll have a much easier time planning development.
You’ll be able to effectively assign resources and set realistic deadlines for your team.
This is important because it will help you avoid one of the biggest project killers – scope creep.
If you want to learn more about why scope creep is so dangerous, DECODE co-founder and CEO, Marko Strizic, talked about how scope creep destroys projects (and how you can avoid it) on a recent episode of The Roadmap:
Also, an accurate estimate will prevent you from going over budget – after all, time is money.
You’ll be able to align the budget with actual project needs.
And that’s the best way to successfully finish a project within or under budget.
Every project is different, but most mid-to-large custom software projects we run follow a similar trajectory. Here’s what it looks like, phase by phase, with the timelines we’re seeing hold up on real projects today.
Discovery takes 2 to 4 weeks. This is where you nail down scope, validate the idea, and get a working requirements doc.
Before AI sped up the research and documentation side of discovery, timelines were longer – from 4 to 8 weeks.
But on a well-scoped project today, 2 to 4 weeks is realistic.
Design takes 4 to 8 weeks. User research, flows, a working prototype, and usability testing all happen here.
The design work itself rarely blows the timeline. Slow stakeholder approvals do.
Development takes 8 to 16 weeks. Team seniority matters more here than anywhere else.
When we built Norfolk Southern’s capacity-planning platform, a team of 20 senior engineers delivered the initial platform in 6 weeks by running front-end and back-end work in parallel rather than sequentially, no relearning the codebase or the domain halfway through.
On projects we’re running today, agentic engineering shortens timelines even further, with senior engineers directing and reviewing AI agents on boilerplate and repetitive implementation work.
Testing takes 1 to 2 weeks. That’s dedicated testing time after development wraps, assuming QA has been integrated from day one rather than bolted on at the end.
Deployment takes 1 to 2 weeks. Infrastructure setup, environment configuration, and a staged rollout with monitoring all belong here. A single big-bang launch can be faster, but it’s much riskier.
Maintenance is ongoing. Budget 15 to 20% of your annual initial development cost for the first year post-launch. This is the phase most people underestimate, because it doesn’t have a fixed end date like the others.
Put together, a mid-sized custom build can realistically move from discovery to launch in 4 to 6 months rather than the 6 to 12 months these timelines used to take.
The teams that hit these numbers aren’t cutting corners. They’re senior enough to use AI agents to move faster without lowering quality.
Now, we’ll cover the key steps you need to follow to estimate software development time.
If you want to accurately estimate development time, you first need to know what you’re building.
And that means defining your project’s scope and requirements.
At this stage, you’ll define:
Once you have those, you can define scope, meaning the deliverables and resources needed to complete the work, and only then start estimating.
I always push back on any request for a firm number before this step is done. A range, yes. A commitment, no.
Once you’ve done this, you’ll have everything you need to start estimating how long a project will take to finish.
But, before you dive in, you also need to identify risks and uncertainties, which we’ll discuss next.
Identifying risks before you start estimating development time is an absolute must.
If you don’t, you risk unnecessary delays and cost overruns derailing your project or even outright killing it.
So, risk identification is clearly important – but, which risks should you look out for?
Here are 7 common project risks you need to be aware of:
Common risks include third-party API dependencies, unclear requirements, key-person dependencies, and integration with unfamiliar systems.
Say your project depends on a third-party API. The real risks are downtime and version compatibility issues, so your estimate needs a time buffer for both, plus a contingency plan for when (not if) something breaks.
I don’t consider an estimate complete until every major risk has an explicit buffer attached to it, not a vague “we’ll add some slack.”
Once you’ve defined your project’s scope and the risks involved, your next step is to break down the project into smaller, more manageable tasks.
Smaller units are easier to estimate accurately and far easier to track once development starts.
Your best bet is to use a work breakdown structure (WBS). Here’s how a WBS works:
In a nutshell, a WBS creates a hierarchy of deliverables, tasks, subtasks, and activities needed to complete your project.
This makes it easier to estimate how long the project will take to complete and plan development accurately.
Once you’ve broken down your project into smaller tasks you need to choose an estimation method. Picking the right one is crucial for your project’s success.
We’ll cover each in more detail later, but the 5 most popular estimation methods you need to know are:
You just need to remember one thing – different projects need different approaches and depending on the project, these methods can give you wildly different estimates.
So, you need to make sure you’re using the right method before you commit to it.
The final step before you calculate your estimate is picking the team that will actually work on the project.
And the team you choose can have a huge impact on your estimate.
If you have a team of mostly senior engineers, they’ll be able to complete the work faster.
But there’s a catch – they’re also more expensive, so you’ll need to be mindful of your budget.
Also, make sure the team you pick is cross-functional.
Cross-functional teams aren’t just a fad, either.
According to a Harvard Business Review study, cross-functional teams supported by management have a 76% project success rate.
And the best part?
In a cross-functional team, the team members can work on different parts of the project simultaneously while also collaborating with each other.
This helps reduce bottlenecks and speeds up their overall progress, so keep that in mind when estimating development time.
Finally, the last thing left to do is to calculate and document your estimate.
Here, you should do a detailed breakdown of the project and then review it with your team.
This is important because you need to make sure your estimate is actually realistic and the team can meet the deadline you set.
Also, make sure you document all of your assumptions behind your estimates and review them with your team. I document every assumption behind an estimate, explicitly.
This way, you’ll know if you’ve made a wrong call in your estimation which you can then correct.
Also, the documentation you write will be a handy reference point you can use to track progress during development.
Here, we’ll go through the 5 most popular methods for estimating development time: analogous estimation, bottom-up estimation, parametric estimation, three-point (PERT) estimation, and story points and velocity estimation.
Analogous estimation is a technique where you predict the time required for a project based on a similar project in the past.
For instance, if you saw that it takes your team six months on average to create a mobile wallet app, you’d give roughly the same estimate for any mobile wallet app in the future.
Analogous estimation is the best route if you don’t have enough time and data available to give a more accurate timeframe.
It’s also relatively easy, requiring no fancy calculations or complex modeling.
However, its biggest flaw is inaccuracy. That’s because analogous estimation is based on the assumption that a past project is exactly the same as your current project.
That’s simply not the case.
Factors such as unexpected issues, a shifting market, and a different tech stack could skew your estimates. Even a new team member can impact development times.
The key to making analogous estimation work is to pick a past project that’s as close to your current one as possible.
It’s also beneficial to look at the project’s metrics, like lead time and code churn, to give a better picture.
For this reason, analogous estimation is impossible for newer development teams with no project history.
In bottom-up estimation, you break a project down into individual tasks. You then use these as the basis for predicting your timeline.
For instance, let’s say you determine that the project requires roughly 50 individual tasks, each requiring an average of four days to complete.
Thus, your software should take 200 days to complete, or around 6 – 7 months.
Bottom-up estimation is a much more accurate method than analogous estimation. That’s because you can account for as many factors as possible.
While breaking down your project, you’ll inevitably tackle bottlenecks and issues, which you’ll need to compensate for.
For example, if your team lacks cybersecurity skills to create a fintech app, you might add additional time to account for training and research.
Of course, the flip side is that bottom-up estimation takes more time and effort because you need to analyze a project and gather requirements.
The larger the project, the longer bottom-up estimation takes, making it hard to scale.
You also can’t rely on past estimations and would need to start from scratch every time.
Despite these shortcomings, bottom-up estimation is one of the better methods for getting accurate timelines. So if you have the time and resources, go for it.
Parametric estimation uses a combination of historical and statistical data to calculate a time prediction.
In other words, it takes the best of both bottom-up and analogous methods.
Because it considers many variables, parametric estimation gives highly accurate predictions.
In practice, however, its accuracy depends on the statistical model used and the quality of the data.
Parametric estimation is also reusable. Once you’ve refined an algorithm and proven it works, you can apply it to future projects.
In fact, a parametric model tends to get more accurate with time.
The number one drawback of this method is the huge time commitment and expertise involved, as you’ll need to gather data and crunch the numbers.
Thus, this approach is best for larger project.
Three-point estimation, also known as PERT (program evaluation and review technique), asks for three numbers per task: an optimistic estimate, a pessimistic estimate, and a most-likely estimate.
You then run them through a weighted formula (typically optimistic + pessimistic + four times the most-likely, divided by six) to get a single expected value.
source: Simplestimate
I like this method specifically because it forces the team to think through the worst case explicitly, instead of just finding the optimistic number and calling it done.
It’s a direct countermeasure to optimism bias, which I’d argue is the single most common cause of blown estimates.
The tradeoff is that it takes longer per task than a single-point guess, so it’s best reserved for higher-risk or higher-stakes components rather than every single task.
Story points assign a relative complexity score to each task, rather than a time estimate, and velocity tracks how many story points your team completes per sprint based on real history.
Once you know your team’s velocity, you can forecast how many sprints a given backlog will take.
This method works well if you’re already running agile sprints with a stable team, since velocity is only meaningful once a team has settled into a rhythm together.
It’s weaker for brand-new teams or teams with heavy churn, because velocity hasn’t stabilized yet and any forecast built on it will be shaky.
I’d pair this with PERT for the first few sprints of a new team, then lean into velocity once you have three or four sprints of real data behind you.
Beyond process failures, a few things about the project itself will stretch your timeline no matter how carefully you plan. If you know them from the start, you can adjust the estimate before you commit to it.
A complex app design, with a customized UI and animations, generally takes more time and effort to develop. And it will stretch your development timeline significantly.
Consider how easy it is to create an app with a simple UI. You can always rely on iOS or Android’s native libraries and pre-built components to quickly put together your app interface.
In these cases, estimating the time you’ll need to design your app is easy.
But it’s not so easy with a large, custom app design. That’s because you must create most of it from scratch, which could complicate your estimation.
Animation, for example, is a complex process that could easily delay your project.
On average, expect to add several more weeks when designing a custom app UI.
As with app design, software complexity is directly proportional to development time.
In other words, the more features or advanced functionality it has, the more time it takes to develop.
To give you an idea, a simple app with only a few screens takes around 2 – 4 months to build.
Contrast that with a feature-packed app like Uber or Facebook, which can easily take 9 months or more to build.
This isn’t at all surprising. Complex features require more coding and testing time to get right.
Plus, you need buffer time to account for problem-solving and other roadblocks. On top of that, feature-rich apps take more time to maintain and update.
To make things worse, development times for complex projects are also harder to estimate. Because of the many variables involved, it’s easy to miscalculate your prediction.
Standalone software is rare today.
Almost everything needs to talk to other systems, and integrating with legacy infrastructure that has no modern API is one of the slowest, least predictable parts of a project.
You end up having to build the connective layer (APIs, DALS, etc.) that should already exist.
Fortunately, there are ways to shorten the integration process and simplify your estimation.
Where possible, I lean on established third-party connectors like Twilio or open banking APIs rather than building integration from scratch.
It’s a meaningful time saver whenever the option exists.
Migrating data from an old system can seem trivial, but only if everything is in perfect order. In the real world, this rarely happens.
In practice, it’s rarely clean: incomplete records, inconsistent formats, and structures that don’t map cleanly to the new system.
That’s why you often need to create scripts to handle this. And that takes time.
And it doesn’t end there. Once the data is imported, you must test and optimize it to ensure good performance. Here’s an overview of the data migration steps:
A clean migration might take a few days.
A messy one, with years of accumulated inconsistency, can stretch to months. It’s genuinely hard to know which one you’re dealing with until you’re in it.
The single biggest change in estimation is agentic engineering, i.e. senior developers supervising AI agents that handle a meaningful share of coding, testing, and documentation work under direct human review.
This isn’t the same as “AI writes your app for you.” I don’t believe in vibe coding, and I wouldn’t ship anything an engineer hasn’t reviewed.
What agentic engineering changes is throughput.
A senior engineer who can direct and review AI-generated code for boilerplate, test scaffolding, and repetitive implementation work gets through a backlog considerably faster than one writing every line by hand, without sacrificing quality.
At DECODE, this approach is core to how we scope timelines now.
It’s a real factor in how we’re cut delivery time in half on projects where the work suits it.
When you’re estimating a project in 2026, ask your team directly how much of the build is agent-assisted and how that changes the numbers.
A team still estimating like it’s 2022 is leaving real time savings on the table.
One word of caution here, though. Agentic engineering only shortens timelines when the engineers reviewing the output are senior enough to catch what the AI gets wrong.
A junior-heavy team leaning on AI agents without that oversight doesn’t move faster, it just accumulates technical debt faster.
Usually because the “original estimate” was given before proper discovery, not because the team is bad at math.
Early estimates are based on partial information by definition. The fix is treating that first number as a range and re-estimating as you learn more through discovery and early development.
Time and cost are directly linked once you know the rate, but a longer estimate doesn’t always mean a worse deal.
A senior team that estimates 12 weeks and delivers on time usually costs less overall than a cheaper team that estimates 8 weeks and takes 16.
Ask why, specifically, before asking for a new date.
A good team can tell you exactly which assumption changed: a scope addition, a risk that materialized, an integration that turned out to be messier than expected. If nobody can name the reason, that’s the bigger problem.
In reality, estimating a project timeline is equal parts art and science.
You can rely on knowledge and tactics. However, it’s the experience that will really help give an accurate prediction.
And that’s what we can bring to the table. With dozens of projects completed on time and on budget, we believe we can help you achieve the same in your project.
Interested? Schedule a free consultation with us today, and let’s talk!
A seasoned software engineering executive, Marin’s role combines his in-depth understanding of software engineering processes (particularly mobile) with product and business strategies. Humbly boasting 20+ years of international experience at the forefront of telecoms, Marin knows how to create and deliver state of the art software products to businesses of all sizes. Plus, his skills as a lifelong basketball player mean he can lead a team to victory. When he’s not hopping from meeting to meeting, you’ll find Marin listening to indie rock, or scouring the latest IT news.
A practical framework for measuring AI developer productivity: DORA metrics, AI adoption rate, code churn, review time, and net time savings.
Fixed price feels safer, but often costs more and delivers less. Here is why time and materials contracts work better for most custom software projects
Real tactics for managing large software projects at scale, from estimates and scope to legacy systems, QA, and stakeholder communication.
