Introduction

In Agile project management, estimation is arguably the most important part of the project cycle.
If you don't have clear estimates of time and effort or incorrect estimates, you can impede your team’s workflow, delay value delivery, and frustrate your stakeholders.

Estimation in agile is not about accurately predicting the future, but rather providing a shared understanding of the work, identifying risks, and improving estimation accuracy over time.

In this blog, you'll discover the top 5 Agile estimation techniques that help teams get the most value. Whether you are estimating test cases, user stories, or a complete project milestone, these estimation techniques will help develop a more consistent and predictable Agile process.



Why Estimation Matters in Agile

Estimation in Agile helps teams:

  • Plan realistically for each sprint

  • Identify dependencies and bottlenecks early

  • Allocate resources wisely

  • Build trust with stakeholders by meeting deadlines more consistently

A McKinsey study found that, on average, large IT projects exceed their budgets by 45%, miss deadlines by 7%, and deliver only 56% of the intended value.

When Agile teams estimate better, they not only reduce risk but also create space for continuous improvement and smarter project governance.

Let’s dive into the most effective techniques that Agile teams use around the world.

1. Planning Poker

Best for: Collaborative teams estimating user stories

How it works:
Everyone on the team has their own set of cards, which generally are Fibonacci: 1,2,3,5,8,13 so on and so forth; each will assign a size estimate to a task or user story. Everyone then turns over their card at the same time, explains the differences, and then tries again until everyone agrees.

Why it works:

  • Encourages participation and discussion

  • Reveals hidden complexities

  • Helps align everyone's understanding of the work

Use case: Ideal for early sprint planning where stories need to be sized quickly and collaboratively.

2. T-Shirt Sizing

Best for: High-level estimates in backlog grooming

How it works:
Tasks are categorized into sizes: XS, S, M, L, XL — based on scope and effort. These are relative categories and not tied to specific hours.

Why it works:

  • Quick and intuitive

  • Helps compare stories without worrying about exact hours

  • Great for long-term roadmap planning

Use case: Use when you want to quickly categorize user stories before working up to more detailed estimates.

3. Three-Point Estimation (PERT)

Best for: Quantitative risk-adjusted estimates

How it works:
You calculate the average of three estimates:

  • Optimistic (O) – Best-case scenario

  • Pessimistic (P) – Worst-case scenario

  • Most Likely (M) – Realistic middle value

Formula: (O + 4M + P) / 6

Why it works:

  • Accounts for uncertainty and variability

  • Reduces the impact of overconfidence or worst-case thinking

  • Provides a more realistic estimate than single-point guesses

Use case: Great for test estimation and high-risk tasks with unknown variables.

4. Bucket System Estimation

Best for: Estimating large backlogs quickly

How it works:
A range of "buckets" is created (e.g., 1, 2, 3, 5, 8, 13, 20). Tasks are placed into the appropriate bucket by team consensus. The process is iterative and can be scaled to hundreds of tasks.

Why it works:

  • Fast and effective for large-scale planning

  • Reduces time spent in endless debates

  • Combines structured size ranges with team discussion

Use case: Best for backlog grooming or roadmap estimation across many epics.

5. Affinity Mapping

Best for: Sorting and prioritising tasks with unclear scope

How it works:
The team first reviews all items silently. Then, they place similar items together in groups based on perceived effort. These groups are then given size labels (e.g., S, M, L).

Why it works:

  • Encourages collaboration without groupthink

  • Helps visualise the distribution of effort

  • Useful when starting a new project or team

Use case: Works well when there is little historical data and the team is forming a shared understanding of effort.

How Baseliner.ai Supports Agile Estimation

Agile estimation is not just a choice of technique – it is an effort to guarantee that those estimates are accurate, flexible, and actionable.

That’s where Baseliner.ai comes in. Designed specifically for Agile teams, Baseliner combines traditional estimation techniques like Three-Point Estimation with the power of GenAI. It helps you:

  • Generate realistic estimates by analysing historical data and task attributes

  • Create and manage project baselines for accurate tracking

  • Visualise progress vs. planned work with smart dashboards

  • Get real-time change impact analysis as your project evolves

If you are tired of guesswork and spreadsheets as with most Agile planning, then we can make your Agile planning a more data-driven, exact, and reliable undertaking.

Final Thoughts

Estimation is not a one-off task – it is a purposeful and active part of Agile that can be continually improved upon. When you are moving to a choice of a technique, we'll reiterate that it all depends upon the project phase, the size of your team, and the number of existing data points you have.

To recap, the top estimation techniques are:

  • Planning Poker

  • T-Shirt Sizing

  • Three-Point Estimation

  • Bucket System

  • Affinity Mapping

Each one helps your team speak the same language about work, manage risk better, and hit deadlines more consistently.

Looking for a tool that takes the guesswork out of Agile estimation? Baseliner.ai helps you turn estimates into actionable plans with accuracy and clarity.

Start applying these methods today and give your Agile projects the structure they need to succeed.