Predictability vs estimations

In many engineering organisations, especially those undergoing agile or delivery transformations, teams still default to traditional metrics such as story points, time estimates, or velocity benchmarks. These methods, often originate from well-intentioned planning practices, aim to bring visibility and control to delivery within an organisation. But more often than not, they create false confidence, friction between teams and stakeholders, and a blurred view of reality.

It’s time we evolve our thinking and shift our focus to predictability.

The problem with estimates and story points

Estimates attempt to answer “how long will this take?” but they are inherently flawed for several reasons:

• Subjectivity and variability: Teams and individuals estimate differently. A 3-point story for one team might be an 8 for another.
• Gaming and anchoring: Once estimates become targets, they get padded or negotiated, losing accuracy and meaning.
• Lack of real-world grounding: Estimates rarely account for context-switching, unplanned work, unvalidated assumptions, dependencies or complexity that emerges during execution.
• Focus on effort over outcome: Estimation culture encourages an output measurement over actual value or delivery reliability.

For leaders, this creates a dangerous disconnect. Sprint burndowns may look green on paper, yet key features still slip and delivery feels inconsistent.

Over time, this gap between what’s reported and what’s actually delivered erodes confidence from cross‑functional stakeholders. The more this happens, the less credibility engineering has in owning or setting delivery dates and that loss of trust is far harder to recover from.

Why predictability matters more

Predictability answers a fundamentally more valuable question: “How reliably do we deliver?” Instead of focusing on how much work can be done, it asks whether we can deliver what we have committed to as a team and can we do that consistently over time.

Here’s why that matters:

• It builds trust with stakeholders: When teams deliver consistently, even if slower, they are seen as dependable.
• It drives better planning: With predictable delivery cycles, roadmap planning and go-to-market coordination become far more accurate.
• It encourages sustainable pace: Teams stop overcommitting to “stretch goals” and focus on continuous improvement.
• It reflects reality: Unlike abstract point systems, predictability is grounded in actual delivery data and outcomes.

How to measure predictability

You don’t need to reinvent the wheel. Here are five proven approaches:

1. Commitment vs. Completion (forecast accuracy): Measure how consistently teams deliver against what they planned whether that be at a project level or within a sprint/timebox. The simplest way to do this is by comparing the committed delivery date with the actual completion date. The delta between the two is the critical signal: the smaller and more stable it becomes, the more predictable the team is becoming. This higher accuracy builds confidence with stakeholders, while recurring gaps expose overcommitment, hidden dependencies, or systemic blockers that need addressing by you and the team.
2. Effective planning: Use clear, bounded timeframes to set realistic commitments. Well‑structured timeboxes create a predictable delivery rhythm, reduce the gap between planned and actual outcomes, and build confidence with stakeholders. In my experience, the sweet spot is planning in weekly cycles, with projects scoped to 4-6 weeks in duration. Anything beyond that significantly increases the risk of missing completion dates.
3. Cycle time stability: Track the average time it takes for work to move from “in progress” to “done.” Don’t just watch the average but watch for variation. Stability is a stronger indicator of predictability than speed alone.
4. Throughput consistency: Monitor how much work is completed per sprint or week. The goal isn’t maximising volume, but maintaining a steady, reliable flow of delivery that stakeholders can plan around.
5. Project retrospectives (Post-Mortems): Look beyond “why did we miss?” and also ask “why did we succeed?” Capture lessons from both delays and early deliveries, and feed them back into future planning to improve predictability over time.

Tools like Airtable or Monday make it easy to track predictability, including a variability calculation field between dates, without adding overhead. They work especially well when you want a simple, high‑level view that’s abstracted from the engineering team’s day‑to‑day tools like Jira. They integrate fairly seamlessly as well.

A cultural shift, not just a metric

Moving from estimation to predictability isn’t just a measurement change it is a mindset shift.

It means empowering teams to say “no” when they’re stretched thin. It means investing in improved planning, reducing work-in-progress, managing dependencies, and improving the flow of work. It means celebrating consistency over heroics.

And most importantly, it repositions engineering as a partner to the business, not just a factory of estimates and potentially missed releases.

Final thought

If your engineering teams are still wrestling with estimate accuracy, it might be time to stop asking “how much can we do?” and start asking “how reliably do we deliver?”

Predictability isn’t just a better metric. It’s a foundation for trust, sustainable delivery, and true agility.