The overlooked reasons our development timelines always slip

Alex Omeyer
5 min readFeb 8, 2024


Software development projects often miss their deadlines.

But this isn’t an article about scope creep or resources. I want to talk about how human limitations have given rise to inefficient “workaround” methods that struggle to prevent timeline slippage as intended.

I want to talk about three causes of timeline slippages which relate to those limitations, and at the end, we’ll talk about what comes next.

1. We’re Only Mimicking Visibility

We humans are simply poorly equipped to maintain a real-time, complete understanding of what’s happening that is actually of consequence.

To compensate, we’ve constructed a variety of systems and methods to help us attain visibility.

But these only serve to mimic visibility.

We primarily rely synchronous (daily standup meetings, 1-to-1s), asynchronous (email, Slack) and systems of record (Jira, GitHub).

But, like us, our methods and systems inherently lack the ability to offer real-time understanding and adaptation. They lead to immediate but not lasting alignment and understanding. The outcomes are decisions and understandings that are quickly outdated as project dynamics evolve.

Visibility, in this guise, is lacking in context and true awareness of ongoing issues.

2. Too Much Data, Not Enough Data Storytelling

The second overlooked reason for the slippage in development timelines is the abundance of data coupled with a lack of effective data storytelling.

We have access in principle to more information than ever before — code repositories, project management tools, customer feedback systems and so much more. But I regularly see teams suffering from analysis paralysis, rather than picking out the insight they need to make informed decisions.

Sheer volume tempts many to invest in resource-intensive, heavy BI tools in a bid to try and herd the data into usable and interesting formats.

Crucially, they lack data storytelling.

Data storytelling is the art of transforming raw data into a narrative that provides context, meaning, and actionable insights.

Without it, data points, graphs, and updates from the team remain just that — data without direction. For example, a dashboard might indicate improvements in velocity or flag a stalling project, but it doesn’t provide the ‘why’ or the ‘what’s next.’

This lack of context diminishes the value in decision-making.

The total cost of report generation, especially when context is manually added, can be exorbitantly high, both in monetary terms and in terms of time.

3. Humans Aren’t Equipped to Spot Delivery Risks

We’re inherently limited in our ability to spot risks to our software delivery amongst all of the incoming data points.

So we miss opportunities to mitigate them.

We’re constrained by cognitive biases, limited attention spans, intra-team alignment, inability to effectively spot patterns data from disparate sources, and — of course — time itself.

Tools like GitHub, Linear, or Jira document progress and decisions, but they don’t necessarily facilitate real-time understanding or adaptation.

Spotting delivery risks, in particular, is a task ripe for disruption by AI.

A solution to our problems?

We already know that we’ve developed workarounds to get us as close as we can to solving these kinds of problems.

But they fall short.

For example, to mimic visibility, we conduct frequent meetings and manually compile updates from various communication channels, which is time-consuming and may not capture real-time project status accurately. To add data storytelling, we do manual work to write reports and try and glean insights ourselves. And to identify delivery risks, we comb through project updates and try to spot patterns from disparate sources.

But today, artificial intelligence presents us with a new opportunity to overcome these hurdles in ways that simply weren’t possible before.

For example:

  • AI can automate the collection and synthesis of project information, providing real-time visibility without the need for continuous manual updates.
  • It can analyse vast amounts of data to identify trends and insights, transforming raw data into actionable narratives without the heavy tooling and manual effort.
  • AI can predict potential delivery risks by analysing patterns and trends across project data, offering proactive alerts to mitigate these risks before they impact the timeline.

And we built a tool that does just this.

Stepsize AI streamlines project management and risk identification with features designed to automate and enhance visibility, data storytelling, and risk detection.

It integrates with your issue tracker and observes everything that happens to produce context-rich reports that surface what matters.

  • Metrics with automatic commentary. Transforms metrics into concise, actionable narratives using AI, making it easier for you to take action.
  • Progress at a glance. Uses AI to highlight themes and patterns in your tasks, and organises your progress.
  • Intelligent delivery risk identification. Proactively identifies potential delivery risks and suggests actionable solutions.

It’s also a security-first tool that protects your data, keeps you in the driver’s seat and never uses your data to train AI.

We think Stepsize AI is a game-changer for teams. We built it to directly address the challenges of achieving real-time visibility, effective data storytelling, and early risk detection.

I’d love to hear what you think.

You can try Stepsize AI for free here.



Alex Omeyer

Building Stepsize, a SaaS company to measure and manage technical debt.