By Priya Shankar, Head of Product at Glue
Every product manager I know has lived through a roadmap slip. The quarterly plan looked solid. Engineering agreed to the estimates. Leadership signed off. Then, three weeks into the quarter, the first milestone slides. Then the second. By month two, you are quietly renegotiating half the roadmap and hoping nobody remembers the version you presented at the all-hands. If your product roadmap keeps slipping, you are not bad at planning. You are planning with missing information.
I managed a 40-person engineering org's roadmap for two years, and the pattern was so consistent it felt like a law of physics. The roadmap slips. It always slips. But once I understood why, I stopped accepting it as inevitable.
This post walks through the three root causes of roadmap slippage that most teams never address, and a practical approach to fixing each one.
Why Roadmaps Slip
Roadmap slippage gets blamed on many things: changing priorities, engineering underperformance, unrealistic expectations. These are symptoms, not causes. The actual causes are structural.
Cause 1: The roadmap is built on estimates, and estimates are wrong. This is not controversial. Every PM knows estimates are imprecise. What most PMs do not appreciate is the scale of that imprecision. Research consistently shows that software estimates are wrong by a factor of 1.5x to 4x, depending on project complexity. When your entire roadmap is a stack of imprecise estimates, the errors compound. A 1.5x miss on each of five projects does not produce a 1.5x roadmap slip. It produces a quarter that is unrecognizable from what you planned.
Cause 2: Hidden dependencies create cascading delays. Projects do not exist in isolation. Feature A depends on a shared service that Team B is also modifying. The checkout flow touches the same database schema as the billing migration. These dependencies are often invisible during planning. When they surface during execution, they create blockers that ripple across the roadmap.
Cause 3: Technical debt adds unplanned work. Teams plan for feature work but not for the debt they will encounter along the way. When an engineer starts working in a module that has not been touched in eighteen months and discovers it is brittle, poorly tested, and tangled with unrelated systems, that is unplanned work. Jellyfish reports that 23% to 42% of developer time goes to technical debt and maintenance. That is a massive chunk of capacity that never appears on the roadmap.
The PMI Pulse of the Profession report puts a dollar figure on this: $109 million wasted per $1 billion invested in software projects. Roadmap slippage is not just a planning inconvenience. It is a financial problem.
The Estimation Problem
Let me be specific about why estimation fails so consistently, because understanding the failure mode points toward the solution.
When an engineer estimates a task, they consider what they know about the system. They think about the module they will modify, the logic they will write, the tests they will need. What they do not consider, because they cannot, is what they do not know.
Unknown complexity is the killer. The engineer estimates based on a mental model of the system. That model is incomplete. It was built through months of working in certain parts of the codebase and has blind spots in the parts they rarely touch.
A study from the Standish Group found that 66% of software projects experience cost overruns. The primary driver is not laziness or poor planning. It is the gap between what teams think they know about the system and what the system actually contains.
For a deeper analysis of why effort estimation in software consistently misses the mark, the data is sobering. The variance between estimated and actual effort follows a predictable pattern: simple tasks are estimated reasonably well, while complex tasks are underestimated by 2x to 4x. Since roadmaps are disproportionately composed of complex work, the aggregate error is significant.
The traditional response to estimation inaccuracy is to add buffers. Pad estimates by 20%. Add a "risk sprint" at the end of the quarter. This helps, but it treats the symptom. The disease is that estimates are made without sufficient codebase context. An engineer estimating a feature in a module they built will be reasonably accurate. An engineer estimating a feature that touches three modules they have never worked in will be wildly wrong.
The fix is not better estimation techniques. It is better information at the point of estimation.
The Hidden Dependencies Problem
Dependencies are the silent killer of roadmaps. And they are almost always hidden.
Picture this: you are planning Q2. You have five major initiatives. Each one has been estimated independently. The estimates look feasible within your team's capacity. You commit to the roadmap.
What you did not account for is that Initiative 2 and Initiative 4 both need to modify the user permissions module. The team working on Initiative 2 gets there first and makes changes that break assumptions Initiative 4 was built on. Now Initiative 4 needs to be re-scoped, and its engineer needs to coordinate with the Initiative 2 engineer. That coordination takes a week. The revised implementation takes an additional sprint.
One hidden dependency just pushed Initiative 4 out by three weeks, which pushed Initiative 5 into Q3.
This scenario plays out in every engineering organization, every quarter. Dependencies are invisible because:
No one maps them proactively. Most teams do not have a system for identifying which modules, services, and schemas overlap across projects. Dependencies are discovered during implementation when one team's changes break another team's assumptions.
Tribal knowledge hoards dependency information. The senior engineer who has been on the team for four years knows that the user permissions module is a bottleneck. But that knowledge lives in their head. If they are not in the planning meeting, or if they are but do not think to mention it, the dependency goes unidentified.
Project management tools do not model code-level dependencies. Jira can track task dependencies, but it has no concept of code-level relationships. You can link tickets, but you cannot see that two seemingly unrelated tickets both touch the same database table. Sprint planning is broken in part because the tools teams use for planning have no connection to the systems being built.
The result is roadmaps that are technically impossible to execute as planned, but nobody knows it until execution begins.
How Visibility Fixes Everything
The three root causes of roadmap slippage share a common thread: the people planning the roadmap cannot see the system they are planning against. They cannot see the true complexity of the work. They cannot see the dependencies between projects. They cannot see the technical debt lurking in the critical path.
Fixing roadmap slippage is not about better estimation techniques, stricter change control, or more conservative planning. It is about giving the planning team real-time, accurate visibility into the codebase.
Visibility fixes estimation. When you can see which files a feature will touch, which dependencies it involves, and how complex those modules are, estimates stop being guesses. They become informed projections. An engineer who can see the full blast radius of a change before estimating will produce an estimate that is fundamentally more accurate than one based on their incomplete mental model.
Visibility fixes dependencies. When you can map which modules each project touches before the quarter starts, you can identify overlaps during planning rather than during execution. "Initiative 2 and Initiative 4 both touch user permissions" becomes a planning input, not a mid-quarter surprise.
Visibility fixes technical debt surprises. When you can see code health metrics, hotspots, and complexity scores for the modules in your roadmap's critical path, you can factor debt into your plans rather than being ambushed by it.
Glue provides this visibility by connecting AI to your actual codebase and surfacing the intelligence that product teams need for accurate planning. You can ask "what would be involved in adding a referral program?" and get a response that includes the affected files, dependencies, and complexity, before anyone writes a line of code.
For product managers specifically, the shift is profound. Instead of taking estimates on faith and discovering problems mid-quarter, you enter planning with a map of the terrain. Your roadmap reflects what the system actually requires, not what the team hopes it requires.
The roadmap will still change. Priorities shift. Markets move. That is healthy adaptation. But the kind of slippage that comes from planning in the dark, the kind where you are constantly discovering that things are harder than expected, is preventable. It just requires seeing the codebase before you plan against it.
Stop building roadmaps on assumptions. Start building them on visibility.
FAQ
Why do software roadmaps always slip?
Software roadmaps slip because they are built on three unstable foundations: inaccurate estimates, hidden code-level dependencies, and unaccounted technical debt. Estimates miss because they are based on incomplete understanding of the codebase. Dependencies surface during implementation rather than planning. Technical debt adds unplanned work that was never on the roadmap. The common thread is insufficient visibility into the system being built.
How do you improve roadmap accuracy?
Improving roadmap accuracy requires improving the information available at planning time. Map system dependencies before committing to scope. Understand which modules each initiative will touch and where overlaps exist. Factor code health and technical debt into estimates rather than treating them as surprises. Use tools that provide codebase visibility to the planning team, not just the engineering team. The more your roadmap reflects the actual system, the less it will slip.
What is the biggest cause of roadmap delays?
Hidden dependencies between projects are the single biggest cause of roadmap delays. When multiple initiatives touch the same modules, services, or database schemas without coordination, they create cascading blockers that push entire quarters off track. These dependencies are invisible in project management tools like Jira because those tools model task relationships, not code relationships. Identifying overlaps during planning rather than execution is the highest-impact improvement most teams can make.