Modernizing Software: Lowering Costs, Reducing Risks

You notice it with every request from the department, every technical inquiry, and every planned update: Before any decision can be made, dependencies must first be clarified, risks assessed, and past decisions understood. Often, the issue isn’t a single weak point, but a system that has grown over the years and is now holding things back in several areas.

Perhaps you recognize one of these situations:

• Your core software runs on Delphi, Access, or an old version of PHP, and IT has been saying for two years that this is a risk.
• The developer who built the system has resigned and is retiring soon
• There is hardly any documentation, but many ad-hoc rules have developed over time.
• Every change triggers an error somewhere else that no one anticipated.
• A new interface to the ERP has been on hold for months because no one dares to touch the old codebase.
• If the system goes down, it directly affects production, shipping, or billing.

These problems are rarely caused by a single technical error. More often than not, there is a lack of structures that evolve alongside the software’s growing complexity: responsibilities remain implicit, knowledge is tied to specific individuals, data flows are unclear, and changes are difficult to test. As a result, releases become less frequent, larger in scope, and riskier—and this is precisely what increases operational pressure.

The pattern is always similar: A system is built to solve a specific problem. It works. So it grows. Requirements are added, edge cases pile up, and interfaces emerge. But the organizational foundation—responsibilities, tests, documentation—doesn’t keep pace. Eventually, the balance tips.

The most common misconception: Modernization means throwing everything away and starting from scratch. The opposite is usually true. Modernization means updating the foundation so that operations, updates, and further development can function reliably again. First, clarify dependencies; then, secure operations; and finally, update the system step by step. This reduces risk at its source, without making you wait months for significant results.

Not everything needs to be rebuilt from scratch. The key is to determine which approach reduces risk and restores predictability.

Gradual modernization is the right choice when the business logic is fundamentally sound, but operations, structure, or testability are lacking. You reduce risks where they arise and regain control without having to wait months for a major outcome.

A complete rewrite is only worthwhile if requirements change fundamentally, the existing foundation cannot be meaningfully stabilized, or if the long-term costs of incremental renewal would be significantly higher. A completely new software system without a clear vision merely shifts risks instead of resolving them.

Criteria for a sound decision

Ask yourself these six questions before deciding on a direction:

• Downtime costs and criticality: What does it cost if the system is down for a day?
• Clarity on data flows and integrations: Does anyone know how data flows between systems and where it is modified?
• Testability and release readiness: Can you deploy a change today and be sure that nothing breaks?
• Dependence on individuals: What happens if the one person who knows the system quits tomorrow?
• Pressure to change: How many requirements are currently piling up because no one wants to implement them?
• Vision for the next three years: Where is the system headed? Is it growing, shrinking, or being replaced?

Rule of thumb: If more than three of these questions don’t have a clear answer, modernization is the right next step, regardless of whether a new build follows afterward or not.

Effort rarely arises where you first expect it to. It usually stems from ambiguity: in data handoffs between systems, in unreliable data, and in edge cases that only a few people truly understand.

In day-to-day operations, this manifests in small changes requiring a disproportionate amount of coordination, tests and acceptance procedures taking longer than planned, and unexpected rework arising after releases.

These issues drive up effort and costs

• many interfaces and unclear data flows
• poor data quality and missing checks
• many special cases, manual workarounds, and exceptions
• Lack of testing and time-consuming manual acceptance procedures
• Unclear responsibilities in operations

Identifying and addressing these factors early on can, in many cases, cut the total effort in half.This isn’t an estimate; it’s a pattern we see regularly.