7 Unexpected Challenges Faced During Information System Implementation and How to Resolve Them
Information system implementations often encounter unexpected roadblocks that can derail even the most carefully planned projects. This article examines seven critical challenges organizations face during implementation, offering practical solutions backed by insights from experienced IT professionals. From addressing customization resource demands to mapping hidden system dependencies, these expert-tested strategies will help technology teams overcome common implementation hurdles.
Extended Customization Requires Resource Reallocation
During our ERP implementation, we faced the unexpected challenge of requiring far more customization than initially anticipated, which extended our timeline from 8 to 14 months. We addressed this by reallocating resources and working closely with the vendor to prioritize critical customizations first while phasing in secondary features. In retrospect, I would conduct a more thorough assessment of our unique business processes before vendor selection and build more flexible timelines into our implementation plan from the start.
Coaching Teams Through Agile Methodology Resistance
During our agile implementation at Nerdigital, we encountered unexpected resistance from cross-functional teams who struggled to align their workflows with the new methodology. To address this challenge, we invested in comprehensive agile coaching across departments and implemented visualization systems that helped teams track progress and identify blockers in real time. Looking back, I would have spent more time upfront assessing team readiness and tailoring our training approach to different departmental needs before beginning the implementation.
Phased Deployment Resolves Legacy Integration Failures
During a major ERP rollout for a healthcare group, legacy integrations failed under real data loads, stalling project milestones. Resolved by splitting deployments into phased modules and early stress testing. Next time, I'd engage user groups earlier and add redundancies to project timelines to manage such risks proactively.
Data Type Inconsistencies Solved Through Validation
Our team encountered an unexpected data transfer bottleneck during the major ERP migration process because the legacy system contained multiple data type inconsistencies and hidden constraints which produced undetectable errors during ETL testing. The data structure contained business logic which made us believe there would be a clear separation of concerns but we were wrong.
The team implemented a reactive mapping validation process through SQL Server stored procedures which worked together with NUnit tests to detect type mismatches at an early stage. A pre-migration data audit should be included in the initial scope according to my experience because running sample queries during the planning phase would have detected these problems before development started.
Resource Matrix Tracks Double-Booked Team Members
During transformation, we faced an unexpected challenge when our organizational change management and content resources were double-booked across implementation waves, which resulted in significant project delays. To resolve this issue, I created a shared capacity matrix and established weekly synchronization meetings that greatly improved workload visibility across teams. If I were to handle a similar situation again, I would implement this resource tracking system from the very beginning of the project rather than as a reactive measure to problems that had already emerged.
Automation Trumps Traditional Change Management
The biggest surprise in digital transformation isn't the code. It's the people.
You can build exactly what was asked for... on time, on budget... and still watch adoption crash. Why? Because people have built their identities around the workarounds. They've survived years of broken systems and disjointed data, and they're proud of how they keep it all running.
We learned early that the best change management isn't messaging or workshops. It's automation. If the new system takes less effort than the old one, people don't resist... they sprint toward it. When there's nothing left to change, there's nothing left to manage.
Our approach is simple: layer clean data and AI on top of existing systems, automate the manual drudgery, and re-skin the workflows so users still recognize their world. We turn data entry into decision-making... approve, revise, archive... not retype.
But AI can't run wild. You still need traceability and humans in the loop. The goal isn't to replace their judgment; it's to remove the friction that kept them from using it.
When you automate the meaningless work, people finally have time for the meaningful work. That's when change sticks.
Map Hidden Dependencies Before System Replacement
Since I monitor many information system modernization projects for my clients, I face many difficulties in the process. Some of them could be predicted, some of them not.
As one of the most unexpected technicalities for legacy system modernization or implementation of a new major system is hidden dependencies or lack of organizational logic for new systems. In particular, old systems may rely on undocumented processes or third-party tools that break when replaced.
To come out on top throughout this, we've invented the internal protocol:
- obligatory rollback of impacted modules in an emergency to restore stability,
- collaboration with experienced personnel to do a thorough forensic assessment of old processes, such as used system logs, user interviews, and shadow IT discovery to construct a dependency map,
- rebuilt important integrations with available APIs and replaced old tools with new ones that work better,
- creation of a central knowledge base where everything was written down for future use.
However, this tech hurdle is easy to overcome with decent preparation. However, the factor which is almost always unpredicted is human. It could be a resistance from unexpected stakeholders, from mid-level managers or power users, not frontline staff or the so-called "Change fatigue". If the implementation follows other major changes, teams may disengage.
It seems a subtle change, but it should be acknowledged. This way, the rollouts should be strictly phased. Additionally, we add trainings where we clearly communicate the goals and try to "sell" the solution to employees. As a result, we get better engagement and the entire implementation goes more smoothly. On average, it's 25% faster and with 15% higher employee engagement rate.
