The Predictable Points of
Implementation Failure
A Phase-by-Phase Analysis
Selection-Phase Failure Patterns
The Superficial Analysis Trap
Organizations often fall victim to impressive demonstrations and feature lists. The system appears perfect during demos but masks critical limitations. Implementation veterans recognize this pattern when vendors focus on showing features rather than demonstrating actual business process flows with real-world complexity.
The Integration Assumption
A recurring pattern: Organizations select systems based on core capabilities while underestimating integration complexity. The system works brilliantly in isolation but creates data bottlenecks when connected to the broader ecosystem. By month six, these bottlenecks begin to strangle operational efficiency.
The Scalability Miscalculation
Perhaps the most costly selection failure pattern: Organizations choose systems that perform flawlessly with test data but collapse under real-world volume. Veterans watch for subtle signs during demonstrations – slight processing delays, careful limitations on test data volume, or vague answers about peak performance capabilities.
Adoption-Phase Failure Patterns
The Training Mirage
A classic pattern: Organizations deliver comprehensive system training, users appear to understand everything, yet actual system use remains superficial. The deeper issue? Training focused on system features rather than business process execution. Users know which buttons to click but not how to actually accomplish their work.
The Process Regression
Implementation veterans recognize this pattern immediately: Users initially adopt new processes but gradually create workarounds that mirror old workflows. By month three, shadow systems emerge. By month six, critical data exists outside the system. By month nine, the promised benefits of standardization evaporate.
The Change Fatigue Spiral
This pattern follows a predictable trajectory: Initial enthusiasm gives way to frustration as users encounter complexity. Support requests spike. Productivity dips. Management pushes harder for adoption. User resistance grows. The spiral continues until either the implementation fails or management abandons key requirements.
Sustainment-Phase Failure Patterns
The Knowledge Erosion Effect
A subtle but devastating pattern: Key system knowledge gradually concentrates in a few expert users. When these users leave, system optimization stops. Workarounds multiply. New employees learn bad habits. Within months, system usage devolves to its most basic level.
The Configuration Drift
This pattern emerges slowly: Small system changes accumulate. Each change seems logical in isolation. But over time, the system’s configuration increasingly diverges from best practices. Performance degrades. Upgrades become more complex. Eventually, the system becomes effectively unmaintainable.
The Data Quality Decay
A particularly insidious pattern: Data quality gradually erodes as users find ways around validation rules. Reports become less reliable. Decision-making suffers. Management loses faith in system data. The system slowly transforms from a single source of truth into a source of confusion.
The Interconnected Nature of Failure
What makes these patterns particularly dangerous is their tendency to reinforce each other:
- Selection failures create adoption hurdles
- Adoption shortcuts lead to sustainment challenges
- Sustainment issues validate user resistance to adoption
- User resistance drives more workarounds
- Workarounds accelerate system decay
Breaking the Failure Patterns
Organizations that successfully navigate these challenges share common characteristics:
In Selection:
- They focus on process execution over feature lists
- They validate scalability with real-world scenario
- They treat integration as a core requirement, not an afterthought
In Adoption:
- They prioritize business process mastery over system knowledge
- They monitor for and actively prevent process regression
- They manage change energy as a finite resource
In Sustainment:
- They build knowledge retention into their operational model
- They actively manage system health and configuration
- They treat data quality as a continuous process
The Path Forward
Understanding these failure patterns provides organizations with a crucial advantage: The ability to recognize and address issues before they become critical. Success requires:
- Pattern recognition capabilities
- Proactive intervention strategies
- Long-term focus on system health
- Continuous attention to value realization
Implementation success isn’t about avoiding all problems – it’s about recognizing patterns early enough to prevent them from becoming critical failures.
About Dokainish & Company
The capital project landscape is impacted with billions of dollars lost from cost overruns. Dokainish & Company stands out with a track record of building award-winning PMOs and lowering cost overages up to 200% on projects in energy, infrastructure, mining, construction, defense, and more. We are the category leaders in project controls and technology consulting. We are ISO 9001:2015 certified, minority owned, and maintain a 97% rate of client retention. We provide integrated project controls, project management, and change management services. Learn more at dokainish.com and follow @Dokainish&Company.
