BY Emre Yenier, Principal Consultant, VFP, and Kristin Hubbard, Managing Consultant at VFP
Summary
Before signing the contract for the next major software platform, look inward. Ensure your governance is robust, your processes are standardized, and your support teams are ready. By focusing on these fundamentals today, you maximize the ROI of your current tools and pave the way for tomorrow’s innovations.
As organizations race to innovate, the pressure to modernize is relentless. Organizations are racing to acquire the latest software suites and are frantically building roadmaps for Artificial Intelligence. However, a troubling pattern is emerging: while companies are increasingly willing to invest in new tools, they often struggle to operationalize them.
Buying the tool is easy. Making it work within the complex ecosystem of a global enterprise is hard. Without bridging this gap, these expensive assets never deliver the promised ROI and cannot serve as the foundation for advanced innovations such as AI.
To maximize value, leaders must shift their focus from “Go-Live” celebrations to the unglamorous but essential work of fundamental operational readiness.
The Illusion of Success: When “Buzz” Isn’t Enough
The disconnect between purchasing a tool and generating value usually stems from neglecting the backend processes that support the technology.
Consider the case of a global management consultancy that recently rolled out a sophisticated new platform. On the surface, the launch looked like a massive success. They invested heavily in Change Management, ensuring high adoption rates. The employees were excited, adoption peaked, and there was a genuine “buzz” about the new system.
However, the ROI failed to materialize. Why? Because while the people were ready, the processes and teams supporting them were not.
The new tool integrated multiple complex business areas, but the IT organization lacked the governance needed to support it. They didn’t have enough subject matter experts to maintain the system, nor did they have the audit trails or Software Development Life Cycle (SDLC) processes required to handle such complexity. It took months and high unbudgeted costs to diagnose and fix these structural gaps. The lesson was clear: User adoption is irrelevant if the backend infrastructure cannot sustain the tool.
The Cost of Silos: A Financial Services Case Study
If the first pitfall is a lack of backend governance, the second is a lack of standardization.
A global financial services firm attempted to implement a Quote-to-Cash (Q2C) program to modernize its revenue operations. The firm was comprised of powerful business units, each with its own culture and top producers.
Initially, the program tried to accommodate everyone. They allowed high levels of customization to keep individual business units happy. While the tool worked well for specific teams, it was a disaster for the company as a whole. The lack of a consistent delivery model led to fragmented billing and revenue processes that required extensive manual intervention.
The result was severe: cash flow was negatively impacted, and the entire program had to be canceled, resulting in a total loss of time and investment.
The Turnaround
The story didn’t end there. Recognizing the failure, the firm enlisted support to establish a Project Management Office (PMO) dedicated to standardization. By aligning the data models and processes across all units before re-implementing the technology, they finally achieved a successful rollout that tangibly improved financial performance.
Now What? Back to Basics
These examples highlight a critical reality: Go-live is not the finish line; it is the starting line.
To avoid the costly mistakes of the past and prepare for an AI-driven future, teams must focus on three fundamental takeaways:
- It’s not just about the tech: You can’t buy software to fix a broken process. Real success only happens when you have the technology, the people, and the process working together. If one of those is off, the tool won’t work.
- Standardize before you automate: Too much customization is a trap. If every team is doing its own thing with different data and rules, a new tool will just magnify the confusion. You need a consistent approach before you try to automate it.
- AI isn’t a magic wand: Artificial Intelligence requires clean data and efficient, standardized workflows to function. It is a layer to be added on top of an efficient implementation. If you try to apply AI to a chaotic process, you will simply accelerate your inefficiency.
