Where does service-heavy modernization leak value?
Enterprise cloud modernization has mostly been delivered as a service. The projects are custom, iterative and people-driven, and pricing has been mostly time and materials. That makes cost highly variable and hard to compare.
These leaks are hard to fix from inside the customer’s cloud estate and data landscape, and both are becoming more dynamic and more important to enterprise AI success. Three inefficiencies stand out.
- Heavy dependence on human expertise
- Skilled architects and tribal knowledge become bottlenecks. Outcomes depend on individual skill, which means uneven quality, longer timelines and higher cost.
- Low repeatability and consistency
- Learnings are rarely captured as code, so teams reinvent the wheel on every migration project.
- Scaling constraints
- Throughput across parallel programs is limited by the number of people available.
The result: technical debt that compounds
80%
of technical debt will be architectural by 2027 (Gartner)
20 to 40%
of the value of the technology estate is tech debt (McKinsey)
60%
of CIOs say their tech debt has risen (McKinsey)
Sources: Gartner, Magic Quadrant for Technical Debt Management Tools, May 2026. McKinsey, Tech debt: Reclaiming tech equity, October 2020.
The industry’s inefficiency is not due to complexity alone. It is due to how modernization is delivered. Putting expertise in software removes the real constraint, human bandwidth, and replaces it with scale, consistency and speed.
Why productize modernization?
AI and ML now make it practical to capture expertise and automate its use. Services delivered as software is becoming the most feasible and cost-efficient way to modernize. Large software vendors are already moving from per-seat pricing to usage and value-based pricing.
Data and cloud modernization is skill-intensive and expensive, and it runs in loops of recurring projects. Each major change in the data or cloud stack is another leap of faith. Time-and-materials delivery suits providers who want recurring revenue, but it keeps the enterprise in an endless loop of modernization projects.
A product breaks that loop. It puts tools in the hands of the employees and partners who know the estate best. The enterprise gets control, ownership and visibility each time a new requirement arises, while the product team acts as a partner and trains your people on low-code and no-code tooling.
Productization rests on three levers:
- Codified expertise
- What experienced engineers know is built into the software.
- Deterministic outcome
- The same input gives the same output.
- Parallel scalability
- The platform scales horizontally across programs.
What does it mean for enterprise tech leaders?
This is a shift in delivery model, not just a better tool. The direct outcomes are:
- Speed
- Faster migrations through automation and parallel execution across programs.
- Cost efficiency
- Less reliance on large service teams and no redundant effort.
- Reduced risk
- Standardized, repeatable output and less human error.
- Scale
- Platform-driven throughput instead of project-based delivery.
The model moves from services to a product platform, from human throughput to system throughput, and from artisanal execution to industrialized modernization. Recurring time-and-materials costs become a product costing model. Training and engineering support from the product company costs far less, because the knowledge is already codified and the tools are low-code.
What do you gain? Before and after
| Dimension | Services model | Productized model |
|---|---|---|
| Execution | Manual | Automated, no T&M dependency |
| Knowledge | Tribal | Codified, integrated, auto-updated |
| Scalability | Linear | Exponential, no service provider lock-in |
| Risk | High variability | Controlled, transparent |
| Speed | Human-limited | System-driven |