Flexible by design, governed by default: what enterprises need to get LLMs right

AI models are evolving quickly, and software testing is already one of the clearest areas where they can add value—from generating test cases and automating tests to creating test data and improving insights. IDC research found that software quality and testing is among the top areas expected to benefit from generative AI.

But for many organizations, the real challenge is not whether to use these models. It is how to use them in a way that remains practical, controlled, and adaptable as the technology continues to change.

And a clear pattern is emerging: successful adoption depends on balancing flexibility with governance. Without both, enterprise use of large language models (LLMs) tends to stall.

The four challenges shaping enterprise LLM adoption

Across software testing teams, a consistent set of challenges is coming into focus.

Governance

Software testing often touches critical business processes and sensitive information. That makes governance, security, and oversight essential. Organizations need confidence that AI is being used in a controlled way—how data is handled, how models are accessed, and how usage is monitored.

Choice

The rapid expansion of AI models introduces both opportunity and complexity. Different teams may require different models depending on their needs—whether for test case generation, performance, or compliance. Locking into a single model path can limit flexibility over time.

Change

AI models evolve frequently, and those updates can affect outputs in subtle ways. For testing teams, consistency and predictability matter. When models change, teams need visibility into how those changes affect results, prompts, and workflows.

Cost

As AI becomes embedded across the testing lifecycle, usage scales quickly. Organizations need a clear way to manage consumption and understand how costs grow over time, especially as more teams begin to rely on AI-driven workflows.

Individually, these challenges are manageable. Together, they point to a broader issue: adopting AI is not just about capability. It is about creating a system that can support that capability over time.

Why flexibility alone is not enough

Flexibility is often positioned as the primary requirement for adopting LLMs. And it matters. Teams need the ability to choose the right model for their needs and adapt as new options emerge.

But flexibility without governance introduces risk.

When AI is introduced without clear controls, organizations can quickly lose visibility into how it is being used, how data is handled, and how decisions are made. Over time, this makes it harder to scale adoption confidently.

The inverse is also true. Governance without flexibility creates friction. Teams become constrained by rigid systems that cannot evolve with the pace of AI innovation.

This is why the most effective approaches combine both. Flexibility by design, so teams can adapt. Governance by default, so usage remains controlled and transparent.

What a practical approach looks like

In practice, this means embedding AI into software testing in a way that aligns with how enterprise teams already work, while adding the controls needed to manage it at scale.

UiPath Test Cloud reflects this approach.

It is designed to allow teams to use the models that best fit their needs—including third-party and custom models—while keeping that usage inside a platform built for governance and control. This gives teams the ability to move forward with AI without being locked into a single model or approach.

At the same time, governance is built in through capabilities such as usage auditing, cost control, context grounding, personally identifiable information (PII) masking, and support for bringing your own model. These controls help organizations understand how AI is being used, protect sensitive data, and manage adoption as it grows.

This combination is what makes AI adoption more practical. Teams can experiment, scale, and evolve their use of LLMs without losing visibility or control.

From isolated tools to integrated systems

Another shift is how AI is introduced into testing workflows.

When AI is treated as a separate tool, it creates fragmentation. Teams have to manage it independently, integrate it manually, and reconcile outputs with existing processes.

A more sustainable approach is to embed AI directly into the testing lifecycle—across design, automation, execution, and management—so it becomes part of how testing is performed rather than an add-on.

This reduces overhead and makes adoption easier to manage over time. It also ensures that AI contributes to the broader goal of improving testing outcomes, rather than introducing new complexity.

What this means for enterprise teams

For enterprise teams, the goal is not simply to adopt AI. It is to adopt AI in a way that can scale.

That means:

• Introducing AI in a structured way, rather than as disconnected tools

• Maintaining visibility into how models are used and how outputs are generated

• Balancing flexibility with governance as adoption grows

• Ensuring that AI supports existing processes instead of creating new silos

Organizations that take this approach are better positioned to expand their use of AI over time without introducing unnecessary risk or operational overhead.

The path forward

AI will continue to evolve, and software testing will evolve with it.

The organizations that succeed will not be those that adopt the most models, but those that build systems capable of supporting ongoing change. Systems that allow teams to take advantage of new capabilities while maintaining control, visibility, and trust.

This is where the idea of being flexible by design and governed by default becomes important. It provides a foundation for adopting AI in a way that is both modern and manageable, regardless of how the underlying technology evolves.

Find out how Test Cloud can help.