Deployment outpacing validation in digital experience

As enterprises across APAC accelerate their digital transformation with AI, most still rely on internal telemetry and infrastructure metrics to assess whether their digital services are working for end-users.

Why and how is AI accelerating digital service deployment faster than enterprises can validate real-world performance, and what that means for CIOs and technology leaders across APAC?

Gargi: AI is significantly reducing the time required to design, build and deploy digital services. With generative AI and agentic workflows, enterprises can now automate parts of development, generate test scenarios and shorten release cycles in ways that were not possible with traditional software delivery models.

The challenge is that while deployment speed has improved, real-world validation has not always kept pace. Applications still need to perform consistently across devices, networks and geographies, especially in a diverse region like APAC where customer environments vary widely.

For CIOs, this means quality assurance can no longer remain a final checkpoint before launch. It needs to become a continuous process that validates customer experience in real time. The focus is shifting from simply releasing faster to ensuring digital services work reliably under real user conditions.

How is the shift from infrastructure monitoring to real-world digital experience assurance reshaping observability strategies for enterprises in BFSI, telecom, OTT and retail?

Gargi: For years, enterprises measured success by asking whether systems were up. Today, the better question is whether the customer journey is actually working. In telecom, for example, operators no longer want to know only whether the network is available. They want to understand whether a customer can successfully stream YouTube, watch Netflix, or access Disney+ Hotstar without buffering or degradation on a specific network. That is a very different kind of observability.

We have moved from monitoring infrastructure to measuring lived digital experience. The same is true in banking and retail. A payment platform can be technically available, but if a customer cannot complete a transaction at the moment it matters, the business still loses trust.

That is why observability is shifting from an inside-out model to an outside-in model, using real devices, real networks and real geographies to understand how digital services behave in the hands of customers.

What does privacy-first, synthetic testing look like in practice and why it is becoming the preferred approach for regulated industries and public sector organizations?

Gargi: Privacy-first testing has become much more important than many organizations anticipated even two years ago.  In regulated sectors like banking, telecom and government, companies can no longer rely on production data being copied into test environments. Regulators increasingly expect customer data to remain protected at every stage of the lifecycle.

For example, in a banking environment, you can simulate, account creation, login authentication, true positive (TP) validation, payment flows etc. without using a single real customer identity.

That matters in markets like India where data sovereignty and digital governance requirements are becoming much stricter, especially with more organizations being asked to keep workloads inside sovereign cloud environments. I think the shift is simple; you should be able to test digital performance without creating additional privacy risk.

How is Singapore’s position as a digital hub making it both a proving ground and a reference market for experience-led digital assurance globally?

Gargi: Singapore is uniquely positioned as one of the most digitally mature markets in Asia. It combines advanced infrastructure, high smartphone penetration, strong regulatory frameworks and a highly connected user base within a compact geography.

That makes it an ideal market for validating digital experiences across multiple devices, networks and languages in a controlled but demanding environment. Because customer expectations are extremely high, organizations operating in Singapore often treat it as an early benchmark for digital service quality.

What works well in Singapore can often serve as a reference point for broader regional and global deployment. For many enterprises, it has become a market where digital assurance strategies are tested before being scaled internationally.

What does the next phase of AI-native testing look like, and why does it matter for enterprises building on AI infrastructure?

Gargi: The next phase of testing will be very different from what enterprises think of as software testing today. We are moving into a world where systems are no longer only serving users, systems are increasingly interacting with other systems.

A payment agent may need to communicate with an identity agent, a fraud agent, a recommendation engine and a partner platform all in real time. That means testing can no longer stop at validating a screen or an API. It has to validate whether autonomous systems are making the right decisions together.

The complexity here is that these systems are not always deterministic. They often produce probabilistic outcomes, which means the same input may not always create the same response. That changes what quality assurance means.

Going forward, AI-native testing will need to evaluate reliability, trust, governance, security and decision consistency. And despite all the automation, human oversight will still remain important. At least for the foreseeable future, human-in-the-loop validation will continue to play a role in ensuring these systems behave responsibly.

.