Testing - Unified, Scalable and AI-Enabled

Estimated read time: 20 min
What you’ll learn: Explore how Exense’s Step platform unifies functional, performance and monitoring into a reusable, scalable, and AI-enabled Testing backbone platform driving enterprise software quality and resilience.
Ideal profile(s): Test Managers, QA Leads, DevOps Engineers, Automation Engineers, SREs, Enterprise Architects, Business Analysts.

Abstract

Enterprises today must innovate faster while keeping systems stable and secure. Testing has become a strategic discipline that ensures resilience in an AI-driven software landscape. Exense’s Step platform is an enterprise Testing platform designed for AI-enabled testing and scalable QA operations that unifies functional, performance, monitoring. Through real deployments in banking, insurance, and government, Step delivers faster ramp-up, broader coverage, and measurable efficiency gains. Built for reusability, scalability, and AI enablement, Step transforms Testing into a continuous source of confidence that links engineering performance with business reliability.

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Context – Why Testing Matters?

Enterprises today operate in a state of constant transformation, yet the speed and impact of that transformation have reached unprecedented levels. Technology is no longer a support function; it has become the backbone of business performance and therefore sits under direct scrutiny from the C-suite. Every strategic initiative, from growth to compliance, now depends on a resilient and adaptable IT landscape. In this context, enterprise software testing and quality engineering have become core enablers of digital transformation and operational resilience.

The Forces Shaping the Modern Enterprise

Digitization of every channel

User journeys have grown increasingly complex, spanning multiple channels, devices, and back-office workflows that must remain consistent from end to end. Customer and partner interactions, from onboarding to service delivery, now depend on a dense mesh of interconnected systems: APIs, mobile applications, cloud platforms, and SaaS tools. Each new integration extends capability while also multiplying potential points of failure.

Relentless innovation pressure

Continuous waves of new technologies such as cloud-native architectures, containerization, and AI-powered applications must be adopted and operationalized without compromising stability. Organizations are expected to innovate rapidly without additional resources or budget increases.

Fast-moving competition

Smaller digital-first challengers use modern stacks and agile delivery to capture market share from established players. Incumbents must match their pace while meeting strict regulatory obligations, creating constant pressure to accelerate without compromising reliability.

Legacy complexity

Large enterprises carry decades of accumulated technology. Mergers, acquisitions, and modernization waves have layered old and new systems together. The result is a multi-tiered architecture where even a small change can trigger cascading effects across business-critical applications.

Economic constraints

In mature markets where growth is slow, IT budgets remain flat or shrink while expectations continue to rise. Teams must balance innovation, security, and quality with limited resources, which forces difficult prioritization.

The New Risk Landscape

These converging forces have dramatically expanded the risk profile of enterprise IT.

Incidents can now affect multiple systems and user groups at once. Failures become instantly visible as customers experience them directly through digital channels, and reputational damage spreads quickly through social media and real-time reporting. The introduction of AI adds another dimension of risk by embedding opaque decision logic and data dependencies into critical workflows, making validation more complex and errors harder to detect.

Also, AI has now entered the software delivery chain itself. Development cycles accelerate as AI tools assist in code generation and testing, but this also multiplies change frequency and risk. Enterprises can no longer rely on periodic testing; validation must evolve as fast as the software it protects.

The Strategic Role of Testing

In this environment, Testing has evolved from a technical checkpoint into a strategic safeguard for the business. It must now be:

• Broader, covering every layer of the digital ecosystem from back-end APIs to long, complex, end-user journeys.
• Deeper, ensuring reliability, performance, and compliance under diverse and unpredictable conditions.
• Continuous, integrated into development, deployment, and production monitoring.
• Transparent, offering clear evidence and traceability for leadership and regulators.

Testing is no longer only about detecting defects and creating alerts; it is about protecting business continuity, customer trust, and brand reputation in a world where technology failure directly equates to business failure.

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Challenge – Why is it so hard to do right?

For most large enterprises, the real complexity of Testing lies not only in its scope (number of applications, features, etc.) but in its diversity (technologies, disciplines, people).

Testing must now cover both long-standing core systems and fast-evolving digital initiatives, from legacy mainframes and ERP platforms to AI-powered customer-facing applications. Each requires different methods, tools, and mindsets. The result is a Testing landscape that is fragmented, duplicated, and difficult to control. Without a unified QA strategy, enterprises struggle to achieve Testing efficiency across tools and disciplines.

Heterogeneity Across the Testing Landscape

Testing today is a patchwork of disciplines, teams, and technologies. Across the disciplines of Testing — functional testing, load testing and production monitoring — the definitions, objectives and implementations often diverge. Within a single discipline, different teams may use their own frameworks or tools. Even within the same application, different testers may test adjacent or similar use cases using different data and approaches.

This heterogeneity is not just inconvenient; it is costly. Each team reinvents the wheel, maintains its own scripts, and duplicates effort. The absence of shared standards or integration leads to inconsistent results, tool fatigue, and reduced confidence in testing outcomes. The organization ends up with more activity but less assurance.

Expanding Breadth and Depth with Limited Throughput

As applications evolve, the number of user journeys, integrations, and data scenarios grows quickly. Yet many enterprises still test with methods designed for a slower era.

Legacy infrastructure slows test execution and limits scalability. Coverage remains partial, leaving key workflows potentially untested due to time or environment constraints. Tested workloads sometimes fail to represent real user behavior, producing results that consume resources without improving application reliability. Long iteration cycles limit how much development and testing can be accomplished within each release, leading to loss of opportunities.

The consequence is a widening gap between what should be tested and what can realistically be tested. Teams are constantly behind schedule, chasing releases rather than enabling them. The broader and more interconnected the system landscape becomes, the more this gap erodes agility and confidence.

Integration in DevOps: The Paradox of Isolated Success

Modern DevOps teams test what they own. Each microservice or module runs in its own pipeline, with independent tests and metrics. Taken in isolation, everything looks healthy: builds pass, reports are green, confidence is high. Yet when these components meet in an end-to-end environment, the system fails.

Integration does not emerge from simple addition. End-to-end scenarios rarely align with component-level ones, and the teams behind them often use different tools, data, or success criteria. Mocked interfaces, unrepresentative test data, or simplified workflows may surface only after integration.

For Test Managers, this creates a recurring paradox: multiple components or pipelines report “OK,” while the user experience is “NOT OK.” (see Figure 1). The result is a systemic challenge in DevOps at scale, where ownership and validation of the user journey fall between teams rather than within them.

The Signal-to-Noise Problem

Even when automation and monitoring are in place, another problem appears: information overload. Automated pipelines and synthetic checks can generate thousands of alerts, many of which are false positives. At the same time, subtle but serious defects may slip through undetected, missed scenarios often referred to as false negatives.

This imbalance leaves QA and operations teams uncertain about which issues truly matter. Attention decreases, investigations slow down, and alerts gradually lose their credibility. The testing and monitoring ecosystem, which was meant to bring clarity, instead creates confusion and noise. The result is paradoxical: more data, but less insight.

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Impact – The price of inefficiency

Testing inefficiency has become a growing enterprise risk in an era of AI-driven development and continuous delivery.

As software development becomes increasingly assisted and accelerated by AI, the speed of change rises while the cost of weak testing multiplies. Inefficient testing drains time, money, and confidence. It slows delivery, hides risk, and turns quality assurance into a bottleneck instead of a competitive advantage.

The Cost of Limited Reuse

Because test assets are rarely standardized or shared across teams, every new project begins with a ramp-up period that involves redefining and reimplementing what already exists elsewhere. Each department builds and maintains its own scripts, environments, and data, leading to unnecessary duplication.

The cost of this duplication compounds over time. Maintaining multiple test suites that serve similar purposes consumes effort that could have been spent expanding coverage or improving reliability. Every change in the application triggers several redundant updates in separate repositories, creating maintenance overhead and risk of inconsistency.

In DevOps settings specifically, this duplication extends beyond code. Teams use different tools, test data, and validation criteria, which makes sharing and troubleshooting complex. When end-to-end issues appear, resolution becomes slow and siloed, since no common assets or frameworks exist to diagnose and reproduce failures across systems. The result is a testing effort that grows with scale, but not in efficiency.

The Cost of Limited Scope

Even with considerable effort, testing coverage remains partial. Some user journeys or integration paths are never exercised, often because test ownership and priorities are defined at the component level rather than across the full system. In large enterprises, each team validates its own domain, yet few tests cover how those domains interact.

This isolation leads to a recurring paradox: components pass their tests, but the application still fails. End-to-end scenarios are not simply the sum of individual ones, and the gaps between systems such as user journeys, data flow, timing, configuration, or dependency alignment are rarely captured by local validation.

The impact is cumulative. Integration defects surface late in the delivery cycle, when fixes are costlier and coordination harder. Test Managers must then arbitrate between release deadlines and unverified risk. Over time, this uneven coverage erodes confidence in automation and slows the pace of change.

The Burden of Noisy Alerts

Automation and monitoring were meant to bring clarity, but when not properly tuned, they produce the opposite effect. Teams receive a flood of alerts, dashboards, and metrics that do not distinguish between benign variations and real incidents.

This noise leads to diluted focus. Support teams waste time triaging non-critical issues while essential risks remain unseen. False positives create alert fatigue, while false negatives undermine confidence in the monitoring system. As a result, decisions are delayed and trust in testing results erodes. What should serve as an early warning system becomes a distraction.

Summary

Inefficient testing is not just a technical issue; it has direct financial and strategic consequences. Repetition increases cost, limited scope extends release cycles, and noisy feedback masks genuine risks. Collectively, these factors slow down innovation, inflate operational expenses, and expose the enterprise to potential service failures and reputational harm.

Enterprises that continue to treat testing as a series of isolated activities rather than a unified discipline find themselves trapped in a cycle of wasted effort and recurring surprises.

Breaking that cycle requires a new approach, one that turns testing into an integrated, reusable, and AI-driven capability.

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Vision – What does better look like?

Enterprises need a testing approach tailored to their current needs and aspirations. In a world where software is built and deployed faster, increasingly assisted by AI, testing must evolve from a cost center into a reusable, scalable, and continuously improving asset. The vision is not to test more, but to test smarter: define once, re-use everywhere.

Our vision anticipates autonomous testing and intelligent QA systems built on reusable, scalable, and AI-enabled foundations (Figure 2).

Let us detail those core pillars in the next sections.

Reusability

Every test encodes knowledge. It is a validation that a specific action on a given input produces an expected and verifiable output. When this knowledge is captured properly, it should not be rebuilt each time an environment, tool, test type, test level or team changes.

A reusable test remains valid across contexts. Consider the example of clicking the “Login” button in an e-banking application. The expected behavior is consistent: the user should be prompted for credentials. This holds true regardless of who performs the action (a customer, a tester, a security engineer, or a developer), how many users perform it at once, or whether the interface is web or mobile. It should also hold regardless of the chosen test framework, whether Selenium, Cypress, or Playwright.

Reusability turns testing from an activity into an asset. It allows teams to build once and validate many times. It creates consistency, reduces duplication, and builds a foundation where quality scales naturally with development.

Scalability

Scalability in testing is not only about speed; it is about maintaining depth and realism as systems grow. Enterprises must be able to test complex user journeys quickly, at scale, and under realistic conditions, without compromising accuracy or spending days configuring infrastructure.

Functional Test Automation Scalability

Functional scalability is the ability to execute complex end-to-end workflows quickly and consistently. It combines technical efficiency, such as running tests in parallel, with organizational coordination across teams and systems, strongly asking for reusability (see above). Scalable functional testing ensures that integrated business processes are validated continuously and collaboratively.

Load and Performance Testing Scalability

Performance scalability focuses on realism at volume. The goal is to simulate thousands of concurrent users or transactions under production-like conditions while preserving accuracy. Many traditional approaches rely on low-level protocol simulations for speed, but true scalability reproduces realistic user behavior and system interactions at full scale.

Unified Scalability

When functional and performance testing converge, scalability becomes more than parallel execution. The same artifacts, scenarios, and data serve both validation and load, making quality continuous across all test levels. Step enables this convergence, turning scalability into a shared capability rather than a separate effort. Testing becomes a natural part of software evolution, maintaining realism, speed, and depth as systems grow, and shifting from a reactive control point to an active enabler of innovation.

AI Enablement

As AI begins to assist developers, quality assurance must evolve alongside it. Testing will increasingly rely on automation that learns, adapts, and collaborates. The tester’s role shifts from manual execution to the design and supervision of intelligent systems that validate software continuously. Step’s architecture anticipates this shift by treating every test and workflow as data-rich and automation-ready, enabling future AI augmentation across the testing lifecycle.

At the same time, AI introduces a new class of applications under test. Applications built with embedded models, adaptive agents, or evaluation-driven development (EDD) cycles require rigorous validation under real-world variability. By fully integrating testing across the Biz-Dev-Sec-Ops lifecycle, the reuse of test scenarios, expected results, and underlying data provides both training targets and guardrails for these AI applications. This alignment ensures that the same testing intelligence that supports developers can also govern the behavior of AI systems under test.

Summary

The vision for testing is clear: reuse what you know, scale what you do, and enable AI systems. A reusable foundation of test assets allows teams to cover more ground with less effort, while scalable execution ensures that quality insights arrive as fast as the code evolves. Together, these principles define the new standard for enterprise testing in an era of AI-accelerated software development and testing.

Turning this vision into reality required a single automation platform capable of abstraction, integration, and scale. This is where Step becomes the backbone of the enterprise’s testing transformation.

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Solution – How does the vision become reality?

The enterprise’s new testing strategy came to life through Step, the unified automation platform by Exense. Step translates the principles of reusability, scalability and AI enablement into a single, open, and extensible system (see Figure 3). It consolidates the three Testing disciplines into one consistent framework that integrates seamlessly with the company’s existing infrastructure.

Reusability in Test Automation: Abstraction and Collaboration

At the heart of Step is the concept of abstracted test artifacts. Tests are designed once, using reusable components called keywords, which represent meaningful business or technical actions rather than low-level scripts. These artifacts can then be reused across functional, performance, monitoring without duplication. Step allows teams to also share these artifacts across teams and between roles. Developers, testers, and business analysts can all contribute using the interface best suited to their skill set :

• Code-based via Java or YAML (Plan-as-Code)
• Low-code through Step’s visual plan editor
• No-code using its natural language plan syntax

This unified model fosters true collaboration. Know-how and processes flow across teams rather than staying siloed, and infrastructure is consolidated instead of multiplied. Tests become standardized assets that evolve with the application, enabling earlier and more continuous validation within the development pipeline. In particular, Step closes the gap between business and engineering by allowing contributors to design, execute, and share tests in their own language, whether through code, low-code, or no-code interfaces.

Step’s open architecture supports multiple testing frameworks and tools out of the box, including Selenium, Cypress, JMeter, SoapUI, Appium, and others. It connects directly with classical test management systems such as Azure DevOps, Jira, and Quality Center, ensuring that results, defects, and coverage data are captured in one place. This interoperability eliminates redundancy and makes end-to-end testing — from GUI to API and document validation — possible within a single orchestration environment.

The result is a testing ecosystem where every test is an asset. Artifacts are reusable, adaptable to any persona, and ready for AI augmentation as intelligent testing assistants mature.

Bridging DevOps and QA: A Unified Delivery Backbone

One of the most persistent enterprise challenges lies in bridging developer-centric DevOps pipelines and QA-centric testing frameworks. Each side relies on different tools, workflows, and languages, which leads to duplicated effort, inconsistent data, and misaligned validation goals.

Step resolves this divide by acting as a neutral automation layer that supports both perspectives. It integrates directly with CI/CD pipelines and developer toolchains, speaking the same frameworks and languages that development teams already use. For QA teams, Step connects seamlessly with classical test management systems and preserves their validation processes and reporting standards.

Most importantly, Step provides a shared foundation where both DevOps and QA teams work from the same artifacts, datasets, and success criteria. This common basis ensures consistent validation across environments and builds trust between technical and testing teams. In this way, Step transforms fragmented validation chains into a continuous testing fabric accepted by both developers and QA, providing one source of truth for automation and quality.

Scalable Test Automation Architecture

Step’s second defining principle is scalability, embodied in its modern hybrid agent grid. The grid is built for massive parallel execution, enabling thousands of automation tasks to run simultaneously across distributed environments. Its hybrid architecture seamlessly combines native Kubernetes and multi-cluster support with agents deployed on classical setups like virtual machines or physical clients, operating both on-premises and in SaaS environments. On Kubernetes, the grid also delivers auto-scaling capabilities, dynamically provisioning and releasing resources on demand during execution to ensure optimal performance and efficiency without manual intervention.

This scalability allows teams to increase both the breadth and depth of testing. Thousands of user journeys can be simulated concurrently, under realistic workloads, within a controlled and repeatable environment. Step supports execution across distributed systems and integrates directly with CI/CD pipelines, enabling continuous testing across all testing disciplines. Step’s distributed engine executes tests in parallel across any framework, removing dependencies on specific infrastructures such as Selenium Grid.

The reporting layer provides unified, extensible analytics via an open API, compatible with Grafana, MongoDB, and custom dashboards. Teams can analyze test trends, benchmark performance, and feed results back into development loops with minimal effort.

Beyond execution performance, scalability also means supporting the organizational reality of large enterprises. Step provides a centralized testing backbone that can serve multiple teams, business units, and compliance domains while respecting internal governance models. Its architecture supports complex role-based access control, project isolation, and approval workflows that align with corporate standards. This allows each team to operate independently while sharing a common infrastructure, data model, and reporting framework, ensuring both autonomy and compliance at scale.

With Step, scalability does not only mean running more tests faster. It means ensuring that testing grows naturally with the organization, maintaining coverage and confidence even as applications, users, and delivery speed expand.

AI-Enabled Testing and Intelligent Automation

Step’s third defining principle is AI enablement. Its architecture transforms testing data into structured, reusable knowledge that intelligent systems can learn from and act upon. Every test, result, and metric is captured and exposed through open APIs, allowing AI assistants to analyze patterns, detect anomalies, and suggest improvements.

This foundation enables testing to evolve from static automation to adaptive validation. AI agents can generate new tests, optimize coverage, and prioritize workloads dynamically. With Step, automation becomes intelligent, transparent, and continuously improving, preparing enterprises for the next generation of autonomous testing, and testing of AI applications.

Summary

Step embodies the enterprise’s vision of reusable, scalable, and AI-enabled testing, as reflected in its current features [1] and roadmap. It abstracts complexity, unifies disciplines, and integrates seamlessly into the DevOps lifecycle. By centralizing artifacts, connecting tools, and scaling execution automatically, Step turns testing into a sustainable, collaborative, and AI-driven practice.

It is not just a tool but the automation backbone that unifies developer and QA workflows, enabling teams to innovate with confidence and speed. Step provides a single platform for all software automation disciplines without compromise. Once established as the central testing platform, its impact quickly became measurable across clients and disciplines. What began as targeted projects evolved into enterprise-wide transformations, preparing organizations for the next generation of intelligent and autonomous testing.

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Proof – What results confirm the value of this approach?

The following enterprise-testing case studies highlight how the Step platform delivers AI-enabled, scalable QA automation across banking, insurance, and public-sector environments.

Each client’s journey illustrates a different stage in adopting Step as a unified testing platform. Some began with performance testing, others with functional automation or even RPA. Together, these cases demonstrate how the same foundation can address distinct needs across the three Testing disciplines and progressively expand into a strategic enterprise capability.

Cross-Discipline Enterprise Testing Success Stories

Some of our clients deployments showcase how Step scales across functional, performance, and robotic process automation testing to unify complex enterprise environments.

Across these organizations, Step became a shared automation backbone used by both business and technical teams. Business testers could design and maintain test specifications without coding, thanks to Step’s separation between test definition and execution. Developers extended the same artifacts into automated pipelines built on frameworks such as Selenium, JMeter, and Appium, creating a consistent validation layer across the delivery lifecycle.

Enterprises achieved measurable improvements in quality and efficiency. Several implementations reached end-to-end coverage above 75–80 percent for mission-critical systems. Public-sector programs applied the same model to enforce uniform automation standards across departments, proving Step’s scalability in regulated contexts.

Together, these cases demonstrate how Step transforms testing from isolated activities into a unified engineering discipline that improves coverage, speed, and confidence across both IT and business domains.

Specialized Adopters: Deep Impact in Single Disciplines

Some other clients of ours exemplify how Step extends across disciplines, from performance testing to robotic process automation and synthetic monitoring.

These organizations demonstrate Step’s ability to handle complex workloads and diverse automation needs at scale. In performance testing, Step sustained throughput exceeding 2'500 transactions per second during multi-day campaigns, validating the full productive load of enterprise systems through a browser- and mobile-based approach that eliminates protocol-level shortcuts. In monitoring, Step’s synthetic journey framework now supports tens of thousands of daily executions across distributed infrastructures, maintaining real-time oversight of critical business services.

On the automation side, RPA implementations [2] achieved massively parallel execution across distributed agents, enabling thousands of automated workflows per minute and delivering measurable efficiency gains within days of deployment. These results underline Step’s strength as a unified platform that scales consistently across testing, monitoring, and process automation, providing enterprise-grade performance, reliability, and traceability.

Technology providers

Adcubum, Hin and enovetic: Input from Jérôme

Impact by Discipline and Cross-Clients

Summary

Across its client base, Step has demonstrated measurable impact on efficiency, coverage, and scalability, as summarized in the below table (see Table 1).

Discipline	Proven outcomes across clients
Functional Testing	Over 80 percent automation coverage in large enterprises; thousands of end-to-end test cases standardized and reused across projects; faster onboarding and maintenance through modular keywords.
Performance Testing	Sustained throughput exceeding 2,500 transactions per second over multi-day campaigns covering the full productive landscape of mission-critical applications. Browser- and mobile-based load testing approach removing dependency on protocol-level shortcuts and enables realistic, end-to-end validation across user interfaces and public web services, including proprietary payment protocols and standard APIs such as SOAP and REST.
Production Monitoring	Continuous validation of production environments using shared test artifacts keeps quality aligned across stages. By reusing the same assets for testing and monitoring, enterprises can detect degradations early and maintain system reliability at scale. In large environments, this approach already supports > 60’000 of daily executions across and > 1000 concurrent users on applications distributed worldwide.
Robotic Process Automation (RPA)	24/7 execution of business-critical processes; automation of document-heavy workflows and legacy systems; significant reduction in manual effort and operational risk.

Table 1 – Result extract from delivered case studies

Each client’s experience, whether in a single discipline or across the full lifecycle, confirms the same principle: Step delivers reusable, scalable, and reliable testing that grows with the enterprise.

Together, these outcomes form a consistent pattern of maturity. From the engineering foundations that enable reuse, scalability, and AI-driven automation, to the testing effectiveness that accelerates ramp-up and broadens coverage, and finally to the business impact of reduced risk and higher efficiency, Step transforms testing into a continuous engine of confidence for the entire organization (Figure 4).

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The Way Forward

How to Start Your Enterprise Testing Transformation?

Testing is entering a new era. The pace of AI-assisted development leaves no room for manual or fragmented validation. Enterprises must unify their testing disciplines, reuse knowledge, and scale automation to match the velocity of change.

Step unifies people, processes, and intelligent automation into a single enterprise Testing platform built for continuous quality and resilience.

For Test Managers and QA Leaders

Test Managers now stand at the center of the new generation of enterprise testing. Their mission extends beyond coordinating tools and teams. They shape the strategy that connects automation, intelligence, and trust across the organization.

Start by identifying where duplication and siloed tooling slow down your testing effort. Explore how shared artifacts and unified reporting can increase collaboration and transparency. Run a pilot that connects one functional suite, one performance scenario, and one monitoring workflow under Step to experience immediate gains in reuse, speed, and visibility.

The next generation of software testing will not be defined by tools, but by the ability to unify people, processes, and intelligent automation. Exense invites Test Managers to lead this transformation and take the next step toward truly adaptive, enterprise-scale quality assurance.

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References

1. Step features
2. Robotic Process Automation for a Swiss insurance company