Matthew Zungu and Riaan Roos, Senior Solution Consultants at Inspired Testing

A business owner once approached us ‘needing’ a CRM system. What he actually needed was simpler: a reliable way to keep track of customers, maintain relationships and generate repeat business.

The solution might well have been a sophisticated platform, but it might also have been a well-managed spreadsheet or even a Rolodex.

Testing technology decisions often follow the same pattern. A major SAP migration begins. Release pressure increases. Automation targets appear on the leadership dashboard. Before long, teams are comparing Tosca, Katalon, BrowserStack, NeoLoad and open-source frameworks.

All may be capable, but that does not make any of them the right investment. Organisations often buy testing technology before knowing what they need it to improve. When that happens, they might well be solving the problem, but they’re also making it much more expensive than it needs to be.

The licence cost is only the beginning

Tool sprawl rarely starts with one poor decision, whether it’s a business unit that wants faster browser testing, or needs to automate an enterprise application, or wants better reporting.

Over time, the organisation accumulates multiple software platforms, each with its own licenses, skills, support arrangements, reporting conventions and maintenance demands. Without centralised governance and objective validation, they quickly end up in a state of technology-stack sprawl.

We have recently seen this first-hand at a large insurance and financial services provider. Different business units had independently adopted Katalon Studio, Tricentis Tosca and BrowserStack. These are not identical products, but their capabilities overlap enough to create duplication when introduced without clear governance.

The cost was not limited to software licences. The organisation had fragmented skills, duplicated frameworks, inconsistent reporting and no reliable enterprise view of quality. This is the coordination tax of tool sprawl. It leaks out through duplicated effort, slower decisions and expensive people compensating for an incoherent technology strategy.

The tool is not the requirement

When a team says, “We need Tosca”, “We need BrowserStack” or “We need an AI testing platform”, the first response should not be to start procurement. It is to ask: what business or delivery problem are we trying to solve?

The answer may be slow SAP regression testing, unstable browser coverage, unreliable environments, poor test data or an automation suite that takes longer to maintain than the testing it replaced. Those are requirements. A named product is already a proposed solution.

Starting with the solution narrows the conversation. It also creates confirmation bias. Once a preferred product has been identified, the evaluation often becomes an exercise in proving the original decision correct. That is not governance; it is theatre with a scoring matrix.

The best testing technology is not the most popular or most advanced tool on the market, it’s the one that fits the business requirements, technology architecture and delivery velocity.

Working through a sound validation process, including a robust, needs-driven proof-of-concept (PoC), identifies the business need, assesses environmental constraints such as CI/CD pipelines and team maturity, and puts competing commercial and open-source frameworks head-to-head against real application code.

The assessment should consider business-risk coverage, maintenance effort, test-data dependencies, licensing cost, skills requirements, reporting needs and the organisation’s ability to support the platform after implementation. Without this, a PoC is little more than a product demonstration with better catering.

Different environments need different answers

Tool-agnostic does not mean avoiding commercial platforms or choosing open source by default, as our work within a retail giant illustrates.

Its SAP landscape involved complex, integrated business processes where transport changes created [LJS1.1]significant regression risk.

For that environment, we led the implementation of the Tricentis toolset, including Tosca. Tosca’s model-based approach and SAP capabilities made it the strongest fit against the agreed requirements, supporting repeatable regression coverage across core processes while reducing reliance on script-heavy automation.

The retailer’s online and omnichannel environment presented a different problem. The teams needed a lightweight, flexible approach that could support frequent changes across web-based customer journeys and integrate with continuous delivery.

For that environment, we designed and delivered an automation framework using the opensource Robot Framework[LJS2.1]. The choice reflected the team’s need for flexibility, maintainability, integration and cost control.

Using one heavy enterprise platform across both environments might have looked consistent, but would not necessarily have delivered better outcomes. Consistency is useful, but uniformity for its own sake is not.

Business evidence, not automated activity

The purpose of a testing platform is to provide faster, more reliable evidence that critical business outcomes still work after change.

For a SAP programme, that might mean proving that an order still moves from capture through fulfilment, invoicing and financial posting. For a digital retailer, it may mean proving that a customer can search, purchase, pay and receive confirmation across supported browsers and devices.

A platform that produces attractive dashboards but cannot tell the business whether those outcomes remain protected is not creating confidence, it’s simply automating activity. The better question is not, “How much have we automated?” It is, “How quickly can we provide trustworthy evidence about the risks that matter?”

AI is making it faster to generate code, test cases and automation assets. That can help, but it can also allow organisations to create maintenance debt at greater speed. Weak test data, unstable environments, duplicated frameworks and unclear ownership do not disappear when AI is introduced, they just become visible faster.

Technology can accelerate quality; it cannot create it. Before buying or renewing another testing platform, organisations should therefore be able to answer five important questions:

• What specific problem are we addressing?
• What evidence will show that the tool has improved it?
• What will it cost to operate and maintain?
• Does it fit the architecture, skills and delivery model we actually have?
• What would cause us to review or reverse the decision?

Start with the outcome, use the PoC to reduce uncertainty and keep only the technology that earns its place. That is how organisations build testing capabilities that provide better business evidence, with less waste and fewer expensive surprises.