For decades, organisations treated software quality assurance as a necessary but rarely strategic stage of the delivery process. This model had its flaws, but it worked as long as the pace of software development could be controlled by humans. Today, development is significantly faster, software is delivered continuously, and tolerance for defects is lower than ever. As a result, the need to consolidate testing environments and toolchains, and to move decisively beyond manual testing, is growing rapidly.
IT and software engineering teams now face a dual challenge. On the one hand, artificial intelligence is dramatically accelerating code generation; this in turn, is increasing the volume of changes that require validation. Research from Microsoft shows that developers using GitHub Copilot complete coding tasks more than 55 percent faster. At the same time, IDC reports that 72 percent of developers modify more than 40 percent of AI-generated code.
Faster development therefore does not reduce the need for testing. On the contrary, it substantially increases the pressure on software testing teams, both in terms of workload and complexity. This requires an evolution in approach and a corresponding technological adaptation.
A structural tooling imbalance
Software testing tools, particularly those dependent on manual processes, scale much more slowly than code-generation tools. Today, software is delivered continuously across enterprise applications, APIs, data platforms, customer-facing interfaces, and AI-powered services. In such environments, changes are increasingly difficult to predict and isolate.
At the same time, defects no longer have only technical consequences. They also create operational, financial, and reputational risks. Organisations that fail to rethink their software quality model will steadily lose the ability to keep pace with the rate of change. Many companies are already experiencing this growing operational burden and recognising that testing can no longer be treated merely as the final step in the software development process.
Fragmentation as a critical bottleneck
In a model where software testing takes place only at the end of the delivery lifecycle, tools are poorly coordinated, handoffs are imprecise, and automation requires significant manual effort. This makes it difficult for development teams to prioritise testing, assess the impact of changes, identify coverage gaps, and respond quickly enough as release cycles continue to shorten.
This legacy model not only creates operational friction but also fails to account for today’s scale and complexity. As a result, quality data becomes fragmented, visibility is limited, and deployment decisions are made based on an incomplete understanding of system readiness.
The root cause of this inefficiency is not the lack of yet another automation layer, but the absence of an intelligent, adaptive operating model closely connected to release decision-making. Moving to such a model requires organisations to treat the reduction of fragmentation as a core architectural objective.
Agentic testing: AI is only part of the answer
Software testing supported by AI agents represents an orchestrated response to the technological and operational shortcomings of previous models. It enables the generation of relevant test cases, the identification of risk, and continuous adaptation as software evolves. The objective is not to maximise the number of tests executed, but to focus effort on the areas that matter most: the changes and dependencies with the greatest business impact.
Agentic testing also improves resilience by reducing dependence on constant reconfiguration whenever user interfaces, timing conditions, or environments change. Equally important, it addresses developers’ need for trustworthy testing signals. Such systems can indicate what has changed, what has been covered, where gaps remain, and whether a release can proceed safely.
A New Scale of Testing
As with any major technological transition, with cloud computing being a prime example, the adoption of an agentic software testing tool depends less on trust in the technology itself than on trust in the control mechanisms surrounding it. Competitive pressure, however, is not slowing down. Organisations that fail to build confidence in AI agents will be forced to maintain extensive layers of manual oversight, undermining much of the value that automation can deliver.
Early adopters of agentic testing are demonstrating results that were difficult to achieve under previous models. The British bank NatWest reduced the time required to initiate testing and significantly lowered maintenance costs. EDF Renewables automated the majority of its SAP regression testing, accelerating releases while reducing costs. Cisco reduced manual effort and anticipates substantial savings through expanded automation across the software lifecycle. According to IDC research, organisations using UiPath Test Cloud achieve millions of dollars in average annual benefits, a three-year ROI of 529 percent, and payback in approximately six months.
What this means for the Middle East and Africa
This shift is especially consequential across the Middle East and Africa, where national digital agendas are compressing release timelines at the same time as regulators are raising the cost of software failure. In the UAE, the Ministry of Finance’s e-invoicing mandate is expected to enter a pilot phase in July 2026, with businesses generating more than AED 50 million in annual revenue required to adopt the structured, Peppol-based framework from 1 January 2027. Saudi Arabia’s Vision 2030 programme, together with the wider Gulf push towards open banking, instant payments, and digital government, is placing comparable pressure on enterprise systems.
Each of these initiatives translates directly into a wave of changes across finance, ERP, and customer-facing platforms that must be tested quickly, repeatedly, and without disrupting live operations.
The organisations feeling this most acutely are the region’s largest and most regulated. Tier-one banks such as First Abu Dhabi Bank and RAKBANK operate core banking, payments, and digital channels where a single regression must not be allowed to reach production. For them, the question is no longer whether to modernise software testing, but how quickly an intelligent, agent-driven model can be trusted to keep pace with the rate of change while satisfying the governance and auditability requirements increasingly demanded by regional regulators.
The emergence of a new operating model
Agentic software testing provides organisations with the means to coordinate previously isolated systems and transform quality assurance into a source of confidence throughout the entire product lifecycle. This enables earlier risk identification, lower automation maintenance costs, and faster, more efficient software releases.
For organisations that successfully make this transition, increasing complexity will strengthen rather than undermine confidence in their software. They will gain a critical advantage: the ability to raise the ceiling for innovation while simultaneously reducing operational risk.
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