Supply chain challenges, cyber security threats, pace of change, and the ongoing skills shortage mean that the landscape for businesses has never been more complex. Investments in robust quality assurance and engineering are the foundations of an organisation’s ability to remain flexible, responsive, and adaptable, which ultimately impacts broader business performance, including profitability and even sustainability and delivers the extra value to customers.

As organisations adjust their strategies into the new year, the aim of generating value for customers and end users will be a key priority for quality assurance.

Capgemini and Sogeti, part of the Capgemini Group most recent World Quality Report outlines the most important trends and developments in quality assurance and testing that evolved in 2022 in order to remain competitive.

The need for value vs speed

In 2021, the UK faced increasing economic pressures to conduct quality assurance and testing at speed to ensure demand was met and companies were able to keep up with the pace of competitors and respond to rapidly changing needs of the business and their customers.

Fast forward to today and the sense of urgency has significantly decreased. Instead, the demand for more commitment to testing and quality assurance across all essential teams within a business is now a key priority and quality is regarded as everyone’s responsibility. Now more than ever, every voice must be heard, and collaboration within and between teams is an essential element in its chances of success.

Using innovation to evaluate priorities

 In order to remain competitive, organisations are determined to try new tools to help monitor and improve their offerings. The use of artificial intelligence (AI) and machine learning (ML) is becoming frequently adopted in risk-based testing and test automation as businesses become more reliant on analytics. However, beyond the need to provide sufficient outcomes with analytics, businesses will be turning to smart tools such as AI to strategically prioritise what needs automation in the first place and capitalise on innovative technologies as a driver for agility.

Another innovative tool driving agility for businesses is the use of synthetic data in quality testing. Synthetic data is created in digital worlds rather than collected from or measured in the real world. More and more businesses are beginning to consider its use for all industries due to a greater understanding of how it works alongside AI and ML and what it can achieve. It’s ability to reproduce the characteristics and structure of original data can help protect sensitive data, improve accuracy, and find and mitigate bias and security weaknesses, and help work within data protection frameworks like GDPR.

Quality drivers in sustainability

 With the commitment to reach net zero by 2050, organisations are starting to measure sustainability as part of their quality assurance routines, and it’s a metric that’s regularly included in requests for proposals.

 An example of how AI can help organisations to reduce their carbon footprint in the year ahead is through route optimisation and fleet management throughout the supply chain. AI can be used to interpret data from electricity or natural gas invoices, and we’ll see the growth of central sustainability tracking models, which will form the basis for footprint reduction projects.

End-to-end quality

In order to achieve quality, value needs to be assessed from end to end. To do so, quality assurance processes need to be integrated, standardised and orchestrated to deliver accurate testing. At present, skills represent the greatest challenge to ensuring quality, mainly due to struggles with keeping up on the latest tools and understanding their niche aspects they work within.

Statistics from the World Quality Report, show there are skills related challenges when it comes to the several tools that are currently on the market. 41% of senior decision makers believe that effective partnership strategies will resolve these skills challenges, whilst a further 38% believe that this struggle for accessing the right skills is the same as that of other niche skills in other part of the Software Development Lifecycle.

In addition, as organisations move to the cloud, we’re seeing a growing need for infrastructure testing skills.

Conclusion

 If anything is clear, it is that quality assurance plays a more pivotal role than ever in enabling organisations to achieve higher levels of flexibility and agility, while assuring positive business outcomes and greater customer satisfaction.

As businesses become more comfortable with trying new approaches involving the use of AI, ML, and synthetic data, there are greater demands for skills. However, even though there is an understandable skills gap, it isn’t all bad news. They are a good indicator of determination and dynamism: after all, organisations that have all bases covered have no new worlds to conquer.