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The rise of AI-informed testing

Andrew Fullen

Over the last five years, artificial intelligence (AI) has moved from being a niche interest into something found everywhere you look within the IT landscape. The pace of change and innovation has been dramatic – and it’s a rare day when there isn’t a news story about AI capabilities, or some new insight or innovation that’s supported by machine learning. So, it’s no surprise that the most recent World Quality Report (2020-21) found that 88% of organizations surveyed wanted to use AI in their quality assurance (QA) work.

Across tech stacks and tool chains, we see AI systems becoming more common. In part, this is being driven by the pace of change globally as businesses and organizations assess and redesign themselves, drawing on the lessons from the last few years. It’s also being driven by the need to be competitive – if your rival is doing it and getting to market faster or with fewer defects, can you afford not to do the same?

Making informed decisions with AI and a quality-engineering-focused, agile DevOps team

The rapid rise of AI is also being pushed by the need to control the process of change and make informed decisions around the deluge of data that sweeps through complex IT estates every day. The time when any one person could look at all stages of the application development and maintenance (ADM) cycle and see what was happening is gone. To make sense of all the data, we need tools to help us cut through the noise and enable us to focus on making these informed decisions.

Such tools are already becoming mainstream and are giving organizations the ability to manage the entire process. This starts with the business need and moves to supporting agile ways of working and building, assuring, testing, and delivering change quickly and securely. Smart and expert systems and AI are brought together in best-in-class reporting tools, visualizing complex information into representations that humans can easily process and base decisions on.

In this respect, testing is using new smart tools as it moves away from being a separate stage on its own to become much more integrated within the entire delivery process. Working with development teams, testing can help build early controls for security and performance during code check ins, through to integrating the QA culture across all teams to bring together a quality-engineering-focused, mature, agile DevOps team.

Achieving continuous quality monitoring with Capgemini’s embedded Quality Engineering in ADMnext

Smart monitoring of the entire process and live operations enable continuous quality monitoring and the identification of defects or emerging issues before they become serious. And that’s where our  ADMnext portfolio of services comes into play. With Capgemini’s embedded Quality Engineering in ADMnext, we have the capabilities and solutions to address a wide array of challenges and help you make better business decisions. We’ve spent a lot of time making sure that monitoring and control are baked in from the start – and that AI is very much a part of the picture. AI is crucial for supporting decision making by helping us make sense of the volumes of data generated every day – enabling us to be in control and well-informed.

This also helps well beyond some of the traditional IT functions. As we have the data and can track decisions, we can be more accountable for what we do and why. In turn, this supports corporate risk management. How? Because any system that you can show you are in control of – with the right information to make the correct decisions – becomes easier to manage in terms of risk across the organization. This is something that more and more organizations are recognizing – it’s also helping drive the adoption of AI tools even further.

If you can use intelligent analytics to manage both risk and change effectively, you have an IT bedrock that is resilient and helps your business respond to a rapidly changing world. Down the line, as the processes embedded into your organization and you’re able to test defects out within the system before they happen, zero-defect development and zero-touch testing will become more common. But firstly, however, they need to be built upon the foundations of AI being deployed right now.

To learn more about what Capgemini’s embedded Quality Engineering in ADMnext can do for your business, visit us here .

You can also learn more about Capgemini’s wider ADMnext offering here. And to continue the AI conversation, drop me a line here.

This blog is authored by Andrew Fullen, Head of Technology and Innovation for Sogeti UK.