Advancing AI in Retail with Pick N Pay's Leon Van Niekerk

Pick N Pay's Head of Testing Leon Van Niekerk tells us at OpenText World Europe 2024 about its partnership with OpenText and how it plans to use AI

AI continues to revolutionise several key industries, with retail sectors in particular starting to reap the benefits of the technology.

In fact, a recent study conducted by IBM highlights that consumer interest in AI-driven shopping experiences has surged. With the sector currently facing immense levels of demand, technologies like AI can work to improve and elevate customer experiences, in addition to optimising workplace productivity.

We spoke with Leon Van Niekerk, Head of Testing at Pick N Pay at OpenText World Europe 2024 about how the company’s partnership with OpenText is helping them to realise the benefits of AI technology. 

Partnering with OpenText to harness automation testing

Pick N Pay is a retail company in South Africa and currently operates one of the largest online grocery platforms in sub-Saharan Africa. Currently responsible for operating more than 1,650 stores across seven countries, it is working to enhance visibility, traceability, quality and test automation across its platforms with OpenText ValueEdge.

Leon explains that he has been using OpenText tools since 1998, having started the journey with Micro Focus (now OpenText), and now seeks to grow customer demand for convenient digital shopping experiences. 

“In my current role, I'm Head of the Test Centre of Excellence. We went through a process about eight years ago where we evaluated a number of tools that were in the Pick N Pay environment, free tools like Selenium, Certify and QA runner, before deciding to consolidate all of them,” he says.

In fact, Pick N Pay was the first customer of OpenText to migrate large amounts of data from its quality centre into Octane, the company’s planning, DevOps and test management solution.

“We had a look at our environment from a technology point of view and we wanted to have a look at what tools are available that will support all of our environments. After six months of investigating, we decided upon the OpenText tools.”

In Pick N Pay’s environment, it runs the Quality Centre and also uses automation tools. It also utilises DigitalApp for its device faults by running service virtualisation so that it can create test environments easily.

“We also use the Micro Focus Connect to integrate into JIRA, which is being used by our third-party development houses that’s doing some of our mobile apps,” Leon explains. “We moved over to Octane about four years ago and that's where the official journey started. 

Realising the benefits of enterprise AI

Via this partnership, Pick N Pay seeks to manually test workflows and offer visibility of test results. To power their services, the retailer uses a wide range of information systems, including CRM, ERP and Enterprise Warehouse Management (EWM).

“There's lots of areas where we’re starting to explore AI. In the Test Centre of Excellence, we are testing the Gen AI within ValueEdge to enhance our processes. We have development houses creating features and user stories within JIRA, which is then integrated into Octane - and then we write the processes. 

“With OpenText explaining to us what the AI will do, we decided to test it and our process was quite simple. It’s not that we didn’t trust the AI, but it’s new - we want to make sure that what we get is unique.

“The features that Pick N Pay has in its app are unique to any other retailer in the world, so we took about 45 different features that we tested manually before we had the test cases. We then ran that through the Gen AI tool, which gave us more test cases.

“It’s adding more localised test cases to our environment. The OpenText AI is spot on, they showed us that it is working for what we want and is definitely adding value.

Leon Van Niekerk speaking with OpenText EVP and Chief Product Officer Muhi Majzoub at OpenText World Europe 2024 (Image: OpenText)

Further down the line, Leon explains that the AI will also lead to his human testers not having to write manual test cases anymore.

“They can now focus on something else,” he says. “We have automation resources built into the Sprint teams and we used to target about 70-75% of the Sprint being automated before it hits our QA environment. We are now upping that to 90-95%.

Customer analytics and keeping data secure

When we asked Leon about the future of AI at Pick N Pay, Leon suggested that moving forward it could be used to improve the customer analytics experience. He explains that harnessing JIRA from a third-party development enables the company to better integrate ValueEdge and have one overall view of its test cases.

“We don't have to get an email from someone anymore, everyone has access. We can ask the questions and we receive the results immediately. So that definitely helps us to shorten the game. 

ValueEdge provides testing and development teams with a rich array of new automation testing, quality control and monitoring tools that can support faster and more data-driven development with the power of AI.

On the other hand, during a climate of intense AI development, there are inevitable risks and concerns over data breaches, or businesses mishandling data. 

With this in mind, Leon explains that in South Africa, businesses are held to data protection laws under The South African Protection of Personal Information Act (POPIA). As a result, Pick N Pay is not allowed to test with real customer data.

“So quite early in our automation journey, we decided we will not touch any customer data,” he says. “We use OpenText tools and create our own data to test with. Using service virtualisation in our environment means we do not have to secure any customer data and we can get accurate results based on the test case we want.”

Moving forward, Leon is very positive about Pick N Pay’s relationship with OpenText. He highlights how the company will continue to help Pick N Pay integrate its assets into one platform with the power of automation, which will continue to be actualised over the next 12 to 18 months.

He says: “[OpenText] was involved from the very start in evaluating if AI will work for us.”

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