QA Automation Leadership Unveiled: Challenges and Triumphs
In 2020, Prajakta Pandit was ready for a career change. She had worked as a lead software test engineer and quality assurance (QA) lead in previous positions and was well-versed in QA automation, so she felt confident when she joined the WhizAI team as a QA lead.
However, she knew she would be working in a new field -- artificial intelligence – but she enthusiastically took up the challenge. WhizAI is a generative AI platform for life sciences companies, pre-trained with domain-specific data and data sources and leveraging large language models (LLMs) to make it user-friendly. Life science commercial teams, executives, data analysts, and users query the platform conversationally. The platform understands how life science users communicate, and the terms common to the domain analyze billions of data points in less than a second and provide them with accurate, contextual insights.
WhizAI gives users autonomy, allowing them to access the information they need to make data-based decisions and improve business outcomes without relying on the IT or data team for assistance.
“I’m working on a project most people only read about,” Prajakta said. “It’s amazing to see an article on Google PaLM or another large language model and see the same things happening at your company.”
Over the past three years, she helped WhizAI streamline and automate 3,000 test cases using various frameworks using different programming languages. With QA automation, she helped to reduce testing time from 2 weeks to 1-2 days, a notable achievement.
She also performed test management activities like test planning, estimation, test artifact creation, review, execution, bug tracking, and status reporting. Additionally, because WhizAI can be adapted to a life sciences company’s specific needs, she also helped the team perform business requirements analysis, workflow identification, and functional requirement documentation, as well as usability testing, integration testing, exploratory testing, regression testing, and UX improvement.
Part of the process was stressing the importance of developing a stable product. “Requirements change frequently,” Prajakta said. “But if the test environment isn’t stable, you can’t automate tests.” She played a role in stressing the benefits of developing test automation so that QA processes could be faster and more efficient.
Growing Into the Role of Senior Manager
In addition to learning a new product and new technologies, Prajakta faced an unexpected challenge early in her time at WhizAI. Just days after she joined the team, she had to fill the shoes of the company’s first QA manager, who had resigned, and over the next year, she took on that role until WhizAI filled the position. It was a role that she had never seen herself in – and she wasn’t sure she was up to the task. She said support from WhizAI co-founders Rohit Vashisht and Amitabh Patil was remarkable.
“We used to connect almost every day to plan tasks to be completed. I was allowed to approach both of them anytime for every small help required,” she explained. “I learned how to communicate with ease to complete tasks and manage a team. Their advice helped me develop as a leader. Even today, my teammates mention that they enjoyed working under my leadership..”
While the situation was a challenge, she also recognizes that it helped her gain new skills she uses in her current role as “senior manager, QA automation,” training and overseeing a team of 7. Prajakta trained each person on her team in test automation and, when necessary, manual processes, providing them with tutorials on coding for UI test automation, data-driven testing concepts, and testing APIs via automation.
“At first, they were hesitant about automation testing, but we provided them with on-the-job training and support to remove that barrier,” she explained. “It feels good that they don’t have to reach out to me anymore and can work on test automation tasks by themselves – it means I’ve done my job.”
Although Prajakta’s career has taken some unexpected turns, she is looking forward to more years at WhizAI, confident in her ability to learn, adapt, and handle whatever lies ahead.
Subscribe to our blog
People also viewed
A New Era of Personalization: Applying Generative AI to Dynamic Customer Engagement
Webinar Series: Part 4 - Give Your Life Sciences Analytics the ExplAIn Advantage
Adding Up Data Analytics Total Cost of Ownership (TCO) and How to Lower It
High data analytics expenditures don’t have to be a cost of doing business for life sciences companies. WhizAI lowers the total cost of ownership (TCO) while making data insights more accessible to business users. Traditionally, data analytics TCO has been a significant expense for life sciences organizations.
Transforming Life Sciences Commercial Function with AI and Augmented Analytics
In this whitepaper, sponsored by WhizAl, Everest Group explores the business case and profound impact Al and Augmented Analytics can have on the life sciences commercial function across various use cases in Sales, Marketing, and Market Access. They examine what top pharma enterprises are looking for in an augmented analytics tool, a roadmap for how to get started, and a blueprint for adoption.
Augmented Analytics is “On the Rise” on Gartner’s 2022 Hype Cycle for Life Science Commercial Operations
The Gartner® 2022 Hype Cycle™ for Life Science Commercial Operations introduces a new category, Augmented Analytics, this year. WhizAI is listed as a sample vendor in the Augmented Analytics category in this Hype Cycle. This summary report covers the following highlights.
Why Domain Expertise Is Essential for Life Science Analytics
Domain expertise matters. Consider this: Enterprises choose transportation and logistics companies that have created the specific types of services they need. Businesses seek professional services with experience in their industries and look for consultants with good track records in their markets. However, remember that domain expertise benefits extend to analytics platforms, particularly those deployed for life sciences.