• Test Status Reporting: This is a necessary evil, and despite not many people reading these reports they have to be sent out. Much manual effort is required to create these status report and I expect that AI tools should be able to send reports and show intelligently what changed to the right stakeholders. There is much scope in this area not only for QA but the overall tech industry.

• Manual Documentation

• Repetition of Test in multiple browsers, device or environments

QA jobs that will be Transformed:

• Automation Testing: If I have to choose one area where AI will have the most significant impact, it will be automation testing. This will be an area that has already seen a huge transformation, but I expect this to continue evolving. AI can revolutionize automated testing making it more accessible to non-technical team members and reducing the amount of Maintenence activity that drastically reduced the ROI on automated testing.

• Security Testing: As we are making progress with making our applications and data more secure, so are hackers using the same technological advancements to exploit security vulnerabilities. Ensuring security cannot remain limited to regular scans or manual penetration testing. AI will make inroads in security testing to make it more diligent, robust, and create stronger protection. However, it should be noted that this will be a journey that we will continue to play catch on.

• Auditing and Compliance: Despite so much advancement made in software development processes and tools, auditing still seems to be a manually intensive task. AI can help dramatically in this area by making the process more automated and reliable reporting.

• Perception of Quality: As the pace of software development intensifies, tech-savvy people have more and more appetite to accept a Beta software than ever before. The idea that everything has to be perfect in terms of quality is shifting, and this will also translate into more emphasis on risk-based testing approaches.

New QA jobs that will be created:

• Test Strategist: Traditionally, we have Test Managers on the projects. With automation assisted with AI becoming a norm, I expect a new role of Test strategist tasked with determining where AI-assisted tools should be used and where human intervention is required. Since the adoption of AI will be a journey, this role will be crucial to help companies transform their QA operations.

• QA Data Scientist: I have already seen this happening; the vast amount of data available to QA teams needs to be harvested, analyzed and used to improve Quality Assurance. For us to make the best use of tools at our disposal, the QA data scientist will help us make decisions based on data and not gut feeling. Every QA team should have one data scientist on-board and we don’t have to wait even for AI; this just makes sense.

• AI Model Trainer: The requirements for these roles are already on the rise as more and more companies are taking on their own AI projects. What is noteworthy is how naturally QA professionals will be fit for this role. With the mindset of finding flaws in software QA, professionals can easily pivot on these roles and help make the AI model more robust and ready for real- world.

• AI Model Tester: This will be one of the most contested but crucial roles that will continue to define itself. There are a lot of open questions, and there is no straightforward answer on how to test an AI model. Do we use training data for testing or testing data for training? Nobody will still be fully confident because real-world data can be unexpected. Take for an example Microsoft twitter AI Bot TAI that had to be terminated within 16 hours of its launch because it started throwing racist twist and guess what it learned it from real Twitter users. So no matter how much effort Microsoft spent on training the model, the realworld corrupted it in hours.

• Crowd Testing for AI Models: There are so many examples of bias of creators or trainers getting introduced in the AI model, and that has an impact on real-world people. Be it an example of self-driving cars that are biased to the color of pedestrian’s skin or recruiting software’s AI model screen out women and minority groups. It only points to the importance of being inclusive when training the model. Crowd testing of AI models for this inclusion is not only recommended but might become a regulatory requirement as more biases are exposed.

• More jobs in the Regulatory Roles: We are still scratching the surface in terms of governments being prepared with the impact of AI interacting with the public everywhere. There are few regulations enacted but certainly will intensify, and more regulatory bodies will have to think about including AI’s impact on how to monitor them.

• More jobs in Security testing roles

• More jobs in testing Smart hardware: More and more hardware around us is using AI. From your robot vacuum to self-driving cars, from smartphone assistant to robotic pets. We are surrounded by AI-enabled hardware, and it will require testing.

Impact of AI on Global Economy:

Machine learning engineers are already one of the highestpaid professionals in the US, and job growth has been a staggering 344% in this field. There are many open positions for AI and Machine learning professionals where there are no qualified candidates available, making it one of the hottest jobs in the market. The scarcity of the candidates to fuel the future AI growth is contingent on re-skilling efforts. Many large companies have already begun working on re-training their employees. For example, Amazon is working on a plan to spend $700 Million to retrain 100,000 employees in the next six years. As per an IBM Survey on AI, 120 Million people will need re-training in the next three years alone due to an increase in automation and robotics around the globe.

We cannot ignore AI anymore. The situation can best be summed up from a famous quote from Paul Allen, the late cofounder of Microsoft: “The promise of artificial intelligence generally vastly outweighs the impact it could have on some jobs in the same way that, while the invention of the airplane negatively affected the railroad industry, it opened a much wider door to human progress.”

As per PWC global Artificial Intelligence survey, AI will contribute nearly $15.7 trillion to the economy by 2030, and it is expected to give a boost of 26% to the economy.

Technology professionals will have to be proactive and invest in learning to be relevant and not only survive but thrive in the future.

The danger of Bias with AI:

The danger of bias being introduced in the algorithm will become a significant issue in the future. Microsoft launched a bot for twitter in 2016, which had to be killed in 16 hours because it quickly started spitting racist tweets learning from humans.

As per a study done by the Georgia Institute of technology, there is a higher risk of an autonomous car running over someone with darker skin as the algorithm might not detect them as a pedestrian. These are direct results of unintentional bias entered in AI by the creators and AI model trainers.

These roadblocks in AI progress also brings new opportunities for Technology professionals, as we will see many openings for people who understand human psychology, AI model trainers who can help train the AI algorithm to remain unbiased, testers who can test the AI model, mathematicians who can help create the algorithms and much more.

Final Thoughts:

AI is here, and it’s going to be part of our lives for a long time to come. This is an opportunity of our lifetimes, which will transform economies, make our lives easier, and bring more jobs for people who are prepared for it.

I will summarize with a final quote from Amit Ray’s book, Mindfulness Meditation for Corporate Leadership and Management:” As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.”