Select Page

What Artificial Intelligence Means to the Future of Software

AI has already transformed our lives from what they were only a few years ago. Case in point: less than a decade ago, self-driving cars were science fiction. Now, 29 U.S. states have passed laws permitting autonomous vehicles. Other AI applications that have become commonplace over the last 10 years include voice assistants, chatbots, and of course the behind-the-scenes algorithms that make shopping and digital media easier to navigate.

It’s still early days, however. The biggest changes are on the way.

User Experience

Personalization is not a new concept, but is one that has become invaluable to the modern-day customer experience. As a result, marketing organizations are embracing AI and machine learning in an effort to differentiate themselves among competitors.

With so much on the line, few are holding back with their AI initiatives. In fact, 82% of marketing leaders are adopting AI and machine learning to help improve personalization, while 64% are relying on AI to deliver precisely targeted content and promotions directly to customers.

Ultimately, the goal is to anticipate the needs of customers before they even know what they need — because that’s what consumers have come to expect. Personalization has shifted from a luxury to the norm, and marketers have no choice but to embrace AI.

Revenue

Need another reason to invest in artificial intelligence? Deloitte found that 82% of enterprises that have adopted machine learning and AI have seen a financial return on their investment. While ROI varies by company, more than half of the companies surveyed felt that AI helped them improve their competitive standing.

Netflix, in particular, has seen incredible results from its AI initiatives. Given the noted difficulty viewers have finding new content to watch, Netflix leverages AI to curate content specifically to each user. As a result, Netflix is keeping its customers happy and limiting churn, preventing losses of $1 billion a year.

Jobs

Worried about robots coming for your job? Don’t be.

“Unfortunately, most calamitous warnings of job losses confuse AI with automation — that overshadows the greatest AI benefit — AI augmentation — a combination of human and artificial intelligence, where both complement each other,” says Svetlana Sicular, research vice president at Gartner.

Artificial intelligence is often seen as a bad thing for actual human beings, who fear being automated out of their current jobs, but that’s not the case. In fact, Gartner predicts that artificial intelligence will create more jobs than it eliminates as soon as 2020, while Forbes estimates that AI initiatives will create 58 million net new jobs by 2022.

AI and the Future of Testing

While many companies might be eager to reap the rewards of AI and ML, few have the resources to take on the challenge themselves. According to SnapLogic, 51% of organizations believe they don’t have the right mix of skilled AI talent in-house to bring their strategies to life. As a result, 71% of companies have outsourced some AI and ML testing, per Alegion.

Which specific activities are being outsourced? A high volume of them are data-specific. There are a wide range of needs though, as demonstrated by the top five services companies are outsourcing for:

  1. Data collection – 36%
  2. Model development and testing – 36%
  3. Data preparation – 28%
  4. Overall strategy and objectives – 27%
  5. Data specialist personnel – 26%

AI has the ability to revolutionize software and the customer experience, but it must be done well for you to benefit. That means committing to thorough testing and continual collection of quality data. This will no doubt be a challenge for any company to accomplish, but it has to be done — whether internally or by a third party.

AI may seem daunting, and it is, but it cannot be ignored. The sooner you embrace the technology of the future, the sooner you’ll reap the present-day rewards.

Want to see more like this?
Kelly McCann
Kelly McCann
Senior Product Marketing Manager
Published On: November 1, 2019
Reading Time: 4 min

How Much Testing Is Enough?

Risk-based testing prioritizes critical tests to reduce risk.

Are AI Tools Improving Accessibility in 2026?

Read the highlights from Applause’s annual survey on the State of Digital Accessibility.

Human Testing vs. AI Testing: What Each Can (and Can’t) Catch

Find the perfect balance for reliable software testing.

From Drift to Deflection: Engineering Trust in AI Systems

Maintaining user trust in your AI chatbots is a continuous process, involving evaluation, observation and adversarial testing.

Test Automation, AI and Gaps in Digital Quality

While AI-generated code and automation can speed releases, they require human oversight to make sure you’re testing what really matters.

What Makes a QA Process Mature?

Mature QA moves from reactive defect-chasing to proactive quality engineering.
No results found.