Part 1 of this two-part blog serves as the foundation, highlighting the critical role that quality engineering (QE) must play in addressing the business risks associated with the accelerating adoption of artificial intelligence or AI-powered software.

‍ They started in awe, which soon turned into desperation to keep up, and it is only now that we have started realizing the utility and business value of our Artificial Intelligence (AI) goals. I like this stage of our industrial revolution, where we are no longer expecting magic from AI but are integrating it nevertheless for all the wonders it can still do for our businesses. This was the same space where our DevOps efforts started yielding enterprise-level transformations.

BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.

When AI first started to gain widespread adoption, it sparked a wave of fear. While much of that fear was overblown, we still need to remain cautious about any new technological innovation. Given AI’s potential to drive change on a massive scale, applying ethical principles to AI is not just important, but urgent. Every company must prioritize responsible AI—not only as an ethical responsibility but as a practical, strategic choice.

Let’s be honest – AI can seem like a bit of a mystery, and with this mystery comes myths and misconceptions. Is it actually that good? Can it handle varying document structures? Can it integrate with my existing systems? Because of this mystery, many companies have yet to take the leap and incorporate AI into their data processes. Today, we’re going to play MythBusters, separate fact from fiction, and show how you can use AI document processing to maximize efficiency and save costs.

Artificial Intelligence (AI) is transforming industries across the globe, driving innovation, improving efficiencies, and unlocking new possibilities. From autonomous vehicles to personalized healthcare, AI’s capabilities are vast. However, as AI systems become more integrated into our daily lives, the question of trust becomes critical. Can we trust AI to make decisions that align with our values? Can we be sure that AI systems are secure from manipulation or bias?

Adtech and martech companies are engaged in a fierce battle for audience attention. Customers are bombarded with thousands of ads and marketing messages every day, and the average attention span is plummeting, so it’s no wonder they tune out — or turn on ad blockers. But it’s not all doom and gloom. The global adtech market is expected to grow at a rate of 22.4% through 2030, and martech’s projected growth rate is 18.5% through 2032.

As businesses increasingly adopt cloud technologies to drive innovation, Cloud Infrastructure Management Services (CIMS) have become a cornerstone of successful digital transformation initiatives. With rapid advancements in generative AI, IoT, and connected devices, enterprises want to modernize their infrastructure, enhance integration, and automate processes to remain competitive.

Whether you agree with Elon's prediction or not, it's hard to ignore AI's far-ranging impact, especially on how we approach work. Over the last two years, we have seen AI progress rapidly, leaving many of us wondering, "Will AI replace my job?" It's a question that software engineers have also been grappling with. As ironic as it may seem, the people writing the code driving the technology revolution face the same uncertainty about whether AI might replace them in the future.