Software testing teams analyse and correct thousands of code on a daily basis to ensure the final product is free of errors. However, the on-demand customer expects software to be comprehensive in functionality and delivered with precision and speed. Current software testing procedures are not scalable to meet these needs, nor are they cost- or time-efficient in the digital economy. As products become more complex to create, the code becomes more challenging to test accurately. Manual testing exposes development teams to many challenges--code changes causing errors elsewhere in the product, the considerable length of regression testing cycles, resourcing constraints of hiring skilled software testers to meet demand, and more.

Artificial intelligence (AI), when integrated correctly, enables organizations to learn and then act on information--powering businesses to make predictions, automate processes and optimize logistics. Although AI has the potential to add almost $16T to the global economy by 2030, 81 percent of business leaders do not understand the data and infrastructure required for AI. Businesses need a prescriptive, strategic approach to successfully modernize their information architectures (IA) with AI. Successful AI models rely on collection and organization of data--a unified and open information architecture is necessary to prime data and ready businesses for an AI and multicloud world. The AI ladder gives organizations a set of guiding principles for the four areas of AI: how to collect, organize and analyze data, and ultimately, how to infuse AI into business practices.

Developments including artificial intelligence, machine learning and robotic process automation bring the promise of transforming work as we know it. Those transformed work processes will operate in a completely different way: fully automated and autonomous, with smart machines doing the work. The vision is to free humans from performing mundane and repetitive business tasks and assist them with better access to better information to better serve customers and the business. For C-suite executives and technologists today, the challenge is to move beyond the hype of digital transformation to use data and automation in ways that make a real difference in the performance of the organization. Automating work may seem like it's just about technology, but the transformation that matters most is strategic.

If you follow developments in cloud architecture, you may have been hearing a lot recently on the importance of an "intelligent cloud" and an "intelligent edge." Cloud providers who have traditionally focused on providing infrastructure and software have begun to realize that there is only so much value they can drive through these as-a-service offerings, and it is no surprise that the word "cognitive" has begun to creep into more marketing and speechifying on cloud. But it's important for developers and data scientists to be able to distinguish between the marketing and the reality of a truly cognitive cloud. IBM is leading in artificial intelligence, with Watson's deep domain expertise helping clients of every size, across all industries, every day. Watson -- which is available only on the IBM Cloud --has the full range of cognitive technology – ML, AI, cognitive -- because that's what is needed for decision making and transformative business outcomes.

In modern IT operations teams, one of the biggest challenges is monitoring an increasingly complex environment--across many different tools--with fewer people. On top of that, teams face more pressure to avoid outages. And due to the immediacy of social media, outages can become very public, very quickly, negatively affecting customer sentiment of the company's brand. Some companies are choosing to employ data scientists to help them overcome challenges like these. The data scientist can use machine learning libraries to build a custom solution to help monitor their environment for potential problems.

No longer only the domain of science fiction, artificial intelligence (AI) is poised to impact everything from how steel is produced to how banks recommend financial products and even to how farmers grow lettuce. It could even change how people move around cities and do business. At its core, AI can be defined as a set of technologies that enable computing systems to sense, comprehend and act. Within 10 years, AI and robotics are expected to make a creative disruption impact estimated of between $14 trillion and $33 trillion dollars in cost reductions across manufacturing and healthcare, enabled by the automation of knowledge work, according to a Bank of America Merrill Lynch report. Rule engine and expert systems have been essential components of symbolic artificial intelligence for decades.

If you follow developments in cloud architecture, you may have been hearing a lot recently on the importance of an "intelligent cloud" and an "intelligent edge." Cloud providers who have traditionally focused on providing infrastructure and software have begun to realize that there is only so much value they can drive through these as-a-service offerings, and it is no surprise that the word "cognitive" has begun to creep into more marketing and speechifying on cloud. But it's important for developers and data scientists to be able to distinguish between the marketing and the reality of a truly cognitive cloud. IBM is leading in artificial intelligence, with Watson's deep domain expertise helping clients of every size, across all industries, every day. Watson -- which is available only on the IBM Cloud --has the full range of cognitive technology – ML, AI, cognitive -- because that's what is needed for decision making and transformative business outcomes.

The buzz in the computing industry is all about cognitive. My clients are at various stages of understanding its implications. They want to know what cognitive truly is and how it solves real business challenges. As many observers have noted, it's not a specific product. Instead, it's an era that includes multiple vendors and various technologies.

Let's take a breath: robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven't happened. Taxing this promising field of innovation is not only reactionary and antithetical to progress, it would discourage the development of technologies and systems that can improve everyday life. Imagine where we would be today if policy makers, fearing the unknown, had feverishly taxed personal computer software to protect the typewriter industry, or slapped imposts on digital cameras to preserve jobs for darkroom technicians.

Digital transformation has become an ongoing process rather than a one-time goal, with market-attuned companies continually on the hunt for the next big technology shift that gives them a competitive advantage. That next big shift is the fusion of artificial intelligence and cloud computing, which promises to be both a source of innovation and a means to accelerate change. With pervasive AI and cognitive capabilities underpinned by the cloud, digital pioneers in today's data-intensive world have the potential to harvest and build value from this unprecedented amount of data. A new IBM study, "The cognitive advantage," reveals that 65 percent of early adopters believe AI is very important to their organizational strategy and success. More than half say AI is essential to digital transformation.