Innovation at Work: Issue 5
We’re already on issue 5 of our new and exciting addition to the Userlane blog! Instead of scouring the internet yourself for high-quality and relevant articles, we’ll do it for you, saving you time and giving you the most newsworthy, important, and insightful articles relating to innovation at work!
This week’s articles and opinion pieces relating to innovation at work include some worrying stats about IT modernization, how to adequately prepare for the future of tech, the world’s best quantum computer, the impact of the novel coronavirus on business, and why we shouldn’t be conceptualizing AI in human terms.
You can find the summaries of each article below or simply click on the link to read the full article!
Modernization of the technological backbone of any business is not simply a competitive advantage: It’s an absolute necessity if businesses are to survive in today’s highly tech-driven market. CIOs definitely have their work cut out for them: They need to keep the business running whilst simultaneously facing pressure to innovate. In a survey commissioned by Insight, 40% of respondents stated that their 2019 IT modernization initiatives were either delayed or scrapped due to other priorities, a lack of a well-defined strategy and in-house experts, and outdated infrastructure. Despite this, over two-thirds of companies agree that IT modernization is crucial for business transformation initiatives. If organizations are to modernize this year, they will need governance strategies to support IT modernization, new IT operating models, and a clear strategy for integrating data infrastructure.
Preparing for major technology shifts over the coming decade is a must for organizations, and that’s why enterprises are becoming more and more interested in looking beyond what’s new to what’s next to lay the foundation for future innovation. When considering the future of tech, prospecting may be more appropriate than prediction. This is because there are many prospective futures and companies can maintain a so-called “matrix of maybes”, which is an “inventory of not-ready-for-prime-time technology prospects” that could have a wide business impact. Technology leaders can decide what’s important by using the macro technology forces construct. There are three emerging macro forces: Ambient experience, exponential intelligence, and quantum. Taken together, these macro forces can help IT leaders plan technology decisions and see any emerging tech prospects as the building blocks of their company’s future. Macro technology forces are developing at a nonlinear pace, so if organizations wait too long to begin exploring them, they will soon fall behind.
While Google and IBM were fighting for quantum supremacy, US company Honeywell has been quietly working on quantum tech. The company plans to make it available to customers via the internet within (as little as) the next three months. Honeywell claims that its computer will be twice as powerful as IBM’s Q System One – but this statement will in all likelihood be contested very soon. Although Honeywell may seem like an outsider in this field, it’s a massive company with a great deal of industrial expertise. In fact, it has experience working with vacuums and cryogenics, which has probably played a large part in its quantum computer-building efforts. What this all indicates is that money for next-gen quantum tech should, in fact, be placed on industrial giants like Honeywell and not just on the traditional tech giants Google and IBM.
If ever there was a time to capitalize on the technology we have at our fingertips, now would be it. With close to 100,000 confirmed cases of the novel coronavirus, enterprises across sectors are adopting preventive measures should employees be exposed to it. Such measures include advising employees to work remotely and prohibiting cross-border travel (which is what Salesforce is currently doing). And as for Adobe and Google Cloud, they have canceled the in-person portions of their annual conferences. Google Cloud will hold its Next ‘20 as a multi-day digital event and the Adobe Summit will happen online. So, if your company doesn’t have collaboration tools to enable remote work, now would be a good time to invest in them!
Almost 30 years ago, The Wall Street Journal published a definition of intelligence, which essentially states that it’s a very general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, and learn from experience. Then there’s specialized intelligence, a task-oriented intelligence that refers to a human’s ability to address clear and specific goals in a given environment. In recent years, smart machines have been able to handle many of these tasks, and AI technologies are certainly approaching – it not surpassing – certain capabilities that once belonged solely to humans. The latest AI advancements are based on deep-learning algorithms, which use data sets and deep neural networks. But while neural networks are inspired by the anatomy of the human brain, deep neural networks are brittle, inefficient and data-hungry, and myopic. David Watson of Oxford University says it’s a mistake to say that these deep-learning algorithms actually recreate human intelligence. For example, when it comes to careers or operations that require high-stake decisions that will have a huge impact on the lives of those involved, we rely on humans for this because of accuracy, trust, and moral responsibility. Whereas AI can meet the quality of accuracy, trust is problematic and moral responsibility simply makes no sense.