Public companies focus less on short-term quarterly earnings. Skeptics have questioned the importance of quarterly earnings for years, but in 2015 this debate picked up some steam as more research on the potential pitfalls appeared. Among the many arguments on the topic that stood out, influential law firm Wachtell, Lipton, Rosen & Katz told the Wall Street Journal they believe earnings can, "...distract executives from long-term goals." Stanford University's Graduate School of Business authored the study "Are Earnings Reports Obsolete?", which echoed similar statements in its research examining quarterly reporting cycles and company productivity. Now I'm not predicting that quarterly earnings go away obviously, but I am a believer that in this New Year we'll see more companies realize the reactive effect earnings cycles have on innovation. A company culture built strictly around making forecasted numbers (which of course are adjusted and altered constantly throughout the year) might not be enough for long-standing, sustainable growth in today's climate if broader trends and patterns are ignored.
AI becomes a boardroom discussion. A very popular topic worth distilling is artificial intelligence (AI). On the doomsday side, Stephen Hawking and Elon Musk have warned too much AI can be harmful (and potentially dangerous to human existence), while Gartner Research predicted, "one in three jobs will be taken by software or robots by 2025." We must remember that with such widespread discussions on the topic, it's important to look at AI in the right context. I'm anticipating that in 2016, we'll be able to better understand, define and classify actual use cases in business. Boards are always looking for a competitive advantage, and their understanding of AI will increase and sharpen throughout the year. At the heart of AI is machine learning, and at the heart of machine learning is data. I expect decision-makers' understandings of AI to mature as more realize it's not about the algorithm, rather it's about the data that feeds the algorithm. Organizations will make better use of data, machine-driven technology and automation to improve operations and performance in an increasingly digital world.
CFOs will finally tweet. Peers of mine at some of the oldest, legacy software companies like SAP, Oracle and IBM have outlined why digitalization should be a CFO topic for the last couple of years. Yet why I think 2016 will be the year CFOs finally embrace social isn't to attract or engage with customers (like most would assume), it's actually to attract the next generation of financial talent. Research from a study conducted by LinkedIn and the Financial Planning Association (FPA) show younger professionals in the finance field increasingly use social media daily. As a result, the more senior financial executives are turning to the same networks to understand and better relate to the tide of upcoming talent. Of course, that’s not to say there aren’t exceptions – some CFOs are accepting social for other reasons. I’ve seen forward-looking CFOs also embrace networks like Twitter because it embodies the digital transformation efforts they are now leading for their organizations.
Blockchain finds a home outside of Bitcoin. If you haven’t heard of Blockchain, it is underlying technology used to power the Bitcoin network. By definition Blockchain is a distributed platform with no-single point of ownership. With smart devices, the internet-of-things (IoT) and just about everything else that’s connected, it won’t be long before we see more applications outside of Bitcoin built on the alternative, secure network. As I write this, Blockchain conferences and summits are enticing developers to attend and show off their applications. The folks over at Andreessen Horowitz share a similar belief, as well as others such as Union Square Ventures.
The grand disappearance of the Business Intelligence (BI) stack. Since the 1980s Business Intelligence (BI) tools have provided enterprises the ability to analyze data sitting in their databases with an eye towards analytical processing not just transactional. Yet historic data is too old to rely on for insights. Today, the market is filled with BI tools that promise contextualized insights to help businesses run better, yet the root problem still lives – BI as a standalone application requires extensive labor, time and extra resources since they are missing they very key ingredient they promise, context. Analytics, visualizations and other characteristics of BI are now embedded directly into specific use-case applications that can connect data from various sources with the day to day web of what matters to business users so that relevant actions can be taken. The ability to gain insights pulled from mulling over streams of data now live directly in HR, sales, marketing and financial planning software, given businesses the speedy analysis they need without the extra cost (or layer on their systems stack).