In a recent blog I stated that "Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That "Crossing the AI Chasm" not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1). A recent Harvard Business Review (HBR) article "Building the AI-Powered Organization" agrees that despite the promise of AI, many organizations' efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. The above points – interdisciplinary collaboration, data-driven at the front-line, and experimental and adaptive – are the hallmarks of an organization where everyone has been trained to "Think Like a Data Scientist." So, how can your organization embrace the liberating and innovative process of getting everyone to "Think Like a Data Scientist"?
Artificial intelligence seems to be on the brink of a boom. It's now guiding decisions on everything from crop harvests to bank loans, and uses like totally automated customer service are on the horizon. Indeed, McKinsey estimates that AI will add $13 trillion to the global economy in the next decade. Yet companies are struggling to scale up their AI efforts. Most have run only ad hoc projects or applied AI in just a single business process.
Google Drive tends to pick up a lot of shared files, especially if your place of business uses G Suite. The "Shared with Me" section can end up rather messy as a result, but Google is now looking to clean it up with the help of artificial intelligence. In the coming days, Drive will begin guessing which files you want to open.
International law firm Taylor Wessing is implementing artificial intelligence (AI) across the organisation and wants to ensure staff have the necessary skills to make the most of the technology. Businesses have identified a serious AI skills gap, which 69% of enterprises have described as "moderate, major or extreme" due to the difficulty involved in finding skilled people to staff their new AI-driven business models. According to Kevin Harris, IT director at Taylor Wessing, AI has the potential to greatly reduce the time lawyers spend reviewing documents, many of which can be hundreds of pages long and filled with technical legal jargon. "We are using [AI] quite extensively in looking at things like lease reviews. We've got large document stores where there's a myriad of quite complex legal terms and the AI is really helping us sort those legal terms out," he said.