Half of current finance artificial intelligence (AI) deployments will be either delayed or cancelled by 2024, while the use of business process outsourcing (BPO) for AI will rise from 6% to 40% within two years, according to Gartner, Inc. CFOs face major barriers to scaling up the use of AI in-house and will increasingly turn to business process outsourcing (BPO) solutions to meet their digital transformation objectives.

…“While finance departments have made reasonable progress in laying the groundwork for AI, the challenges come when attempting to scale up solutions that can manage the complexities of function-wide use,” said Sanjay Champaneri, senior director analyst in the Gartner Finance practice. “The upfront costs of building scalable infrastructure in house, and the overreliance on stretched citizen developers, will lead many CFOs to rethink their current strategies.” Read More

It happened before and it will happen again — very soon. It is not a matter of if, but a matter of when.

The term AI winter is not even a fancy term made up by me to clickbait you into reading this article but is actually a well-known term in the AI industry. The reason for that are the two AI winters we already have experienced in the 20th century. Read More

The AI 100 is CB Insights’ annual list of the 100 most promising private AI companies in the world. This year’s winners are working on diverse solutions designed to recycle plastic waste, improve hearing aids, combat toxic online gaming behavior, and more. Read More

#strategy

The mad scramble to adopt Artificial Intelligence amid the Covid-19 crisis is officially old news. We interact with AI as seamlessly as we do our smartphones, through voice assistants, customer service, automated tasks, self-checkout, fraud detection, in healthcare decisions and infinitely more invisible applications that affect our daily lives. Investments in AI research and applications are set to hit $500 billion by 2024, according to research firm IDC. And PwC predicts AI will contribute $15.7 trillion to the global economy by 2030. With all that money flowing, it can be hard to figure out what the coming thing is, but certain trends do emerge.

Our fourth annual AI 50 list, produced in partnership with Sequoia Capital, recognizes standouts in privately-held North American companies making the most interesting and effective use of artificial intelligence technology. This year’s list launches with new AI-generated design and and multiple funding round announcements that came about after our esteemed panel of judges laid down their metaphorical pencils. Inductees reflect the booming VC interest as well as the growing variability in AI-focused startups making unique uses of existing technologies, others developing their own and many simply enabling other companies to add AI to their business model. Read More

For the past five years, I’ve been advising AI leaders at Fortune 1000 companies on how to recruit and retain AI talent. Because none of them have Google-sized compensation budgets, I have advised them to ignore the hype about talent shortages and focus on building the high-performing team necessary to achieve their business goals. The most successful companies have built effective teams by investing in their existing employees and recruiting from nontraditional regions and backgrounds. The least successful have copied Google’s job descriptions and sent recruiters scurrying around for Stanford and MIT PhDs.

In other words, I helped these companies avoid budget-busting salary costs through better recruiting strategies. But these tactics are no longer working. The AI talent shortage has evolved into a crisis, and every AI leader needs to begin resetting expectations with their leadership. Read More

According to a new report by LXT, artificial intelligence (AI) spending is strong at mid-to-large U.S. organizations, and 40% rate themselves at the three highest levels of AI maturity, having already achieved operational to transformative implementations. A key component to success across all organizations is AI training data, in terms of both quality and investment.

The survey found that over a third of high-revenue companies are spending between $51 million to $100 million on AI, and seven in ten organizations are spending $1 million or more of their budget on AI. Enterprises are using AI to innovate, scale up and drive competitive advantage as well as gain internal efficiencies. Read More

In 2005, years before Apple’s Siri and Amazon’s Alexa came on the scene, two startups—ScanSoft and Nuance Communications—merged to pursue a burgeoning opportunity in speech recognition. The new company developed powerful speech-processing software and grew rapidly for almost a decade—an average of 27% per year in sales. Then suddenly, around 2014, it stopped growing. Revenues in 2019 were roughly the same as revenues in 2013. Nuance had run into strong headwinds, as large computer firms that were once its partners became its competitors. 

Nuance’s story is far from unique. In all major industries and technology domains, startups are facing unprecedented obstacles. New companies are still springing up to exploit innovative opportunities. And these companies can now tap into an extraordinary flood of venture capital. Yet all is not healthy in the startup economy. Innovative startups are growing much more slowly than comparable companies did in the past.  Read More

The AI pioneer says it’s time for smart-sized, “data-centric” solutions to big issues

ANDREW NG HAS SERIOUS STREET CRED in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Read More

AI adoption is on the rise. According to a recent McKinsey survey, 55% of companies use artificial intelligence in at least one function, and 27% attribute at least 5% of earnings before interest and taxes to AI, much of that in the form of  cost savings.

As AI will dramatically transform nearly every industry it touches, it’s no surprise that vendors and enterprises are looking for opportunities to deploy AI everywhere they can. But not every project can benefit from AI and attempting to apply AI inappropriately can not only cost time and money but also sour employees, customers, and corporate leaders on future AI projects.

The key factors for determining whether a project is suitable for AI are business value, availability of training data, and cultural readiness for change. Here’s a look at how to ensure those criteria are in line for your proposed AI project before your foray into artificial intelligence becomes a sunk cost. Read More