We are in the early days of generative AI, and there's a gold rush to gain position and prominence in the sector as it takes off. But with this rush to implementation across a bewildering range of use cases come associated risks. Get artificial intelligence (AI) right, and it can be an incredibly creative, labor-saving, and efficiency-improving solution. Employ it badly, and you risk social, financial, and even legal consequences. First, there's the difficulty of predicting their value or likely success, given the unpredictability of AI outputs.

Joe McKendrick is an author and independent analyst who tracks the impact of information technology on management and markets. As an independent analyst, he has authored numerous research reports in partnership with Forbes Insights, IDC, and Unisphere Research, a division of Information Today, Inc. Building and supporting the artificial intelligence infrastructure that is guiding our businesses is not an easy job. The applications, data and networks behind the scenes have to perform as close to flawlessly as possible, in real time. The good news is AI itself can be employed to provide relief to stressed IT teams. AIOps - artificial intelligence for IT operations - is paving the way to autonomous operations of critical enterprise systems.

This article originally appeared in Forbes. Artificial intelligence (AI) is transforming work as we know it, but how do executives embrace the idea of an autonomous enterprise? In this article, we'll explore the characteristics of a self-driving company, where to start with your vision and how to lay the groundwork for an autonomous future. The autonomous enterprise is a self-driving business that has applied artificial intelligence and automation to the problems of operations and management. Stages of self-sufficiency are marked by the ability to operate with less human intervention, and a fully autonomous enterprise is one that is able to configure, monitor and maintain itself independently.

Cognitive applications run mission-critical business systems in a continuous, self-driving, self-learning, auto-compliant, self-securing, and self-healing approach. These AI driven systems intelligently automate transactional systems and processes such as campaign to lead, order to cash, procure to pay, incident to resolution, concept to market, and hire to retire. The goal of an autonomous enterprise is to continuously automate precision decisions at scale. Why? Transactional applications have run their course. Pressure to reduce margins, technical debt, and investment in core systems create tremendous pressure for automation.

My co-author Henry King and I have been asked several times since launching our series on Flow whether the model can tell us anything about the coronavirus pandemic as it is currently being defined by the WHO (as of March 15, 2020). We aren't health professionals, so we are reluctant to comment on the virus, how it works and spreads, or on medical interventions and antiviral technologies. But we can make observations about the emerging, and now normalizing, response to the virus at least in the USA, what we may learn from it, and what, if any, long term implications there might be for business and our society more generally. From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. The mandate we have all been given, from health professionals, from politicians, from our employers, is "stay at home".

If the Consumer Electronic Show (CES), that took place in Las Vegas earlier this month, is anything to judge by, Artificial Intelligence (AI) and Machine Learning (ML) will be the buzz words for 2019. Reports of "Jaw-dropping' AI technology abound, including: AI enhanced products are coming into our consumer lives at a dizzying pace. Can we expect to see the same flurry of AI and ML solutions coming into hospitals, schools, retail stores, and hotels in 2019? The short answer is yes. While adoption is slow, and we are unlikely to see the sheer number or diversity of AI and ML solutions increase, AI and ML technology is permeating into enterprise environments.

We are still in the early days of artificial intelligence and machine learning transforming many aspects of our world, including how work gets done. For those of you who can remember how work got done before computers, the internet, and mobile, you have a sense of just how transformational new technologies can be. The previous wave of transformational technologies led to organizations that were inherently more connected than before. Information and decisions flowed much faster as a result. The current wave will lead to what I've started to call the autonomous enterprise: an organization where many decisions are made with the help of machine learning.

The Hive, is a venture fund and co-creation studio which actively builds and fosters start-ups that focus on AI-powered applications in the enterprise. It announced its closing of the third-fund (The Hive III) at $26.5 Million in capital investments. The investment earned would help The Hive co-create more than seven start-ups in the next three years. The Hive III fund is focused on early-stage companies which help in creating value and driving disruption using AI in the enterprise and other key indicators. Until date, The Hive III has co-created two startups, Decision Engines, and Live Objects.

All bets are off on how quickly learning occurs, how fast it's implemented via autonomous enterprises, and the extent to which machines drive the global economy. The digital AI future is here, and it's going to radically change everything about the way we live and work. If that sounds far-fetched, just follow the money: From 2011 to 2015, investments in AI startups skyrocketed from $282 million to $2.4 billion. Advances in big data technologies combined with inexpensive, massively scalable infrastructure and storage solutions are opening up new applications for AI. The technology helps us perform tasks better and infinitely faster.