Chintan is a serial entrepreneur and currently the CEO and founder of Brainvire. Artificial intelligence (AI) and machine learning (ML), or AI/ML, are quickly becoming a crucial next step for business growth. Recent years have seen more and more businesses adopting this technology and witnessing significant benefits in several areas. A McKinsey survey indicated that AI adoption rose from 50% in 2020 to 56% in 2021. As per another survey, "76% of organizations say they prioritize AI/ML over other IT initiatives, and 64% say the priority of AI/ML has increased relative to other IT initiatives."

From the mundane to breathtaking, AI is disrupting virtually every business process in all sectors. People are ceasing to associate Artificial Intelligence with science-fiction dystopias as Artificial Intelligence (AI) is taking more commonplace in our daily lives. While such acceptance in mainstream society is a new phenomenon, it is not a new concept in business. Today, AI has become imperative to maintain a competitive edge. This guide will help business owners who are serious about maintaining a competitive edge in their business sector by adopting AI.

This story features COMMONWEALTH BANK OF AUSTRALIA. With Australian finance services providers increasingly looking towards adoption of Artificial Intelligence, successful implementation of AI processes will be complicated and a question of timing in many instances. As we drive further into the 2020s many technological features of what were once regarded – even in relatively recent years – as from an out-of-reach futuristic world are now becoming commonplace. Essentially all of us use daily modern marvels such as smartphones, Wi-Fi, and Bluetooth, and the ways in which we can use them constantly increases. Yet this decade is also expected to deliver tremendous advances in areas such as wearables like smartwatches and fitness trackers, Virtual Reality (VR), Augmented Reality (AR), and in an array of other fields.

The first barrier is data availability. ML and deep learning models require large datasets to accurately classify or predict different tasks.27 Sectors where ML has seen immense progression are those with large datasets available to enable more complex, precise algorithms.28 In healthcare, however, the availability of data is a complex issue. On the organizational level, health data is not only expensive,27 but there is ingrained reluctance towards data sharing between hospitals as they are considered the property of each hospital to manage their individual patients.29

Artificial intelligence is at the forefront of the minds of many pharmaceutical and health care executives. We know this because, as life sciences consultants, our clients frequently ask us for advice on how best to navigate AI. But along with enthusiasm in areas as diverse as phenotypic screening, drug repositioning, and analysis of CT scans, we are also finding a growing skepticism: What is real and what is hype? An example often cited by skeptical clients are the problems surrounding IBM Watson Health, especially in the cancer treatment sphere, where reporting by STAT and the Wall Street Journal, among others, has revealed a chasm between the public relations stories and the reality as experienced by clinicians. Now is an appropriate time to ask: What is holding back artificial intelligence in health care and the life sciences?

While interest in Machine Learning/Artificial Intelligence/ (ML/AI) has never been higher, the number of companies deploying it is only a subset, and successful implementations a smaller proportion still. The problem isn't the technology; that part is working great. What's missing is the attitude, appreciation, and approach necessary to drive adoption and working solutions. To learn more, join us for this free 1-hour webinar from GigaOm Research. Our panel members are seasoned veterans in the database and analytics consulting world, each with a track record of successful implementations.

Though Artificial Intelligence (AI) is a hot topic for businesses right now, it has so far failed to shake up the real estate industry and the use of property software in the same way it has transformed sectors such as banking and healthcare. Tom Shrive explains how the sector is ripe for AI disruption, and why this burgeoning tech will not jeopardise jobs. AI is an inescapable buzzword at the moment and has become an essential part of the technology industry. However, the emergence of Artificial Intelligence (AI) has not come without controversy, provoking polarized responses from the general public. By definition, artificial intelligence is technology that can perform human-like tasks.

What we've learned from the early days of CX technology is that forcing customers to conform to rigid mechanical processes (language choices, service versus support options) doesn't provide a customer-centric approach. It didn't take long for people to figure out pressing "O" to get a live operator--or shouting "agent" in frustration--was almost always the best choice. Thankfully, AI, powered by Machine Learning and evaluated and fine-tuned by humans, has come a long way in the last decade. AI-powered virtual assistants are now able to ask-- and respond to--open-ended questions like, "What can I do for you today?" Marketers fulfill a dual function within their organizations. But because of the unique insights that they gain into how their customers think, feel, act, behave, and buy, they're also customer advocates.

To achieve IA implementation success plan for a partnership, and not replacement, between humans and machines. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Engage the IA experts early to ensure data quality, even if you're just starting to collect data. Depiction of the helpful AI "GERTY" from Moon (2009), directed by Duncan Jones There are several keys to implementing a reliable Intelligence Augmentation (IA) solution that scales and provides maximum value. The technology for AI and IA is the same, but the difference in the end-result can be traced to involvement of subject matter expert humans in the planning of the application and in the use of it (for the previous articles in the series, see the links at the bottom of this post).

There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go one pondering about how people will lose their jobs due to AI, or before we actually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe. AI phobia is not a new kind of fear, it is a fear which we have been living with all our lives due to the irrational works of fiction writers and movies. This fear has been around long before the technology was even developed if you have watched movies like Terminator, you know exactly what I am talking about. This phobia is so rampant that even great minds like Stephen Hawkings and Elon Musk have been very vocal about their irrational fear of AI.