For the third straight year, Deloitte surveyed executives about their companies' sentiments and practices regarding AI technologies. We were particularly interested in understanding what it will take to stay ahead of the pack as AI adoption grows--and we wanted to learn how adopters are managing risk around the technologies as AI governance, trust, and ethics become more of a boardroom issue. Get the Deloitte Insights app. Adopters continue to have confidence in AI technologies' ability to drive value and advantage. We see increasing levels of AI technology implementation and financial investment. Adopters say they are realizing competitive advantage and expect AI-powered transformation to happen for both their organization and industry. Early-mover advantage may fade soon. As adoption becomes ubiquitous, AI-powered organizations may have to work harder to maintain an edge over their industry peers.

The term last-mile problem comes from the telecom industry, which observed that it costs inordinately more to build and manage the last-mile of infrastructure to the home than to bring infrastructure to the hub city or residential perimeter. Businesses are starting to discover a similar last-mile delivery problem in AI: It is much harder to weave AI technologies into business processes that actually run companies than it is to build or buy the AI and machine learning (ML) models that promise to improve those processes. "The path to deploying ML is still expensive," said Ian Xiao, manager at Deloitte Omnia, Deloitte Canada's AI consulting practice. He estimates that most companies deploy only between 10% and 40% of their machine learning projects depending on their size and technology readiness. In fact, the last-mile problem is a bit of a misnomer when applied to AI deployment in the enterprise.

Artificial Intelligence in Internet of Things presents an array of Predictive solution to the manufacturers, thus showcasing an intelligent behavior, with smart data-driven decisions. Technology is driven by the Internet of Things (IoT). Cloud computing acts as the foundation of IoT to connect, store, and compute data. The Internet of Things (IoT), is an interrelated network of computing devices that enables the system to transfer data without any human interaction. A PwC report states that 73% of global companies invest in IoT.

Artificial intelligence holds great potential for public-sector institutions around the world to improve government operations as well as service to citizens. But governments don't necessarily have experience in acquiring modern AI solutions and can tend to be cautious about harnessing new technology. By helping to guide the process of procuring AI, we aim to address major AI adoption pain points early in the process and make it easier for governments to implement this advanced technology. Overall, the guidelines aim to assist all parties involved in the procurement life cycle – policy officials, procurement officials and government commercial teams, data practitioners, and AI-solutions providers – in safeguarding public benefit and well-being.

This year "is the year that AI is going to enter the enterprise mainstream adoption," said Jeff Loucks, executive director of The Center for Technology, Media & Telecommunications at Deloitte Services LP. Deloitte's 2020 edition of its annual "State of AI in the Enterprise" report, released in July, indicates that many enterprises are investing heavily in AI, and many are buying cloud-based AI products instead of building their own. The technology and consulting company surveyed 2,737 IT and line-of-business executives across nine countries. All of the respondents use some form of AI in their companies. The survey showed that 53% of the adopters spent more than $20 million over the past year on AI-related technology and talent, with 71% of them expecting to increase spending in the next fiscal year.

You may not be entirely comfortable talking about automation as there are so many technical terms such as, for instance, «Artificial Intelligence» or «Robotic Process Automation». A lack of dedicated automation functions and roles in your organisation, and potentially significant workforce implications, can make automation as an initiative intimidating. These sentiments toward automation are common: You are not the only one facing a challenge in building sustainable, scalable automation projects. Business processes today are still designed for the days when large organisations had to rely on manual labour forces. It is especially important today that workforces focus on tasks that require social skills and creativity, instead of making them execute manual and repetitive rule-based processes.

Artificial intelligence (AI) is gradually being implemented in almost every aspect of our lives. In medicine, geology, customer data analysis, autonomous vehicles and even art, its applications are everywhere and its uses are constantly evolving. However, AI has raised at least as many questions as it has answered, including how the technology is defined and used (assisted vs. augmented vs. autonomous intelligence, for example), whether computers are capable of thinking in the same way as humans (the so-called Turing test), the broader impact of automation on society, and the unanticipated ethical and moral dilemmas it may cause.

GlobalData research has found the top big data influencers based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in big data on Twitter during Q2 2020. Evan Kirstel is a B2B thought leader with extensive experience across enterprises sales, alliances, and business development. He currently serves as chief digital officer and advisor of NYDLA.ORG, a remote, distance/digital learning and collaboration association. Kirstel highlights the challenges of data ingestion within the healthcare context, and also stated that enterprises are collecting massive amounts of data but do not how to leverage it effectively.

Companies increasingly rely on artificial intelligence to automate crucial tasks. Machines with brains work alongside humans in warehouses, make recommendations about who should get credit, triage patients seeking care, and analyze dizzying quantities of financial data. Lately, corporate boards have begun to worry about the ethical ramifications of turning so much power over to the machines. A study of 2,737 executives released this month by consulting firm Deloitte found that a majority of those who used AI in their business reported "major" or "extreme" concerns about ethical risks. "Even 18 months ago, ethics was not as much a part of the conversation as it is today," said Beena Ammanath, who leads the AI Institute at Deloitte.

AI solutions are proliferating, from custom offerings to enterprise applications to devices with embedded capabilities. However, this year's Deloitte AI survey found a growing awareness of the associated risks and wide variation in companies' preparedness for mitigating them. Over the past few years, more and more companies have been experimenting with AI, advancing their data-related capabilities, acquiring new technologies and talent, and integrating AI into their business processes. In coming years, AI will likely become even more pervasive. Just as companies no longer talk about isolated mobile strategies--they're just part of doing business--AI will soon become standard and routine, maybe even sooner than expected.